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Difference Between Descriptive and Experimental Research

The main difference between descriptive and experimental research is that the descriptive research describes the characteristics of the study group or a certain occurrence while the experimental research manipulates the variables to arrive at conclusions.

Descriptive research and experimental research are two types of research people use when doing varied research studies. Both these research types have their own methods that facilitate the researcher to gain maximum outcomes.

Key Areas Covered

1. What is Descriptive Research      – Definition, Aim, Methods 2. What is Experimental Research      – Definition, Aim, Methods 3. What is the Difference Between Descriptive and Experimental Research       – Comparison of Key Differences

Descriptive Research, Experimental Research, Research

What is Descriptive Research

Descriptive research is a type of research that studies the participants that take part in the research or a certain situation. Descriptive research does not limit to either of quantitative or qualitative research methodologies, but instead, it uses elements of both, often within the same study. Therefore, a descriptive researcher often uses three major ways to collect and analyse the data. They are observations, case studies and surveys.

Descriptive studies are aimed at finding out “what is,” therefore, observational and survey methods are frequently used to collect descriptive data (Borg & Gall, 1989). Thus, the main focus of descriptive research is to answer the question ‘what’ with concern to the study group. Moreover, descriptive research, primarily concerned with finding out “what is,” that might be applied to investigate the particular study group or the situation. Therefore,  descriptive research does not give answers to the cause and effect of the particular occurrence that is studied. 

Therefore, descriptive research assists to make specific conclusions regarding situations such as marketing products according to the needs of the customers, to estimate the percentages of units in a specified population according to a certain behaviour, etc. Some examples of descriptive researches include population census and product marketing surveys.

What is Experimental Research?

Experimental research is the research study where the scientist actively influences something to observe the consequences. Experimental research uses manipulation and controlled testing to understand causal processes. Therefore, in this type of research, the researcher manipulates one given variable and controls the others to come to a conclusion.

This type of research typically includes a hypothesis, a variable that can be manipulated, measured, calculated and compared. Eventually, the collected data and results will either support or reject the hypothesis of the researcher. Therefore, one could call this research type as a true experiment.

In this research type, the researcher manipulates the independent variables such as treatment method and teaching methodology, and measures the impact it has on the dependent variables such as cure and student comprehension in order to establish a cause-effect relationship between these two variables. Therefore, this research type can answer the questions of cause, effect and results, thus, making it possible to make hypothetical assumptions based on the gathered data. Therefore, unlike descriptive research which answers’ what is’, experimental research answers the question ‘what if’. Therefore, usually, this type of research uses quantitative data collection methodology.

Evidently, this type of research is mostly conducted in a controlled environment, usually a laboratory. Experimental research is mostly used in sciences such as sociology and psychology, physics, chemistry, biology, medicine, etc.

Descriptive research is the type of research where characteristics of the study group or a certain occurrence are described while experimental research is the research type that manipulates variables to come to a conclusion. This is the main difference between descriptive and experimental research.

Descriptive research is useful in gathering data on a certain population or a specific occurrence while experimental research is useful in finding out the cause-effect of a causal relationship, correlation etc

The aim of the descriptive research is to describe the characteristics of the study group, thus answering the question ‘what is’ while the aim of the experimental research is to manipulate the given variables so as to support or reject the assumed hypothesis. Hence it answers the question ‘what if’.

Type of Studies

Descriptive research typically includes sociological and psychological studies while experimental research typically includes forensic studies, biological and other laboratory studies, etc.

Data Collection

Descriptive research uses both qualitative and quantitative methodologies while experimental research primarily uses quantitative methodology.

Descriptive and experimental research are two significant types of research. Both these research types are helpful in analysing certain occurrences and study groups. The main difference between descriptive and experimental research is that descriptive research describes the characteristics of the research subject while the experimental research manipulates the research subject or the variables to come to a conclusion. Similarly, descriptive research answers the question ‘what is’ while experimental research answers the question ‘what if’.

1. “Descriptive Research.” Wikipedia, Wikimedia Foundation, 19 June 2018, Available here . 2. “WHAT IS DESCRIPTIVE RESEARCH?”, The Handbook of Research for Educational Communications and Technologies, Available here . 3. ” Descriptive Research Design: Definition, Examples & Types” Study.com, Available here . 4. “Experimental Research – A Guide to Scientific Experiments.” Observation Bias, Available here . 5. Wattoo, Shafqat. “Experimental Research.” LinkedIn SlideShare, 3 Feb. 2012, Available here .

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Upen, BA (Honours) in Languages and Linguistics, has academic experiences and knowledge on international relations and politics. Her academic interests are English language, European and Oriental Languages, Internal Affairs and International Politics, and Psychology.

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What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

• In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
• What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

• Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
• Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
• Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
• Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
• Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

• Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
• Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
• Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
• Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
• Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
• Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
• Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

• Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
• Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

• In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
• In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
• In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

• Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
• It is a versatile research method and allows flexibility.
• Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
• It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
• It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

• It is limited in its scope as it does not determine cause-and-effect relationships.
• The approach does not generate new information and simply depends on existing data.
• Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
• Descriptive research findings may not be generalizable to other populations.
• Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

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Descriptive vs experimental research

• October 7, 2021

Exclusive Step by Step guide to Descriptive Research

Get ready to uncover the how, when, what, and where questions in a research problem

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Descriptive research and experimental research are both types of quantitative research. Quantitative research refers to the process of analyzing data in its numeric form. The objective of quantitative research is to examine social phenomena by collecting objective data. 

But there is a difference in the way descriptive research and experimental research are performed and the insights they deliver. We will explore how different the two research types are from one another. 

Before we jump into exploring descriptive vs experimental research, let’s define the two types.

What is Descriptive Research?

Descriptive research is a method to describe the demographics of the research variables. The demographics being “why, what, when, how” regarding the subject variable. Rather than limiting its approach to qualitative or quantitative, descriptive research is mostly observational. The reason being obvious, the variables are not influenced by any external variables and are observed to derive results from it. 

Descriptive research aims to statistically analyze the data collected through observations and surveys or case studies. The variables that are being observed are not controlled. As descriptive research digs out the patterns in the data, it helps researchers get future insights depending on the pattern. 

Methods of descriptive research:

• Observation – as the name suggests, this includes observing a variable in the study. It can be qualitative or quantitative in nature. Quantitative observations will give data that is numerically represented, whereas qualitative observations are more brief and long to analyze. 

For example, a company owner decides to implement new soft skill training among the employees. After the training is over he observes their speech and performance to figure out how effective the training program was. 

• Surveys – are the most common form of gathering feedback from the customers. This includes questionnaires regarding the topic which the responders will answer. It can be conducted online as well as offline and provides vast areas of channels to circulate them through. 

The main advantage of surveys is that it gets your hands on large amounts of data in a short time span. 

For example, a company owner wants to get feedback on a recent meeting. He will ask both open-ended as well as close-ended questions.

• Case studies – it is a deep study of an individual or group. It helps your frame hypothesis or theories. As it studies a natural phenomenon, researchers’ biases are avoided. Another reason is, a not-so-genuine responder. It would be unfair to study this responder who is a lot different from the general population and then generalize his results to the entire population. 

For example, a company owner studies an employee who travels far to come to the office. He may have a different experience with his traveling and its effect on his work, then the other employees. 

What is Experimental Research?

Experimental research is a scientific approach to dealing with two or more variables. It is basically an experiment conducted to bring out the cause-effect relationship between those variables. 

The experiment has two groups, a treatment group, and a control group. A researcher starts an experiment by keeping a problem statement in mind, and that includes a control variable. The treatment group undergoes the changes that the researcher wants to experiment with, and the control group doesn’t go through any treatment. At the end of the experiment, the researcher concludes how the independent variable affects the dependent variable when the course is changed. 

Experimental research aims to help you make meaningful insights out of the gathered data. It is useful in testing your hypothesis and making decisions about it. Experimental research is said to be successful when the manipulation of the independent variable brings about a change in the variable that is under study. 

Methods of experimental research:

Pre-experimental Design

It is sort of a dry run before a true experiment takes place. It studies one or two groups when they are put under the researcher’s treatment. This gives an idea of whether the treatment will solve the problem at hand or not. And if yes, then what is the right way to carry out the experiment when it actually takes place. 

The 3 kinds are; 

• One-shot case study research design
• One-group pretest-posttest research design
• Static group comparison 

[Related read: Pre-experimental Design ]

True-experimental Research Design  

It is hypothesis-testing research, which at the end of the study, will either support or refute the hypothesis. You can say this research is based on the foreground of the pre-experimental research. 

True experiments work on hypothesis testing with the help of independent and dependent variables, pre-testing and post-testing, treatment groups and control groups, and control variables. In addition to that, the samples are selected at random. 

For example, a teacher wants to know the average maths marks of her class. She will randomly select students to take the math test. 

Quasi-experimental Research  

It is similar to a true experiment but surely not the same. Just like true experiments, it also includes independent and dependent variables, pre-tests and post-tests, and treatment and control groups. 

The major difference is that it does not include randomization of samples and control variables. As a result of which, the participants are assigned to the experimental groups through a study that decides which participants to put in which experimental group. 

For example, a teacher wants to know how her class is doing in math, but more importantly, she wants to study the students that have an average score on a math test. So she will select only those students who have an average score in math. 

Descriptive Vs. Experimental Research

Definition .

Descriptive research is a method that describes a study or a topic. It defines the characteristics of the variable under research and answers the questions related to it. 

Whereas experimental research is a scientific approach to testing a theory or a hypothesis using experimental groups and control variables. 

Descriptive research will help you gather data on a subject or understand a population or group. 

Experimental research will help you establish a cause-effect relationship between two or more variables. 

Descriptive research aims towards studying the demographics related to a subject group. Experimental research aims to test hypotheses and theories, which include cause-effect variables. 

Descriptive research is sociological and psychological in nature. 

Experimental research uses a more scientific experimental approach to test the problems. 

Both of them differ in terms of external interventions. Descriptive research doesn’t face any, while experimental research has control variables. 

Method to gather data

In descriptive research , the study can be done by collecting qualitative and quantitative data types. 

But when it comes to experimental research , the data has to be quantitative in nature. 

Descriptive Vs. Experimental Research: Comparison Chart

Conclusion;

Despite falling under the types of quantitative research, descriptive research & experimental research differ significantly. This concludes all points of difference between the two research types. Next time you have to decide which research method, you can refer to this blog.

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The main difference between the two is that – descriptive research is a qualitative or quantitative approach dedicated to observing the variable demographics under its natural habitat. While experimental research includes a scientific quantitative approach to test hypotheses and theories using control variables.

One example can be, a software company wants to develop a new shopping application. For that, they will observe the regular shopping experiences of the customers and what are current options they are preferring. Second example can be a researcher who wants to study social media experiences for different people belonging to different age groups.

Two things that will differentiate the two prime research methodologies can be:

• Descriptive research deals with observation and no external intervention while experimental research totally depends on the intervention. This intervention is caused by manipulation of the independent variable. 
• The use of descriptive research is done when you want to observe a certain group or an individual while experimental research is used when you have a theory and you want to test it out by experimenting on the variables. 

For instance, a new teaching strategy for math is tested for its effects. A random selection of students is done to undergo the special training for the subject. At the end of the training, results of the math tests are compared with the results before the training program. This will let the management know how effective the training is. 

• It has dependent and independent variables that give the cause-effect relationship between the variables. 
• It has pre-test and post-test study to compare the results of the experiment before the treatment and after the treatment. 
• Random sampling helps both the treatment group and control groups to have equal quality of participants. 

As descriptive research is an observational and experimental research is, well, experiment based, both have their own importance depending on the research problem. Use descriptive research when you just have to observe a group in its environment and develop an understanding on the subject. Use experimental research when you have to test a hypothesis or establish a cause-effect relation between two or more variables. 

Experimental research includes independent and dependent variables, it compares the pretest and post-tests while including randomization of samples and control variables. While non-experimental research doesn’t have randomization of the samples and it doesn’t manipulate the independent variables even if it is about establishing causal relationships between the variables. 

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Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

What is Descriptive Research and How is it Used?

Introduction

What does descriptive research mean, why would you use a descriptive research design, what are the characteristics of descriptive research, examples of descriptive research, what are the data collection methods in descriptive research, how do you analyze descriptive research data, ensuring validity and reliability in the findings.

Conducting descriptive research offers researchers a way to present phenomena as they naturally occur. Rooted in an open-ended and non-experimental nature, this type of research focuses on portraying the details of specific phenomena or contexts, helping readers gain a clearer understanding of topics of interest.

From businesses gauging customer satisfaction to educators assessing classroom dynamics, the data collected from descriptive research provides invaluable insights across various fields.

This article aims to illuminate the essence, utility, characteristics, and methods associated with descriptive research, guiding those who wish to harness its potential in their respective domains.

At its core, descriptive research refers to a systematic approach used by researchers to collect, analyze, and present data about real-life phenomena to describe it in its natural context. It primarily aims to describe what exists, based on empirical observations .

Unlike experimental research, where variables are manipulated to observe outcomes, descriptive research deals with the "as-is" scenario to facilitate further research by providing a framework or new insights on which continuing studies can build.

Definition of descriptive research

Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon.

The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.

The difference between descriptive and exploratory research

While both descriptive and exploratory research seek to provide insights into a topic or phenomenon, they differ in their focus. Exploratory research is more about investigating a topic to develop preliminary insights or to identify potential areas of interest.

In contrast, descriptive research offers detailed accounts and descriptions of the observed phenomenon, seeking to paint a full picture of what's happening.

The evolution of descriptive research in academia

Historically, descriptive research has played a foundational role in numerous academic disciplines. Anthropologists, for instance, used this approach to document cultures and societies. Psychologists have employed it to capture behaviors, emotions, and reactions.

Over time, the method has evolved, incorporating technological advancements and adapting to contemporary needs, yet its essence remains rooted in describing a phenomenon or setting as it is.

Descriptive research serves as a cornerstone in the research landscape for its ability to provide a detailed snapshot of life. Its unique qualities and methods make it an invaluable method for various research purposes. Here's why:

Benefits of obtaining a clear picture

Descriptive research captures the present state of phenomena, offering researchers a detailed reflection of situations. This unaltered representation is crucial for sectors like marketing, where understanding current consumer behavior can shape future strategies.

Facilitating data interpretation

Given its straightforward nature, descriptive research can provide data that's easier to interpret, both for researchers and their audiences. Rather than analyzing complex statistical relationships among variables, researchers present detailed descriptions of their qualitative observations . Researchers can engage in in depth analysis relating to their research question , but audiences can also draw insights from their own interpretations or reflections on potential underlying patterns.

Enhancing the clarity of the research problem

By presenting things as they are, descriptive research can help elucidate ambiguous research questions. A well-executed descriptive study can shine light on overlooked aspects of a problem, paving the way for further investigative research.

Addressing practical problems

In real-world scenarios, it's not always feasible to manipulate variables or set up controlled experiments. For instance, in social sciences, understanding cultural norms without interference is paramount. Descriptive research allows for such non-intrusive insights, ensuring genuine understanding.

Building a foundation for future research

Often, descriptive studies act as stepping stones for more complex research endeavors. By establishing baseline data and highlighting patterns, they create a platform upon which more intricate hypotheses can be built and tested in subsequent studies.

Descriptive research is distinguished by a set of hallmark characteristics that set it apart from other research methodologies . Recognizing these features can help researchers effectively design, implement , and interpret descriptive studies.

Specificity in the research question

As with all research, descriptive research starts with a well-defined research question aiming to detail a particular phenomenon. The specificity ensures that the study remains focused on gathering relevant data without unnecessary deviations.

Focus on the present situation

While some research methods aim to predict future trends or uncover historical truths, descriptive research is predominantly concerned with the present. It seeks to capture the current state of affairs, such as understanding today's consumer habits or documenting a newly observed phenomenon.

Standardized and structured methodology

To ensure credibility and consistency in results, descriptive research often employs standardized methods. Whether it's using a fixed set of survey questions or adhering to specific observation protocols, this structured approach ensures that data is collected uniformly, making it easier to compare and analyze.

Non-manipulative approach in observation

One of the standout features of descriptive research is its non-invasive nature. Researchers observe and document without influencing the research subject or the environment. This passive stance ensures that the data gathered is a genuine reflection of the phenomenon under study.

Replicability and consistency in results

Due to its structured methodology, findings from descriptive research can often be replicated in different settings or with different samples. This consistency adds to the credibility of the results, reinforcing the validity of the insights drawn from the study.

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Numerous fields and sectors conduct descriptive research for its versatile and detailed nature. Through its focus on presenting things as they naturally occur, it provides insights into a myriad of scenarios. Here are some tangible examples from diverse domains:

Conducting market research

Businesses often turn to data analysis through descriptive research to understand the demographics of their target market. For instance, a company launching a new product might survey potential customers to understand their age, gender, income level, and purchasing habits, offering valuable data for targeted marketing strategies.

Evaluating employee behaviors

Organizations rely on descriptive research designs to assess the behavior and attitudes of their employees. By conducting observations or surveys , companies can gather data on workplace satisfaction, collaboration patterns, or the impact of a new office layout on productivity.

Understanding consumer preferences

Brands aiming to understand their consumers' likes and dislikes often use descriptive research. By observing shopping behaviors or conducting product feedback surveys , they can gauge preferences and adjust their offerings accordingly.

Documenting historical patterns

Historians and anthropologists employ descriptive research to identify patterns through analysis of events or cultural practices. For instance, a historian might detail the daily life in a particular era, while an anthropologist might document rituals and ceremonies of a specific tribe.

Assessing student performance

Educational researchers can utilize descriptive studies to understand the effectiveness of teaching methodologies. By observing classrooms or surveying students, they can measure data trends and gauge the impact of a new teaching technique or curriculum on student engagement and performance.

Descriptive research methods aim to authentically represent situations and phenomena. These techniques ensure the collection of comprehensive and reliable data about the subject of interest.

The most appropriate descriptive research method depends on the research question and resources available for your research study.

Surveys and questionnaires

One of the most familiar tools in the researcher's arsenal, surveys and questionnaires offer a structured means of collecting data from a vast audience. Through carefully designed questions, researchers can obtain standardized responses that lend themselves to straightforward comparison and analysis in quantitative and qualitative research .

Survey research can manifest in various formats, from face-to-face interactions and telephone conversations to digital platforms. While surveys can reach a broad audience and generate quantitative data ripe for statistical analysis, they also come with the challenge of potential biases in design and rely heavily on respondent honesty.

Observations and case studies

Direct or participant observation is a method wherein researchers actively watch and document behaviors or events. A researcher might, for instance, observe the dynamics within a classroom or the behaviors of shoppers in a market setting.

Case studies provide an even deeper dive, focusing on a thorough analysis of a specific individual, group, or event. These methods present the advantage of capturing real-time, detailed data, but they might also be time-intensive and can sometimes introduce observer bias .

Interviews and focus groups

Interviews , whether they follow a structured script or flow more organically, are a powerful means to extract detailed insights directly from participants. On the other hand, focus groups gather multiple participants for discussions, aiming to gather diverse and collective opinions on a particular topic or product.

These methods offer the benefit of deep insights and adaptability in data collection . However, they necessitate skilled interviewers, and focus group settings might see individual opinions being influenced by group dynamics.

Document and content analysis

Here, instead of generating new data, researchers examine existing documents or content . This can range from studying historical records and newspapers to analyzing media content or literature.

Analyzing existing content offers the advantage of accessibility and can provide insights over longer time frames. However, the reliability and relevance of the content are paramount, and researchers must approach this method with a discerning eye.

Descriptive research data, rich in details and insights, necessitates meticulous analysis to derive meaningful conclusions. The analysis process transforms raw data into structured findings that can be communicated and acted upon.

Qualitative content analysis

For data collected through interviews , focus groups , observations , or open-ended survey questions , qualitative content analysis is a popular choice. This involves examining non-numerical data to identify patterns, themes, or categories.

By coding responses or observations , researchers can identify recurring elements, making it easier to comprehend larger data sets and draw insights.

Using descriptive statistics

When dealing with quantitative data from surveys or experiments, descriptive statistics are invaluable. Measures such as mean, median, mode, standard deviation, and frequency distributions help summarize data sets, providing a snapshot of the overall patterns.

Graphical representations like histograms, pie charts, or bar graphs can further help in visualizing these statistics.

Coding and categorizing the data

Both qualitative and quantitative data often require coding. Coding involves assigning labels to specific responses or behaviors to group similar segments of data. This categorization aids in identifying patterns, especially in vast data sets.

For instance, responses to open-ended questions in a survey can be coded based on keywords or sentiments, allowing for a more structured analysis.

Visual representation through graphs and charts

Visual aids like graphs, charts, and plots can simplify complex data, making it more accessible and understandable. Whether it's showcasing frequency distributions through histograms or mapping out relationships with networks, visual representations can elucidate trends and patterns effectively.

In the realm of research , the credibility of findings is paramount. Without trustworthiness in the results, even the most meticulously gathered data can lose its value. Two cornerstones that bolster the credibility of research outcomes are validity and reliability .

Validity: Measuring the right thing

Validity addresses the accuracy of the research. It seeks to answer the question: Is the research genuinely measuring what it aims to measure? In descriptive research, where the objective is to paint an authentic picture of the current state of affairs, ensuring validity is crucial.

For instance, if a study aims to understand consumer preferences for a product category, the questions posed should genuinely reflect those preferences and not veer into unrelated territories. Multiple forms of validity, including content, criterion, and construct validity, can be examined to ensure that the research instruments and processes are aligned with the research goals.

Reliability: Consistency in findings

Reliability, on the other hand, pertains to the consistency of the research findings. When a study demonstrates reliability, this suggests that others could repeat the study and the outcomes would remain consistent across repetitions.

In descriptive research, factors like the clarity of survey questions , the training of observers , and the standardization of interview protocols play a role in enhancing reliability. Techniques such as test-retest and internal consistency measurements can be employed to assess and improve reliability.

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• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

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Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

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Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

• Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

• Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

• Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

• Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

• Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

• Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

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10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2 Research Designs in Psychology

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs, and explain the advantages and disadvantages of each.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. Researchers have a variety of research designs available to them in testing their predictions. A research design  is the specific method a researcher uses to collect, analyze, and interpret data. Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is designed to provide a snapshot of the current state of affairs. Correlational research  is designed to discover relationships among variables. Experimental research is designed to assess cause and effect. Each of the three research designs has specific strengths and limitations, and it is important to understand how each differs. See the table below for a summary.

Descriptive research: Assessing the current state of affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews four types of descriptive research: case studies, surveys and tests, naturalistic observation, and laboratory observation.

Sometimes the data in a descriptive research project are collected from only a small set of individuals, often only one person or a single small group. These research designs are known as case studies , which are descriptive records of one or more individual’s experiences and behaviour. Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics, this may include those who find themselves in particularly difficult or stressful situations. The assumption is that carefully studying individuals can give us results that tell us something about human nature. Of course, one individual cannot necessarily represent a larger group of people who were in the same circumstances.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses was interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is of Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Milton Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

Research using case studies has some unique challenges when it comes to interpreting the data. By definition, case studies are based on one or a very small number of individuals. While their situations may be unique, we cannot know how well they represent what would be found in other cases. Furthermore, the information obtained in a case study may be inaccurate or incomplete. While researchers do their best to objectively understand one case, making any generalizations to other people is problematic. Researchers can usually only speculate about cause and effect, and even then, they must do so with great caution. Case studies are particularly useful when researchers are starting out to study something about which there is not much research or as a source for generating hypotheses that can be tested using other research designs.

In other cases, the data from descriptive research projects come in the form of a survey , which is a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest. The people chosen to participate in the research, known as the sample , are selected to be representative of all the people that the researcher wishes to know about, known as the population . The representativeness of samples is enormously important. For example, a representative sample of Canadians must reflect Canada’s demographic make-up in terms of age, sex, gender orientation, socioeconomic status, ethnicity, and so on. Research based on unrepresentative samples is limited in generalizability , meaning it will not apply well to anyone who was not represented in the sample. Psychologists use surveys to measure a wide variety of behaviours, attitudes, opinions, and facts. Surveys could be used to measure the amount of exercise people get every week, eating or drinking habits, attitudes towards climate change, and so on. These days, many surveys are available online, and they tend to be aimed at a wide audience. Statistics Canada is a rich source of surveys of Canadians on a diverse array of topics. Their databases are searchable and downloadable, and many deal with topics of interest to psychologists, such as mental health, wellness, and so on. Their raw data may be used by psychologists who are able to take advantage of the fact that the data have already been collected. This is called archival research .

Related to surveys are psychological tests . These are measures developed by psychologists to assess one’s score on a psychological construct, such as extroversion, self-esteem, or aptitude for a particular career. The difference between surveys and tests is really down to what is being measured, with surveys more likely to be fact-gathering and tests more likely to provide a score on a psychological construct.

As you might imagine, respondents to surveys and psychological tests are not always accurate or truthful in their replies. Respondents may also skew their answers in the direction they think is more socially desirable or in line with what the researcher expects. Sometimes people do not have good insight into their own behaviour and are not accurate in judging themselves. Sometimes tests have built-in social desirability or lie scales that attempt to help researchers understand when someone’s scores might need to be discarded from the research because they are not accurate.

Tests and surveys are only useful if they are valid and reliable . Validity exists when an instrument actually measures what you think it measures (e.g., a test of intelligence that actually measures how many years of education you have lacks validity). Demonstrating the validity of a test or survey is the responsibility of any researcher who uses the instrument. Reliability is a related but different construct; it exists when a test or survey gives the same responses from time to time or in different situations. For example, if you took an intelligence test three times and every time it gave you a different score, that would not be a reliable test. Demonstrating the reliability of tests and surveys is another responsibility of researchers. There are different types of validity and reliability, and there is a branch of psychology devoted to understanding not only how to demonstrate that tests and surveys are valid and reliable, but also how to improve them.

An important criticism of psychological research is its reliance on so-called WEIRD samples (Henrich, Heine, & Norenzayan, 2010). WEIRD stands for Western, educated, industrialized, rich, and democratic. People fitting the WEIRD description have been over-represented in psychological research, while people from poorer, less-educated backgrounds, for example, have participated far less often. This criticism is important because in psychology we may be trying to understand something about people in general. For example, if we want to understand whether early enrichment programs can boost IQ scores later, we need to conduct this research using people from a variety of backgrounds and situations. Most of the world’s population is not WEIRD, so psychologists trying to conduct research that has broad generalizability need to expand their participant pool to include a more representative sample.

Another type of descriptive research is  naturalistic observation , which refers to research based on the observation of everyday events. For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting naturalistic observation, as is a biopsychologist who observes animals in their natural habitats. Naturalistic observation is challenging because, in order for it to be accurate, the observer must be effectively invisible. Imagine walking onto a playground, armed with a clipboard and pencil to watch children a few feet away. The presence of an adult may change the way the children behave; if the children know they are being watched, they may not behave in the same ways as they would when no adult is present. Researchers conducting naturalistic observation studies have to find ways to recede into the background so that their presence does not cause the behaviour they are watching to change. They also must find ways to record their observations systematically and completely — not an easy task if you are watching children, for example. As such, it is common to have multiple observers working independently; their combined observations can provide a more accurate record of what occurred.

Sometimes, researchers conducting observational research move out of the natural world and into a laboratory. Laboratory observation allows much more control over the situation and setting in which the participants will be observed. The downside to moving into a laboratory is the potential artificiality of the setting; the participants may not behave the same way in the lab as they would in the natural world, so the behaviour that is observed may not be completely authentic. Consider the researcher who is interested in aggression in children. They might go to a school playground and record what occurs; however, this could be quite time-consuming if the frequency is low or if the children are playing some distance away and their behaviour is difficult to interpret. Instead, the researcher could construct a play setting in a laboratory and attempt to observe aggressive behaviours in this smaller and more controlled context; for instance, they could only provide one highly desirable toy instead of one for each child. What they gain in control, they lose in artificiality. In this example, the possibility for children to act differently in the lab than they would in the real world would create a challenge in interpreting results.

Correlational research: Seeking relationships among variables

In contrast to descriptive research — which is designed primarily to provide a snapshot of behaviour, attitudes, and so on — correlational research involves measuring the relationship between two variables. Variables can be behaviours, attitudes, and so on. Anything that can be measured is a potential variable. The key aspect of correlational research is that the researchers are not asking some of their participants to do one thing and others to do something else; all of the participants are providing scores on the same two variables. Correlational research is not about how an individual scores; rather, it seeks to understand the association between two things in a larger sample of people. The previous comments about the representativeness of the sample all apply in correlational research. Researchers try to find a sample that represents the population of interest.

An example of correlation research would be to measure the association between height and weight. We should expect that there is a relationship because taller people have more mass and therefore should weigh more than short people. We know from observation, however, that there are many tall, thin people just as there are many short, overweight people. In other words, we would expect that in a group of people, height and weight should be systematically related (i.e., correlated), but the degree of relatedness is not expected to be perfect. Imagine we repeated this study with samples representing different populations: elite athletes, women over 50, children under 5, and so on. We might make different predictions about the relationship between height and weight based on the characteristics of the sample. This highlights the importance of obtaining a representative sample.

Psychologists make frequent use of correlational research designs. Examples might be the association between shyness and number of Facebook friends, between age and conservatism, between time spent on social media and grades in school, and so on. Correlational research designs tend to be relatively less expensive because they are time-limited and can often be conducted without much equipment. Online survey platforms have made data collection easier than ever. Some correlational research does not even necessitate collecting data; researchers using archival data sets as described above simply download the raw data from another source. For example, suppose you were interested in whether or not height is related to the number of points scored in hockey players. You could extract data for both variables from nhl.com , the official National Hockey League website, and conduct archival research using the data that have already been collected.

Correlational research designs look for associations between variables. A statistic that measures that association is the correlation coefficient. Correlation coefficients can be either positive or negative, and they range in value from -1.0 through 0 to 1.0. The most common statistical measure is the Pearson correlation coefficient , which is symbolized by the letter r . Positive values of r (e.g., r = .54 or r = .67) indicate that the relationship is positive, whereas negative values of r (e.g., r = –.30 or r = –.72) indicate negative relationships. The closer the coefficient is to -1 or +1, and the further away from zero, the greater the size of the association between the two variables. For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Correlations of 0 indicate no relationship between the two variables.

Examples of positive correlation coefficients would include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher, or lower, on one of the variables also tend to score higher, or lower, on the other variable. Negative correlations occur when people score high on one variable and low on the other. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses and between time practising and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable. Note that the correlation coefficient does not tell you anything about one specific person’s score.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatterplot. A scatterplot  is a visual image of the relationship between two variables (see Figure 2.3 ). A point is plotted for each individual at the intersection of his or her scores for the two variables. In this example, data extracted from the official National Hockey League (NHL) website of 30 randomly picked hockey players for the 2017/18 season. For each of these players, there is a dot representing player height and number of points (i.e., goals plus assists). The slope or angle of the dotted line through the middle of the scatter tells us something about the strength and direction of the correlation. In this case, the line slopes up slightly to the right, indicating a positive but small correlation. In these NHL players, there is not much of relationship between height and points. The Pearson correlation calculated for this sample is r = 0.14. It is possible that the correlation would be totally different in a different sample of players, such as a greater number, only those who played a full season, only rookies, only forwards, and so on.

For practise constructing and interpreting scatterplots, see the following:

• Interactive Quiz: Positive and Negative Associations in Scatterplots (Khan Academy, 2018)

When the association between the variables on the scatterplot can be easily approximated with a straight line, the variables are said to have a linear relationship . We are only going to consider linear relationships here. Just be aware that some pairs of variables have non-linear relationships, such as the relationship between physiological arousal and performance. Both high and low arousal are associated with sub-optimal performance, shown by a U-shaped scatterplot curve.

The most important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables; in other words, we cannot know what causes what in correlational research. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. The researcher has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week as well as a measure of how aggressively each child plays on the school playground. From the data collected, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite of what has been hypothesized; perhaps children who have behaved aggressively at school are more likely to prefer violent television shows at home.

Still another possible explanation for the observed correlation is that it has been produced by a so-called third variable , one that is not part of the research hypothesis but that causes both of the observed variables and, thus, the correlation between them. In our example, a potential third variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may allow children to watch violent television and to behave aggressively in comparison to children whose parents use less different types of discipline.

To review, whenever we have a correlation that is not zero, there are three potential pathways of cause and effect that must be acknowledged. The easiest way to practise understanding this challenge is to automatically designate the two variables X and Y. It does not matter which is which. Then, think through any ways in which X might cause Y. Then, flip the direction of cause and effect, and consider how Y might cause X. Finally, and possibly the most challenging, try to think of other variables — let’s call these C — that were not part of the original correlation, which cause both X and Y. Understanding these potential explanations for correlational research is an important aspect of scientific literacy. In the above example, we have shown how X (i.e., viewing violent TV) could cause Y (i.e., aggressive behaviour), how Y could cause X, and how C (i.e., parenting) could cause both X and Y.

Test your understanding with each example below. Find three different interpretations of cause and effect using the procedure outlined above. In each case, identify variables X, Y, and C:

• A positive correlation between dark chocolate consumption and health
• A negative correlation between sleep and smartphone use
• A positive correlation between children’s aggressiveness and time spent playing video games
• A negative association between time spent exercising and consumption of junk food

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible or when fewer resources are available. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. We can also use correlational designs to make predictions, such as predicting the success of job trainees based on their test scores during training. They are also excellent sources of suggested avenues for further research, but we cannot use such correlational information to understand cause and effect. For that, researchers rely on experiments.

Experimental research: Understanding the causes of behaviour

The goal of experimental research design is to provide definitive conclusions about the causal relationships among the variables in the research hypothesis. In an experimental research design, there are independent variables and dependent variables. The independent variable  is the one manipulated by the researchers so that there is more than one condition. The dependent variable is the outcome or score on the measure of interest that is dependent on the actions of the independent variable. Let’s consider a classic drug study to illustrate the relationship between independent and dependent variables. To begin, a sample of people with a medical condition are randomly assigned to one of two conditions. In one condition, they are given a drug over a period of time. In the other condition, a placebo is given for the same period of time. To be clear, a placebo is a type of medication that looks like the real thing but is actually chemically inert, sometimes referred to as a”sugar pill.” After the testing period, the groups are compared to see if the drug condition shows better improvement in health than the placebo condition.

While the basic design of experiments is quite simple, the success of experimental research rests on meeting a number of criteria. Some important criteria are:

• Participants must be randomly assigned to the conditions so that there are no differences between the groups. In the drug study example, you could not assign the males to the drug condition and the females to the placebo condition. The groups must be demographically equivalent.
• There must be a control condition. Having a condition that does not receive treatment allows experimenters to compare the results of the drug to the results of placebo.
• The only thing that can change between the conditions is the independent variable. For example, the participants in the drug study should receive the medication at the same place, from the same person, at the same time, and so on, for both conditions. Experiments often employ double-blind procedures in which neither the experimenter nor the participants know which condition any participant is in during the experiment. In a single-blind procedure, the participants do not know which condition they are in.
• The sample size has to be large and diverse enough to represent the population of interest. For example, a pharmaceutical company should not use only men in their drug study if the drug will eventually be prescribed to women as well.
• Experimenter effects should be minimized. This means that if there is a difference in scores on the dependent variable, they should not be attributable to something the experimenter did or did not do. For example, if an experiment involved comparing a yoga condition with an exercise condition, experimenters would need to make sure that they treated the participants exactly the same in each condition. They would need to control the amount of time they spent with the participants, how much they interacted verbally, smiled at the participants, and so on. Experimenters often employ research assistants who are blind to the participants’ condition to interact with the participants.

As you can probably see, much of experimental design is about control. The experimenters have a high degree of control over who does what. All of this tight control is to try to ensure that if there is a difference between the different levels of the independent variable, it is detectable. In other words, if there is even a small difference between a drug and placebo, it is detected. Furthermore, this level of control is aimed at ensuring that the only difference between conditions is the one the experimenters are testing while making correct and accurate determinations about cause and effect.

Research Focus

Video games and aggression

Consider an experiment conducted by Craig Anderson and Karen Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (e.g., Wolfenstein 3D) or a nonviolent video game (e.g., Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (i.e., aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown below (see Figure 2.4 ).

There are two strong advantages of the experimental research design. First, there is assurance that the independent variable, also known as the experimental manipulation , occurs prior to the measured dependent variable; second, there is creation of initial equivalence between the conditions of the experiment, which is made possible by using random assignment to conditions.

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table. Anderson and Dill first randomly assigned about 100 participants to each of their two groups: Group A and Group B. Since they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation; they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then, they compared the dependent variable (i.e., the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable, and not some other variable, that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Sometimes, experimental research has a confound. A confound is a variable that has slipped unwanted into the research and potentially caused the results because it has created a systematic difference between the levels of the independent variable. In other words, the confound caused the results, not the independent variable. For example, suppose you were a researcher who wanted to know if eating sugar just before an exam was beneficial. You obtain a large sample of students, divide them randomly into two groups, give everyone the same material to study, and then give half of the sample a chocolate bar containing high levels of sugar and the other half a glass of water before they write their test. Lo and behold, you find the chocolate bar group does better. However, the chocolate bar also contains caffeine, fat and other ingredients. These other substances besides sugar are potential confounds; for example, perhaps caffeine rather than sugar caused the group to perform better. Confounds introduce a systematic difference between levels of the independent variable such that it is impossible to distinguish between effects due to the independent variable and effects due to the confound.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Do people act the same in a laboratory as they do in real life? Often researchers are forced to balance the need for experimental control with the use of laboratory conditions that can only approximate real life.

Additionally, it is very important to understand that many of the variables that psychologists are interested in are not things that can be manipulated experimentally. For example, psychologists interested in sex differences cannot randomly assign participants to be men or women. If a researcher wants to know if early attachments to parents are important for the development of empathy, or in the formation of adult romantic relationships, the participants cannot be randomly assigned to childhood attachments. Thus, a large number of human characteristics cannot be manipulated or assigned. This means that research may look experimental because it has different conditions (e.g., men or women, rich or poor, highly intelligent or not so intelligent, etc.); however, it is quasi-experimental . The challenge in interpreting quasi-experimental research is that the inability to randomly assign the participants to condition results in uncertainty about cause and effect. For example, if you find that men and women differ in some ability, it could be biology that is the cause, but it is equally likely it could be the societal experience of being male or female that is responsible.

Of particular note, while experiments are the gold standard for understanding cause and effect, a large proportion of psychology research is not experimental for a variety of practical and ethical reasons.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, psychological tests, naturalistic observation, and laboratory observation. The goal of these designs is to get a picture of the participants’ current thoughts, feelings, or behaviours.
• Correlational research designs measure the relationship between two or more variables. The variables may be presented on a scatterplot to visually show the relationships. The Pearson correlation coefficient is a measure of the strength of linear relationship between two variables. Correlations have three potential pathways for interpreting cause and effect.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Done correctly, experiments allow researchers to make conclusions about cause and effect. There are a number of criteria that must be met in experimental design. Not everything can be studied experimentally, and laboratory experiments may not replicate real-life conditions well.

Exercises and Critical Thinking

• There is a negative correlation between how close students sit to the front of the classroom and their final grade in the class. Explain some possible reasons for this.
• Imagine you are tasked with creating a survey of online habits of Canadian teenagers. What questions would you ask and why? How valid and reliable would your test be?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 2.2. This Might Be Me in a Few Years by Frank Kovalchek is used under a CC BY 2.0 license.

Figure 2.3. Used under a CC BY-NC-SA 4.0 license.

Figure 2.4. Used under a CC BY-NC-SA 4.0 license.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Henrich, J., Heine, S. J., & Norenzaya, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 , 61–83.

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.) . Mountain View, CA: Cengage.

Psychology - 1st Canadian Edition Copyright © 2020 by Sally Walters is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

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Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

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What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Difference between Descriptive Research and Experimental Research

Descriptive research is a method that focuses on detailing and explaining the characteristics of the subject being studied. It aims to answer questions about who, what, where, and when regarding the topic. On the other hand, experimental research is a scientific method used to test a theory or hypothesis by manipulating certain variables and observing the effects. This approach typically involves comparing an experimental group, which experiences the manipulation, with a control group that does not.

Descriptive Research

Descriptive research refers to research which describes a phenomenon or else a group under study and it is easy to do in social sciences due to manipulating variables. It is mainly useful for gathering data on a certain population, situations and events. Descriptive research is more towards collecting data and try to find out some insight out of that data using statistical analysis.

Example of descriptive research includes population census and product marketing surveys etc.

Experimental Research

Experimental research refers to research where the researcher manipulates the variable to come to an conclusion or finding and it is difficult to do in social sciences due to manipulating variables. It is useful in finding out the cause effect of a casual relationship and correlation. Experimental research is also do this same sort of analysis but also it always analyzes where the data of an experiment comes from.

Example of experimental research includes laboratory experiments.

Frequently Asked Questions on Descriptive Research vs Experimental Research – FAQs

1. what is the difference between descriptive correlational and experimental research.

Experimental research uses the independent variable to determine how it impacts the dependent variable, whereas descriptive correlational research simply describes the relationship between variables.

2. Is descriptive research qualitative or quantitative?

Descriptive research is typically classified as a sort of quantitative research, but qualitative research can also be utilized for descriptive purposes.

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Part 1. Overview Information

National Institutes of Health ( NIH )

National Institute on Aging ( NIA )

National Institute on Alcohol Abuse and Alcoholism ( NIAAA )

National Institute of Arthritis and Musculoskeletal and Skin Diseases ( NIAMS )

Eunice Kennedy Shriver National Institute of Child Health and Human Development ( NICHD )

National Institute of Neurological Disorders and Stroke ( NINDS )

R15 Research Enhancement Award (REA)

• April 4, 2024  - Overview of Grant Application and Review Changes for Due Dates on or after January 25, 2025. See Notice NOT-OD-24-084 .
• August 31, 2022 - Implementation Changes for Genomic Data Sharing Plans Included with Applications Due on or after January 25, 2023. See Notice  NOT-OD-22-198 .
• August 5, 2022 - Implementation Details for the NIH Data Management and Sharing Policy. See Notice  NOT-OD-22-189 .

See Part 2, Section III. 3. Additional Information on Eligibility.

The purpose of this HEAL Initiative program is to: (1) support basic and mechanistic pain research from R15-eligible undergraduate-focused serving institutions, health professional schools or graduate schools; (2) promote integrated, interdisciplinary research partnerships between Principal Investigators (PIs) from R15-eligible institutions and investigators from U.S. domestic institutions; and (3) enhance the pain research environment at the R15-eligible institution for health professional students, undergraduate and/or graduate students through active engagement in pain research.

Applications in response to this notice of funding opportunity (NOFO) should include plans to accomplish these goals. Specifically, applications should include a rigorous plan for conducting basic and mechanistic pain research in the Research Strategy section of the application . In addition, a research partnership between the PI’s institution and at least one investigator from a separate U.S. domestic institution that provides resources and/or expertise that will enhance the proposed pain research program must be included in a separate Team Management Plan. The proposed partnership will be a sub-award agreement(s) with at least one partnering institution, which does not need to be R15-eligible. The budget of all sub-awards must not exceed one third of the total budget. Furthermore, applications must include a Facilities & Other Resources document that demonstrates active involvement of health professional students, undergraduate and/or graduate students from the R15-eligible institution(s) in the proposed pain research projects.

This Notice of Funding Opportunity (NOFO) requires a Plan for Enhancing Diverse Perspectives (PEDP).

 30 days before application due date.

All applications are due by 5:00 PM local time of applicant organization. 

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

No late applications will be accepted for this Notice of Funding Opportunity (NOFO).

Not Applicable

It is critical that applicants follow the instructions in the Research (R) Instructions in the  How to Apply - Application Guide , except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts ).

Conformance to all requirements (both in the How to Apply - Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the How to Apply - Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the How to Apply - Application Guide , follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

• Use the NIH ASSIST system to prepare, submit and track your application online.
• Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
• Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.

Part 2. Full Text of Announcement

Section i. notice of funding opportunity description.

Applications in response to this notice of funding opportunity (NOFO) should include plans to accomplish these goals. Specifically, applications should include a rigorous plan for conducting basic and mechanistic pain research projects in the Research Strategy section of the application. In addition, a research partnership between the PI’s institution and at least one investigator from a separate U.S. domestic institution that provides resources and/or expertise that will enhance the proposed pain research program must be included in a separate Team Management Plan. The proposed partnership will be a sub-award agreement(s) with at least one partnering institution, which does not need to be R15-eligible. The budget of all sub-awards must not exceed one third of the total budget. Furthermore, applications must include a Facilities & Other Resources document   that demonstrates active involvement of health professional students or undergraduate and/or graduate students from the R15-eligible institution(s) in the proposed pain research projects.

The National Institutes of Health (NIH) Helping to End Addiction Long-term ® Initiative, or NIH HEAL Initiative ® , bolsters research across NIH to (1) improve treatment for opioid misuse and addiction and (2) enhance pain management. More information about the NIH HEAL Initiative is available at  https://heal.nih.gov/ . Research shows that diverse teams working together and capitalizing on innovative ideas and distinct perspectives outperform homogeneous teams. Scientists and trainees from diverse backgrounds and life experiences bring different perspectives, creativity, and individual enterprise to address complex scientific problems. See  the Notice of NIH’s Interest in Diversity ( NOT-OD-20-031 ) for more details. Promoting diversity in the pain research workforce is crucial to promoting future scientific advances in this area and to achieve the NIH HEAL Initiative’s workforce development goals.The initiative has funded multiple pain workforce enhancement programs that support early-career investigators. Despite these efforts, the NIH HEAL Initiative can benefit from additionally supporting  R-15 eligible institutions that involve undergraduate, graduate or health professional school/colleges students in pain research.

Since Fiscal Year (FY) 1985, NIH has made a special effort to stimulate research at educational institutions that provide baccalaureate and/or advanced degrees for a significant number of the nation’s research scientists who have not been major recipients of NIH support. NIH has implemented two parent award programs, the Academic Research Enhancement Award (AREA) program ( PAR-21-155 ) and Research Enhancement Award Program (REAP) ( PAR-22-060 ), to provide research experiences to health professional or undergraduate and/or graduate students pursuing biomedical or behavioral research at U.S. higher education institutions. Utilizing these two programs will further promote a diverse pain research workforce. This  Pain Research Enhancement Program (“PREP”) will further support meritorious collaborative pain research from designated educational levels  in the NIH HEAL Initiative, using the NIH Research Enhancement Award programs as a guide. Specifically, this NOFO aims to support new scientific solutions to the national opioid public health crisis byestablishing new research partnerships that will lead to research experiences for undergraduate, graduate, and health professional students,  to further enhance the pool of potential participants in the pain research pipeline.

Program Objectives:

The purpose of this HEAL Initiative program is to: (1) support basic and mechanistic pain research from R15-eligible undergraduate-focused serving institutions, health professional schools or graduate schools; (2) promote integrated, interdisciplinary research partnerships between Principal Investigators (PIs) from R15-eligible institutions and investigators from U.S. domestic institutions; and (3) enhance the pain research environment at the R15-eligible institution for health professional students, undergraduate and/or graduate students through active engagement in pain research. Successful applications will include plans detailing how they intend to accomplish all three goals. Please refer to Section III for specific R15 eligibility information. Although preliminary data are not required for an R15 application, they may be included if available. The scientific foundation for the proposed research should be based on published research and/or any available preliminary data.

Objective 1: Develop Small-Scale Basic and Mechanistic Pain Research Projects

Proposed research projects should be hypothesis driven and use a rigorous scientific design to generate research data/evidence and advance scientific knowledge. Applications should include objectives that are attainable within the 3-year grant period.

Pain research projects may include, but are not limited to, the study of: nociception and/or pain processing in non-pain populations, acute pain, cancer pain, chemotherapy-induced neuropathy, chronic pain, diabetic neuropathy, eye pain, gynecologic pain, headache, musculoskeletal pain, myofascial pain, obstetric pain, osteoarthritis, pain conditions across the lifespan (including in the context of aging), pain co-occurring with substance use disorders (SUDs), painful disorders of the orofacial region, painful neuropathy, post-stroke pain, post-surgical pain, sickle cell pain, and/or visceral pain. Innovative pain research topics that propose an interdisciplinary mechanistic pain research are considered high program priority under this initiative.

Projects may focus on basic  pain research with pre-clinical ( e.g., animal or in silico ) models or involve research participants ( e.g., observational studies, epidemiological studies, secondary data analyses, or device development). Alternatively, investigators may propose a mechanistic and/or “Basic Experimental Studies involving Humans” (BESH) clinical trial as described below.  Clinical trials designed primarily to determine the safety, tolerability, and/or clinical efficacy of an intervention will be considered non-responsive to this NOFO and withdrawn without review .

For this NOFO, only the following types of clinical trials will be supported:

• Basic Experimental Studies with Humans (BESH) ,  defined as basic research studies involving humans that seek to understand the fundamental aspects of phenomena
• Mechanistic trials ,  defined as studies designed to understand a biological or behavioral process, the pathophysiology of a disease, or the mechanism of action of an intervention (i.e., how an intervention works, but not if it works or is safe)

NIH defines a clinical trial as a research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes ( https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-015.html ). For further clarification on how NIH defines the different types of clinical trials, please refer to the following resources:

• NOT-OD-15-015: Notice of Revised NIH Definition of Clinical Trial
• NIH's Definition of a Clinical Trial
• Decision Tree for NIH Clinical Trial Definition
• Guidance for Basic Experimental Studies with Humans (BESH) Funding Opportunities
• NIH Definition of Clinical Trial Case Studies

Objective 2: Promote Integrated, Interdisciplinary Research Partnerships

A second key objective of this NOFO is to promote new research partnerships among investigators at R15-eligible institutions with separate (legally distinct) investigators at domestic research institutions. Investigators can have a multitude of research expertise that aligns with the proposed research projects and/or resources that can be shared to enhance the proposed research. Applications must propose a collaboration with at least one sub-award holder from a separate U.S. domestic research institution and should include details of how the collaboration will enhance the R15-research program must be described. Applications are permitted to have a subaward to a non-R15-eligible institution. However, it is expected that PD/PI(s) from R15-eligible institution(s) will lead the proposed project and complete most of the research at the R15-eligible institution. As such, PI(s) from R15-eligible institutions must serve as the contact program director (PD)/PI for the project. Additionally, no more than one third of the total budget for the project may be used by the identified sub-award institution.

Applications that propose new interdisciplinary are considered a high program priority under this NOFO. Interdisciplinary partnership could include, but are not limited to, any two or more areas of research expertise from the following:

• Clinical pain management (e.g., nonpharmacologic or pharmacologic interventions)
• Clinical pain research
• Preclinical/basic pain biology and modeling
• Specific disease and/or pathological conditions (either human or preclinical models)
• Animal behavior
• Artificial intelligence
• Data science

In addition, a Team Management Plan is required as part of Objective 2.  Studies of team science have highlighted the need for effective management structures to achieve program goals. Many resources exist to aid in developing effective team-based programs (e.g., the  National Cancer Institute Collaboration and Team Science Field Guide ). The Team Management Plan focuses on management of the whole team/key personnel. Because teams will likely include individuals from widely divergent scientific backgrounds, teams must have a shared vision and a defined plan for communication and management of shared responsibilities, interpersonal interactions, and professional credit. The Team Management Plan should be included as an attachment (three pages maximum) to this application. It should address how the research team, including the PI from R15-eligible Instiution and collaborator(s), will work together to accomplish program objectives. See the application instructions for “Other Attachments” on the SF424(R&R) Other Project Information in Section IV.2 Instructions for Application Submission for details. The Team Management Plan should address the following points:

• Organizational structure and team composition and roles
• Shared leadership, contributions, and distributed responsibility for decision-making
• Resource sharing and allocation
• Credit assignment and/or intellectual property (IP) rights
• Coordination and communication plans
• Intra-team data sharing, archiving, and preservation

Objective 3: Enhance the Research Environment by Engaging Students

The third objective of this program is to enhance the pain research environment at the R15-eligible institution by engaging and providing research opportunities to health professional students or undergraduate and/or graduate students. A Facilities & Other Resources document is required to describe how the proposed research will enhance the pain research environment at the R15-eligible institution. Two-thirds of the proposed research project team should comprise personnel from the R15-eligible institutions, including health professional students, or graduate students or undergraduate students from the primary R15-eligible institution. Although the proposed research project must be led by the identified PD/PI, applications with strong and innovative student engagement are of high program priority.  If participating students have not yet been identified, the number and academic stage of those to be involved should be provided. Applications should identify which aspects of the proposed research will include student participation. Student involvement may include participation in the design of experiments, collection and analysis of data, execution and troubleshooting of experiments, participation in research meetings, and discussion of future research directions. When applicable, it is highly desirable that student participation also include presentation of research at local and/or national meetings (including the HEAL Annual Scientific Meeting and "Positively Uniting Researchers of Pain to Opine, Synthesize, & Engage" {PURPOSE} meeting), publication of journal articles, and collaborative interactions. By engaging in these activities and collaborating on pain-focused research projects at early stages of training, students will be better prepared and motivated to pursue careers in  pain research. Please see Section III for a list of eligible students.

This NOFO  aims to support pain research grants, not training or fellowship program s. As such, applications should not include training plans such as didactic training or non-research activities related to professional development.  Likewise, applications should not include independent student research projects. For applications that propose a clinical trial, the PD/PI must be the responsible individual of record for oversight of the trial though students can take part in all components of a clinical trial. Oversight includes (but is not limited to): interacting with relevant Institutional Review Board (IRB) staff; reviewing all informed consent documents; reporting potential serious adverse events; and maintaining responsibility for patient safety. However, the student can gain experience in all these components in conjunction with the individual leading the trial. Applications submitted to this NOFO may include additional investigators to those outlined above, including additional collaborators or consultants, or other individuals such as high school students, post-baccalaureate participants, postdoctoral fellows, or clinical fellows. However, involvement of such individuals does not fulfill the goal of enhancing the R15-eligible institutional environment and should account for less than one third of the overall proposed project team.

Additional Information

Non-responsiveness Criteria:

Applications deemed to be non-responsive will not proceed to review and will be withdrawn. Applications with one or more of the following characteristics are considered non-responsive to this NOFO:

• Research that does not address the NIH HEAL Initiative mission to enhance pain management.
• Failure to describe a proposed Research plan and specific aims primarily led by a PI from a R15-eligible Institution.
• Omission of a domestic research partnership and accompanying sub award(s), or that include sub-award(s) that account for more than one third of the total project budget.
• Failure to include the required Facilities & Other Resources document and Other Attachments, including a Team Management Planand, letters of support, including a letter of support from the identified subaward holder(s) and a letter of support from the R15-eligible institution’s provost. Please see s ection IV.2 “Instructions for Application Submission” for details. 
• Proposing a clinical trial addressing safety, tolerability, efficacy, and/or effectiveness of pharmacologic, behavioral, biologic, surgical, or device (invasive or noninvasive) interventions.

Contacting Program Officers Prior to Submission

Applicants are strongly encouraged to consult with  program staff as plans for an application are being developed.

Rigor and Reproducibility

NIH strives for rigor and transparency in all research it funds. For this reason, the NIH HEAL Initiative   explicitly emphasizes the NIH application instructions related to rigor and transparency ( https://grants.nih.gov/policy/reproducibility/guidance.htm ) and provides additional guidance from individual NIH institutes and centers (ICs) to the scientific community. For example, the biological rationale for the proposed experiments must be based on rigorous and robust supporting data, which means that data should be collected via methods that minimize the risk of bias and be reported in a transparent manner. If previously published or preliminary studies do not meet these standards, applicants should address how the current study design addresses the deficiencies in rigor and transparency. Proposed experiments should likewise be designed in a manner that minimizes the risk of bias and ensures validity of experimental results.

Proposed research projects should incorporate adequate methodological rigor where applicable, including but not limited to a clear rationale for the chosen model(s) and primary/secondary endpoint(s), clear descriptions of tools and parameters, blinding, randomization, adequate sample size, prespecified inclusion/exclusion criteria, appropriate handling of missing data and outliers, appropriate controls, pre-planned analyses, and appropriate quantitative techniques.

Applications should also clearly indicate the exploratory vs. confirmatory components of the study, consider study limitations, and plan for transparent reporting of all methods, analyses, and results so that other investigators can evaluate the quality of the work and potentially perform replications. NIH intends to maximize the impact of NIH HEAL Initiative-supported projects through broad and rapid data sharing and immediate access to publications ( https://heal.nih.gov/about/public-access-data ). Guidelines for complying with the HEAL Public Access and Data Sharing Policy can be found at  https://heal.nih.gov/data/complying-heal-data-sharing-policy . More details about NIH HEAL Initiative data sharing are described in Section IV.

Clinical Trial Accrual Policy:

For applications that are proposing to conduct a clinical trial, a series of clinical recruitment milestones detailing completion of the clinical trial and providing contingency plans to proactively confront potential delays or disturbances in attaining the clinical recruitment milestones must be included along with a study timeline in the PHS Human Subjects and Clinical Trials Information form. Continuation of the award is conditional upon satisfactory progress, availability of funds, and scientific priorities of the NIH HEAL Initiative. If, at any time, recruitment falls significantly below the projected milestones for recruitment, NIH will consider ending support and negotiating an orderly phaseout of the award. NIH retains the option of periodic external peer review of progress. NIH program staff will closely monitor progress at all stages for milestones, accrual, and safety.  

Expected Activities of Coordination

NIH HEAL Initiative awardees are strongly encouraged to cooperate and coordinate their activities. It is expected that NIH HEAL Initiative awardees will cooperate and coordinate their activities after post award by participating in PD/PI meetings, including:

NIH HEAL Initiative Scientific Meeting Attendance

Applicants and students are highly encouraged to attend the annual NIH HEAL Initiative Scientific Meetings. The NIH HEAL Initiative hosts an annual meeting of more than 800 NIH HEAL Initiative-funded researchers across the initiative’s research portfolio and career stage spectrum, NIH staff, people with lived and living experience, community partners advising initiative-funded projects, advocacy groups, and other stakeholders to

• Share research advances and cutting-edge science
• Discover opportunities, challenges, and approaches to build on the initiative’s progress
• Connect and explore collaboration with other NIH HEAL Initiative-funded researchers and collaborators to enhance initiative-funded research.

Annual National Pain Scientists Career Development Program (PURPOSE) Meeting

Applicants and students are also highly encouraged to enroll in the HEAL Initiative: Positively Uniting Researchers of Pain to Opine, Synthesize, and Engage (PURPOSE) network and attend its annual meetings. Details can be found at https://painresearchers.com . The HEAL R24 Coordinating Center for National Pain Scientists works to improve the collaboration between basic, translational, and clinical researchers who do not regularly collaborate or work together. One function of the HEAL R24 Coordinating Center for National Pain Scientists is to organize an annual meeting for established scientists as well as early-career pain investigators. This annual meeting facilitates the creation of a network of pain research mentors and mentees as well as fostering communication between scientists and clinicians of different disciplines and providing enhanced mentorship, leadership courses, and any additional training that might be helpful for early-career scientists. R15 recipients are encouraged to attend the annual PURPOSE meeting, either virtually or in person.

See Section VIII. Other Information for award authorities and regulations.

Plan for Enhancing Diverse Perspectives (PEDP) The NIH recognizes that teams comprised of investigators with diverse perspectives working together and capitalizing on innovative ideas and distinct viewpoints outperform homogeneous teams. There are many benefits that flow from a scientific workforce rich with diverse perspectives, including: fostering scientific innovation, enhancing global competitiveness, contributing to robust learning environments, improving the quality of the research, advancing the likelihood that underserved populations participate in, and benefit from research, and enhancing public trust. To support the best science, the NIH encourages inclusivity in research guided by the consideration of diverse perspectives. Broadly, diverse perspectives can include but are not limited to the educational background and scientific expertise of the people who perform the research; the populations who participate as human subjects in research studies; and the places where research is done. This NOFO requires a Plan for Enhancing Diverse Perspectives (PEDP), which will be assessed as part of the scientific and technical peer review evaluation.  Assessment of applications containing a PEDP are based on the scientific and technical merit of the proposed project. Consistent with federal law, the race, ethnicity, or sex (including gender identify, sexual orientation, or transgender status) of a researcher, award participant, or trainee will not be considered during the application review process or when making funding decisions.  Applications that fail to include a PEDP will be considered incomplete and will be administratively withdrawn before review. The PEDP will be submitted as Other Project Information as an attachment (see Section IV).  Applicants are strongly encouraged to read the NOFO instructions carefully and view the available PEDP guidance materials .

Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.

Section II. Award Information

Grant: A financial assistance mechanism providing money, property, or both to an eligible entity to carry out an approved project or activity.

The  OER Glossary  and the How to Apply - Application Guide provide details on these application types. Only those application types listed here are allowed for this NOFO.

Optional: Accepting applications that either propose or do not propose clinical trial(s).

Need help determining whether you are doing a clinical trial?

The NIH HEAL Initiative intends to commit an estimated total of $1.25 million to fund up to three awards per year for FY 2025, FY 2026, and FY 2027. Support for this funding opportunity is contingent upon annual NIH appropriations and the submission of a sufficient number of meritorious applications

Applicants may request up to $375,000 in direct costs for the entire project period. No more than one third of total project costs may go to non-R15-eligible institutions. Annual inflationary increases are not allowed.

The scope of the proposed project should determine the project period. The maximum project period is 3 years. 

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. eligible applicants eligible organizations higher education institutions public/state controlled institutions of higher education private institutions of higher education the following types of higher education institutions are always encouraged to apply for nih support as public or private institutions of higher education: hispanic-serving institutions historically black colleges and universities (hbcus) tribally controlled colleges and universities (tccus) alaska native and native hawaiian serving institutions asian american native american pacific islander serving institutions (aanapisis) in addition, applicant organizations must meet the following criteria at the time of submission: the applicant organization must be an accredited public or nonprofit private school that grants baccalaureate or advanced degrees in health professions (see section below for more details) or biomedical and behavioral sciences. the application must be submitted by the eligible organization with a unique entity identifier (such as uei or duns) and a unique nih era institutional profile file (ipf) number. at the time of application submission, determination of eligibility will be based in part on nih institutional support. a year is defined as a federal fiscal year: from october 1 through september 30.   note that collaborating subawardees do not need to adhere to the r15 eligibility criteria stated above. however, they must be separate legal entities that fulfill the terms of an eligible subaward agreement. for this particular nofo, they must also be u.s. domestic institutions. more details can be found at https://grants.nih.gov/policy/subawards . undergraduate focused institutions: at the time of application submission, all the non-health professional components of the institution combined must not have received support from the nih totaling more than $6 million per year (in both direct and f&a/indirect costs) in 4 of the last 7 years. for institutions composed of multiple schools and colleges, the $6 million funding limit is based on the amount of nih funding received by all the non-health professional schools and colleges within the institution as a whole. note that all activity codes are included in this calculation except the following: c06, s10, and all activity codes starting with a g. help determining the organization funding level can be found at https://grants.nih.gov/grants/funding/determing-organization-funding-levels-r15-eligibility.pdf    an academic component is any school/college that is not a health professional school or college. a qualifying academic component (i.e., school/college) within an institution (e.g., school of arts and sciences) has greater undergraduate student enrollment than graduate student enrollment. all types of health professional schools and colleges are not eligible to apply and are not considered in this calculation.  for institutions with multiple campuses, eligibility can be considered for each individual campus (e.g., main, satellite, etc.) only if separate ueis and nih ipf numbers are established for each campus. for institutions that use one uei or nih ipf number for all campuses, eligibility is determined for all campuses (e.g., main, satellite, etc.) combined.   health professional and graduate schools   at the time of application submission, all components of the institution combined must not have received support from the nih totaling more than $6 million per year (in both direct and f&a/indirect costs) in 4 of the last 7 years. for institutions composed of multiple schools and colleges, the $6 million funding limit is based on the amount of nih funding received by all of the schools and colleges within the institution as a whole. note that all activity codes are included in this calculation except the following: c06, s10, and all activity codes starting with a g. a graduate school offers advanced degrees, beyond the undergraduate level, in an academic discipline including m.a., m.s., and ph.d. degrees. health professional schools and colleges are accredited institutions that provide education and training leading to a health professional degree, including but not limited to: b.s.n., m.s.n., d.n.p., m.d., d.d.s., d.o., pharm.d., d.v.m., o.d., d.p.t., d.c., n.d., d.p.m., m.o.t., o.t.d., d.p.t., m.s.-s.l.p., c.sc.d., s.l.p.d., au.d., m.s.p.o., m.s.a.t., and m.p.h. eligible health professional schools/colleges may include schools or colleges of nursing, medicine, dentistry, osteopathy, pharmacy, veterinary medicine, public health, optometry, allied health, chiropractic, naturopathy, podiatry, rehabilitation medicine, physical therapy, orthotics and prosthetics, kinesiology, occupational therapy, and psychology. accreditation must be provided by a body approved for such purpose by the secretary of education. for institutions with multiple campuses, eligibility can be considered for each individual campus (e.g., main, satellite, etc.) only if a unique identifier number and nih ipf number are established for each campus. for institutions that use one identifier number or nih ipf number for all campuses, eligibility is determined for all campuses (e.g., main, satellite, etc.) together. additional eligibility guidance a signed letter is required from the provost or similar official with institution-wide responsibility verifying the eligibility of the applicant institution at the time of application submission according to the eligibility criteria indicated above. see the application instructions for “other attachments” on the sf424(r&r) other project information form in section iv.2 instructions for application submission. final eligibility will be validated by nih prior to award. to assist in determining eligibility, organizations are encouraged to use the nih report website under nih awards by location & organization . a prep application must provide evidence of a subaward to a separate institution , and the grantee may partner with a non-r15-eligible institution. however, applicants should keep the goals of the prep in mind when preparing the application, which include strengthening the research environment of eligible institutions and engaging students from eligible institutions in pain research. it is expected that the project, and two-thirds of the total project budget, will be directed by the pd(s)/pi(s) at r15-eligible institution(s). a letter of support from each collaborator is required verifying the research collaboration at the time of application submission according to the eligibility criteria indicated above. the letter(s) should detail how the proposed research partnership will help to accomplish the proposed pain research project, enhance the r15-eligible institution’s research program, and promote synergy from an integrated, interdisciplinary research partnership(s) among the multiple proposed institutions. see the application instructions for “other attachments” on the sf424(r&r) other project information form in section iv.2 instructions for application submission. foreign organizations non-domestic (non-u.s.) entities (foreign organizations) are not eligible to apply. non-domestic (non-u.s.) components of u.s. organizations are not eligible to apply. foreign components, as defined in the nih grants policy statement , are allowed.  required registrations applicant organizations applicant organizations must complete and maintain the following registrations as described in the how to apply - application guide to be eligible to apply for or receive an award. all registrations must be completed prior to the application being submitted. registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference nih grants policy statement section 2.3.9.2 electronically submitted applications for additional information system for award management (sam) – applicants must complete and maintain an active registration, which requires renewal at least annually . the renewal process may require as much time as the initial registration. sam registration includes the assignment of a commercial and government entity (cage) code for domestic organizations which have not already been assigned a cage code. nato commercial and government entity (ncage) code – foreign organizations must obtain an ncage code (in lieu of a cage code) in order to register in sam. unique entity identifier (uei) - a uei is issued as part of the sam.gov registration process. the same uei must be used for all registrations, as well as on the grant application. era commons - once the unique organization identifier is established, organizations can register with era commons in tandem with completing their grants.gov registrations; all registrations must be in place by time of submission. era commons requires organizations to identify at least one signing official (so) and at least one program director/principal investigator (pd/pi) account in order to submit an application. grants.gov – applicants must have an active sam registration in order to complete the grants.gov registration. program directors/principal investigators (pd(s)/pi(s)) all pd(s)/pi(s) must have an era commons account.  pd(s)/pi(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in era commons. if the pd/pi is also the organizational signing official, they must have two distinct era commons accounts, one for each role. obtaining an era commons account can take up to 2 weeks. eligible individuals (program director/principal investigator) any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the program director(s)/principal investigator(s) (pd(s)/pi(s)) is invited to work with their organization to develop an application for support. individuals from diverse backgrounds, including individuals from underrepresented racial and ethnic groups, individuals with disabilities, and women are always encouraged to apply for nih support. see, reminder: notice of nih's encouragement of applications supporting individuals from underrepresented ethnic and racial groups as well as individuals with disabilities , not-od-22-019 . for institutions/organizations proposing multiple pds/pis, visit the multiple program director/principal investigator policy and submission details in the senior/key person profile (expanded) component of the how to apply - application guide . to be eligible for support under a prep grant, the pd(s)/pi(s) must meet the following additional criteria: each pd/pi must have a primary appointment at either an r15-eligible institution, including professional or graduate schools, undergraduate-focused organizations, or a college within the applicant institution, as defined in “eligible organizations,” above. if proposing multiple pd(s)/pi(s), each pd/pi must be at an r15-eligible institution. each pd/pi may not be the pd/pi of an active nih research grant, including another r15 grant, at the time of award of a prep grant, although they may be one of the key personnel for an active nih grant held by another pd/pi. each pd/pi may not be awarded support under more than one r15 grant at a time, although he or she may have support under successive new or renewal grants. 2. cost sharing.

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement NIH Grants Policy Statement Section 1.2 Definition of Terms.

3. Additional Information on Eligibility

Number of Applications

Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time, per NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application . This means that the NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review (see  NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications ).

Section IV. Application and Submission Information

1. requesting an application package.

The application forms package specific to this opportunity must be accessed through ASSIST, Grants.gov Workspace or an institutional system-to-system solution. Links to apply using ASSIST or Grants.gov Workspace are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the instructions in the Research (R) Instructions in the  How to Apply - Application Guide  except where instructed in this notice of funding opportunity to do otherwise. Conformance to the requirements in the How to Apply - Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

Letter of Intent

Although a letter of intent is not required, is not binding, and does not enter into the review of a subsequent application, the information that it contains allows IC staff to estimate the potential review workload and plan the review.

By the date listed in Part 1. Overview Information , prospective applicants are asked to submit a letter of intent that includes the following information:

• Descriptive title of proposed activity
• Name(s), address(es), and telephone number(s) of the PD(s)/PI(s)
• Names of other key personnel
• Participating institution(s)
• Number and title of this funding opportunity

The letter of intent should be sent to:

Jessica McKlveen, PhD National Center for Complementary & Integrative Health (NCCIH) Telephone: 301-594-8018 Email:  [email protected]

Page Limitations

All page limitations described in the How to Apply – Application Guide and the Table of Page Limits must be followed.

The following section supplements the instructions found in the How to Apply – Application Guide and should be used for preparing an application to this NOFO.

SF424(R&R) Cover

All instructions in the How to Apply - Application Guide must be followed.

SF424(R&R) Project/Performance Site Locations

Sf424(r&r) other project information.

Facilities & Other Resources (Required):

• A profile of the scientific background, academic level, and expertise of the students of the applicant institution and any information or estimate of the number who have obtained a health professional baccalaureate or advanced degree and gone on to obtain an academic or professional doctoral or other advanced degree in the health-related sciences during the last 5 years.
• Description of plans to build a broad team of prospective researchers, including students, with a variety of backgrounds, expertise, and skills, and to arrive at major decisions, accounting for different points of view. Personnel from the primary R15-eligible institution(s) should compose a two-thirds majority of the project team .
• Description of the special characteristics of the applicant institution that make it appropriate for an PREP grant awarded through this NOFO to: (1) support the efforts by R15-eligible principal investigators (PIs) at undergraduate-focused institutions or health professional schools and graduate schools to conduct small-scale basic and mechanistic pain research projects; (2) promote integrated, interdisciplinary research partnerships between R15-eligible PIs and additional investigators from U.S. domestic institutions; and (3) enhance the pain research environment at the R15-eligible institution for health professional students or undergraduate and/or graduate students by actively engaging them in the proposed pain research projects.
• Description of the likely impact of a PREP grant on the ability of the PD(s)/PI(s) to engage students in research.
• Description of the likely impact of a PREP grant on the research environment of the applicant institution.
• Description of the likely impact of the PREP grant on the ability of health professional or undergraduate and/or graduate students at the institution to gain experience conducting biomedical research.
• Description of the resources of the grantee institution available for the proposed research (e.g., equipment, supplies, laboratory space, release time, matching funds).
• Although the majority of the research project should be conducted at the R15-eligible institution, the use of special facilities or equipment at another institution is permitted. For any proposed research sites other than the applicant institution, provide a brief description of the resources and access students will need and have to these resources.

Applications without a Facilities & Other Resources document will be withdrawn. 

Other Attachments:

Applications that fail to include the following three required ‘other’ attachments will be considered incomplete and will be withdrawn.

1.Team Management Plan (Required three pages maximum):

A key goal of this program is to establish new research partnerships among R15-eligible investigators and other domestic research centers, programs, or institutions with complementary research expertise and/or resources. To ensure that prospective research teams fit the goals of the PREP, a team management plan is required. Applications with team management plans that exceed the three-page limit will be withdrawn.

As an “Other Attachment” entitled Team-Management-Plan.pdf, applications should describe how the research collaborators will function to accomplish program objectives. Team management approaches raised in the subsections listed below should be described in the plan. Note that a “Multiple PD/PI Leadership Plan” may also be submitted as a separate attachment, and if it is included the information in that plan should not be duplicated here. Whereas the Multiple PD/PI Leadership Plan focuses on leadership by and interactions across the PD/PIs, the Team Management Plan focuses on management of the whole team/key personnel. Applicants are encouraged to consult resources to aid in developing effective team-based programs (see e.g., the  NCI Collaboration and Team Science Field Guide ).

Organizational structure and team composition: The Team Management Plan should clearly show the organizational structure and composition of the proposed project team. Two-thirds of the proposed research project team should be health professional students trainees or graduate students or undergraduate from the primary R15-eligible institution. The plan should describe a management structure based on project objectives that effectively promote the proposed research. The structure should account for team composition, institutional resources, and policies that conform with PREP objectives outlined in Section I.

Shared leadership, contributions, and distributed responsibility for decision-making: The Team Management Plan should include a description of how the proposed collaborators will work together to direct the overall scientific team to leverage the diverse perspectives, expertise, and skills of the team members to successfully accomplish the goals of the project. One key consideration is that teams employing multidisciplinary approaches and having diverse areas of intellectual and technical expertise are more productive if the process for making decisions incorporates different points of view. The Team Management Plan should describe how major decisions will be made or how conflicts will be resolved.

Resource sharing and allocation across the team: Applications should describe management and decision-making processes that promote collective input for allocation of program resources with flexibility when resources may need to be dynamically reallocated to achieve programmatic goals. A plan for how intra-team, institutional, and regional resources that are integral to the team goals will be shared and made accessible to team members should also be included.

Credit assignment: A plan for how credit and IP will be shared, especially with the R15 institution’s students, should be included. Methods for attributing contributions to publications should be described to enable individual professional assessment in joint projects.

Coordination and communication plans: Practical aspects should be described, including frequency and logistics of real-time communication across all key personnel, consultants, scholars, early-stage investigators etc., and other significant contributors regardless of effort level.

An important and meaningful impact of team science may come from shaping the next generation of pain scientists. Because of the interdisciplinary expertise of the research groups, students are exposed to and can learn a variety of scientific approaches and methodologies, resulting in multifaceted early-stage investigators. Plans for how students trainees will be immersed in and benefit from different approaches taken by the collective team program should be described. This could include shared mentorship, inter-laboratory meetings, all-hands tutorials, shared meeting and document space, inter-laboratory visits, and student presentations.

2. Provost Letter(s) of Support: The application must include a PDF-formatted letter named “ProvostLetter.pdf” (without quotation marks). For MPI applications a signed provost letter is required from each involved institution. The letter must be signed by the provost or similar official with institution-wide responsibility attesting to the following information:

For Undergraduate Focused Institutions:

• The eligible academic component(s) (i.e., the college/school level) must have more undergraduates than graduate students as of the date of submission.
• All the non-health professional components of the institution together have received support from the NIH totaling no more than $6 million per year (in both direct and F&A/indirect costs) in 4 of the last 7 years, as described in Section III, "Eligible Organization".
• Validation that the PD/PI has (or in the case of a multiple PD/PI application that all PD(s)/PI(s) have) a primary appointment at the qualifying component (i.e., the college/school level).  

For Health Professional and Graduate Schools:

• The eligible academic component(s) (i.e., the college/school level) must be a health professional or graduate school that awards health professional baccalaureate or advanced degrees in biomedical and/or biobehavioral sciences.
• All components of the institution together have received support from NIH totaling no more than $6 million per year (in both direct and F&A/indirect costs) in 4 of the last 7 years, as described in Section III, “Eligible Organization.”
• Validation that the PD/PI has (or in the case of a multiple PD/PI application that all PD(s)/PI(s) have) a primary appointment at the qualifying component (i.e., the college/school level).

3. Collaborator Letter(s) of Support:  Applications must include additional PDF-formatted letter(s) from collaborating subaward holder(s) named “CollaboratorLetter_ Initials .pdf” (without quotation marks). For multiple collaborators, a signed letter is required from each involved collaborator. Note that collaborators do not need to meet the R15-eligibility criteria outlined above. The letter should demonstrate the collaborator's willingness to collaborate with the study lead as well as briefly outline their contributions to the project that will result in a well-integrated, interdisciplinary research approach to the understanding of pain. If the proposed collaboration is a new research partnership among investigators, this information should also be included.

Plan for Enhancing Diverse Perspectives (PEDP)

• In an "Other Attachment" entitled "Plan for Enhancing Diverse Perspectives," all applicants must include a summary of actionable strategies to advance the scientific and technical merit of the proposed project through expanded inclusivity.
• Applicants should align their proposed strategies for PEDP with the research strategy section, providing a holistic and integrated view of how enhancing diverse perspectives and inclusivity are buoyed throughout the application.
• The PEDP will vary depending on the scientific aims, expertise required, the environment and performance site(s), as well as how the project aims are structured.
• Actionable strategies using defined approaches for the inclusion of diverse perspectives in the project;
• Description of how the PEDP will advance the scientific and technical merit of the proposed project;
• Anticipated timeline of proposed PEDP activities;
• Evaluation methods for assessing the progress and success of PEDP activities.

Examples of items that advance inclusivity in research and may be appropriate for a PEDP can include, but are not limited to:

• Partnerships with different types of institutions and organizations (e.g., research-intensive; undergraduate-focused; HBCUs; emerging research institutions; community-based organizations).
• Project frameworks that enable communities and researchers to work collaboratively as equal partners in all phases of the research process.
• Outreach and planned engagement activities to enhance recruitment of individuals from diverse groups as human subjects in clinical trials, including those from underrepresented backgrounds.
• Description of planned partnerships that may enhance geographic and regional diversity.
• Outreach and recruiting activities intended to diversify the pool of applicants for research training programs, such as outreach to prospective applicants from groups underrepresented in the biomedical sciences, for example, individuals from underrepresented racial and ethnic groups, those with disabilities, those from disadvantaged backgrounds, and women.
• Plans to utilize the project infrastructure (i.e., research and structure) to enhance the research environment and support career-advancing opportunities for junior, early- and mid-career researchers.
• Transdisciplinary research projects and collaborations among researchers from fields beyond the biological sciences, such as physics, engineering, mathematics, computational biology, computer and data sciences, as well as bioethics.

Examples of items that are not appropriate in a PEDP include, but are not limited to:

• Selection or hiring of personnel for a research team based on their race, ethnicity, or sex (including gender identify, sexual orientation, or transgender status).
• A training or mentorship program limited to certain researchers based on their race, ethnicity, or sex (including gender identify, sexual orientation, or transgender status).

For further information on the Plan for Enhancing Diverse Perspectives (PEDP), please see PEDP guidance materials .

SF424(R&R) Senior/Key Person Profile

R&r or modular budget.

• The total budget for all years of the proposed project must be requested in Budget Period 1. Do not complete Budget Periods 2 or 3. They are not required and will not be accepted with the application.
• Applicants submitting an application with direct costs of $250,000 or less (total for all years, excluding consortium Facilities and Administrative [F&A] costs) must use the Modular Budget.
• Applicants submitting an application with direct costs of $250,001 - $375,000 (total for all years, excluding consortium Facilities and Administrative [F&A] costs) must use the R&R Budget.
• Students must be compensated for their participation in the laboratory's research and in accord with institutional policies. Student salaries can be requested in the R15 budget, or other resources at the university can be used to pay them for their participation. Undergraduate students who are compensated from the R15 grant or other institutional funds should receive at least the national minimum wage . Compensation through course credit hours towards graduation is allowable, but must be justified. If universities/colleges provide room and board for summer research students, details must be provided in the application.
• NIH does not fund stipends for undergraduates on R15 awards.

Budget Justification:

Personnel Justification: Since a primary objective of the PREP is to expose and incorporate students into multidisciplinary pain research, PD(s)/PI(s) must include health professional or undergraduate and/or graduate students from the applicant institution/applicant component in the proposed research. Students from the R15-eligible institution should compose the majority of the research team (two thirds or more). Indicate aspects of the proposed research in which students will participate. If participating students have not yet been identified, the number and academic level of those to be involved should be provided. Collaborators or consultants for the project should provide additional budget information, including their names, their organizational affiliations, and the services they will perform.

PEDP implementation costs: Applicants may include allowable costs associated with PEDP implementation (as outlined in the Grants Policy Statement section 7): https://grants.nih.gov/grants/policy/nihgps/html5/section_7/7.1_general.htm.

R&R Subaward Budget

Phs 398 cover page supplement, phs 398 research plan.

All instructions in the  How to Apply - Application Guide must be followed, with the following additional instructions:

Research Strategy:  

The research strategy must address how the proposed project intends to accomplish all three objectives of this program, including: 1) Supporting the efforts by R15-eligible principal investigators (PIs) at undergraduate-focused institutions OR health professional schools and graduate schools to conduct small-scale basic and mechanistic pain research; (2) promoting integrated, interdisciplinary research partnerships between R15-eligible PIs and additional investigators from U.S. domestic institutions; and (3) enhancing the pain research environment at the R15-eligible institution for health professional students or undergraduate and/or graduate students by actively engaging them in the proposed pain research projects.

Applications should include a detailed description of a research approach that will  produce rigorous data that can be disseminated and advance our basic and mechanistic understanding of pain.  Additionally, the research strategy should detail how the proposed research partnership includes sufficient integrative pain expertise and related resources and/or institutional infrastructure that increase the likelihood of success. The application should detail how the proposed scientific research and proposed program and research partnership will have a substantial effect on strengthening the research environment at the proposed applicant’s institution.

Applications should provide details on how the research project will be directed by the R15-eligible PI and how two-thirds of the research project will be conducted at the R15-eligible institution. The research strategy should detail how the research team will recruit additional prospective investigators, including students, from a range of backgrounds, skills, and expertise for the broad pool of researchers who may apply to participate and contribute to the project. Applications should include details about how the investigators will cooperate and coordinate their activities with other HEAL investigators at PD/PI meetings, including (but not limited to) other investigators in the R15 program, the HEAL Annual Scientific and PURPOSE meetings.Proposed PD/PI(s) should include evidence of experience supervising students in previous research efforts, as well as describing any innovative approaches to engage students in the proposed pain research project. Applications should provide additional details outlining student involvement in the research project by addressing the following questions:

• How will students engage in conducting hands-on rigorous research?
• How will students participate in research activities such as planning, execution, and/or analysis of the research?
• Are there any additional plans for student involvement, such as presentation at local or national meetings, participation in publication of research findings, and development of, or participation in, collaborative activities?
• How will the project provide students with adequate opportunities to improve their research capabilities and support their progress toward a future career in pain research?
• Note-The purpose of this program is to support pain research projects, not student training. Formal training plans (e.g., non-research activities, didactic training, seminars) should not be provided, although a brief description of activities related to enhancing students’ research capabilities and progress (e.g., the use of individual development plans) is permitted. Furthermore, applications should not include independent student research projects.

Resource Sharing Plan : Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the  How to Apply - Application Guide .

Other Plan(s): 

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions:

• All applicants planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan. 

The NIH HEAL Initiative has additional requirements that must be addressed in the Data Management and Sharing Plan. All HEAL-generated data must be shared through the HEAL Initiative Data Ecosystem following HEAL’s compliance guidance ( https://heal.nih.gov/data/complying-heal-data-sharing-policy ). Specifically, HEAL applicants must include:

• Plans to submit data and metadata (and code, if applicable) to a HEAL-compliant data repository ( https://www.healdatafair.org/resources/guidance/selection ) and follow requirements of the selected repository.
• Plans to register your study with the HEAL platform within one year of award ( https://heal.github.io/platform-documentation/study-registration/ ).
• Plans to submit HEAL-defined study-level metadata within one year of award (HTTP ://github.com/HEAL/heal-metadata-schemas/blob/main/for-investigators-how-to/study-level-metadata-fields/study-metadata-schema-for-humans.pdf ) and  https://heal.github.io/platform-documentation/slmd_submission/ .
• Plans to submit data dictionaries to the HEAL Data Ecosystem, if applicable.
• HEAL pain clinical studies must include a plan to use HEAL core Common Data Elements (CDEs) ( https://heal.nih.gov/data/common-data-elements ). NIH HEAL Initiative clinical studies that are using copyrighted questionaries are required to obtain licenses for use prior to initiating data collection. Licenses must be shared with the HEAL CDE team and the program officer prior to use of copyrighted materials.
• To the extent possible, all other (nonpain) HEAL studies conducting clinical trials or research involving human subjects are expected to use questionnaires by the HEAL Common Data Elements (CDE) Program ( https://heal.nih.gov/data/common-data-elements ) if applicable and relevant to their research.
• Studies using CDEs, regardless of whether they are part of the HEAL repository, will be required to report which questionnaires are being used.
• To the extent possible, NIH HEAL Initiative awardees are expected to integrate broad data sharing consent language into their informed consent forms.

The NIH HEAL Initiative has developed additional details and resources to fulfill these requirements ( https://www.healdatafair.org/resources/road-map ). Budgeting guidance for data sharing can be found in NOT-OD-21-015 and the NIH Scientific Data Sharing site .

Appendix:  Only limited Appendix materials are allowed. Follow all instructions for the Appendix as described in the How to Apply - Application Guide .

• No publications or other material, with the exception of blank questionnaires or blank surveys, may be included in the Appendix.

PHS Human Subjects and Clinical Trials Information

When involving human subjects research, clinical research, and/or NIH-defined clinical trials (and when applicable, clinical trials research experience) follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply - Application Guide , with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or Delayed Onset Study record.

Study Record: PHS Human Subjects and Clinical Trials Information

Section 2 - Study Population Characteristics

2.5 Recruitment and Retention Plan

Describe the following: 

• Recruitment milestones; 
• The planned recruitment methods, including use of contact lists (participants and/or sites), databases or other pre-screening resources, advertisements, outreach, media / social media and referral networks or groups;
• If there are known participant or study-related barriers to accrual or participation (based on literature or prior experience), please list these barriers and describe plans to address them to optimize success; 
• Contingency plans for participant accrual if enrollment significantly lags behind accrual benchmarks; 5) participant retention and adherence strategies; and 6) possible competition from other trials for study participants.

2.7 Study Timeline

Include a table or graph of the overall study timeline. This is expected to be a visual representation (such as a Gantt chart) of recruitment milestones and key project management activities. A narrative is not expected in this section.

The study timeline should include recruitment milestones that need to be met throughout the life cycle of the clinical trial to ensure its success, and the subtasks that will be used to reach the recruitment milestones. In the timeline, the study duration is expected to be displayed in months. The timeline should include, but is not limited to, the following:

(a) When the study opens to enrollment (b) When recruitment milestones (see below) are met (c) What subtasks are needed to reach of the recruitment milestones (d) When final transfer of the data will occur (e) When analysis of the study data will occur (f) When the primary study manuscript will be submitted for publication

Delayed Onset Study

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start). All instructions in the How to Apply - Application Guide must be followed.

PHS Assignment Request Form

3. unique entity identifier and system for award management (sam).

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I.  contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday , the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies). Applicants must then complete the submission process by tracking the status of the application in the eRA Commons , NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time.  If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications .

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the How to Apply – Application Guide .

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review.

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Pre-award costs are allowable only as described in the NIH Grants Policy Statement Section 7.9.1 Selected Items of Cost .

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide . Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit How to Apply – Application Guide . If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII .

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form . Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide .

See more tips for avoiding common errors.

Applications must include a PEDP submitted as Other Project Information as an attachment. Applications that fail to include a PEDP will be considered incomplete and will be administratively withdrawn before review.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and responsiveness by components of participating organizations , NIH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

In order to expedite review, applicants are requested to notify the NCCIH Referral Office by email at  [email protected] when the application has been submitted. Please include the NOFO and title, PD/PI name, and title of the application.

Recipients or subrecipients must submit any information related to violations of federal criminal law involving fraud, bribery, or gratuity violations potentially affecting the federal award. See Mandatory Disclosures, 2 CFR 200.113 and NIH Grants Policy Statement Section 4.1.35 .

Send written disclosures to the NIH Chief Grants Management Officer listed on the Notice of Award for the IC that funded the award and to the HHS Office of Inspector Grant Self Disclosure Program at [email protected] . 

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy

The following post-submission materials will be accepted: Team Management Plan (e.g., due to the hiring, replacement, or loss of an investigator).

Section V. Application Review Information

1. criteria.

Only the review criteria described below will be considered in the review process.  Applications submitted to the NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

For this particular NOFO, note the following:

The purpose of this HEAL Initiative program is to (1) support the efforts by R15-eligible principal investigators (PIs) at primarily undergraduate-focused serving institutions or health professional schools and graduate schools to conduct small-scale basic and mechanistic pain research projects ; (2) promote integrated, interdisciplinary research partnerships between R15-eligible PIs and investigators from U.S. domestic institutions; and (3) enhance the pain research environment at the R15-eligible institution for health professional students or undergraduate and/or graduate students by actively engaging them in the proposed pain research projects.

Applications in response to this notice of funding opportunity (NOFO) should include plans to accomplish these goals. Specifically, applications should include a rigorous plan for conducting basic and mechanistic pain research projects in the Research Strategy section of the application . In addition, a research partnership between the PI’s institution and at least one investigator from a separate U.S. domestic institution that provides resources and/or expertise that will enhance the proposed pain research program must be included in a separate Team Management Plan. The proposed partnership will be a sub-award agreement(s) with at least one partnering institution, which does not need to be R15-eligible. The budget of all sub-awards must not exceed one third of the total budget. Furthermore, applications must include a Facilities & Other Resources document  that demonstrates active involvement of health professional students or undergraduate and/or graduate students from the R15-eligible institution(s) in the proposed pain research projects.

Although preliminary data are not required for an R15 application, they may be included if available. The scientific foundation for the proposed research should be based on published research and/or any available preliminary data.

A proposed Clinical Trial application may include study design, methods, and intervention that are not by themselves innovative but address important questions or unmet needs. Additionally, the results of the clinical trial may indicate that further clinical development of the intervention is unwarranted or lead to new avenues of scientific investigation.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the project proposed).As part of the overall impact score, reviewers should consider and indicate how the Plan for Enhancing Diverse Perspectives affects the scientific merit of the project.

Reviewers will consider each of the review criteria below in the determination of scientific merit and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact. For example, a project that by its nature is not innovative may be essential to advance a field.

Does the project address an important problem or a critical barrier to progress in the field? Is the prior research that serves as the key support for the proposed project rigorous? If the aims of the project are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?

In addition, for applications involving clinical trials

Are the scientific rationale and need for a clinical trial to test the proposed hypothesis or intervention well supported by preliminary data, clinical and/or preclinical studies, or information in the literature or knowledge of biological mechanisms? For trials focusing on clinical or public health endpoints, is this clinical trial necessary for testing the safety, efficacy or effectiveness of an intervention that could lead to a change in clinical practice, community behaviors or health care policy? For trials focusing on mechanistic, behavioral, physiological, biochemical, or other biomedical endpoints, is this trial needed to advance scientific understanding?

Specific to this NOFO:

Taking into consideration the type of R15-eligible institution the application has been submitted from, if funded, will this grant have a substantial effect on strengthening the research environment at the applicant institution and exposing students to research ?

Does the project adequately describe how the research partnership will advance our understanding of pain conditions? 

If the aims of the project are achieved, will the project yield rigorous data that can be disseminated and is likely to be important to the field?

Will the proposed collaboration appropriately improve the R15 institutional environment in a manner to support more students to engage in pain research at that institution?

Are the PD(s)/PI(s), collaborators, and other researchers well suited to the project? If Early Stage Investigators or those in the early stages of independent careers, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance and organizational structure appropriate for the project?

With regard to the proposed leadership for the project, do the PD/PI(s) and key personnel have the expertise, experience, and ability to organize, manage and implement the proposed clinical trial and meet milestones and timelines? Do they have appropriate expertise in study coordination, data management and statistics? For a multicenter trial, is the organizational structure appropriate and does the application identify a core of potential center investigators and staffing for a coordinating center?

Does the application provide details about how the research project will be directed by the R15-eligible PI and how two-thirds of the research project will be conducted at the R15-eligible institution?

Is it clear how the applicant intends to recruit additional prospective investigators, including students, from a range of backgrounds, skills, and expertise for the pool of researchers who may apply to address the proposed scientific problem?

Will the combined scientific expertise (of the proposed collaborative research team) likely result in a well-integrated, interdisciplinary research approach to the understanding of pain?

Does the team of investigators include sufficient integrative pain expertise for the proposed research?

How appropriate is the PD/PI’'s experience in supervising and engaging students in research?

Does the application include details about how the investigators will cooperate and coordinate their activities with other HEAL investigators at PD/PI meetings, including (but not limited to) other investigators in the R15 program, the HEAL Annual Scientific and PURPOSE meetings?

Team Management Plan (Attachment):

How fair and adequate are the governance processes for decision making, conflict resolution, and resource allocation outlined in the plan? 

How effective is the plan for team leadership and management with sufficient examples of distributed responsibility?

How well would the program leadership create a sustainable environment for maintaining cohesiveness, productivity, and shared vision?

How adequate are the management plans for shared professional credit?

If shared research resources will be utilized, how adequate are the plans for resource sharing and allocation to ensure that all team members will have the access they require?

How well does the plan include examples of team coordination and communication?

How clearly does the plan include details about which personnel are available at the R15-eligible institution(s), including health professional students or graduate students or undergraduate students, that would compose a two-thirds majority of the project team and how they would contribute to the research project?

How well does the management plan outline how the collaborative partnership will supervise and engage students?

Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?

Does the design/research plan include innovative elements, as appropriate, that enhance its sensitivity, potential for information or potential to advance scientific knowledge or clinical practice?

Does the proposed research include innovative interdisciplinary pain research topics?

Is the proposed research partnership a new collaboration between investigators?

Are innovative approaches for engaging health professional or undergraduate and/or graduate students in research proposed?

Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Have the investigators included plans to address weaknesses in the rigor of prior research that serves as the key support for the proposed project? Have the investigators presented strategies to ensure a robust and unbiased approach, as appropriate for the work proposed? Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects? 

If the project involves human subjects and/or NIH-defined clinical research, are the plans to address 1) the protection of human subjects from research risks, and 2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of individuals of all ages (including children and older adults), justified in terms of the scientific goals and research strategy proposed?

Does the application adequately address the following, if applicable

Study Design

Is the study design justified and appropriate to address primary and secondary outcome variable(s)/endpoints that will be clear, informative and relevant to the hypothesis being tested? Is the scientific rationale/premise of the study based on previously well-designed preclinical and/or clinical research? Given the methods used to assign participants and deliver interventions, is the study design adequately powered to answer the research question(s), test the proposed hypothesis/hypotheses, and provide interpretable results? Is the trial appropriately designed to conduct the research efficiently? Are the study populations (size, gender, age, demographic group), proposed intervention arms/dose, and duration of the trial, appropriate and well justified?

Are potential ethical issues adequately addressed? Is the process for obtaining informed consent or assent appropriate? Is the eligible population available? Are the plans for recruitment outreach, enrollment, retention, handling dropouts, missed visits, and losses to follow-up appropriate to ensure robust data collection? Are the planned recruitment timelines feasible and is the plan to monitor accrual adequate? Has the need for randomization (or not), masking (if appropriate), controls, and inclusion/exclusion criteria been addressed? Are differences addressed, if applicable, in the intervention effect due to sex/gender and race/ethnicity?

Are the plans to standardize, assure quality of, and monitor adherence to, the trial protocol and data collection or distribution guidelines appropriate? Is there a plan to obtain required study agent(s)? Does the application propose to use existing available resources, as applicable?

Data Management and Statistical Analysis

Are planned analyses and statistical approach appropriate for the proposed study design and methods used to assign participants and deliver interventions? Are the procedures for data management and quality control of data adequate at clinical site(s) or at center laboratories, as applicable? Have the methods for standardization of procedures for data management to assess the effect of the intervention and quality control been addressed? Is there a plan to complete data analysis within the proposed period of the award?

Taking into consideration the type of R15-eligible institution the application has been submitted from, how suitable are the plans for ensuring that students are well integrated into the research program?

How will this project provide students with a high-quality research experience focused on the execution, analysis, and reporting of the study? 

Would students have adequate opportunities to present at national or local meetings, publish research findings, and/or participate in other collaborative activities? 

Would the proposed research project provide adequate opportunities for students to improve their research capabilities and support their progress toward a biomedical research career? 

Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?

If proposed, are the administrative, data coordinating, enrollment and laboratory/testing centers, appropriate for the trial proposed?

Does the application adequately address the capability and ability to conduct the trial at the proposed site(s) or centers? Are the plans to add or drop enrollment centers, as needed, appropriate?

If international site(s) is/are proposed, does the application adequately address the complexity of executing the clinical trial?

If multi-sites/centers, is there evidence of the ability of the individual site or center to: (1) enroll the proposed numbers; (2) adhere to the protocol; (3) collect and transmit data in an accurate and timely fashion; and, (4) operate within the proposed organizational structure?

Does the "Facilities & Other Resources" attachment describe strong and innovative approaches to how students or trainees will participate in the research project?

Does the application demonstrate appropriate plans to recruit health professional or undergraduate and/or graduate students from diverse backgrounds to participate in the research project?

Does the application provide a plan to aid students at the R15-eligible institution/academic component to pursue careers in the biomedical sciences?

Do(es) the PD/PI(s) have sufficient time and institutional support to conduct the proposed project?

Is there synergy to be gained from the integrated, interdisciplinary research partnership(s) among the multiple proposed institutions?

As applicable for the project proposed, reviewers will evaluate the following additional items while determining scientific and technical merit, and in providing an overall impact score, but will not give separate scores for these items.

Specific to applications involving clinical trials

Is the study timeline described in detail, taking into account start-up activities, the anticipated rate of enrollment, and planned follow-up assessment? Is the projected timeline feasible and well justified? Does the project incorporate efficiencies and utilize existing resources (e.g., CTSAs, practice-based research networks, electronic medical records, administrative database, or patient registries) to increase the efficiency of participant enrollment and data collection, as appropriate?

Are potential challenges and corresponding solutions discussed (e.g., strategies that can be implemented in the event of enrollment shortfalls)?

Specific to this NOFO: Are the clinical trial recruitment milestones feasible given the proposed study timeline?

For research that involves human subjects but does not involve one of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring for clinical trials.

For research that involves human subjects and meets the criteria for one or more of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the Guidelines for the Review of Human Subjects .

When the proposed project involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed. For additional information on review of the Inclusion section, please refer to the Guidelines for the Review of Inclusion in Clinical Research .

The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following three points: (1) a complete description of all proposed procedures including the species, strains, ages, sex, and total numbers of animals to be used; (2) justifications that the species is appropriate for the proposed research and why the research goals cannot be accomplished using an alternative non-animal model; and (3) interventions including analgesia, anesthesia, sedation, palliative care, and humane endpoints that will be used to limit any unavoidable discomfort, distress, pain and injury in the conduct of scientifically valuable research. Methods of euthanasia and justification for selected methods, if NOT consistent with the AVMA Guidelines for the Euthanasia of Animals, is also required but is found in a separate section of the application. For additional information on review of the Vertebrate Animals Section, please refer to the Worksheet for Review of the Vertebrate Animals Section.

Reviewers will assess whether materials or procedures proposed are potentially hazardous to research personnel and/or the environment, and if needed, determine whether adequate protection is proposed.

For Resubmissions, the committee will evaluate the application as now presented, taking into consideration the responses to comments from the previous scientific review group and changes made to the project.

Not applicable. 

Not applicable.  

As applicable for the project proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

Reviewers will assess whether the project presents special opportunities for furthering research programs through the use of unusual talent, resources, populations, or environmental conditions that exist in other countries and either are not readily available in the United States or augment existing U.S. resources.

Reviewers will assess the information provided in this section of the application, including 1) the Select Agent(s) to be used in the proposed research, 2) the registration status of all entities where Select Agent(s) will be used, 3) the procedures that will be used to monitor possession use and transfer of Select Agent(s), and 4) plans for appropriate biosafety, biocontainment, and security of the Select Agent(s).

Reviewers will comment on whether the Resource Sharing Plan(s) (e.g., Sharing Model Organisms ) or the rationale for not sharing the resources, is reasonable.

For projects involving key biological and/or chemical resources, reviewers will comment on the brief plans proposed for identifying and ensuring the validity of those resources.

Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

2. Review and Selection Process Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s) convened by NCCIH, in accordance with NIH peer review policies and practices , using the stated review criteria. Assignment to a Scientific Review Group will be shown in the eRA Commons. As part of the scientific peer review, all applications will receive a written critique. Applications may undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score. Appeals of initial peer review will not be accepted for applications submitted in response to this NOFO. Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this NOFO. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions: Scientific and technical merit of the proposed project, including the PEDP, as determined by scientific peer review Availability of funds. Relevance of the proposed project to program priorities. Please note that reviewers will not consider race, ethnicity, age, or sex (including gender identity, sexual orientation or transgender status) of a researcher, award participant, or trainee, even in part, in providing critiques, scores, or funding recommendations. NIH will not consider such factors in making its funding decisions. If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the  NIH Grants Policy Statement Section 2.5.1. Just-in-Time Procedures . This request is not a Notice of Award nor should it be construed to be an indicator of possible funding. Prior to making an award, NIH reviews an applicant’s federal award history in SAM.gov to ensure sound business practices. An applicant can review and comment on any information in the Responsibility/Qualification records available in SAM.gov.  NIH will consider any comments by the applicant in the Responsibility/Qualification records in SAM.gov to ascertain the applicant’s integrity, business ethics, and performance record of managing Federal awards per 2 CFR Part 200.206 “Federal awarding agency review of risk posed by applicants.”  This provision will apply to all NIH grants and cooperative agreements except fellowships. 3. Anticipated Announcement and Award Dates

After the peer review of the application is completed, the PD/PI will be able to access his or her Summary Statement (written critique) via the  eRA Commons . Refer to Part 1 for dates for peer review, advisory council review, and earliest start date.

Information regarding the disposition of applications is available in the  NIH Grants Policy Statement Section 2.4.4 Disposition of Applications .

Section VI. Award Administration Information

1. award notices.

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• HHS recognizes that NIH research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research. For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this NOFO.

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Recipients are responsible for ensuring that their activities comply with all applicable federal regulations.  NIH may terminate awards under certain circumstances.  See  2 CFR Part 200.340 Termination and  NIH Grants Policy Statement Section 8.5.2 Remedies for Noncompliance or Enforcement Actions: Suspension, Termination, and Withholding of Support . 

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NIH intends to maximize the impact of NIH HEAL Initiative-supported projects through broad and rapid data sharing. All NIH HEAL Initiative award recipients, regardless of the amount of direct costs requested for any one year, are required to comply with the HEAL Public Access and Data Sharing Policy. NIH HEAL Initiative award recipients must follow all requirements and timelines developed through the HEAL Initiative Data Ecosystem ( https://heal.nih.gov/about/heal-data-ecosystem ), as described in the initiative’s  compliance guidance (See “Already Funded” section:  https://heal.nih.gov/data/complying-heal-data-sharing-policy ):   

1. Select a HEAL-compliant data repository ( https://www.healdatafair.org/resources/guidance/selection )

• Data generated by NIH HEAL Initiative-funded projects must be submitted to study-appropriate, HEAL-compliant data repositories to ensure the data is accessible via the HEAL Initiative Data Ecosystem.
• Some repositories require use of specific data dictionaries or structured data elements, so knowing your repository’s requirements up front can help reduce the burden of preparing data for submission.
• HEAL-funded awardees must follow requirements for selected repository.

2. Within one year of award,  register your study with the HEAL platform ( https://heal.github.io/platform-documentation/study-registration/ )

• This process will connect the platform to information about your study and data, including metadata, and identify the selected repository. HEAL requests initial submission within one year of award, with annual updates, and to be updated in accordance with any release of study data.

3.  Within one year of award, submit HEAL-specific study-level metadata.

• Some of the required study-level metadata ( https://github.com/HEAL/heal-metadata-schemas/blob/main/for-investigators-how-to/study-level-metadata-fields/study-metadata-schema-for-humans.pdf ) will be autopopulated as part of the registration process.  

4. Submit data and metadata (and code, if applicable) to HEAL-compliant repository

• At the completion of the study and/or when prepared to make the final data deposits in the repositor(ies) of choice, ensure your  study registration ( https://heal.github.io/platform-documentation/study-registration/ ) is complete.
• Submit data dictionaries to the HEAL data ecosystem, if applicable.
• The NIH HEAL Initiative expects data sharing timelines to align with timeline requirements stated in the Final NIH Policy for Data Management and Sharing ( NOT-OD-21-013 ).

6. Additional Requirements for NIH HEAL Initiative studies conducting clinical research or research involving human subjects.

These studies must meet the following additional requirements:

• NIH HEAL Initiative trials that are required to register in clinicaltrials.gov should reference support from and inclusion in the NIH HEAL Initiative by including the standardized term “the HEAL Initiative ( https://heal.nih.gov/ )” in the Study Description Section.
• Studies that wish to use questionnaires not already included in the HEAL CDE repository should consult with their program official and the HEAL CDE team. New questionnaires will be considered for inclusion in the repository on a case-by-case basis and only when appropriate justification is provided.
• NIH HEAL Initiative clinical studies that are using copyrighted questionaries are required to obtain licenses for use prior to initiating data collection. Licenses must be shared with the HEAL CDE team and the program officer prior to use of copyrighted materials. For additional information, visit the HEAL CDE Program ( https://heal.nih.gov/data/common-data-elements ).
• To the extent possible, all other (nonpain) HEAL studies conducting clinical trials or research involving human subjects are expected to use questionnaires by the HEAL CDE Program ( https://heal.nih.gov/data/common-data-elements ) if applicable and relevant to their research.

Additional details, resources, and tools to assist with data-related activities can be found at https://www.healdatafair.org .  Budgeting guidance for data sharing can be found in  NOT-OD-21-015 and the  NIH Scientific Data Sharing site .

All data collected as part of the NIH HEAL Initiative are collected under a Certificate of Confidentiality and entitled to the protections thereof. Institutions who receive data and/or materials from this award for performance of activities under this award are required to use the data and/or materials only as outlined by the NIH HEAL Initiative, in a manner that is consistent with applicable state and Federal laws and regulations, including any informed consent requirements and the terms of the institution’s NIH funding, including NOT-OD-17-109 and 42 U.S.C. 241(d). Failure to adhere to this criterion may result in enforcement actions.

4. Reporting

Progress reports for multi-year funded awards are due annually on or before the anniversary of the budget/project period start date of award. The reporting period for multi-year funded award progress report is the calendar year preceding the anniversary date of the award. Information on the content of the progress report and instructions on how to submit the report using the RPPR are posted at http://grants.nih.gov/grants/policy/myf.htm

• Recipients will provide updates at least annually on implementation of the PEDP.

( To follow the next section ):

Report and ensure immediate public access to HEAL-funded publications

Publications resulting from NIH HEAL Initiative-funded studies must be immediately publicly available upon publication. 

• For manuscripts published in journals that are not immediately open access, authors should arrange with journals in advance to pay for immediate open access. 
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Prior to publication, the NIH HEAL Initiative expects investigators to alert their program officers of upcoming manuscripts to ensure coordination of communication and outreach efforts.

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“This research was supported by the National Institutes of Health through the NIH HEAL Initiative ( https://heal.nih.gov ) under award number [include specific grant/contract/award number; with NIH grant number(s) in this format: R01GM987654].” 

A final RPPR, invention statement, and the expenditure data portion of the Federal Financial Report are required for closeout of an award, as described in the NIH Grants Policy Statement Section 8.6 Closeout . NIH NOFOs outline intended research goals and objectives. Post award, NIH will review and measure performance based on the details and outcomes that are shared within the RPPR, as described at 2 CFR Part 200.301.

Section VII. Agency Contacts

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten submission by the due date, and post-submission issues)

Finding Help Online:  https://www.era.nih.gov/need-help  (preferred method of contact) Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

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Rebecca N Lenzi, Ph.D. NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES (NIAMS) Phone: (301) 402-2446 E-mail: [email protected]

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Elizabeth Sypek, PhD National Institute of Neurological Disorders and Stroke (NINDS) Email:  [email protected]

Examine your eRA Commons account for review assignment and contact information (information appears 2 weeks after the submission due date).

Debbie Chen National Center for Complementary and Integrative Health (NCCIH) Phone: 301-594-3788 Email:  [email protected]

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Erik Edgerton NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES (NIAMS) Phone: 301-594-7760 E-mail: [email protected]

Ryan Blakeney National Institute on Aging (NIA) Phone: 301-451-9802 Email: [email protected]

Margaret Young Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Telephone: 301-642-4552 Email: [email protected]

Section VIII. Other Information

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts . All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Awards are made under the authorization of Sections 301 and 405 of the Public Health Service Act as amended (42 USC 241 and 284) and under Federal Regulations 42 CFR Part 52 and 2 CFR Part 200.

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• Open access
• Published: 04 September 2024

Mechanisms that clear mutations drive field cancerization in mammary tissue

• Marta Ciwinska 1   na1 ,
• Hendrik A. Messal   ORCID: orcid.org/0000-0003-2259-0286 2   na1 ,
• Hristina R. Hristova 2   na1 ,
• Catrin Lutz 2 ,
• Laura Bornes   ORCID: orcid.org/0000-0001-9721-1223 2 ,
• Theofilos Chalkiadakis 3 ,
• Rolf Harkes 4 ,
• Nathalia S. M. Langedijk   ORCID: orcid.org/0000-0002-1069-2704 2 ,
• Stefan J. Hutten   ORCID: orcid.org/0000-0001-5395-8392 2 ,
• Renée X. Menezes 5 ,
• Jos Jonkers   ORCID: orcid.org/0000-0002-9264-9792 2 ,
• Stefan Prekovic 3 ,
• Grand Challenge PRECISION consortium ,
• Benjamin D. Simons   ORCID: orcid.org/0000-0002-3875-7071 6 , 7 , 8   na2 ,
• Colinda L. G. J. Scheele   ORCID: orcid.org/0000-0001-8999-5451 1   na2 &
• Jacco van Rheenen   ORCID: orcid.org/0000-0001-8175-1647 2   na2  

Nature volume  633 ,  pages 198–206 ( 2024 ) Cite this article

Metrics details

• Breast cancer
• Cancer imaging
• Self-renewal

Oncogenic mutations are abundant in the tissues of healthy individuals, but rarely form tumours 1 , 2 , 3 . Yet, the underlying protection mechanisms are largely unknown. To resolve these mechanisms in mouse mammary tissue, we use lineage tracing to map the fate of wild-type and Brca1 −/− ;Trp53 −/− cells, and find that both follow a similar pattern of loss and spread within ducts. Clonal analysis reveals that ducts consist of small repetitive units of self-renewing cells that give rise to short-lived descendants. This offers a first layer of protection as any descendants, including oncogenic mutant cells, are constantly lost, thereby limiting the spread of mutations to a single stem cell-descendant unit. Local tissue remodelling during consecutive oestrous cycles leads to the cooperative and stochastic loss and replacement of self-renewing cells. This process provides a second layer of protection, leading to the elimination of most mutant clones while enabling the minority that by chance survive to expand beyond the stem cell-descendant unit. This leads to fields of mutant cells spanning large parts of the epithelial network, predisposing it for transformation. Eventually, clone expansion becomes restrained by the geometry of the ducts, providing a third layer of protection. Together, these mechanisms act to eliminate most cells that acquire somatic mutations at the expense of driving the accelerated expansion of a minority of cells, which can colonize large areas, leading to field cancerization.

The acquisition of genetic aberrations in oncogenes and tumour suppressor genes is considered fundamental to tumorigenesis. Seventy per cent of women with germline mutations in BRCA1 or BRCA2 , two well-studied tumour suppressor genes, develop breast cancer by the age of 80 (refs. 4 , 5 ). However, sequencing studies show that mutant cells with alterations in key driver genes, such as P53 , are abundant in a wide variety of tissues in healthy individuals, including the breast 1 , 2 , 3 , 6 . This high abundance of mutant cells could be associated with a high frequency of independent mutagenic events, or could arise from a minority of mutant cells that spread over large fields of tissue. With the latter, such behaviour has the potential to create areas predisposed to transformation, a process referred to as field cancerization, and may play a crucial role in the initiation and recurrence of human breast cancer—the ‘sick lobe theory’ 7 , 8 . Yet, despite its importance, the underlying cellular mechanisms that protect breast tissue from the accumulation or spread of mutant cells remain largely unknown.

The mammary epithelium is a branched network of tubes with an outer layer of basal cells and an inner layer of hormone receptor-positive (HR + ) and -negative (HR − ) luminal cells. During the 4–7 days of the oestrous cycle, the mouse variant of the menstrual cycle, hormones act on HR + luminal cells, triggering the secretion of mitogenic paracrine signalling factors 9 , 10 , 11 . These factors initiate coordinated rounds of proliferation, also in basal and luminal cells negative for HR, thereby driving the growth of side branches, referred to as alveolar buds. In pregnancy, these side branches progress into lobuloalveolar structures, capable of milk production and secretion 12 , 13 . However, outside pregnancy, these side branches regress through coordinated cell death at the end of the cycle 14 , 15 . Thus, throughout life, each oestrous cycle drives coordinated rounds of localized proliferation and cell death 9 , 16 , 17 , 18 , 19 , 20 , 21 .

Long-term maintenance under conditions of continuous remodelling requires self-renewal activity, and is therefore thought to be driven by adult stem cells 22 . On the basis of transplantation assays and lineage tracing studies, the mouse mammary gland is known to consist of self-renewing unipotent mammary stem cells (MaSCs), also termed enduring progenitors 23 , and their short-lived descendants 14 , 24 , 25 , 26 , 27 , 28 , 29 . In line with the turnover of side branches, the self-renewing capacity of MaSCs is found to fluctuate during the oestrous cycle 9 , 19 , 20 , 21 , 30 . However, the size of MaSC-descendant units and their spatial distribution is as present unknown. As a result, it is unclear whether field cancerization in breast tissue involves the expansion of mutant clones within a single unit or across several units. Moreover, it remains unknown whether and how the normal cellular organization of the mammary gland epithelium protects against the retention of mutant cells and field cancerization. Yet, because the initiation and recurrence of breast cancer may depend on the spread of mutations over larger fields, such protection mechanisms are crucial to resolve. Here, to gain insight into the cellular mechanisms that inhibit the mutant clone expansion, and how they may be overcome by mutant cells to drive field cancerization, we map the fate of cells that acquire mutations in the mouse mammary epithelium.

Extensive spread of mutant cells precedes tumorigenesis

To map the fate of mutant clones, we studied confetti mice carrying homozygous floxed alleles of the tumour suppressor genes Brca1 and Trp53 (Fig. 1a ). At the beginning of adulthood (between 10 and 15 weeks) 31 , luminal and basal cells were recombined at a low induction frequency by intraductal TAT-Cre injection (Fig. 1a,b ), with a bias towards luminal cells (Extended Data Fig. 1a ). By quantitative PCR (qPCR) we confirmed that, in more than 87% of confetti-labelled cells, the Brca1 or Trp53 gene was recombined (Extended Data Fig. 1b–d ). By contrast, most confetti-negative cells were wild-type (WT), although a fraction of these cells also harboured recombined Brca1 or Trp53 genes (Extended Data Fig. 1b–d ), indicating that, in some cases, mutant confetti clones neighbour an unlabelled mutant clone. By imaging the whole mammary glands at cellular resolution, we observed outgrowth of clones throughout the ductal tree, without preferential localization (Extended Data Fig. 1e ). No stromal recombination was detected (Extended Data Fig. 1e ). While multipotent clones have been reported in the adult mammary gland 27 , 29 , especially under mutant conditions or following widespread damage 32 , 33 , 34 , 35 , we found only lineage-restricted clones based on E-cadherin (ECAD) staining for luminal cells and α-smooth muscle actin (SMA) staining for basal cells (Extended Data Fig. 2a–f ). However, a potential minority of the multipotent population may be missed by the low induction frequency. Moreover, as Brca1;Trp53 confetti clones are genomically unstable, we cannot exclude the possibility that some of the luminal clones originate from basal cells that acquired multipotency as a result of accumulating genetic alterations.

a , Schematic of the Brca fl/fl ;Trp53 fl/fl ;R26R-Confetti mouse model used in this study. Recombination was induced by an intraductal injection method with TAT-Cre recombinant protein, leading to sporadic deletion of the Brca1;Trp53 alleles and at the same time stochastic recombination of the Confetti construct resulting in the expression of one of the four fluorophores. b , Timeline of lineage tracing experiments performed in the adult mammary gland. c , d , Brca1;Trp53 confetti lesions with a transformed ductal morphology ( c ) and partially transformed ductal morphology and local invasion ( d ). e , Transformed luminal (L, orange) and basal (B, red) clones as a percentage of the total number of luminal and basal clones, respectively. Each dot indicates an individual mouse; boxplots mark the 25th and 75th percentile, the line indicates the median and the whiskers mark the minimum and maximum values. f , Representative whole-mount confocal images of non-transformed Brca1 −/− ;Trp53 −/− confetti clones showing extensive field cancerization within the existing ductal structure. c , d , f , Images show 3D rendering of Z -stacks, with the confetti-labelled cells in their respective colours and the mammary ducts labelled with an antibody against SMA (white). Representative examples of n  = 6 mice. g , Charts representing the fraction of non-transformed luminal and basal clones (grey), the transformed luminal clones (orange) and the transformed basal clones (red) at different time points after recombination for all analysed glands combined. The total number of quantified Brca1 −/− ;Trp53 −/− confetti clones is indicated below the charts and the number of transformed clones is indicated within the charts. See Supplementary Information  1 for sample sizes and descriptive statistics for e and g . Scale bars, 100 μm.

Source Data

As reported previously 31 , within 200–250 days of induction, mice developed palpable mammary tumours (Extended Data Fig. 2g ). At the microscopic level, transformation was determined on the basis of ductal deformation (Fig. 1c ), aberrant branch formation (Fig. 1d ) and (local) invasion (Fig. 1d and Extended Data Fig. 2h ). Most transformed clones were of luminal origin (Fig. 1e ), in line with previous reports 36 , 37 , 38 , 39 . We also observed many large non-transformed Brca1;Trp53 confetti clones (Fig. 1f,g and Extended Data Figs. 1e and 2e,f ). Quantification showed that only a minority of mutant clones transitioned to a transformed phenotype at 225 days post-induction (Fig. 1g ). In contrast to transformed Brca1 ; Trp53 confetti clones, non-transformed clones did not alter the ductal morphology, but extended over large areas of normal-looking ducts, as previously suggested by the sick lobe theory 7 , 8 .

Next, we isolated both normal-looking and transformed Brca1;Trp53 clones and performed low-coverage DNA sequencing. As anticipated for genetically unstable Brca1 ; Trp53 clones, we identified a heterogeneous but substantial number of chromosomal aberrations in both normal-looking and transformed clones (Supplementary Information 3 , copy number aberrations (CNA)). When examining the average CNA of all clones, common patterns of chromosomal loss emerged (Extended Data Fig. 3a ). To assess whether these patterns were also present in fully developed late-stage tumours, we compared our sequencing data with published CNA data from late-stage Brca1;Trp53 tumours 40 . Many genomic regions that are lost in the late-stage tumours were already lost in normal-looking and transformed clones (Extended Data Fig. 3a,b ). At the same time, late-stage tumours also featured chromosomal gains, which were not yet present in our early clones (Extended Data Fig. 3c ). This suggests that the loss of genomic regions is an early event in Brca1;Trp53 -driven tumorigenesis, occurring even before transformation, whereas genomic amplifications represent late-stage events in this tumour model. Moreover, these data illustrate that we are studying the earliest phases of tumorigenesis, in which clones have already accumulated large genomic alterations, yet still present a normal phenotype.

Spread of mutant cells is intrinsic to ductal turnover

To understand what drives clonal expansion, we compared the spread of mutant confetti clones with WT confetti clones (that is, recombined cells in R26R-Confetti mice). Sporadic recombination of WT cells was induced throughout the ductal tree by either intraductal injection of TAT-Cre recombinant protein or activation of CreERT2 with a low dose of tamoxifen (Extended Data Fig. 4a–e ). Similar to the Brca1;Trp53 confetti clones, we found that WT clones also showed the capacity to spread extensively, occupying substantial areas of the ductal network (Fig. 2a,b and Extended Data Fig. 5a–d ). Hereafter, we refer to this process as ‘field clonalization’. To study their spread, we quantified the size of 2,250 WT confetti clones in 75 glands of 17 mice using whole-gland three-dimensional (3D) imaging (Fig. 2b,c and Extended Data Fig. 5b,d–f ). Despite differences in the relative labelling efficiency between the luminal and basal lineage (Extended Data Figs. 1a and 4c,e ), WT and mutant clones showed a similar size distribution, suggesting a common mechanism of expansion. Moreover, the sparsity of labelling and the cohesive nature of clones provided confidence in the integrity of clonal assignments (Extended Data Fig. 4c,e ). Together, these data suggest that, similar to Brca1;Trp53 confetti-labelled cells, WT clones can spread over large areas of the ductal network, leading to field clonalization.

a , b , Representative confocal whole-mount images showing clonal expansion of luminal Brca1;Trp53 confetti clones ( a ) and luminal WT confetti clones ( b ) in the adult mammary gland over a lineage tracing time period of 225 days. Persisting clones form cohesive clusters of cells spanning many ducts and branch points. Images show 3D rendering of Z -stacks, with the confetti-labelled cells in their respective colours and the mammary ducts labelled with SMA or Keratin 14 (KRT14), both depicted in white. c , Clone size quantification of luminal (cyan dots) and basal (blue dots) Brca1;Trp53 confetti clones (left) and WT confetti clones represented on a logarithmic scale. For each time point at least n  = 6 glands from three mice were analysed. Morphologically transformed clones are indicated in orange (luminal) and red (basal). The analysed numbers of clones for each time point are indicated. Boxplots mark the 25th and 75th percentiles, the line indicates the median and the whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann–Whitney test, **** P  < 0.0001. d , Average surviving basal and luminal clone fraction as a function of time normalized to the average number of confetti + cells 14 days after recombination. Each data point shows the average of at least n  = 3 mice per time point. Error bars represent ±s.e.m. From a longitudinal data analysis between the Brca1;Trp53 and WT clones there is no significant difference between the groups (Supplementary Information 2 ). See Supplementary Information 1 for sample sizes, P values and statistics for c and d . Scale bars, 50 µm.

Ductal turnover in small MaSC-descendant units

Next, we questioned the factors that drive field clonalization. The mammary epithelium contains a hierarchy of MaSCs 23 and their short-lived descendants 19 , 21 , 24 , 25 . It is therefore expected that confetti-labelled WT descendants should be lost over time, whereas MaSC-derived clones should spread to their descendants, potentially leading to field clonalization. To test this hypothesis, we quantified the size and position of WT confetti clones over time. Because clones were found to change transiently during the oestrous cycle as a result of growth and regression of side branches 9 , 19 , 21 (Extended Data Fig. 6a–c ), quantifications were made at the oestrus stage to avoid potential inconsistencies. Consistent with a MaSC-descendant hierarchy, we found that the number of surviving WT confetti clones showed a steep decrease in the first few months following induction (Fig. 2d ). Between 10 and 20% of stochastically recombined cells seemed to show long-term renewal capacity, whereas 80–90% were relatively short-lived, consistent with a small MaSC-descendant hierarchy of five to ten cells. With ductal homeostasis supported by repeated small MaSC-descendant units, we reasoned that MaSCs must be distributed widely throughout the ductal network. To test this, we reconstructed the topology of the ductal network (Extended Data Fig. 7a ) and identified for each WT clone its relative position within the tree based on its ‘branch level’, defined as the number of branch points that separate the clone from the main duct close to the nipple (Extended Data Fig. 7b ). This analysis indeed showed that WT clones were scattered uniformly throughout the ductal network, even after 550 days of tracing (Extended Data Fig. 7c,d ).

Altogether, our data showed that the behaviour of WT clones is in line with a model in which the homeostatic renewal of the mammary epithelium is organized in repetitive MaSC-descendant units that are distributed evenly throughout the ductal network. This tissue hierarchy supports a model in which field clonalization is preceded by the loss of most mutations that are acquired in short-lived descendants, followed by the spread of mutations from the randomly distributed MaSCs to their adjacent short-lived progenies.

It remains to be determined how the MaSC populations labelled by our unbiased lineage tracing approach relate to the populations identified through promoter-specific tracing strategies, including those based on Bcl11b , Tspan8 or ProcR expression 29 , 41 , 42 . For example, long-lived, quiescent stem cells have been identified in label-retaining assays, and might have a specialized function under perturbed conditions, such as pregnancy or repair 35 , 41 , 42 .

Clonal spread follows a simple statistical rule

In an MaSC-descendant unit of five to ten cells, a WT clone originating from an MaSC could never exceed around ten cells. However, we observed clones that become hundreds of times larger (Fig. 2c ). In other hierarchically organized epithelial tissues, clones can continually expand through a ‘neutral’ process of stochastic stem cell loss and replacement 43 . In homeostasis, such behaviour finds a signature in the statistical scaling behaviour of clone sizes 44 . However, when analysing the distribution of WT clones, we could not find evidence for such statistical scaling behaviour in individual animals across different time points (Extended Data Fig. 8a,b and Supplementary Information 4 ).

We therefore questioned whether other statistical features of the clone size distribution could provide insight into the underlying dynamics. On the basis of the huge variability of WT clone sizes (Figs. 2c and 3a and Extended Data Fig. 8c ), we questioned whether the distribution of the logarithm of clone size, ln  n , might show evidence of scaling. Notably, defining \(C(w,t)\) as the probability of finding a clone with a size larger than w  = ln  n , we found that, when plotted as a function of \(\left(w-\mu \left(t\right)\right)/\sigma (t)\) , where \(\mu (t)=\langle {\rm{l}}{\rm{n}}\,n\rangle \) denotes the average of the logarithm of clone size and \({\sigma }^{2}(t)=\langle ({\rm{l}}{\rm{n}}\,n-\langle {\rm{l}}{\rm{n}}\,n\rangle {)}^{2}\rangle \) represents its variance, the clone size data collapsed onto a single scaling curve (Fig. 3b , Extended Data Fig. 8d and Supplementary Information 4 ); that is, \(C(w,t)=C((w-\mu (t))/\sigma (t))\) , where the scaling function \(C(x)\) is time independent ( Supplementary Information 4 ). Further, inspection of the distribution showed that the scaling function fits well with a log-normal size dependence, with \(C(x)=(1/2){\rm{e}}{\rm{r}}{\rm{f}}{\rm{c}}(x/\sqrt{2})\) , where erfc denotes the complementary error function. These findings provide evidence of a statistical scaling behaviour of WT clone sizes distinct from that encountered in systems supported by local stochastic stem cell loss and replacement 44 , pointing to a different pattern of homeostatic turnover. Yet, the emergence of a simple and conserved pattern of WT clone size expansion, dependent only on the average and variance, \(\mu (t)\) and \({{\sigma }}^{2}(t)\) , suggested that the variability of WT clone sizes derived from cells conforming to a common statistical rule (Fig. 3c,d and Extended Data Fig. 8e,f ).

a , Cumulative distribution of WT (left) and Brca1;Trp53 (right) luminal confetti clone sizes showing the probability of finding a clone larger than the given size (log scale). To account for the impact of large-scale mouse-to-mouse variability on clone size, the curves are shown for a representative set of individual mice (distributions are shown for all mice in Supplementary Information 4 ). n  ≥ 3 mice per time point. b , Rescaled cumulative distribution of the logarithm of WT (left) and Brca1;Trp53 (right) luminal confetti clone size, ln  n , showing the probability of finding a clone with a size larger than \(({\rm{l}}{\rm{n}}\,n-\mu )/\sigma \) , where \(\mu =\langle {\rm{l}}{\rm{n}}\,n\rangle \) denotes the average of the logarithm of clone size and \({\sigma }^{2}=\langle ({\rm{l}}{\rm{n}}\,n-\langle {\rm{l}}{\rm{n}}\,n\rangle {)}^{2}\rangle \) represents the variance. Points show data from a . Once rescaled, data from different time points collapse onto a single curve that fits well with the scaling function \((1/2){\rm{erfc}}(x/\sqrt{2})\) (cyan dashed line), consistent with a log-normal size dependence. For details of statistical significance tests, see  Supplementary Information 4 . c , Variance of the logarithm clone size, \({\sigma }^{2}(t)\) , as a function of the inferred oestrous cycle number for luminal (black) and basal (blue) WT (left) and Brca1;Trp53 (right) confetti clones. Points show data from individual mice and lines (dashed) show a fit to a linear growth characteristic, as predicted by a minimal model of clonal fate based on stochastic growth and regression (main text and Supplementary Information 4 ). d , Average of the logarithm clone size, μ ( t ), as a function of the inferred oestrous cycle number for luminal (black) and basal (blue) WT (left) and Brca1;Trp53 (right) confetti clones. Points show data collected from individual mice and lines (dashed) show a fit to a linear growth characteristic, as predicted by a minimal model. See Supplementary Information 1 for sample sizes and statistics for a – d .

Oestrous cycle drives loss and replacement of MaSCs

Log-normal clonal distributions typically emerge from statistical processes in which the fate of proximate cells—amplification or loss—is positively correlated 45 . This could arise artefactually as the result of clone fragmentation or merger events 46 . However, the contiguity of WT clonal patches and sparsity of clonal labelling ruled against this possibility (Extended Data Fig. 4c,e ). We therefore considered whether previous observations of cyclic expansion of the pool of MaSCs 19 , 21 and regionally localized bouts of proliferation and cell death following the turnover of side branches during the oestrous cycle (Extended Data Fig. 6a–c ) 9 , 19 , 21 could locally correlate MaSC fate. To test this, we used an 5-ethynyl-2′-deoxyuridine (EdU) incorporation assay throughout at least one oestrous cycle. Even though EdU labelling showed heterogeneity along the ductal network (Fig. 4a ), Ripley’s L function-based cluster analysis of the position of EdU + cells provided evidence for a local correlation in proliferative activity (Fig. 4b and Extended Data Fig. 9a ). This clustered proliferation decreased when we halted the oestrous cycle by ovariectomy (Fig. 4b ).

a , 3D views of mammary ducts showing EdU incorporation over 1 week in oestrous-cycling mice (top, three mice) and after ovariectomy (bottom, five mice), stained for CK8 and SMA. b , Ripley cluster analysis of EdU + cell clusters along mammary ducts in cycling (green) and ovariectomized (black) mice. Data are mean ± s.e.m., five regions per mouse, three cycling mice, five ovariectomized mice. c , Branching dynamics in KikGR mice imaged through a mammary imaging window over 1 week. Representative examples of side branch expansion and regression are shown as indicated, n  = 5 mice. d , Schematic of the repeated skin-flap procedure to visualize the mammary tree using intravital microscopy. e , Top panels show in vivo overviews of the fourth mammary gland of a R26-mTmG mouse at 3 (left) and 6 months of age (right) during oestrus. Bottom panels show outlines of the ductal tree with the main ducts in blue and side branches in red. Representative of four animals. f , Top panels show in vivo confocal images of the ductal area (red box in b ) at 3 (left) and 6 months of age (right). Bottom panels show outlines with the main ducts in blue and side branches in red. g – j , Quantification of segment length of ducts ( g ), tertiary branch length ( h ) and tertiary branch complexity ( i ) at 3 and 6 months of age. j , Difference (Δ) in the number of tertiary branches between 6 and 3 months of age. Data derived from i . Colours indicate different mice, lines connect measurements of the same structures. Significance tested using a paired t -test, two-sided. See Supplementary Information 1 for more sample sizes, P values and statistics. Scale bars, 100 μm ( a ), 500 μm ( c , e , f ).

To find further evidence for local tissue remodelling during the oestrous cycle, we visualized ductal remodelling by repeated rounds of intravital microscopy 47 , 48 . Indeed, over the course of a single cycle, we observed both the local formation and loss of side branches (Fig. 4c ). Imaged over the course of 3 months (more than 12 cycles) using a repeated skin-flap method (Fig. 4d ), the spatial organization of main ducts remained largely unaltered (depicted in blue, Fig. 4e–h ). By contrast, the number of side branches increased over time, as well as their size and morphology, suggesting a small bias towards ‘bursts’ of localized growth over loss (as depicted in magenta, Fig. 4e,f,i,j ). Together, these findings are in line with previous studies 9 , 19 , 21 and support the presence of regional bursts of proliferation linked to oestrous cycle-mediated turnover of side branches.

Modelling local MaSC fate reproduces clonal spread

To test quantitatively whether the observed bouts of local proliferation lead to the observed variation in sizes and temporal growth characteristics of WT clones, we derived a minimal model of MaSC fate in which individual MaSCs become active with some probability on side branch turnover during each oestrous cycle, after which they collectively expand or become altogether lost, with a relative probability that ensures long-term homeostasis (Extended Data Fig. 9b and Supplementary Information 4 ). As well as recapitulating the observed log-normal size dependence of WT clones, this minimal model predicted the dynamics of clone growth, including the emergence of a linear-like increase in the variance and average of the logarithm of clone size as a function of time (Fig. 3c,d ), as well as the average clone size and the decay in the fraction of single-cell clones (Extended Data Fig. 8e,f ). Note that, here, to account for large-scale mouse-to-mouse variation in the clonal dynamics, we used the coscaling of the average and variance of the logarithm of clone size to regress out a timescale, measured in terms of an effective oestrous cycle number, benchmarked against measurements obtained from previous studies (see  Supplementary Information 4 for further details and fit parameters).

From a quantitative fit to the clone size data, we estimated that each MaSC supports just a few short-lived descendants on average, consistent with the pattern of clonal loss observed soon after induction (Fig. 2d ). From a fit to the average growth characteristics, we estimated that each MaSC becomes active, contributing to the formation of a side branch, roughly once per ten oestrous cycles. Indeed, proliferation and apoptosis vary spatially during the oestrous cycle 9 , 19 , 21 and the same cells are not in a proliferative state each cycle 14 , 15 , 49 . Owing to MaSC turnover, the model predicted that over a single cycle, 50% of ‘activated clones’ are lost while the surviving clones expand proportionately (that is, field clonalization), a result consistent with the observed formation and loss of side branches (Fig. 4c ). With such a localized and cooperative pattern of turnover, it is plausible that MaSC fate would be highly correlated spatially (schematic in Extended Data Fig. 6a ). Although neighbouring MaSCs labelled with different confetti colours were extremely rare owing to the sparse labelling, when such events did occur we found that their expansion was highly correlated, even when clones belonged to independent HR + and HR − sublineages (Extended Data Fig. 9c ).

Spread of mutant and WT clones follows similar rules

Having traced the origin of WT clone expansion and field clonalization during the oestrous cycle, we turned to consider the dynamics of mutant confetti clones, quantifying 1,745 Brca1;Trp53 clones in 64 glands from 19 mice at time points from 14 to 225 days after labelling (Fig. 2c and Extended Data Fig. 5e,f ). To focus on non-transformed or early transformed lesions, we excluded the palpable lesions (if present) from our analyses. This analysis showed that the spread of Brca1;Trp53 confetti clones mirrored that of WT clones, showing the same conserved pattern of expansion with a log-normal size dependence signature (Fig. 3a,b , Extended Data Fig. 8c,d and Supplementary Information 4 ), and a corresponding linear-like increase in the average and variance of the logarithm of clone size (Fig. 3c,d and Supplementary Information 4 ). Moreover, the total number of surviving Brca1;Trp53 confetti clones showed a similarly steep decrease in the first few months following induction (Fig. 2d ). A fit to the linear growth characteristics showed that luminal Brca1;Trp53 confetti clones experience a net spreading advantage over WT confetti clones, with a marginal increase in the degree of amplification during the oestrous cycle, suggesting a resistance to loss during regression ( Supplementary Information 4 ), a result that we could confirm in vitro (Extended Data Fig. 9d ).

We then investigated whether the spreading advantage of Brca1;Trp53 confetti clones had a regional dependence, distinguishing between clones localized in the static main ducts and those in the more dynamic side branches. Focusing on the 225-day time point, for WT confetti clones, we found no regional dependence (Extended Data Fig. 9e ). By contrast, although clone numbers were small, the distribution of Brca1;Trp53 confetti clones in side branches showed a bias towards larger clone sizes characterized by a much narrower size distribution when compared to the main ducts (Extended Data Fig. 9e ), which coincided with higher chance of transformation (Extended Data Fig. 9f ). As Brca1;Trp53 cells seem to be more resistant to loss (Extended Data Fig. 9d ), mutant side branches generated during the oestrous cycle may be more durable than those formed by WT cells, enabling clones to spread more readily.

Clonal spread is restrained by the ductal geometry

Within the framework of the minimal (non-spatial) model of clone growth, surviving WT and Brca1;Trp53 clones are predicted to expand in size indefinitely at an exponential rate. Yet, such behaviour must become untenable in a tissue context. Therefore, to investigate how the tissue geometry might influence clone expansion, we developed a spatial model, representing the ductal epithelium as a one-dimensional ‘lattice-like’ ribbon of cells. By modelling the observed localized and cooperative loss and replacement of MaSCs during the turnover of side branches each oestrous cycle (Extended Data Figs. 6a and 9b ), we could reproduce quantitatively the log-normal size distribution (Extended Data Fig. 9g and Supplementary Information 4 ). However, at long times, the lattice model predicted that the expansion of clones should become suppressed at the length scale of the remodelled regions, and the clone size distribution cross over from log-normal to a narrower Gaussian-like dependence, consistent with the dynamics expected for neutral cell competition between neighbouring cells in one dimension (Extended Data Fig. 9h ). Consistently, applied to the experimental data, departures from the log-normal dependence could be observed for both WT and Brca1;Trp53 confetti clones when clone sizes spanned hundreds of cells or more (Extended Data Fig. 9i,j ). Together, these results indicate that the one-dimensional ductal organization provides a geometrical constraint that ultimately limits the range expansion of clones, limiting field clonalization and cancerization.

Pregnancy does not increase spread of mutant clones

Next, we questioned how the fate of mutant clones is perturbed during pregnancy, which is associated with massive tissue remodelling. Following induction of Brca1;Trp53 mutant clones in 8–12-week-old mice, at day 64 post-induction, mice went through a round of pregnancy, lactation and involution (Extended Data Fig. 10a ). Parous Brca1;Trp53 mice were analysed at 120 days post-induction and compared to their nulliparous counterparts. Although pregnancy-driven reshaping of the ductal network leads to massive expansion and involution of localized areas 12 , this did not lead to significant changes in clone sizes in the parous versus nulliparous mice (Extended Data Fig. 10b ). Further inspection of mutant clone sizes in parous and nulliparous mice revealed a striking shift in the clone size distribution, with the former having a relatively low number of large clones, potentially the result of skipping several oestrous cycles (Extended Data Fig. 10b ). In contrast to the nulliparous mice, no transformed clones were observed at 120 days post-induction in the mammary glands of parous Brca1;Trp53 mice (Extended Data Fig. 10b–d ), consistent with the conclusion that clonal expansions make ducts more susceptible to transformation. It also echoes the results of previous studies demonstrating the protective role of early parity in breast cancer 50 . In line with this reasoning, in nulliparous glands mutant clones localized at the dynamic side branches showed a bias towards large sizes and have a proportionately higher propensity to transform (Extended Data Fig. 9e,f ). Such behaviour mirrors that found in humans, in which transformation is also thought to occur predominantly in side branches—the terminal ductal lobular units 51 , 52 .

Abolishing mutant spread reduces transformation

To further test the relation between clone size and transformation susceptibility, we blocked experimentally the expansion of Brca1;Trp53 confetti clones by performing an ovariectomy. If rapid amplification of clone sizes is associated with oestrous cycle-driven side branch turnover, we reasoned that, in its absence, the susceptibility for transformation should be largely abolished 53 . Indeed, at later time points (120 and 225 days), when large-scale expansion of clones becomes evident in non-ovariectomized glands, no extensive spread of WT and mutant clones was observed in ovariectomized glands (Fig. 5a–f and Extended Data Figs. 11 and 12a–d ). By contrast, the surviving clone fraction still decreased (Fig. 5g ), consistent with the turnover of cells within the MaSC-descendant units; although for Brca1;Trp53 clones there was a slower loss rate as compared to non-ovariectomized mice ( P  < 0.05, Supplementary Information 2 ). Notably, there was a trend towards a faster loss of WT over Brca1;Trp53 confetti clones, consistent with a survival advantage of the former over WT neighbours (Fig. 5g and Supplementary Information 2 ). The limited spread of the Brca1;Trp53 confetti clones was accompanied by a near complete loss of susceptibility for transformation (Fig. 5h,i ).

a , Luminal (cyan) and basal (blue) WT confetti clone sizes in the homeostatic gland (left, same as Fig. 2c ), and after ovariectomy (right). b , c , Representative whole-mount confocal images of luminal WT confetti clones 120 days ( b ) and 225 days ( c ) after recombination in ovariectomized mice ( n  ≥ 3 mice per condition). Luminal cells are labelled with ECAD ( b ), basal cells are labelled with SMA ( c ). Images depict 3D rendering of Z -stacks, unless otherwise indicated. d , Luminal (cyan) and basal (blue) Brca1;Trp53 confetti clone sizes in the oestrous-cycling condition (left, same as Fig. 2c ), and after ovariectomy (right). e , f , Representative whole-mount confocal images of luminal Brca1;Trp53 confetti clones 120 ( e ) and 225 days ( f ) after recombination in ovariectomized mice ( n  ≥ 3 mice per condition). Luminal cells labelled with ECAD ( e ), basal cells labelled with SMA ( f ). Images depict 3D rendering of Z -stacks, unless otherwise indicated. g , Surviving clone fraction in ovariectomized mice as function of time normalized to the average number of confetti + cells at 14 days. Error bars represent mean ± s.e.m. h , Non-transformed luminal or basal clones (grey) and transformed luminal clones (orange) in the ovariectomized Brca1;Trp53 confetti mouse model. i , Transformed luminal (L, orange) and basal (B, red) clones as percentages of the total number of luminal or basal clones, respectively, in the cycling and ovariectomized conditions. Each dot indicates an individual mouse. a , d , h , Clone numbers are indicated. a , d , i , Boxplots mark 25th and 75th percentile, the line indicates median and the whiskers mark minimum and maximum values. Significance was tested using a two-sided Mann–Whitney test, **** P  < 0.0001. See Supplementary Information 1 for more sample sizes, P values and statistics. Scale bars, 100 µm.

The abundance of mutant cells in human breast tissue from healthy individuals, and its potential relevance for the initiation and recurrence of breast cancer, have long been recognized 1 , 7 , 8 . Here, by tracing the dynamics of WT and Brca1;Trp53 confetti clones in mouse tissue, we have addressed the cellular basis of field cancerization and the sick lobe theory. In contrast to prevailing models of the mouse mammary gland 29 , 42 , 54 , 55 and human breast tissue 56 , 57 , our results indicate that MaSCs are distributed uniformly along the ductal tree, a finding resonant with a previous study of human breast tissue 34 . Following the induction of WT or mutant clones, only those clones that are ‘rooted’ in the MaSC compartment survive over the short term, with their spread limited to their descendant units. In the absence of the oestrous cycle, the spread of mutant clones beyond these units remains limited, even over the long term. However, during stages of the oestrous cycle, ovarian hormones act on HR + luminal cells, triggering remodelling of side branches leading to localized proliferation and apoptosis of HR + , HR − luminal and basal cells 14 , 15 , 20 , 30 . These bouts of growth and regression drive the local and coordinated expansion and loss of MaSCs (regardless of their HR expression), leading to the elimination of most mutant clones, including those bearing oncogenic driver mutations, whereas those that by chance survive expand exponentially (Extended Data Fig. 12e ). Therefore, any clone, regardless of its size, HR status or proliferation capacity, can by chance either increase or decrease in size at any given oestrous cycle. This explains why the spread of clones negative for the HR (for example, basal or Brca1;Trp53 confetti clones) still depends on the oestrous cycle. This process of field clonalization enables entire ductal subtrees to become predisposed to the development of aberrant ductal lesions and transformation, a behaviour resonant with the abundance of signature genomic aberrations observed in both large transformed and non-transformed Brca1;Trp53 confetti clones. Moreover, consistent with these findings, clinical observations show that the risk of tumorigenesis increases with the number of menstrual cycles, the human variant of the oestrous cycle. In particular, the risk of breast cancer correlates with the age of entry into menopause, with increasing age indicating an enhanced number of cycles 58 , 59 , 60 . Yet, it is essential to emphasize that the dependence of clonal expansion on the oestrous cycle is different at later stages of tumour development. At the stage when Brca1;Trp53 tumours grow invasively, clonal expansion is no longer affected by the tissue remodelling that accompanies the oestrous cycle or limited by the one-dimensional architecture of the ductal network, and thus tumour growth is unaffected by ovariectomy 53 .

All mice used for experiments were adult females from a mixed background, housed under standard laboratory conditions and receiving food and water ad libitum. All experiments were performed in accordance with the guidelines of the Animal Welfare Committees of the Netherlands Cancer Institute and KU Leuven. Sample size was determined using a resource equation approach, mice were randomly assigned to experimental groups and blinding was performed during data analysis. R26R-Confetti het (JAX stock no. 013731) 61 , 62 ; R26-CreERT2 het (JAX stock no. 008463) 63 mice were injected intraperitoneally with tamoxifen (Sigma-Aldrich), diluted in sunflower oil, to activate Cre recombinase. To achieve clonal density labelling (fewer than one MaSC per duct on average), R26R-Confetti het ;R26-CreERT2 het mice were injected with 1 mg of tamoxifen per 25 g of body weight between 10 and 15 weeks of age. Ovariectomies were performed between 10 and 15 weeks of age, at least 7 days before lineage tracing initiation. The third, fourth and fifth mammary glands of R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl (refs. 31 , 64 ) or R26R-Confetti mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl of PBS, Sigma-Aldrich) between 10 and 15 weeks of age. This TAT-Cre injection resulted in roughly one labelled cell for every 100–200 cells (Extended Data Fig. 4e ). As the confetti construct comprises four distinct colours, there is, on average, one cell labelled with a confetti colour per 400 cells. Considering that a MaSC-progeny unit consists of roughly five to ten cells, a single confetti-labelled cell is induced in one out of 40–80 units. Over time, many clones become extinct, leading to a dilution in the number of clones and making collisions even less likely. For each of the experiments, mice were analysed at different time points after lineage tracing initiation as indicated in Fig. 1b . The injected mammary glands of R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl mice at the latest time point (225 days) were analysed when one of the injected glands developed a palpable tumour of at least 5 × 5 mm, which was between 200 and 250 days after recombination. Tumour sizes did not exceed 1,500 mm 3 in accordance with the guidelines of the Animal Welfare Committee of the Royal Netherlands Academy of Arts and Sciences, the Netherlands Cancer Institute and KU Leuven. Samples were randomly allocated to the experimental groups, sample size was not determined a priori and investigators were not blinded to experimental conditions, except where indicated. For clonal analysis of the R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl model, we analysed n  = 4 mice (14 days), n  = 4 mice (64 days), n  = 5 mice (120 days) and n  = 6 mice (225 days). For clonal analysis of the R26R-Confetti model, we analysed n  = 3 mice (14 days), n  = 3 mice (64 days), n  = 5 mice (120 days) and n  = 6 mice (225 days). Clonal analysis of the ovariectomized R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl and R26R-Confetti het models was performed on at least n  = 3 mice per time point. Adult CAG;;KikGR female mice 65 (RIKEN no. CLSTCDB0201T-117830853340) were used to visualize the short-term dynamics of the mammary gland by repeated imaging through a mammary imaging window as described below. R26-mTmG female mice (JAX no. 007676) 66 were used to visualize the long-term stability of the mammary gland through a repeated skin-flap procedure as described below.

Mammary imaging window implantation, repeated skin-flap procedure and intravital imaging

Mice were anaesthetized using isoflurane (Isovet) inhalation (1.5/2% isoflurane/air mixture). The fourth mammary gland of adult CAG;;KikGR female mice was imaged repeatedly through a mammary imaging window as previously described 47 , 48 . The fourth mammary gland of adult R26-mTmG mice was imaged repeatedly with a skin flap as previously described 47 . To visualize the mammary gland, mice were placed in a facemask within a custom designed imaging box. Isoflurane was introduced through the facemask and ventilated by an outlet on the other side of the box. The imaging box and microscope were kept at 34 °C by a climate chamber surrounding the entire stage of the microscope including the objectives. Imaging was performed on an inverted Leica SP8 Dive system (Leica Microsystems) equipped with four tuneable hybrid detectors, a MaiTai eHP DeepSee laser (Spectra-Physics) and an InSight X3 laser (Spectra-Physics) using the Leica Application Suite X (LAS X) software. All images were collected at 8 bit and acquired with a ×25 water immersion objective with a free working distance of 2.40 mm (HC FLUOTAR L ×25/0.95W VISIR 0.17). For the CAG;;KikGR model, Kikume Green was excited at 960 nm and detected at 490–550 nm. Each imaging session, all visible ducts through the imaging window were imaged using a tiled z scan with ×1–2 zoom and a z -step size of 5–10 μm. For the skin-flap imaging, TdTomato was excited at 1,040 nm and detected at 540–730 nm. All visible ducts were imaged together in one tile z scan with a ×0.75 zoom, a z -step size of 10–20 μm. These parameters allowed to scan large regions of up to 2 cm 2 in less than 3 h. At the end of the first skin-flap imaging session, the skin was closed with a continuous, non-resorbable suture. After 3 months, the skin flap was re-opened for the second imaging session, and the same imaging fields were retraced using the nipple and collagen I structures of the first imaging session as landmarks.

Staging of the mice

To determine the oestrous cycle stage of the mice, a vaginal swab was collected as described 67 . In short, the vagina was flushed using a plastic pipette filled with 50 µl PBS, and the liquid was transferred to a dry glass slide. After air drying, the slide was stained with Crystal Violet and the cell cytology was examined using a light microscope.

Clone isolation and CNA sequencing

Here, 225 days after Cre mediated recombination, fourth mammary glands were extracted, fixed overnight in 1% paraformaldehyde (PFA), incubated in sucrose overnight and stored in optimal cutting temperature (OCT) at −80 °C. For microdissection of individual clones, OCT blocks were thawed at room temperature in the dark for 30 min and mammary glands removed from OCT and washed in 50 ml of PBS on ice. Mammary glands were dissected under a benchtop fluorescent macroscope (Zeiss) using Dumont forceps and fine scissors, using the clone morphology to distinguish between transformed and untransformed clones. Each dissected clone was washed in 1 ml of PBS on ice for 2–5 h (until the end of the dissection procedure). Per mammary gland, one piece of non-fluorescent tissue from the inguinal lymph node was dissected as internal sequencing control. After washing, pieces were lysed in 70 µl of Arcturus lysis buffer following the instructions of the ThermoFisher Scientific Arcturus PicoPure kit KIT0103. Lysis was carried out in a PCR cycler for 18 h at 65 °C followed by 30 min at 75 °C and holding at 4 °C. Samples were then purified using the Roche FFPE DNA extraction kit 06650767001 50-588-384 following the manufacturer’s instructions with elution in 25 µl of PCR grade water. For DNA sequencing, library preparation was carried out with a KAPA Hyper kit (Roche; KK8504) according to the manufacturer protocol with four PCR cycles, before the samples were sequenced by low-coverage whole genome sequencing. The copy number alteration (CNA) analysis was conducted in R, using QDNAseq with 50 kb bins and the mm10 mouse reference genome. This methodology yielded copy number values from both normal and transformed clones, along with an internal control sample. Normalization was achieved by first converting copy number values to log 2 , then subtracting the internal control sample’s values from those of the normal and transformed tumours. We averaged these adjusted copy number values for each replicate across both clone types (13 early normal clones and 13 early transformed clones). Data visualization was executed using the ggplot2 package in R, with specific emphasis on certain chromosomes. Regarding the late-stage tumours published in ref. 40 , copy number profiling data corresponding to ten Wap-Cre;Brca1 fl / fl ;Trp53 fl/fl (WB1P) female mice harbouring a WB1P late-stage mammary tumour, along with internal control samples (spleen) was used. The CNA sequence analysis included the use of cutadapt for adaptor sequence removal and BWA for sequence alignment (using bwa aln, bwa mem) to the mm10 mouse genome. This procedure mirrored the earlier steps up to plotting with ggplot2, repeated for ten WB1P replicates.

Quantification of the distribution of proliferation

Oestrous-cycling mice and mice that had undergone ovariectomy with a 2-week recovery period (all above 8 weeks of age) received 0.5 mg ml −1 EdU in drinking water (refreshed every second day) for 1 week. 3D imaging was performed on three cycling mice and five ovariectomized mice. Per mouse, one-quarter of the mammary gland was taken for subsequent analysis. Samples were fixed in 4% PFA overnight and stained using the FLASH protocol with FLASH Reagent 2 (ref. 68 ). Before adding the primary antibodies, samples were stained for EdU as follows. Tissues were incubated in 5 ml of 3% bovine serum albumin for 1 h, followed by three washes in PBS for 20 min each. EdU was detected with an Alexa-647 azide. The reaction cocktail for EdU fluorescent labelling was prepared according to the manufacturer’s guidelines using the Click-It EdU imaging kit (ThermoFisher Scientific). Per gland, 0.5 ml of reaction cocktail was added for incubation for 4 h at room temperature with gentle agitation on a nutator. The cocktail was removed, samples washed once in 3% bovine serum albumin in PBS for 20 min, followed by three washes in FLASH blocking buffer for 20 min each. Subsequently, samples were stained with primary and secondary antibodies overnight each. Primary antibodies used were KRT8 (rat, Troma-I, Merck Millipore, 1:800) and αSMA (mouse IgG2a, clone 1A4, ThermoFisher Scientific, 1:600). Secondary antibodies used were donkey antirat Alexa-488 and donkey antimouse Alexa546 (ThermoFisher Scientific, catalogue nos. A21208 and A10036, respectively, 1:400), combined with Hoechst 33342 for nucleus detection. Samples were imaged on an Andor Dragonfly spinning disc system, installed on an inverted Leica DMI8 microscope with an Andor Zyla 4+sCMOS camera using a ×10, 0.45 NA Fluo objective (Leica). Imaging was carried out with a 40 μm disc using 405 nm excitation, a 561 nm optically pumped semiconductor laser and 637 nm diode lasers. Images were visualized with Imaris Viewer using gamma correction, ortho slicers and cutting planes to depict deeper tissue layers. For each mammary gland, distribution of proliferation was quantified in five regions. Ripley analysis using QuPath 69 was performed with a custom-made script (available at https://github.com/BioImaging-NKI/qupath_ripley ). The image was opened in QuPath and a freehand line was drawn by hand to outline the duct for analysis. A multipoint annotation was drawn by hand to mark the positions of proliferating cells along the duct. The script calculated Ripleys K function and normalized it to an unclustered distribution resulting in Ripley’s L function. Data were plotted in GraphPad Prism v.10. For simulations, we have generated clustered and unclustered data in Python.

Whole-mount immunofluorescence staining of mammary glands

The third, fourth and fifth mammary glands were dissected and incubated in a mixture of collagenase I (1 mg ml −1 , Roche Diagnostics) and hyaluronidase (50 μg ml −1 , Sigma-Aldrich) at 37 °C for optical clearance, fixed in periodate–lysine–PFA buffer (1% PFA; Electron Microscopy Science), 0.01 M sodium periodate, 0.075 M l -lysine and 0.0375 M P-buffer (0.081 M Na 2 HPO 4 and 0.019 M NaH 2 PO 4 ; pH 7.4) for 2 h at room temperature, and incubated for at least 3 h in blocking buffer containing 1% bovine serum albumin (Roche Diagnostics), 5% normal goat serum (Monosan) and 0.8% Triton X-100 (Sigma-Aldrich) in PBS. Primary antibodies were diluted in blocking buffer and incubated overnight at room temperature. Secondary antibodies diluted in blocking buffer were incubated for at least 6 h. Nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI) (0.1 μg ml −1 ; Sigma-Aldrich) in PBS. Glands were washed with PBS and mounted on a microscopy slide with Vectashield hard set (H-1400, Vector Laboratories). Primary antibodies used were: anti-KRT14 (rabbit, Covance, PRB155P, 1:700), anti-ECAD (rat, eBioscience, 14-3249-82, 1:700), anti-oestrogen receptor (rabbit, no. 13258, Cell Signaling, 1:100), anti-progesterone receptor (rabbit, Clone SP2, MA5-14505, ThermoFisher Scientific, 1:200) and anti-SMA (mouse IgG2a, clone 1A4, Sigma-Aldrich, 1:600). Alexa Fluor 647 and Alexa Fluor 488 Phalloidin were used 1:500 (A-22287 and A-12379, ThermoFisher Scientific) and incubated together with the secondary antibodies. Secondary antibodies used were: goat antirabbit, goat antirat or goat antimouse IgG2a, all conjugated to Alexa-647 (ThermoFisher Scientific, catalogue nos. A21245, A21247 and A21241, respectively, 1:400).

Whole-mount imaging of mammary glands

Imaging of whole-mount mammary glands was performed using an inverted Leica TCS SP8 confocal microscope, equipped with a 405 nm laser, an argon laser, a diode-pumped solid-state laser 561 nm laser and a HeNe 633 nm laser. Different fluorophores were excited as follows: DAPI at 405 nm, cyan fluorescent protein (CFP) at 458 nm, green fluorescent protein (GFP) at 488 nm, yellow fluorescent protein (YFP) at 514 nm, red fluorescent protein (RFP) at 561 nm and Alexa-647 at 633 nm. DAPI was collected at 440–470 nm, CFP at 470–485 nm, GFP at 495–510 nm, YFP at 540–570 nm, RFP at 610–640 nm and Alexa-647 at 650–700 nm. All images were acquired with a ×20 (HCX IRAPO N.A. 0.70 WD 0.5 mm) dry objective using a Z -step size of 1–5 μm (total Z -stack around 200 μm). 3D overview tile scans of the mammary glands were acquired by scanning large tile-scan areas ( xyz ). Next, detailed images were obtained of the individual clones. All images were stitched and processed in the true 3D real-time Rendering LAS X 3D Visualization module (Leica Microsystems) and further processed using ImageJ software ( https://imagej.nih.gov/ij/ ).

Clonal analysis on whole-mount glands

Three-dimensional tile-scan images of whole-mount and fully intact mammary glands were used to manually reconstruct the ductal network by outlining the ducts based on the labelling by ECAD, SMA or KRT14 (between 400 and 600 tiles, ×10 objective, Z -step size of 5–10 µm). After localization of the confetti clones in these 3D overview scans, each clone was imaged in detail with a ×25 water objective using confocal imaging by taking a Z -stack with step size between 1 and 3 µm. On the basis of the overlap with the luminal- or basal-cell-specific labelling and cellular morphology (that is, a cuboidal shape for luminal cells and an elongated shape for basal cell), the labelled confetti cells were identified and annotated in the schematic outline of the mammary tree, including information on their confetti colour (GFP, green; YFP, yellow; RFP, red and CFP, cyan) and their identity: that is, luminal or basal. Regions in which, for technical reasons, the gland could not be visualized well were omitted from analysis (in three out of 160 glands). Clone sizes, referring to the number of cells within each clone, were determined through manual visual inspection of tissue samples, with the quantification performed by eye using detailed Z -stack images and 3D rendering of each individual clone. Using custom-made.NET software (available on request from J.v.R.), the coordinates of the branch points, and the position of the labelled cells in ducts and in ductal ends were scored. To calculate the surviving clone fraction, the total number of clones was determined for each of the indicated lineage tracing time points by analysing the entire mammary gland in three dimensions using our whole-gland imaging approach ( n  = 3 glands per time point of two individual mice). Next, the average numbers of clones identified at 64, 120 and 225 days after lineage tracing initiation were divided by the average number of clones identified 14 days after lineage tracing initiation resulting in the surviving clone fraction as depicted in Figs. 2d and 5g .

Mammary epithelial cell sorting and real-time qPCR

The third, fourth and fifth mammary glands of R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl PBS, produced in-house) between 10 and 13 weeks of age. Then 120 to 180 days after injection, mammary glands were harvested, minced and digested at 37 °C for 30 min in a mixture of collagenase A (2 mg ml −1 , Roche Diagnostics), hyaluronidase (300 μg ml −1 , Sigma-Aldrich) and DNase (1 mg ml −1 ) in DMEM/F12 (Gibco). After 10 min incubation with TripLE (Gibco) at 37 °C cells were strained through a 100 μm cell strainer (Fisher Scientific) to obtain single cells. Cells were spun down for 10 min at 550 RCF (relative centrifugal force) at 4 °C followed by blocking for 15 min on ice in 5 mM EDTA/PBS with 2% sterile filtered normal goat serum (Gibco). CD45-Alexa-647 (clone 30-F11, 03123, Biolegend, 1:200) and EpCAM-APC/Cy7 (clone G8.8, 118218, Biolegend, 1:200) were diluted in 5 mM EDTA/PBS with 2% normal goat serum and incubated for 30–45 min on ice to label the immune population (CD45) and the epithelial population (epithelial cell adhesion molecule). Cells were centrifuged for 5 min at 800 RCF at 4 °C and pushed through a 35 μm cell strainer. The FACS Aria III Special Ordered Research Product (BD Biosciences) was used to sort confetti + and confetti cells, by applying a broad FSC/SSC gate, followed by gates excluding doublets (for the gating strategy, see Extended Data Fig. 1d ). Afterwards, non-immune (AF647 – ; 670/30) confetti-positive (RFP + (YG610/20), GFP + /YFP + (BL530/30), CFP + (V450/50)) and, separately, confetti-negative ((RFP – (YG610/20), GFP – /YFP – (BL530/30), CFP – (V450/50)) epithelial cells (APC/Cy7 + ; 780/60) were collected. Similarly, non-immune (AF647 – ; 670/30) epithelial cells (APC/Cy7 + ; 780/60) were collected from three R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl mice that had not received TAT-Cre intraductally as a negative control. Data were analysed in FlowJo v.10 for the gating strategy (Extended Data Fig. 1d ). Cells were spun down for 10 min at 800 RCF at 4 °C and DNA was isolated using the PicoPure DNA extraction kit (Applied Biosciences; KIT0103) according to the manufacturer’s instructions. The same method was applied to isolate genomic DNA (gDNA) from K14-Cre;Brca1 fl/fl ;Trp53 fl/fl mammary tumour organoids, representing fully recombined samples as a positive control. gDNA concentration was measured using the DeNovix DS-11 spectrophotometer. DNA was diluted to 50–75 ng ml −1 and used for real-time qPCR using the SYBR Green Master Mix (ThermoFisher Scientific, catalogue no. 4309155) in a QuantStudio 6 Flex Real-Time PCR system using the primers listed in the table below. Reactions contained roughly 75 ng of template gDNA and 1 µM of both forward and reverse primers in 20 µl reaction volume. Expression values were calculated by transforming delta–delta Ct values (2 -ΔΔCt ). Ribosomal Protein L38 (Rpl38) was used as a housekeeping gene. To confirm correct qPCR product amplification, 25 μl of qPCR product of the control samples ( R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl mice that had not received TAT-Cre intraductally) was loaded on an 2% agarose gel with loading buffer (Bioxline, catalogue no. BIO-37045) and a DNA ladder (Meridian Bioscience, catalogue no. BIO-33056) and run at 80 V for 1.5 h, after which the qPCR product was cut out of the gel and purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel, catalogue no. 740609.50) according to the manufacturer’s instructions, and was confirmed by sequencing using the qPCR primers.

Generation of Brca1;Trp53 mutant and WT organoids followed by Ki-67/CC3 staining

The fourth and fifth mammary glands of R26R-Confetti;Brca1 fl/fl ;Trp53 fl/fl mice were intraductally injected with recombinant TAT-Cre protein (20 units per gland diluted in 20 µl PBS, Sigma-Aldrich) between 10 and 15 weeks of age. Then, 64 or 225 days post-induction, mammary glands were harvested and prepared for fluorescence-activated cell sorting (FACS) as described before. Both confetti-positive Brca1 −/− ;Trp53 −/− and non-recombined control cells were seeded in a 24-well plate, 10,000 cells per drop of Cultrex Basement Membrane Extract (Type 2, 3532-010-02, R&D Systems) and cultured in the DMEM/F12 (Gibco) supplemented with iInsulin-transferrin-selenium (100×, catalogue no. 41400045, Gibco), B-27 Supplement (50×, catalogue no. 17504044, Gibco), NAC 1.25 mM ( N -acetyl- l -cysteine, 0.125 M in PBS, catalogue no. 6169116, Biogems), mFGF2 2.5 nM (Fibroblast Growth Factor 2, catalogue no. 100-18B, PeproTech) and mEGF 2 nM (Epidermal Growth Factor, catalogue no. 3165-09, PeproTech). After 2 weeks of culture, organoids were fixed with 4% PFA (catalogue no. 47347, AlfaAesar) for 10–15 min at room temperature inside the Basement Membrane Extract droplet on an orbital shaker at 25 rpm. Afterwards, organoids were washed three times for 10 min with PBS, followed by incubation in permeabilization buffer (5% normal goat serum (catalogue no. 16210072, Gibco) and 0.5% Triton X-100 ((Sigma-Aldrich) in PBS) for 3 h. To stain for cell proliferation and cell death, primary antibodies Ki-67 (rat, clone SolA15, 14-5698-82, eBioscience, 1:100) and Cleaved Caspase-3 (rabbit, Asp175, no. 9661, Cell Signaling Technology, 1:400), respectively, were added in the blocking buffer (5% normal goat serum (Gibco) in PBS), and incubated overnight at 4 °C. Organoids were washed three times for 15 min with PBS and secondary antibodies goat antirabbit IgG Antibody, Alexa Fluor 647 (catalogue no. A21244, Thermo Scientific, 1:400), goat antirat IgG Antibody, Alexa Fluor 647 (catalogue no. A21247, Thermo Scientific, 1:400) were added and incubated for more than 5 h at room temperature covered in aluminium foil on an orbital shaker at 25 rpm. Organoids were washed three times for 15 min with PBS and stained organoids were mounted by adding 200 µl of Vectashield mounting medium (VECTASHIELD HardSet Antifade Mounting Medium, H-1400, Vector Laboratories). Organoid imaging was performed on an inverted Leica SP8 Dive system (Leica Microsystems), in which Alexa-647 secondary antibodies were excited at 635 nm and detected between 660 and 700 nm, and organoids were imaged using brightfield. The Ki-67/CC3 ratio was derived by first calculating the organoid area and Ki-67 + or CC3 + areas using ImageJ software ( https://imagej.nih.gov/ij/ ), then calculating the percentage of Ki-67 or CC3 expressing cells per organoid, followed by calculation of the Ki-67/CC3 ratio for every organoid.

P values and statistical tests performed are included in the figure legends or Supplementary Information 1 . The longitudinal data of the clone fractions (Figs. 2d and 5g ) was analysed using a regression model with a time effect, for which the interaction between time and group was tested. For full details, see Supplementary Information 2 . Details on statistics concerning the mathematical modelling can be found in the  Supplementary Information 4 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw clonal data are all provided in the source data. DNA sequencing data are available at the European Genome-Phenome Archive (ENA, https://www.ebi.ac.uk/ena/browser/home ) under accession number PRJEB71510 , secondary accession ERP156311 and PRJEB30443 (Sample accession numbers SAMEA5202116 – 5202120, 5202122 – 5202126).  Source data are provided with this paper.

Code availability

The procedures used to fit the parameters of the phenomenological theory to the experimental data are defined in Supplementary Information 4 . The basis of the cell-based model is also defined in Supplementary Information 4 . Stochastic simulations of the cell-based model were made using a dedicated Fortran code and the Mathematica software package. The code for the computational and statistical analyses is deposited on the GitHub repository ( https://github.com/BenSimonsLab/Ciwinska_Nature_2024 ). Data supporting the findings of Fig. 4b and Extended Data Fig. 9a , including computer code for stochastic simulations, are available at https://github.com/BioImaging-NKI/qupath_ripley . NET code for branching analysis used in Extended Data Fig. 7 is available from J.v.R on reasonable request. Code to determine longitudinal data statistics is provided in the Supplementary Information 2 .

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Acknowledgements

We thank the laboratories of van Rheenen and Scheele for critically reading the manuscript, and the Netherlands Cancer Institute (NKI) Animal facility, NKI BioImaging facility and the NKI genomics core facility for their technical support. This work was supported by the Boehringer Ingelheim Foundation (PhD Fellowship to C.L.G.J.S.), a Federation of European Biochemical Societies excellence award (to C.L.G.J.S.), the Research Foundation Flanders (PhD grant fundamental research no. 11L7222N to M.C.), EMBO (postdoctoral fellowship grant nos. ALTF 452-2019 to H.A.M. and ALTF 1035-2020 to C.L.G.J.S.) and the European Research Council (consolidator grant no. 648804 to J.v.R.), the Doctor Josef Steiner Foundation (to J.v.R.), the Netherlands Organization of Scientific Research (NWO) (Vici grant no. 09150182110004 to J.v.R., and Veni grant no. 09150161910151 to H.A.M.) and a joint grant of the Cancer Research UK and KWF Kankerbestrijding (ref. C38317/A24043). B.D.S. acknowledges funding from the Royal Society E.P. Abraham Research Professorship (grant nos. RP\R1\180165 and RSRP\R\231004) and Wellcome (grant nos. 098357/Z/12/Z and 219478/Z/19/Z). We regret that we could not cite all the important contributions in this field due to the constraint of being limited to citing only 60 studies. This research was funded, in part, by the Wellcome Trust (098357/Z/12/Z and 219478/Z/19/Z). For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Author information

These authors contributed equally: Marta Ciwinska, Hendrik A. Messal, Hristina R. Hristova

These authors jointly supervised this work: Benjamin D. Simons, Colinda L. G. J. Scheele, Jacco van Rheenen

Authors and Affiliations

VIB-KULeuven Centre for Cancer Biology, Department of Oncology, Leuven, Belgium

Marta Ciwinska & Colinda L. G. J. Scheele

Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands

Hendrik A. Messal, Hristina R. Hristova, Catrin Lutz, Laura Bornes, Nathalia S. M. Langedijk, Stefan J. Hutten, Jos Jonkers & Jacco van Rheenen

Centre for Molecular Medicine, UMC Utrecht, Utrecht, the Netherlands

Theofilos Chalkiadakis & Stefan Prekovic

Bioimaging Facility, The Netherlands Cancer Institute, Amsterdam, the Netherlands

Rolf Harkes

Biostatistics Centre and Department of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

Renée X. Menezes

Gurdon Institute, University of Cambridge, Cambridge, UK

Benjamin D. Simons

Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, UK

The Netherlands Cancer Institute, Amsterdam, the Netherlands

Jelle Wesseling, Jos Jonkers, Jacco van Rheenen, Esther H. Lips, Marjanka Schmidt, Lodewyk F. A. Wessels & Proteeti Bhattacharjee

Baylor College of Medicine, Houston, TX, USA

Alastair M. Thompson

University of Cambridge, Cambridge, UK

Serena Nik-Zainal & Helen R. Davies

King’s College London, London, UK

Elinor J. Sawyer

MD Anderson Cancer Center, Houston, TX, USA

Andrew Futreal & Nicholas E. Navin

Duke University School of Medicine, Durham, NC, USA

E. Shelley Hwang

Kansas University Medical Center, Kansas City, KS, USA

Fariba Behbod

University of Birmingham, Birmingham, UK

Independent Cancer Patients’ Voice, London, UK

Hilary Stobart

Patient Advocates in Research, Danville, CA, USA

Deborah Collyar

DCIS 411, San Diego, CA, USA

Donna Pinto

Borstkankervereniging Nederland, Utrecht, the Netherlands

Ellen Verschuur & Marja van Oirsouw

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Grand Challenge PRECISION consortium

• Jelle Wesseling
• , Alastair M. Thompson
• , Serena Nik-Zainal
• , Elinor J. Sawyer
• , Helen R. Davies
• , Andrew Futreal
• , Nicholas E. Navin
• , E. Shelley Hwang
• , Jos Jonkers
• , Jacco van Rheenen
• , Fariba Behbod
• , Esther H. Lips
• , Marjanka Schmidt
• , Lodewyk F. A. Wessels
• , Daniel Rea
• , Proteeti Bhattacharjee
• , Hilary Stobart
• , Deborah Collyar
• , Donna Pinto
• , Ellen Verschuur
•  & Marja van Oirsouw

Contributions

C.L.G.J.S. and J.v.R. conceived the study and designed experiments. C.L.G.J.S., with the help of M.C., H.A.M., H.R.H., L.B. and N.S.M.L., performed experiments and analyses. S.J.H. and C.L. performed intraductal injections, supervised by J.J. R.H. wrote the Ripley’s K script. T.C. and S.P. supported the analysis of chromosomal aberrations. R.X.M. designed and performed the longitudinal data analysis. B.D.S. performed theoretical and statistical analyses. C.L.G.J.S., B.D.S. and J.v.R wrote the paper, which was approved by all authors.

Corresponding authors

Correspondence to Benjamin D. Simons , Colinda L. G. J. Scheele or Jacco van Rheenen .

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Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Mohamed Bentires-Alj, Alexander Anderson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended data fig. 1 stochastic recombination in the brca1 fl/fl ;trp53 fl/fl ;r26r-confetti mouse model..

a , Quantification of the total number of Brca1;Trp53 confetti clones after TAT-Cre mediated recombination in at least 4 different 4th mammary glands derived from different mice. Number of clones was determined by using large tilescans (xyz) of the entire mammary glands (whole-mount) labelled with SMA (basal cells) or ECAD (luminal cells). Luminal clones are depicted in cyan, basal clones are depicted in blue. b , qPCR for the Brca1 allele in sorted confetti-positive cells and sorted confetti-negative cells derived from TAT-Cre recombined Brca1 fl/fl ;Trp53 fl/fl ;R26R-Confetti mammary glands and a positive control ( Brca1 Δ /Δ ) normalized to a non-recombined control ( Brca1 F/F ). The recombined samples demonstrate that the vast majority of confetti-positive cells have fully recombined Brca1 alleles. n  = 3 biological replicates. Each dot represents a biological replicate and error bars indicate s.e.m. c , qPCR for the Trp53 allele in sorted confetti-positive cells and sorted confetti-negative cells derived from TAT-Cre recombined Brca1 fl/fl ;Trp53 fl/fl ;R26R-Confetti mammary glands and a positive control ( Trp53 Δ /Δ ) normalized to non-recombined control ( Trp53 F/F ). The recombined samples demonstrate that the vast majority of confetti-positive cells have fully recombined Trp53 alleles. Note that the confetti-negative cells show some loss of the Trp53 allele as well. Each dot represents a biological replicate and error bars indicate s.e.m. n  = 3 biological replicates. d , Representative plots depicting the gating strategy to sort the Confetti positive and negative mammary epithelial cells from TAT-Cre recombined Brca1;Trp53 ; R26R-Confetti mammary glands (left panels) and mammary epithelial cells from non-recombined mammary glands (right panels). Sorted cells were used to determine Brca1 and Trp53 gene levels by qPCR in Extended Data Fig. 1b and c . Numbers in panels indicate order of gating. The Brca1;Trp53 mammary cells in the RFP-CFP- gate in panel 5 were selected to sort GFP_YFP+ and Confetti- cells in panel 6. e , 3D rendering of a Z-stack confocal image of a whole-mount Brca1;Trp53 confetti mammary gland 225 days after recombination labelled with SMA, confetti clones are represented in their respective colours. Brca1;Trp53 mutant confetti clones are distributed throughout the mammary ductal tree and span large areas of the ducts without changing the ductal morphology. Note the recruitment of SMA-positive stromal cells near the GFP clone in panel 1. Scale bar represents 1 mm (overview image) and 100 µm (panel 1 and 2). Representative image of n  = 6 mice.

Extended Data Fig. 2 Characterization of Brca1;Trp53 confetti clones using antibody labelling in intact mammary glands.

a , b , Whole-mount confocal images (3D rendering of a Z-stack in top panels and a representative 2D section of the Z-stack in the bottom panel) of luminal Brca1;Trp53 confetti cells. Luminal confetti cells show overlap with E-cadherin (ECAD) labelling ( a ) and no overlap with alpha-smooth muscle actin (SMA) expression ( b ) depicted in white. Scale bars represent 100 µm. c, d , Whole-mount confocal images (3D rendering of a Z-stack in top panels and a representative 2D section of the Z-stack in the bottom panel) of basal Brca1;Trp53 confetti cells. Basal confetti cells show no overlap with E-cadherin (ECAD) labelling ( c ) and overlap with alpha-smooth muscle actin (SMA) expressing cells ( d ) depicted in white. Scale bars represent 100 µm. e , f , Representative whole-mount confocal images (3D rendering of a Z-stack) showing luminal ( e ) and basal ( f ) Brca1;Trp53 confetti cells that expanded within the ducts leading to clonal fields of mutant cells without morphological transformation of the ducts. Ducts are labelled with SMA, depicted in white. Scale bars represent 100 µm (e, left top panel) or 1 mm (other panels). g , Tumour growth dynamics of palpable transformed lesions within the Brca1;Trp53 confetti model after recombination. h , Representative whole-mount confocal image (3D rendering of a Z-stack) showing a Brca1;Trp53 luminal confetti clone that expanded within the ducts leading to transformation of the ductal morphology (hyperbranching), including local invasion (white arrows denote Brca1;Trp53 RFP cells within the stroma). Ducts are labelled with SMA, depicted in white. Scale bar represents 1 mm. All images represent n  ≥ 4 biological repeats (mice).

Extended Data Fig. 3 Genomic alterations in Brca1;Trp53 confetti clones and end stage tumours.

a , DNA copy number profiles in untransformed (top) and transformed (middle) Brca1;Trp53 confetti clones 225 days after Cre-recombination, and in Brca1;Trp53 end-stage tumours (bottom). b , Example of genomic events that occur early in Brca1;Trp53 tumorigenesis. DNA copy number profiles of chromosome 12 showing genomic losses that are found in early transformed and untransformed Brca1;Trp53 clones, as well as in end-stage tumours. c , Example of genomic events occurring late in Brca1;Trp53 tumorigenesis. DNA copy number plots of chromosome 6 showing copy number changes that are unique to end-stage Brca1;Trp53 tumours, but not found in early clones. All plots show averages of 3 mice (225d timepoint: 13 transformed clones and 13 untransformed clones) and 10 mice (end-stage tumours).

Extended Data Fig. 4 Recombination of wild-type confetti clones with TAT-Cre or tamoxifen results in similar labelling efficiencies.

a , Schematic representation of the R26R-Confetti construct (left), which was recombined sporadically through an intraperitoneal injection with a low dose of tamoxifen in the presence of R26-CreERT2 (R26R-Confetti het ;R26-CreERT2 het mouse model), which is the gold standard method, or through intraductal injection of TAT-Cre recombinant protein. b , Confocal overview image of a whole-mount 4th mammary gland derived from a R26R-Confetti het ;R26-CreERT2 het adult female mouse 14 days after tracing initiation by tamoxifen-mediated recombination. Zooms show ducts containing single confetti-labelled cells of both basal and luminal origin, representative of the initial labelling density after tracing initiation. Ductal tree is stained with alpha-smooth muscle actin (SMA) depicted in white, which marks the basal cell layer. Scale bar left image represents 1 mm, scale bar right images represents 100 µm. c , Quantification of the confetti-positive cell fraction 14 days after tamoxifen-mediated recombination. Each dot represents the fraction of recombined cells in a randomly selected area within each mammary gland of approximately 1 ×1 mm, n  = 6 glands derived from 6 different mice. Basal cells are normalized to the total number of basal cells in the selected area (blue dots) and luminal cells are normalized to the total number of luminal cells in the selected area (cyan dots). Error bar represents mean ± s.d. d , Confocal overview image of a whole-mount 4th mammary gland derived from a R26R-Confetti het adult female mouse, 14 days after tracing initiation recombined by the TAT-Cre intraductal injection method. Zooms show ducts containing single confetti-labelled cells of both basal and luminal origin, representative of the initial labelling density after tracing initiation. Ductal tree is stained with E-cadherin (ECAD) depicted in white, which marks the luminal cell layer. Scale bar left image represents 1 mm, scale bar right images represents 100 µm. e , Quantification of the confetti-positive cell fraction 14 days after TAT-Cre-mediated recombination for basal (blue dots) and luminal (cyan dots) cells. Each dot represents the fraction of recombined cells in a randomly selected area within each mammary gland of approximately 1 ×1 mm, n  = 3 glands derived from 3 different mice. Error bar represents mean ± s.d. Note that recombination efficiencies of luminal and basal cell populations are similar between the tamoxifen- and TAT-Cre-induced recombination techniques. Both induction methods result in the recombination of approximately one labelled cell for every 100–200 cells. As the Confetti construct comprises four distinct colours, there is, on average, one cell labeled with a confetti colour per 400–800 cells. Considering that a MaSC-progeny unit consists of approximately 5 to 10 cells, a single confetti-labeled cell is induced in 1 out of 40–80 units. Importantly, over time, many clones become extinct (Fig. 2d ), leading to a dilution in the number of clones and making collisions even less likely.

Extended Data Fig. 5 Wild-type and Brca1;Trp53 basal and luminal confetti cells form large cohesive clones spanning multiple ducts and branch points.

a , b , Representative whole-mount confocal images of wild-type luminal confetti clones ( a ) and wild-type basal confetti clones ( b ) showing extensive field clonalization within the existing ductal structure. Ducts are labelled with alpha-smooth muscle actin (SMA), confetti fluorophores are represented in their respective colours. Scale bars represent 100 µm. Images in a and b represent n  ≥ 3 biological repeats (mice). c , Representative whole-mount confocal images (3D rendering of Z-stacks) of basal Brca1;Trp53 confetti clones at different timepoints after recombination showing clonal expansion within the ductal tree over a period of 225 days. Ducts are labelled with SMA. d , Representative whole-mount confocal images of wild-type basal confetti clones showing extensive field clonalization within the existing ductal structure over a period of 225 days. Ducts are labelled with Keratin 14 (KRT14). c , d , Persisting clones form cohesive clusters of cells spanning multiple ducts and branch points. Scale bars represent 100 µm. e , f , Quantification of clone sizes in the Brca1;Trp53 and wild-type confetti conditions at different timepoints after tracing initiation for the luminal ( e ) and basal ( f ) clones separately. The number of quantified clones is indicated within the graph, transformed clones are shown in orange (luminal) and red (basal). Boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, * P  < 0,05, ** P  < 0.01, **** P  < 0.0001. Same data as depicted in Fig. 2c . See Supplementary Information  1 for more sample sizes, P values and statistics for e and f .

Extended Data Fig. 6 Wild-type clone sizes transiently increase during each oestrous cycle.

a , Schematic depicting the cellular basis of the cell-based model of mammary epithelial turnover (see also Extended Data Fig. 9b ). Note that, during one round of oestrous cycle, some clones are collectively lost (e.g., yellow and green clone), while others expand (e.g., blue and red clones). b , Quantification of wild-type confetti + clone sizes during oestrus (O) and dioestrus (D) stage 120 days after lineage tracing initiation, demonstrating a temporary increase of clone sizes during dioestrus stage. n  = 3 mice for oestrus stage (300 luminal clones, 101 basal clones) and n  = 3 mice for dioestrus stage (43 luminal clones, 9 basal clones). Error bars represent mean ± s.d. Significance was tested using a two-sided Mann-Whitney test, **** P  < 0.0001. c , Representative whole-mount confocal images of wild-type confetti + clones 120 days after lineage tracing initiation during oestrus (left panels) and dioestrus stage (right panels). Both luminal (top panels) and basal clones (bottom panels) show an increase in clone size during dioestrus stage. Confetti-labelled cells are depicted in their respective colour, and the mammary ducts are labelled with Keratin 14 (KRT14) or Phalloidin in white. Scale bars represent 100 µm.

Extended Data Fig. 7 Spatial and size distribution of wild-type confetti clones in the mammary gland.

a , Representative confocal overview image of a whole mount 5th mammary gland after 550 days of lineage tracing, illustrating the distribution of wild-type confetti clones within the ductal tree. Images depict 3D-rendering of Z-stacks, with the confetti labelled cells in their respective colour and the mammary ducts labelled with Keratin 14 (KRT14) shown in white. Scale bars represent 1 mm (left panel), 100 µm (panel 1 and 2), and 50 µm (panel 3 and 4). Representative image of n  = 8 glands from 4 biological repeats (mice). b , Branch levels are defined as the number of branch points starting from the main duct close to the nipple. c , Quantification of the wild-type confetti clone size by branch level. Each dot represents a clone, cyan dots for luminal clones and blue dots for basal clones. Line indicates linear regression of the luminal and basal clone sizes with R 2 , slope and 95% confidence interval of the slope indicated in the graph, n  = 6 glands from 3 mice. d , Number of wild-type confetti clones represented by bars for each branch level after 550 days of tracing in n  = 6 glands from 3 mice.

Extended Data Fig. 8 Wild-type and Brca1;Trp53 confetti clones follow a log-normal distribution.

a , b , Cumulative distribution of luminal ( a ) and basal ( b ) wild-type confetti clone size as a function of the scaled clone size n / ⟨ n ⟩ , where ⟨ n ⟩ denotes the average clone size. To account for the impact of large-scale mouse-to-mouse variability in clone size, curves are shown for a representative set of individual mice (shown in Fig. 3a, b ) with corresponding distributions shown for all mice in  Supplementary Information 4 . Note that the data does not show evidence for collapse towards a statistical scaling behavior, as would be predicted for clonal dynamics based on local stochastic stem cell loss and replacement (see main text and Supplementary Information 4 ). n  ≥ 3 mice per time point. c , Cumulative distribution of wild-type (left) and Brca1;Trp53 (right) basal confetti clone size showing the probability of finding a clone larger than the given size (log scale) across time points. To account for the impact of large-scale mouse-to-mouse variability in clone size, the curves are shown for a representative set of individual mice with corresponding distributions shown for all mice in  Supplementary Information 4 . n  ≥ 3 mice per time point. d , Rescaled cumulative distribution of the logarithm of wild-type (left) and Brca1;Trp53 (right) basal confetti clone size, ln n , showing the probability of finding a clone with a size larger than \(({\rm{l}}{\rm{n}}\,n-\mu )/\sigma \) , where \(\mu =\langle {\rm{l}}{\rm{n}}\,n\rangle \) denotes the average of the logarithm of clone size and \({\sigma }^{2}=\langle ({\rm{l}}{\rm{n}}\,n-\langle {\rm{l}}{\rm{n}}\,n\rangle {)}^{2}\rangle \) represents the variance. Points show data from panel ( d ). Once rescaled, data from different time points collapse onto a single curve that fits well with the scaling function \((1/2){\rm{erfc}}(x/\sqrt{2})\) (dashed line), consistent with a log-normal size dependence (see main text). For details of statistical significance tests, see  Supplementary Information 4 . e , Average luminal clone size as a function of the inferred oestrous cycle number for wild-type confetti clones. Points show data from individual mice and line shows the theoretical prediction of the model. (Note that the average of the logarithm of clone size is not the same as the logarithm of the average.) f , Fraction of single-cell luminal wild-type confetti clones as a function of inferred oestrous cycle number. Points show data and line (dashed) shows theoretical prediction of the model based on the fits in Fig. 3c, d . Bars in e and f denote mean values +/− SEM. For details of the model, the model fits, and the inference of oestrous cycle number, see main text and Supplementary Information 4 .

Extended Data Fig. 9 Fits and predictions of the phenomenological theory of the turnover of the mammary gland epithelium.

a , Simulation of unclustered and clustered data analyzed with the Ripley’s K analysis leading to the Ripley’s L function. Two datasets were used for the analysis; 50 points from a uniform random distribution and 50 points from a normal distribution were generated for the clustered simulation, 100 random points from a uniform random distribution for the unclustered simulation. Details on the code and data can be found at https://github.com/BioImaging-NKI/qupath_ripley . b , Schematic depicting spatial model of ductal turnover (for details, see  Supplementary Information 4 ). The mammary ductal epithelium is represented as a one-dimensional lattice. During the oestrous cycle, random non-overlapping domains of size l cells are turned over so that the central domain of l /2 cells are lost and replaced by the stochastic expansion of the 2 × l /4 neighboring sites. Through iterations of this process, clones are continuously lost, while others expand. Once clones extend beyond the size of the activated domain, l , their further expansion proceeds as a process of stochastic expansion and contraction on the clone boundary. c , 3D-rendering of confocal Z-stacks (overview image) and single Z-plane (zoom images) showing full labelling of two luminal lineages in the same part of the mammary gland after 550 days of lineage tracing; a PR + clone labelling all PR + luminal cells in this region (confetti RFP), and a PR − clone labelling all PR − in this part of the mammary gland (confetti YFP). PR + luminal cells are shown in white, confetti YFP cells are shown in green. Scale bars represent 50 µm (overview image) and 10 µm (zoom images). Representative example of n  = 3 mice. d , Quantification of ratio between Ki-67 +  (proliferative) and cleaved caspase3 + (CC3) cells within organoids derived from wild-type (WT) or Brca1;Trp53 confetti+ mammary epithelial cells, 64 and 225 days post-recombination. Each dot represents an organoid ( n  = 33 organoids for WT condition, n  = 30 organoids for the Brca1;Trp53 64 days condition, and n  = 74 organoids for the Brca1;Trp53 225 days condition. Violin plots depict distribution of data points, horizontal lines denote median, 1st and 3rd quartile. Significance was tested using a two-sided Mann Whitney Test, *** P  < 0.0005, **** P  < 0.0001, ns P  = 0.1572. e , Quantification of luminal (cyan dots) and basal (blue dots) wild-type confetti clones (left) and Brca1;Trp53 confetti clones (right) in the ducts and side branches, represented on a logarithmic scale. For each timepoint at least n  = 6 glands from 3 mice were analyzed. Morphologically transformed clones are indicated in orange (luminal clones) and red (basal clones). Boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, **** P  < 0.0001. f , Transformed luminal (L, orange) and basal (B, red) clones in the ducts and side branches as percentage of the total number of luminal or basal clones respectively. Each dot indicates an individual mouse and boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, ** P  < 0.01. g , Cumulative distribution of the logarithm of clone size, ln n , obtained from the spatial cell-based model in ( b ) showing the probability of finding a clone with a size larger than \(({\rm{l}}{\rm{n}}\,n-\mu )/\sigma \) , where μ and \({\sigma }^{2}\) are obtained from a least-square fit of the data for n < l /2 to the log-normal size dependence, \((1/2){\rm{erfc}}(x/\sqrt{2})\) (dashed line) (cf. Fig. 3b and Extended Data Fig. 8d ). Here, each lattice site is associated with a renewing MaSC with a total domain size of l =1000 lattice sites. The points show the results of stochastic simulation of the spatial model (averaged over an ensemble of 1000 realizations of the model on a periodic lattice of 10 6 sites) for different numbers of oestrous cycles. In line with the quantitative analysis of the experimental data, the activation rate of domains is taken as 0.1 per oestrous cycle, with a loss probability set by the model of 0.5. For further details of the spatial model, see  Supplementary Information 4 . The code can be obtained from https://github.com/BenSimonsLab/Ciwinska_Nature_2024 . Note that, at large time scales, the data departs from a log-normal size dependence. h , When plot on a log scale, the cumulative distribution of clone size shows the suppression at size scales in excess of the domain size l /2, a manifestation of the constraints imposed by the one-dimensional geometry of the ductal network. Points show the results of stochastic simulation and lines show the corresponding fits to the log-normal size dependence obtained from the fits in panel g. i , j , Cumulative distribution of luminal clone size for wild-type ( i ) and Brca1;Trp53 ( j ) confetti clones for mice showing the largest effective oestrous cycle number from the 225 and 64 day time points, respectively (see  Supplementary Information 4 ). Points show data and lines show the least-squares fits to a log-normal size dependence at small clone sizes. The respective colours are matched to the data shown in  Supplementary Information 4 . Note that, when plot on a log scale, the data reveals a departure from a log-normal size dependence, with a suppression at the largest clone sizes, mirroring the behavior of the spatial model ( h ). See Supplementary Information  1 for more sample sizes, P values and statistics for e and f .

Extended Data Fig. 10 Pregnancy and lactation do not increase the spread of Brca1;Trp53 mutant clones.

a , Schematic depicting the experimental timeline of the pregnancy and lactation experiments in induced Brca1;Trp53 confetti glands. b , Quantification of Brca1;Trp53 confetti clone sizes in nulliparous (left) and parous (right) glands, 120 days after recombination and lineage tracing initiation represented on a logarithmic scale. For each timepoint, at least n  = 3 glands from 3 different mice were analyzed. Boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, **** P  < 0.0001. c , Transformed luminal (L, orange) and basal (B, red) clones as a percentage of the total number of luminal or basal clones respectively. Each dot indicates an individual mouse and boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. n  = 5 mice (nulliparous) and n  = 3 mice (parous). d , Representative whole-mount confocal images of Brca1;Trp53 confetti clones in parous glands (one pregnancy-involution cycle), 120 days after recombination. Luminal cells are labelled with E-cadherin (ECAD), basal cells are labelled with alpha-smooth muscle actin (SMA). Images depict 3D-rendering of Z-stacks. Scale bars represent 100 µm. Representative image of n  = 3 biological repeats (mice). See Supplementary Information  1 for more sample sizes, P values and statistics for b, c .

Extended Data Fig. 11 Ovariectomy abolishes field clonalization and cancerization of basal and luminal cell clones.

a , b , Clone size quantification of luminal ( a ) and basal ( b ) wild-type confetti clones in the homeostatic gland (left), and after ovariectomy (right) represented on a logarithmic scale. Ovariectomy abolishes clonal expansion and field clonalization. Same data as Fig. 5a , but now with basal and luminal clones presented in separate graphs. For each timepoint at least n  = 6 glands from 3 different mice were analyzed. Analyzed number of clones for each timepoint are indicated in the graphs. Boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, *** P  < 0.001, **** P  < 0.0001. c , d , Clone size quantification of luminal ( c ) and basal ( d ) Brca1;Trp53 confetti clones in the presence of oestrous cycling (left) and after ovariectomy (right) represented on a logarithmic scale. Ovariectomy abolishes clonal expansion and field cancerization. Same data as Fig. 5d , but now with basal and luminal clones presented in separate graphs. For each timepoint at least n  = 6 glands from 3 different mice were analyzed. Analyzed number of clones for each timepoint are indicated in the graphs. Boxplots mark the 25th and 75th percentile, line indicates the median, and whiskers mark the minimum and maximum values. Significance was tested using a two-sided Mann-Whitney test, *** P  < 0.001, **** P  < 0.0001. e , f , Representative whole-mount confocal images of basal wild-type confetti clones 120 days ( e ) and basal and luminal wild-type confetti clones 225 days ( f ) after recombination in ovariectomized condition. Luminal cells are labelled with E-cadherin (ECAD) ( e ), basal cells are labelled with alpha-smooth muscle actin (SMA) ( f ). Images depict 3D-rendering of Z-stacks, unless otherwise indicated. Scale bars represent 100 µm, except for the scale bar in 2D section ( e ) which represents 10 µm. Representative images of n  = 3 biological repeats (mice). g , h , Representative whole-mount confocal images of basal Brca1;Trp53 confetti clones 120 days ( g ) and 225 days ( h ) after recombination in ovariectomized condition. Luminal cells are labelled with E-cadherin (ECAD) ( g ), basal cells are labelled with alpha-smooth muscle actin (SMA) ( h ). Images depict 3D-rendering of Z-stacks, unless otherwise indicated. Scale bars represent 100 µm. Representative images of n  = 3 biological repeats (mice). See Supplementary Information  1 for more sample sizes, P values and statistics for a-d .

Extended Data Fig. 12 Tissue protection mechanisms against field cancerization in the mammary gland.

a , b , Cumulative distribution of luminal ( a ) and basal ( b ) clone size of ovariectomized wild-type confetti mice showing the probability (log scale) of finding a clone larger than the given size across a range of time points. c , d , Cumulative distribution of luminal ( c ) and basal ( d ) clone size of ovariectomized Brca1;Trp53 confetti mice showing the probability (log scale) of finding a clone larger than the given size across a range of time point. e , Model depicting how tissue protection mechanisms drive field cancerization in the mammary gland. The mammary ductal epithelium confers several layers of protection against field cancerization by mutant cells. Protection mechanism #1: The ductal epithelial network is supported by a short, lineage-restricted MaSC-descendant cell hierarchy. As a result, the majority of mutant cells will be lost through homeostatic tissue turnover, and only a few mutations rooted in the stem cell compartment can survive in the medium term. Protection mechanism #2: Local stem cell loss and replacement driven by the oestrous cycle leads to large-scale elimination of the majority of mutant stem cell clones over time. This large-scale clonal loss occurs at the expense of an accelerated (exponential-like) expansion of the minority of clones that survive, allowing them to colonize large areas of the epithelium. Protection mechanism #3: Once clones extend beyond the size of regions activated during the oestrous cycle, their expansion becomes limited by the one-dimensional geometry of the ducts, a phenomenon that is particularly effective in restricting mutant clone expansion.

Supplementary information

Supplementary information 1.

Overview of sample sizes, P values and statistical tests.

Reporting Summary

Supplementary information 2.

Longitudinal data statistics. Statistical analysis code used to compare the different groups in Figs. 2d and 5g.

Supplementary Information 3

Genomic alterations in Brca1;Trp53 confetti clones. Individual DNA copy number profiles of each 225d Brca1;Trp53 clone and chromosome. The profiles are sorted by clone transformation status as indicated.

Supplementary Information 4

This file contains a Supplementary Note and Sections 1–5 including Figs. 1–10 and Tables.

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Ciwinska, M., Messal, H.A., Hristova, H.R. et al. Mechanisms that clear mutations drive field cancerization in mammary tissue. Nature 633 , 198–206 (2024). https://doi.org/10.1038/s41586-024-07882-3

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Received : 13 December 2022

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Issue Date : 05 September 2024

DOI : https://doi.org/10.1038/s41586-024-07882-3

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