purpose of quantitative research and qualitative

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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BSc (Hons) Psychology, MSc Psychology of Education

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Quantitative vs. Qualitative Research in Psychology

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

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By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Reference management. Clean and simple.

Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

Psst... there’s more!

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thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

Prevent plagiarism, run a free check.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Raimo Streefkerk

Quantitative and Qualitative Research: An Overview of Approaches

• First Online: 03 January 2022

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In Chap. 1 , the nature and scope of research were outlined and included an overview of quantitative and qualitative research and a brief description of research designs. In this chapter, both quantitative and qualitative research will be described in a little more detail with respect to essential features and characteristics. Furthermore, the research designs used in each of these approaches will be reviewed. Finally, this chapter will conclude with examples of published quantitative and qualitative research in medical imaging and radiation therapy.

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Seeram, E. (2021). Quantitative and Qualitative Research: An Overview of Approaches. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_2

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qualitative and quantitative header

Learning Objective

Differentiate between qualitative and quantitative approaches.

Hong is a physical therapist who teaches injury assessment classes at the University of Utah. With the recent change to online for the remainder of the semester, Hong is interested in the impact on students’ skills acquisition for injury assessment. He wants to utilize both quantitative and qualitative approaches—he plans to compare previous student test scores to current student test scores. He also plans to interview current students about their experiences practicing injury assessment skills virtually. What specific study design methods will Hong use?

Making sense of the evidence

hen conducting a literature search and reviewing research articles, it is important to have a general understanding of the types of research and data you anticipate from different types of studies.

In this article, we review two broad categories of study methods, quantitative and qualitative, and discuss some of their subtypes, or designs, and the type of data that they generate.

Quantitative vs. qualitative approaches

Quantitative is measurable. It is often associated with a more traditional scientific method of gathering data in an organized, objective manner so that findings can be generalized to other persons or populations. Quantitative designs are based on probabilities or likelihood—it utilizes ‘p’ values, power analysis, and other scientific methods to ensure the rigor and reproducibility of the results to other populations. Quantitative designs can be experimental, quasi-experimental, descriptive, or correlational.

Qualitative is usually more subjective , although like quantitative research, it also uses a systematic approach. Qualitative research is generally preferred when the clinical question centers around life experiences or meaning. Qualitative research explores the complexity, depth, and richness of a particular situation from the perspective of the informants—referring to the person or persons providing the information. This may be the patient, the patient’s caregivers, the patient’s family members, etc. The information may also come from the investigator’s or researcher’s observations. At the heart of qualitative research is the belief that reality is based on perceptions and can be different for each person, often changing over time.

Study design differences

Quantitative design methods

Quantitative designs typically fall into four categories: experimental, quasi-experimental, descriptive, or correlational. Let’s talk about these different types. But before we begin, we need to briefly review the difference between independent and dependent variables.

The independent variable is the variable that is being manipulated, or the one that varies. It is sometimes called the ‘predictor’ or ‘treatment’ variable.

The dependent variable is the outcome (or response) variable. Changes in the dependent variables are presumed to be caused or influenced by the independent variable.

Experimental

In experimental designs, there are often treatment groups and control groups. This study design looks for cause and effect (if A, then B), so it requires having control over at least one of the independent, or treatment variables. Experimental design administers the treatment to some of the subjects (called the ‘experimental group’) and not to others (called the ‘control group’). Subjects are randomly assigned—meaning that they would have an equal chance of being assigned to the control group or the experimental group. This is the strongest design for testing cause and effect relationships because randomization reduces bias. In fact, most researchers believe that a randomized controlled trail is the only kind of research study where we can infer cause (if A, then B). The difficulty with a randomized controlled trial is that the results may not be generalizable in all circumstances with all patient populations, so as with any research study, you need to consider the application of the findings to your patients in your setting. 

Quasi-experimental

Quasi-Experimental studies also seek to identify a cause and effect (causal) relationship, although they are less powerful than experimental designs. This is because they lack one or more characteristics of a true experiment. For instance, they may not include random assignment or they may not have a control group. As is often the case in the ‘real world’, clinical care variables often cannot be controlled due to ethical, practical, or fiscal concerns. So, the quasi experimental approach is utilized when a randomized controlled trial is not possible. For example, if it was found that the new treatment stopped disease progression, it would no longer be ethical to withhold it from others by establishing a control group.

Descriptive

Descriptive studies give us an accurate account of the characteristics of a particular situation or group. They are often used to determine how often something occurs, the likelihood of something occurring, or to provide a way to categorize information. For example, let’s say we wanted to look at the visiting policy in the ICU and describe how implementing an open-visiting policy affected nurse satisfaction. We could use a research tool, such as a Likert scale (5 = very satisfied and 1 = very dissatisfied), to help us gain an understanding of how satisfied nurses are as a group with this policy.

Correlational

Correlational research involves the study of the relationship between two or more variables. The primary purpose is to explain the nature of the relationship, not to determine the cause and effect. For example, if you wanted to examine whether first-time moms who have an elective induction are more likely to have a cesarean birth than first-time moms who go into labor naturally, the independent variables would be ‘elective induction’ and ‘go into labor naturally’ (because they are the variables that ‘vary’) and the outcome variable is ‘cesarean section.’ Even if you find a strong relationship between elective inductions and an increased likelihood of cesarean birth, you cannot state that elective inductions ‘cause’ cesarean births because we have no control over the variables. We can only report an increased likelihood.   

Qualitative design methods

Qualitative methods delve deeply into experiences, social processes, and subcultures. Qualitative study generally falls under three types of designs: phenomenology, ethnography and grounded theory.

Phenomenology

In this approach, we want to understand and describe the lived experience or meaning of persons with a particular condition or situation. For example, phenomenological questions might ask “What is it like for an adolescent to have a younger sibling with a terminal illness?” or “What is the lived experience of caring for an older house-bound dependent parent?”

Ethnography

Ethnographic studies focus on the culture of a group of people. The assumption behind ethnographies is that groups of individuals evolve into a kind of ‘culture’ that guides the way members of that culture or group view the world. In this kind of study, the research focuses on participant observation, where the researcher becomes an active participant in that culture to understand its experiences. For example, nursing could be considered a professional culture, and the unit of a hospital can be viewed as a subculture. One example specific to nursing culture was a study done in 2006 by Deitrick and colleagues . They used ethnographic methods to examine problems related to answering patient call lights on one medical surgical inpatient unit. The single nursing unit was the ‘culture’ under study.

Grounded theory

Grounded theory research begins with a general research problem, selects persons most likely to clarify the initial understanding of the question, and uses a variety of techniques (interviewing, observation, document review to name a few) to discover and develop a theory. For example, one nurse researcher used a grounded theory approach to explain how African American women from different socioeconomic backgrounds make decisions about mammography screening. Because African American women historically have fewer mammograms (and therefore lower survival rates for later stage detection), understanding their decision-making process may help the provider support more effective health promotion efforts. 

Being able to identify the differences between qualitative and quantitative research and becoming familiar with the subtypes of each can make a literature search a little less daunting.

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This article originally appeared July 2, 2020. It was updated to reflect current practice on March 21, 2021.

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Performing a rapid critical appraisal helps evaluate a study for its worth by ensuring validity, meaningful data, and significance to the patient. Contributors Barb Wilson, Mary Jean Austria, and Tallie Casucci share a checklist of questions to complete a rapid critical appraisal efficiently and effectively.

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Medical students Rachel Tsolinas and Sam Wilkinson, along with SOM professor Kathryn Moore, share a practical tool all health care professionals can use to broaden our understanding of how culture influences decisions and events.

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather data in your research projects. So what do qualitative and quantitative mean exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research is all about language, expression, body language and other forms of human communication. That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people feel the way they do, why do they act in a certain way, what opinions do they have and what motivates them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data.

This qualitative data is called unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyze this structured data to assist computers and the human brain?

Free eBook: Quantitative and qualitative research design

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to understand something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to test or confirm a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain insights around a topic and propose a hypothesis. Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted quantitative research could then reveal whether or not those stores selling children’s clothes achieve higher customer satisfaction scores and a rise in profits for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey question options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analyzing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for quantitative data are now used for interpreting non numerical data too.

A rtificial intelligence programs can now be used to analyze open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organizations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyze than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

Related resources

Qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, request demo.

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

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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Qualitative vs Quantitative Research – What Is the difference?

Bryn Farnsworth

In the world of research, across many different research fields, there are two main approaches that researchers employ: qualitative and quantitative research. In this article, we will explore the key differences between qualitative and quantitative research and look at what the main differences are, including their respective strengths and weaknesses, as well as some common methods used in each approach. By the end of this article, readers will have a better understanding of the two approaches and be able to choose the best approach for their research question.

Table of Contents

The core difference between qualitative and quantitative research.

Once you get started with human behavior research you soon find yourself running into the question of whether your research project is qualitative or quantitative in nature . There are inherent differences between qualitative and quantitative research methods, although their objectives and applications overlap in many ways.

In a nutshell, qualitative research generates “textual data” (non-numerical). Quantitative research, on the contrary, produces “numerical data” or information that can be converted into numbers.

In analogous terms, qualitative research is like exploring a new city. You walk around, talk to people, and soak up the atmosphere to understand the culture and the vibe. It’s all about getting a feel for the place and the people who live there.

Quantitative research, on the other hand, is more like counting cars on a busy street. You collect data, run some calculations, and analyze the numbers to understand how things work and what factors might be influencing them.

So, while qualitative research is about understanding the deeper meaning and nuances of human experiences, quantitative research is about measuring and analyzing data in a systematic way.

Both methodologies have their place in science and research in terms of understanding the world around us, but it is important to know how and when to implement them.

What is Quantitative Research?

What is qualitative research and why is it important.

Qualitative research is considered to be particularly suitable for exploratory research (e.g. during the pilot stage of a research project, for example). It is primarily used to discover and gain an in-depth understanding of individual experiences, thoughts, opinions, and trends, and to dig deeper into the problem at hand.

The data collection toolkit of a qualitative researcher is quite versatile, ranging from completely unstructured to semi-structured techniques.

Most common applied Qualitative Methods:

• Individual interviews
• Group discussions
• Focus groups
• Behavioral observations

Check out: How to measure Human Behavior: Survey vs. Focus Groups vs. Biometric

In addition, eye tracking or automatic facial expressions can be collected and analyzed qualitatively, for example in usability research, where gaze patterns (such as with heatmaps) or moments of expressions of frustration/confusion can be used to track the journey of an individual respondent within a software interface.

Check out: What is Eye Tracking and How Does it Work?

Typically, qualitative research focuses on individual cases and their subjective impressions. This requires an iterative study design – data collection and research questions are adjusted according to what is learned.

Often, qualitative projects are done with few respondents and are supposed to provide insights into the setting of a problem, serving as a source of inspiration to generate hypotheses for subsequent quantitative projects.

Advantages of qualitative research

Qualitative research methods are the tool of choice when a researcher wants to gain in-depth data from a small sample size, such as a local community, closed demographic, or situations where in-depth data lets you extrapolate responses to a broader perspective. Some of the main strengths of qualitative research are;

• Provides in-depth understanding: Qualitative research allows researchers to gain a detailed and comprehensive understanding of a particular phenomenon or concept. By gathering rich and detailed data, qualitative research can provide insights into complex and multifaceted issues.
• Flexibility: By working with much smaller sample sizes, qualitative research is flexible, adaptable, and agile. It can be adjusted as the research progresses to explore emerging themes and ideas that were not initially anticipated.
• Emphasis on context: Qualitative research emphasizes the importance of understanding the social and cultural context in which a phenomenon occurs. This allows for a more nuanced interpretation of the data and a deeper understanding of the factors that influence behavior.
• Participant perspectives: Qualitative research often involves direct engagement with participants, which allows researchers to explore their perspectives, experiences, and attitudes toward the phenomenon being studied.
• Generates new theories: Qualitative research is often used in exploratory studies and can generate new theories and hypotheses. It can be particularly useful in situations where little is known about a phenomenon and more research is needed to generate insights and understanding.

Limitations of qualitative research

• Subjectivity: Qualitative research can be more subjective than is advisable, as it relies heavily on the researcher’s interpretation and understanding of the data. This can lead to bias and affect the validity and reliability of the findings.
• Limited generalizability: Qualitative research is typically based on a small sample size and is context-specific, which means the findings may not be generalizable to other populations or settings.
• Time-consuming: Qualitative research can be time-consuming and require significant resources. Collecting and analyzing data can take a long time, and the data may be difficult to analyze due to its complexity.
• Lack of standardization: Qualitative research methods are often not standardized, which can make it difficult to compare and replicate studies. This can also affect the reliability and validity of the findings.
• Difficulty in data analysis: Qualitative research often involves the analysis of large amounts of data, which can be challenging and time-consuming. It can be difficult to identify patterns and themes in the data, and the findings may be difficult to interpret.

What is quantitative research and how to measure it

Simply put, quantitative research is all about numbers and figures. It is used to quantify opinions, attitudes, behaviors, and other defined variables with the goal to support or refute hypotheses about a specific phenomenon, and potentially contextualize the results from the study sample in a wider population (or specific groups).

As quantitative research explicitly specifies what is measured and how it is measured in order to uncover patterns in – for example – behavior, motivation, emotion, and cognition, quantitative data collection is considered to be much more structured than qualitative methods.

Check out: How To Do Behavioral Coding in iMotions

Advantages of quantitative research

As should be apparent by now, quantitative research should be your method of choice if you are looking to work with a large amount of data. Spotting trends, fluctuations across demographics, and objective generalizations in responses is where the quantitative research methods shine. Here is a list of the best reasons why you should choose quantitative research as your method.

• Objectivity: Quantitative research is often viewed as more objective and reliable than qualitative research due to its focus on numerical data and statistical analysis. In short, if you have done your data collection properly, the data will not lie.
• Generalizability: Quantitative research allows for the generalization of results to larger populations because it relies on representative samples and statistical techniques to draw conclusions and make predictions about a larger group.
• Replication: The use of standardized and objective measures in quantitative research allows for easy replication of studies, enabling other researchers to verify and build upon existing findings.
• Easy to analyze: Quantitative data is often straightforward to analyze using statistical software , allowing researchers to quickly and efficiently identify trends and patterns in the data.

Limitations of quantitative research

Research and data collection methods focused on quantity rather than quality, will inevitably come with certain drawbacks and limitations. These are dependent on the research scope, but at the very least they should be considered when building a study design. Following here is a list of the main limitations or considerations of using quantitative research as a method.

• Limited scope: Quantitative research is focused on data and statistical analysis, which can be limiting in terms of the range of topics it can explore and the depth of insights it can obtain.
• Lack of context : Quantitative research may not provide the depth of information and context that qualitative research can offer, as the main focus is on statistics rather than the experiences and perceptions of participants.
• Difficulty in measuring complex constructs : It can be challenging to measure complex constructs such as emotions, attitudes, and beliefs using quantitative methods, as they are often difficult to define and measure precisely when dealing with large datasets.
• Potential for researcher bias : The collection and analysis of numerical data can be influenced by researcher bias, leading to inaccurate or incomplete results.
• Limited ability to capture individual experiences : Quantitative research may not be able to capture the unique experiences and perspectives of individual participants, as it typically focuses on group-level trends and patterns.

Quantitative research techniques

Quantitative techniques typically comprise various forms of questionnaires and surveys, structured interviews as well as a behavioral observation based on explicit coding and categorization schemes.

In addition to these traditional techniques, biosensor recordings such as eye tracking , EEG , EDA / GSR , EMG , and ECG , as well as computer-guided automatic facial expression analysis procedures, are used.

Check out: What Is Facial Expression Analysis? (And How Does It Work?)

All of these quantify the behavioral processes in such a way that numerical results can be obtained – for example, fixation duration from eye tracking (representing the amount of visual attention), the number of GSR peaks (indicating the amount of physiological arousal) or the power of a specific EEG band.

After data collection, quantitative analysis techniques and statistics can be applied, such as t-tests and ANOVAs, to non-parametric methods. This often necessitates much bigger sample sizes compared to qualitative research but allows you to make more solid conclusions, that are backed up with data.

Check Out: How to perform a qualitative research interview

Qualitative or quantitative research study design?

Ultimately, whether to pursue a qualitative or a quantitative study approach is up to you – however, be sure to base your decision on the nature of your project and the kind of information you seek in the context of your study, and the resources available to you. Qualitative will offer you an in-depth understanding of your research problem and hopefully help answer your hypothesis. Quantitative will allow you to scale your research to provide larger sets of data for reliability and validity. A combination of the two provides you with objectivity.

Measurements to avoid bias

This is generally described with respect to the following criteria:

Objectivity

Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.

Reliability

A measure is said to have a high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.

This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.

If you would like to learn more about qualitative and quantitative research designs, contact our experts at iMotions. We’re happy to help!

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Frequently asked questions

What’s the difference between quantitative and qualitative methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Quantitative and Qualitative Research

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What is qualitative research?

Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives. Rather than by logical and statistical procedures, qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

University of Utah College of Nursing, (n.d.). What is qualitative research? [Guide] Retrieved from  https://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php#what 

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Conducting and Writing Quantitative and Qualitative Research

Edward barroga.

1 Department of Medical Education, Showa University School of Medicine, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

Atsuko Furuta

Makiko arima, shizuma tsuchiya, chikako kawahara, yusuke takamiya.

Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Graphical Abstract

INTRODUCTION

Novel research studies are conceptualized by scientific researchers first by asking excellent research questions and developing hypotheses, then answering these questions by testing their hypotheses in ethical research. 1 , 2 , 3 Before they conduct novel research studies, scientific researchers must possess considerable knowledge of both quantitative and qualitative research. 2

In quantitative research, researchers describe existing theories, generate and test a hypothesis in novel research, and re-evaluate existing theories deductively based on their experimental results. 1 , 4 , 5 In qualitative research, scientific researchers raise and answer research questions by performing a novel study, then propose new theories by clarifying their results inductively. 1 , 6

RATIONALE OF THIS ARTICLE

When researchers have a limited knowledge of both research types and how to conduct them, this can result in substandard investigation. Researchers must be familiar with both types of research and skilled to conduct their investigations within the frames of their chosen type of research. Thus, meticulous care is needed when planning quantitative and qualitative research studies to avoid unethical research and poor outcomes.

Understanding the methodological and writing assumptions 7 , 8 underpinning quantitative and qualitative research, especially by non-Anglophone researchers, is essential for their successful conduct. Scientific researchers, especially in the academe, face pressure to publish in international journals 9 where English is the language of scientific communication. 10 , 11 In particular, non-Anglophone researchers face challenges related to linguistic, stylistic, and discourse differences. 11 , 12 Knowing the assumptions of the different types of research will help clarify research questions and methodologies, easing the challenge and help.

SEARCH FOR RELEVANT ARTICLES

To identify articles relevant to this topic, we adhered to the search strategy recommended by Gasparyan et al. 7 We searched through PubMed, Scopus, Directory of Open Access Journals, and Google Scholar databases using the following keywords: quantitative research, qualitative research, mixed-method research, deductive reasoning, inductive reasoning, study design, descriptive research, correlational research, experimental research, causal-comparative research, quasi-experimental research, historical research, ethnographic research, meta-analysis, narrative research, grounded theory, phenomenology, case study, and field research.

AIMS OF THIS ARTICLE

This article aims to provide a comparative appraisal of qualitative and quantitative research for scientific researchers. At present, there is still a need to define the scope of qualitative research, especially its essential elements. 13 Consensus on the critical appraisal tools to assess the methodological quality of qualitative research remains lacking. 14 Framing and testing research questions can be challenging in qualitative research. 2 In the healthcare system, it is essential that research questions address increasingly complex situations. Therefore, research has to be driven by the kinds of questions asked and the corresponding methodologies to answer these questions. 15 The mixed-method approach also needs to be clarified as this would appear to arise from different philosophical underpinnings. 16

This article also aims to discuss how particular types of research should be conducted and how they should be written in adherence to international standards. In the US, Europe, and other countries, responsible research and innovation was conceptualized and promoted with six key action points: engagement, gender equality, science education, open access, ethics and governance. 17 , 18 International ethics standards in research 19 as well as academic integrity during doctoral trainings are now integral to the research process. 20

POTENTIAL BENEFITS FROM THIS ARTICLE

This article would be beneficial for researchers in further enhancing their understanding of the theoretical, methodological, and writing aspects of qualitative and quantitative research, and their combination.

Moreover, this article reviews the basic features of both research types and overviews the rationale for their conduct. It imparts information on the most common forms of quantitative and qualitative research, and how they are carried out. These aspects would be helpful for selecting the optimal methodology to use for research based on the researcher’s objectives and topic.

This article also provides information on the strengths and weaknesses of quantitative and qualitative research. Such information would help researchers appreciate the roles and applications of both research types and how to gain from each or their combination. As different research questions require different types of research and analyses, this article is anticipated to assist researchers better recognize the questions answered by quantitative and qualitative research.

Finally, this article would help researchers to have a balanced perspective of qualitative and quantitative research without considering one as superior to the other.

TYPES OF RESEARCH

Research can be classified into two general types, quantitative and qualitative. 21 Both types of research entail writing a research question and developing a hypothesis. 22 Quantitative research involves a deductive approach to prove or disprove the hypothesis that was developed, whereas qualitative research involves an inductive approach to create a hypothesis. 23 , 24 , 25 , 26

In quantitative research, the hypothesis is stated before testing. In qualitative research, the hypothesis is developed through inductive reasoning based on the data collected. 27 , 28 For types of data and their analysis, qualitative research usually includes data in the form of words instead of numbers more commonly used in quantitative research. 29

Quantitative research usually includes descriptive, correlational, causal-comparative / quasi-experimental, and experimental research. 21 On the other hand, qualitative research usually encompasses historical, ethnographic, meta-analysis, narrative, grounded theory, phenomenology, case study, and field research. 23 , 25 , 28 , 30 A summary of the features, writing approach, and examples of published articles for each type of qualitative and quantitative research is shown in Table 1 . 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43

QUANTITATIVE RESEARCH

Deductive approach.

The deductive approach is used to prove or disprove the hypothesis in quantitative research. 21 , 25 Using this approach, researchers 1) make observations about an unclear or new phenomenon, 2) investigate the current theory surrounding the phenomenon, and 3) hypothesize an explanation for the observations. Afterwards, researchers will 4) predict outcomes based on the hypotheses, 5) formulate a plan to test the prediction, and 6) collect and process the data (or revise the hypothesis if the original hypothesis was false). Finally, researchers will then 7) verify the results, 8) make the final conclusions, and 9) present and disseminate their findings ( Fig. 1A ).

Types of quantitative research

The common types of quantitative research include (a) descriptive, (b) correlational, c) experimental research, and (d) causal-comparative/quasi-experimental. 21

Descriptive research is conducted and written by describing the status of an identified variable to provide systematic information about a phenomenon. A hypothesis is developed and tested after data collection, analysis, and synthesis. This type of research attempts to factually present comparisons and interpretations of findings based on analyses of the characteristics, progression, or relationships of a certain phenomenon by manipulating the employed variables or controlling the involved conditions. 44 Here, the researcher examines, observes, and describes a situation, sample, or variable as it occurs without investigator interference. 31 , 45 To be meaningful, the systematic collection of information requires careful selection of study units by precise measurement of individual variables 21 often expressed as ranges, means, frequencies, and/or percentages. 31 , 45 Descriptive statistical analysis using ANOVA, Student’s t -test, or the Pearson coefficient method has been used to analyze descriptive research data. 46

Correlational research is performed by determining and interpreting the extent of a relationship between two or more variables using statistical data. This involves recognizing data trends and patterns without necessarily proving their causes. The researcher studies only the data, relationships, and distributions of variables in a natural setting, but does not manipulate them. 21 , 45 Afterwards, the researcher establishes reliability and validity, provides converging evidence, describes relationship, and makes predictions. 47

Experimental research is usually referred to as true experimentation. The researcher establishes the cause-effect relationship among a group of variables making up a study using the scientific method or process. This type of research attempts to identify the causal relationships between variables through experiments by arbitrarily controlling the conditions or manipulating the variables used. 44 The scientific manuscript would include an explanation of how the independent variable was manipulated to determine its effects on the dependent variables. The write-up would also describe the random assignments of subjects to experimental treatments. 21

Causal-comparative/quasi-experimental research closely resembles true experimentation but is conducted by establishing the cause-effect relationships among variables. It may also be conducted to establish the cause or consequences of differences that already exist between, or among groups of individuals. 48 This type of research compares outcomes between the intervention groups in which participants are not randomized to their respective interventions because of ethics- or feasibility-related reasons. 49 As in true experiments, the researcher identifies and measures the effects of the independent variable on the dependent variable. However, unlike true experiments, the researchers do not manipulate the independent variable.

In quasi-experimental research, naturally formed or pre-existing groups that are not randomly assigned are used, particularly when an ethical, randomized controlled trial is not feasible or logical. 50 The researcher identifies control groups as those which have been exposed to the treatment variable, and then compares these with the unexposed groups. The causes are determined and described after data analysis, after which conclusions are made. The known and unknown variables that could still affect the outcome are also included. 7

QUALITATIVE RESEARCH

Inductive approach.

Qualitative research involves an inductive approach to develop a hypothesis. 21 , 25 Using this approach, researchers answer research questions and develop new theories, but they do not test hypotheses or previous theories. The researcher seldom examines the effectiveness of an intervention, but rather explores the perceptions, actions, and feelings of participants using interviews, content analysis, observations, or focus groups. 25 , 45 , 51

Distinctive features of qualitative research

Qualitative research seeks to elucidate about the lives of people, including their lived experiences, behaviors, attitudes, beliefs, personality characteristics, emotions, and feelings. 27 , 30 It also explores societal, organizational, and cultural issues. 30 This type of research provides a good story mimicking an adventure which results in a “thick” description that puts readers in the research setting. 52

The qualitative research questions are open-ended, evolving, and non-directional. 26 The research design is usually flexible and iterative, commonly employing purposive sampling. The sample size depends on theoretical saturation, and data is collected using in-depth interviews, focus groups, and observations. 27

In various instances, excellent qualitative research may offer insights that quantitative research cannot. Moreover, qualitative research approaches can describe the ‘lived experience’ perspectives of patients, practitioners, and the public. 53 Interestingly, recent developments have looked into the use of technology in shaping qualitative research protocol development, data collection, and analysis phases. 54

Qualitative research employs various techniques, including conversational and discourse analysis, biographies, interviews, case-studies, oral history, surveys, documentary and archival research, audiovisual analysis, and participant observations. 26

Conducting qualitative research

To conduct qualitative research, investigators 1) identify a general research question, 2) choose the main methods, sites, and subjects, and 3) determine methods of data documentation access to subjects. Researchers also 4) decide on the various aspects for collecting data (e.g., questions, behaviors to observe, issues to look for in documents, how much (number of questions, interviews, or observations), 5) clarify researchers’ roles, and 6) evaluate the study’s ethical implications in terms of confidentiality and sensitivity. Afterwards, researchers 7) collect data until saturation, 8) interpret data by identifying concepts and theories, and 9) revise the research question if necessary and form hypotheses. In the final stages of the research, investigators 10) collect and verify data to address revisions, 11) complete the conceptual and theoretical framework to finalize their findings, and 12) present and disseminate findings ( Fig. 1B ).

Types of qualitative research

The different types of qualitative research include (a) historical research, (b) ethnographic research, (c) meta-analysis, (d) narrative research, (e) grounded theory, (f) phenomenology, (g) case study, and (h) field research. 23 , 25 , 28 , 30

Historical research is conducted by describing past events, problems, issues, and facts. The researcher gathers data from written or oral descriptions of past events and attempts to recreate the past without interpreting the events and their influence on the present. 6 Data is collected using documents, interviews, and surveys. 55 The researcher analyzes these data by describing the development of events and writes the research based on historical reports. 2

Ethnographic research is performed by observing everyday life details as they naturally unfold. 2 It can also be conducted by developing in-depth analytical descriptions of current systems, processes, and phenomena or by understanding the shared beliefs and practices of a particular group or culture. 21 The researcher collects extensive narrative non-numerical data based on many variables over an extended period, in a natural setting within a specific context. To do this, the researcher uses interviews, observations, and active participation. These data are analyzed by describing and interpreting them and developing themes. A detailed report of the interpreted data is then provided. 2 The researcher immerses himself/herself into the study population and describes the actions, behaviors, and events from the perspective of someone involved in the population. 23 As examples of its application, ethnographic research has helped to understand a cultural model of family and community nursing during the coronavirus disease 2019 outbreak. 56 It has also been used to observe the organization of people’s environment in relation to cardiovascular disease management in order to clarify people’s real expectations during follow-up consultations, possibly contributing to the development of innovative solutions in care practices. 57

Meta-analysis is carried out by accumulating experimental and correlational results across independent studies using a statistical method. 21 The report is written by specifying the topic and meta-analysis type. In the write-up, reporting guidelines are followed, which include description of inclusion criteria and key variables, explanation of the systematic search of databases, and details of data extraction. Meta-analysis offers in-depth data gathering and analysis to achieve deeper inner reflection and phenomenon examination. 58

Narrative research is performed by collecting stories for constructing a narrative about an individual’s experiences and the meanings attributed to them by the individual. 9 It aims to hear the voice of individuals through their account or experiences. 17 The researcher usually conducts interviews and analyzes data by storytelling, content review, and theme development. The report is written as an in-depth narration of events or situations focused on the participants. 2 , 59 Narrative research weaves together sequential events from one or two individuals to create a “thick” description of a cohesive story or narrative. 23 It facilitates understanding of individuals’ lives based on their own actions and interpretations. 60

Grounded theory is conducted by engaging in an inductive ground-up or bottom-up strategy of generating a theory from data. 24 The researcher incorporates deductive reasoning when using constant comparisons. Patterns are detected in observations and then a working hypothesis is created which directs the progression of inquiry. The researcher collects data using interviews and questionnaires. These data are analyzed by coding the data, categorizing themes, and describing implications. The research is written as a theory and theoretical models. 2 In the write-up, the researcher describes the data analysis procedure (i.e., theoretical coding used) for developing hypotheses based on what the participants say. 61 As an example, a qualitative approach has been used to understand the process of skill development of a nurse preceptor in clinical teaching. 62 A researcher can also develop a theory using the grounded theory approach to explain the phenomena of interest by observing a population. 23

Phenomenology is carried out by attempting to understand the subjects’ perspectives. This approach is pertinent in social work research where empathy and perspective are keys to success. 21 Phenomenology studies an individual’s lived experience in the world. 63 The researcher collects data by interviews, observations, and surveys. 16 These data are analyzed by describing experiences, examining meanings, and developing themes. The researcher writes the report by contextualizing and reporting the subjects’ experience. This research approach describes and explains an event or phenomenon from the perspective of those who have experienced it. 23 Phenomenology understands the participants’ experiences as conditioned by their worldviews. 52 It is suitable for a deeper understanding of non-measurable aspects related to the meanings and senses attributed by individuals’ lived experiences. 60

Case study is conducted by collecting data through interviews, observations, document content examination, and physical inspections. The researcher analyzes the data through a detailed identification of themes and the development of narratives. The report is written as an in-depth study of possible lessons learned from the case. 2

Field research is performed using a group of methodologies for undertaking qualitative inquiries. The researcher goes directly to the social phenomenon being studied and observes it extensively. In the write-up, the researcher describes the phenomenon under the natural environment over time with no implantation of controls or experimental conditions. 45

DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Scientific researchers must be aware of the differences between quantitative and qualitative research in terms of their working mechanisms to better understand their specific applications. This knowledge will be of significant benefit to researchers, especially during the planning process, to ensure that the appropriate type of research is undertaken to fulfill the research aims.

In terms of quantitative research data evaluation, four well-established criteria are used: internal validity, external validity, reliability, and objectivity. 23 The respective correlating concepts in qualitative research data evaluation are credibility, transferability, dependability, and confirmability. 30 Regarding write-up, quantitative research papers are usually shorter than their qualitative counterparts, which allows the latter to pursue a deeper understanding and thus producing the so-called “thick” description. 29

Interestingly, a major characteristic of qualitative research is that the research process is reversible and the research methods can be modified. This is in contrast to quantitative research in which hypothesis setting and testing take place unidirectionally. This means that in qualitative research, the research topic and question may change during literature analysis, and that the theoretical and analytical methods could be altered during data collection. 44

Quantitative research focuses on natural, quantitative, and objective phenomena, whereas qualitative research focuses on social, qualitative, and subjective phenomena. 26 Quantitative research answers the questions “what?” and “when?,” whereas qualitative research answers the questions “why?,” “how?,” and “how come?.” 64

Perhaps the most important distinction between quantitative and qualitative research lies in the nature of the data being investigated and analyzed. Quantitative research focuses on statistical, numerical, and quantitative aspects of phenomena, and employ the same data collection and analysis, whereas qualitative research focuses on the humanistic, descriptive, and qualitative aspects of phenomena. 26 , 28

Structured versus unstructured processes

The aims and types of inquiries determine the difference between quantitative and qualitative research. In quantitative research, statistical data and a structured process are usually employed by the researcher. Quantitative research usually suggests quantities (i.e., numbers). 65 On the other hand, researchers typically use opinions, reasons, verbal statements, and an unstructured process in qualitative research. 63 Qualitative research is more related to quality or kind. 65

In quantitative research, the researcher employs a structured process for collecting quantifiable data. Often, a close-ended questionnaire is used wherein the response categories for each question are designed in which values can be assigned and analyzed quantitatively using a common scale. 66 Quantitative research data is processed consecutively from data management, then data analysis, and finally to data interpretation. Data should be free from errors and missing values. In data management, variables are defined and coded. In data analysis, statistics (e.g., descriptive, inferential) as well as central tendency (i.e., mean, median, mode), spread (standard deviation), and parameter estimation (confidence intervals) measures are used. 67

In qualitative research, the researcher uses an unstructured process for collecting data. These non-statistical data may be in the form of statements, stories, or long explanations. Various responses according to respondents may not be easily quantified using a common scale. 66

Composing a qualitative research paper resembles writing a quantitative research paper. Both papers consist of a title, an abstract, an introduction, objectives, methods, findings, and discussion. However, a qualitative research paper is less regimented than a quantitative research paper. 27

Quantitative research as a deductive hypothesis-testing design

Quantitative research can be considered as a hypothesis-testing design as it involves quantification, statistics, and explanations. It flows from theory to data (i.e., deductive), focuses on objective data, and applies theories to address problems. 45 , 68 It collects numerical or statistical data; answers questions such as how many, how often, how much; uses questionnaires, structured interview schedules, or surveys 55 as data collection tools; analyzes quantitative data in terms of percentages, frequencies, statistical comparisons, graphs, and tables showing statistical values; and reports the final findings in the form of statistical information. 66 It uses variable-based models from individual cases and findings are stated in quantified sentences derived by deductive reasoning. 24

In quantitative research, a phenomenon is investigated in terms of the relationship between an independent variable and a dependent variable which are numerically measurable. The research objective is to statistically test whether the hypothesized relationship is true. 68 Here, the researcher studies what others have performed, examines current theories of the phenomenon being investigated, and then tests hypotheses that emerge from those theories. 4

Quantitative hypothesis-testing research has certain limitations. These limitations include (a) problems with selection of meaningful independent and dependent variables, (b) the inability to reflect subjective experiences as variables since variables are usually defined numerically, and (c) the need to state a hypothesis before the investigation starts. 61

Qualitative research as an inductive hypothesis-generating design

Qualitative research can be considered as a hypothesis-generating design since it involves understanding and descriptions in terms of context. It flows from data to theory (i.e., inductive), focuses on observation, and examines what happens in specific situations with the aim of developing new theories based on the situation. 45 , 68 This type of research (a) collects qualitative data (e.g., ideas, statements, reasons, characteristics, qualities), (b) answers questions such as what, why, and how, (c) uses interviews, observations, or focused-group discussions as data collection tools, (d) analyzes data by discovering patterns of changes, causal relationships, or themes in the data; and (e) reports the final findings as descriptive information. 61 Qualitative research favors case-based models from individual characteristics, and findings are stated using context-dependent existential sentences that are justifiable by inductive reasoning. 24

In qualitative research, texts and interviews are analyzed and interpreted to discover meaningful patterns characteristic of a particular phenomenon. 61 Here, the researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences. 4

Qualitative hypothesis-generating research involves collecting interview data from study participants regarding a phenomenon of interest, and then using what they say to develop hypotheses. It involves the process of questioning more than obtaining measurements; it generates hypotheses using theoretical coding. 61 When using large interview teams, the key to promoting high-level qualitative research and cohesion in large team methods and successful research outcomes is the balance between autonomy and collaboration. 69

Qualitative data may also include observed behavior, participant observation, media accounts, and cultural artifacts. 61 Focus group interviews are usually conducted, audiotaped or videotaped, and transcribed. Afterwards, the transcript is analyzed by several researchers.

Qualitative research also involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions. Such approach uncovers meaningful patterns that describe a particular phenomenon. 2 Thus, qualitative research requires skills in grasping and contextualizing data, as well as communicating data analysis and results in a scientific manner. The reflective process of the inquiry underscores the strengths of a qualitative research approach. 2

Combination of quantitative and qualitative research

When both quantitative and qualitative research methods are used in the same research, mixed-method research is applied. 25 This combination provides a complete view of the research problem and achieves triangulation to corroborate findings, complementarity to clarify results, expansion to extend the study’s breadth, and explanation to elucidate unexpected results. 29

Moreover, quantitative and qualitative findings are integrated to address the weakness of both research methods 29 , 66 and to have a more comprehensive understanding of the phenomenon spectrum. 66

For data analysis in mixed-method research, real non-quantitized qualitative data and quantitative data must both be analyzed. 70 The data obtained from quantitative analysis can be further expanded and deepened by qualitative analysis. 23

In terms of assessment criteria, Hammersley 71 opined that qualitative and quantitative findings should be judged using the same standards of validity and value-relevance. Both approaches can be mutually supportive. 52

Quantitative and qualitative research must be carefully studied and conducted by scientific researchers to avoid unethical research and inadequate outcomes. Quantitative research involves a deductive process wherein a research question is answered with a hypothesis that describes the relationship between independent and dependent variables, and the testing of the hypothesis. This investigation can be aptly termed as hypothesis-testing research involving the analysis of hypothesis-driven experimental studies resulting in a test of significance. Qualitative research involves an inductive process wherein a research question is explored to generate a hypothesis, which then leads to the development of a theory. This investigation can be aptly termed as hypothesis-generating research. When the whole spectrum of inductive and deductive research approaches is combined using both quantitative and qualitative research methodologies, mixed-method research is applied, and this can facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Data curation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Formal analysis: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C.
• Investigation: Barroga E, Matanguihan GJ, Takamiya Y, Izumi M.
• Methodology: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Project administration: Barroga E, Matanguihan GJ.
• Resources: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Supervision: Barroga E.
• Validation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Visualization: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Jump to DSE Guide

Purpose statement overview.

The purpose statement succinctly explains (on no more than 1 page) the objectives of the research study. These objectives must directly address the problem and help close the stated gap. Expressed as a formula:

Good purpose statements:

• Flow from the problem statement and actually address the proposed problem
• Are concise and clear
• Answer the question ‘Why are you doing this research?’
• Match the methodology (similar to research questions)
• Have a ‘hook’ to get the reader’s attention
• Set the stage by clearly stating, “The purpose of this (qualitative or quantitative) study is to ...

In PhD studies, the purpose usually involves applying a theory to solve the problem. In other words, the purpose tells the reader what the goal of the study is, and what your study will accomplish, through which theoretical lens. The purpose statement also includes brief information about direction, scope, and where the data will come from.

A problem and gap in combination can lead to different research objectives, and hence, different purpose statements. In the example from above where the problem was severe underrepresentation of female CEOs in Fortune 500 companies and the identified gap related to lack of research of male-dominated boards; one purpose might be to explore implicit biases in male-dominated boards through the lens of feminist theory. Another purpose may be to determine how board members rated female and male candidates on scales of competency, professionalism, and experience to predict which candidate will be selected for the CEO position. The first purpose may involve a qualitative ethnographic study in which the researcher observes board meetings and hiring interviews; the second may involve a quantitative regression analysis. The outcomes will be very different, so it’s important that you find out exactly how you want to address a problem and help close a gap!

The purpose of the study must not only align with the problem and address a gap; it must also align with the chosen research method. In fact, the DP/DM template requires you to name the  research method at the very beginning of the purpose statement. The research verb must match the chosen method. In general, quantitative studies involve “closed-ended” research verbs such as determine , measure , correlate , explain , compare , validate , identify , or examine ; whereas qualitative studies involve “open-ended” research verbs such as explore , understand , narrate , articulate [meanings], discover , or develop .

A qualitative purpose statement following the color-coded problem statement (assumed here to be low well-being among financial sector employees) + gap (lack of research on followers of mid-level managers), might start like this:

In response to declining levels of employee well-being, the purpose of the qualitative phenomenology was to explore and understand the lived experiences related to the well-being of the followers of novice mid-level managers in the financial services industry. The levels of follower well-being have been shown to correlate to employee morale, turnover intention, and customer orientation (Eren et al., 2013). A combined framework of Leader-Member Exchange (LMX) Theory and the employee well-being concept informed the research questions and supported the inquiry, analysis, and interpretation of the experiences of followers of novice managers in the financial services industry.

A quantitative purpose statement for the same problem and gap might start like this:

In response to declining levels of employee well-being, the purpose of the quantitative correlational study was to determine which leadership factors predict employee well-being of the followers of novice mid-level managers in the financial services industry. Leadership factors were measured by the Leader-Member Exchange (LMX) assessment framework  by Mantlekow (2015), and employee well-being was conceptualized as a compound variable consisting of self-reported turnover-intent and psychological test scores from the Mental Health Survey (MHS) developed by Johns Hopkins University researchers.

Both of these purpose statements reflect viable research strategies and both align with the problem and gap so it’s up to the researcher to design a study in a manner that reflects personal preferences and desired study outcomes. Note that the quantitative research purpose incorporates operationalized concepts  or variables ; that reflect the way the researcher intends to measure the key concepts under study; whereas the qualitative purpose statement isn’t about translating the concepts under study as variables but instead aim to explore and understand the core research phenomenon.  

Best Practices for Writing your Purpose Statement

Always keep in mind that the dissertation process is iterative, and your writing, over time, will be refined as clarity is gradually achieved. Most of the time, greater clarity for the purpose statement and other components of the Dissertation is the result of a growing understanding of the literature in the field. As you increasingly master the literature you will also increasingly clarify the purpose of your study.

The purpose statement should flow directly from the problem statement. There should be clear and obvious alignment between the two and that alignment will get tighter and more pronounced as your work progresses.

The purpose statement should specifically address the reason for conducting the study, with emphasis on the word specifically. There should not be any doubt in your readers’ minds as to the purpose of your study. To achieve this level of clarity you will need to also insure there is no doubt in your mind as to the purpose of your study.

Many researchers benefit from stopping your work during the research process when insight strikes you and write about it while it is still fresh in your mind. This can help you clarify all aspects of a dissertation, including clarifying its purpose.

Your Chair and your committee members can help you to clarify your study’s purpose so carefully attend to any feedback they offer.

The purpose statement should reflect the research questions and vice versa. The chain of alignment that began with the research problem description and continues on to the research purpose, research questions, and methodology must be respected at all times during dissertation development. You are to succinctly describe the overarching goal of the study that reflects the research questions. Each research question narrows and focuses the purpose statement. Conversely, the purpose statement encompasses all of the research questions.

Identify in the purpose statement the research method as quantitative, qualitative or mixed (i.e., “The purpose of this [qualitative/quantitative/mixed] study is to ...)

Avoid the use of the phrase “research study” since the two words together are redundant.

Follow the initial declaration of purpose with a brief overview of how, with what instruments/data, with whom and where (as applicable) the study will be conducted. Identify variables/constructs and/or phenomenon/concept/idea. Since this section is to be a concise paragraph, emphasis must be placed on the word brief. However, adding these details will give your readers a very clear picture of the purpose of your research.

Developing the purpose section of your dissertation is usually not achieved in a single flash of insight. The process involves a great deal of reading to find out what other scholars have done to address the research topic and problem you have identified. The purpose section of your dissertation could well be the most important paragraph you write during your academic career, and every word should be carefully selected. Think of it as the DNA of your dissertation. Everything else you write should emerge directly and clearly from your purpose statement. In turn, your purpose statement should emerge directly and clearly from your research problem description. It is good practice to print out your problem statement and purpose statement and keep them in front of you as you work on each part of your dissertation in order to insure alignment.

It is helpful to collect several dissertations similar to the one you envision creating. Extract the problem descriptions and purpose statements of other dissertation authors and compare them in order to sharpen your thinking about your own work.  Comparing how other dissertation authors have handled the many challenges you are facing can be an invaluable exercise. Keep in mind that individual universities use their own tailored protocols for presenting key components of the dissertation so your review of these purpose statements should focus on content rather than form.

Once your purpose statement is set it must be consistently presented throughout the dissertation. This may require some recursive editing because the way you articulate your purpose may evolve as you work on various aspects of your dissertation. Whenever you make an adjustment to your purpose statement you should carefully follow up on the editing and conceptual ramifications throughout the entire document.

In establishing your purpose you should NOT advocate for a particular outcome. Research should be done to answer questions not prove a point. As a researcher, you are to inquire with an open mind, and even when you come to the work with clear assumptions, your job is to prove the validity of the conclusions reached. For example, you would not say the purpose of your research project is to demonstrate that there is a relationship between two variables. Such a statement presupposes you know the answer before your research is conducted and promotes or supports (advocates on behalf of) a particular outcome. A more appropriate purpose statement would be to examine or explore the relationship between two variables.

Your purpose statement should not imply that you are going to prove something. You may be surprised to learn that we cannot prove anything in scholarly research for two reasons. First, in quantitative analyses, statistical tests calculate the probability that something is true rather than establishing it as true. Second, in qualitative research, the study can only purport to describe what is occurring from the perspective of the participants. Whether or not the phenomenon they are describing is true in a larger context is not knowable. We cannot observe the phenomenon in all settings and in all circumstances.

Writing your Purpose Statement

It is important to distinguish in your mind the differences between the Problem Statement and Purpose Statement.

The Problem Statement is why I am doing the research

The Purpose Statement is what type of research I am doing to fit or address the problem

The Purpose Statement includes:

• Method of Study
• Specific Population

Remember, as you are contemplating what to include in your purpose statement and then when you are writing it, the purpose statement is a concise paragraph that describes the intent of the study, and it should flow directly from the problem statement.  It should specifically address the reason for conducting the study, and reflect the research questions.  Further, it should identify the research method as qualitative, quantitative, or mixed.  Then provide a brief overview of how the study will be conducted, with what instruments/data collection methods, and with whom (subjects) and where (as applicable). Finally, you should identify variables/constructs and/or phenomenon/concept/idea.

Qualitative Purpose Statement

Creswell (2002) suggested for writing purpose statements in qualitative research include using deliberate phrasing to alert the reader to the purpose statement. Verbs that indicate what will take place in the research and the use of non-directional language that do not suggest an outcome are key. A purpose statement should focus on a single idea or concept, with a broad definition of the idea or concept. How the concept was investigated should also be included, as well as participants in the study and locations for the research to give the reader a sense of with whom and where the study took place. 

Creswell (2003) advised the following script for purpose statements in qualitative research:

“The purpose of this qualitative_________________ (strategy of inquiry, such as ethnography, case study, or other type) study is (was? will be?) to ________________ (understand? describe? develop? discover?) the _________________(central phenomenon being studied) for ______________ (the participants, such as the individual, groups, organization) at __________(research site). At this stage in the research, the __________ (central phenomenon being studied) will be generally defined as ___________________ (provide a general definition)” (pg. 90).

Quantitative Purpose Statement

Creswell (2003) offers vast differences between the purpose statements written for qualitative research and those written for quantitative research, particularly with respect to language and the inclusion of variables. The comparison of variables is often a focus of quantitative research, with the variables distinguishable by either the temporal order or how they are measured. As with qualitative research purpose statements, Creswell (2003) recommends the use of deliberate language to alert the reader to the purpose of the study, but quantitative purpose statements also include the theory or conceptual framework guiding the study and the variables that are being studied and how they are related. 

Creswell (2003) suggests the following script for drafting purpose statements in quantitative research:

“The purpose of this _____________________ (experiment? survey?) study is (was? will be?) to test the theory of _________________that _________________ (compares? relates?) the ___________(independent variable) to _________________________(dependent variable), controlling for _______________________ (control variables) for ___________________ (participants) at _________________________ (the research site). The independent variable(s) _____________________ will be generally defined as _______________________ (provide a general definition). The dependent variable(s) will be generally defined as _____________________ (provide a general definition), and the control and intervening variables(s), _________________ (identify the control and intervening variables) will be statistically controlled in this study” (pg. 97).

Sample Purpose Statements

• The purpose of this qualitative study was to determine how participation in service-learning in an alternative school impacted students academically, civically, and personally.  There is ample evidence demonstrating the failure of schools for students at-risk; however, there is still a need to demonstrate why these students are successful in non-traditional educational programs like the service-learning model used at TDS.  This study was unique in that it examined one alternative school’s approach to service-learning in a setting where students not only serve, but faculty serve as volunteer teachers.  The use of a constructivist approach in service-learning in an alternative school setting was examined in an effort to determine whether service-learning participation contributes positively to academic, personal, and civic gain for students, and to examine student and teacher views regarding the overall outcomes of service-learning.  This study was completed using an ethnographic approach that included observations, content analysis, and interviews with teachers at The David School.
• The purpose of this quantitative non-experimental cross-sectional linear multiple regression design was to investigate the relationship among early childhood teachers’ self-reported assessment of multicultural awareness as measured by responses from the Teacher Multicultural Attitude Survey (TMAS) and supervisors’ observed assessment of teachers’ multicultural competency skills as measured by the Multicultural Teaching Competency Scale (MTCS) survey. Demographic data such as number of multicultural training hours, years teaching in Dubai, curriculum program at current school, and age were also examined and their relationship to multicultural teaching competency. The study took place in the emirate of Dubai where there were 14,333 expatriate teachers employed in private schools (KHDA, 2013b).
• The purpose of this quantitative, non-experimental study is to examine the degree to which stages of change, gender, acculturation level and trauma types predicts the reluctance of Arab refugees, aged 18 and over, in the Dearborn, MI area, to seek professional help for their mental health needs. This study will utilize four instruments to measure these variables: University of Rhode Island Change Assessment (URICA: DiClemente & Hughes, 1990); Cumulative Trauma Scale (Kira, 2012); Acculturation Rating Scale for Arabic Americans-II Arabic and English (ARSAA-IIA, ARSAA-IIE: Jadalla & Lee, 2013), and a demographic survey. This study will examine 1) the relationship between stages of change, gender, acculturation levels, and trauma types and Arab refugees’ help-seeking behavior, 2) the degree to which any of these variables can predict Arab refugee help-seeking behavior.  Additionally, the outcome of this study could provide researchers and clinicians with a stage-based model, TTM, for measuring Arab refugees’ help-seeking behavior and lay a foundation for how TTM can help target the clinical needs of Arab refugees. Lastly, this attempt to apply the TTM model to Arab refugees’ condition could lay the foundation for future research to investigate the application of TTM to clinical work among refugee populations.
• The purpose of this qualitative, phenomenological study is to describe the lived experiences of LLM for 10 EFL learners in rural Guatemala and to utilize that data to determine how it conforms to, or possibly challenges, current theoretical conceptions of LLM. In accordance with Morse’s (1994) suggestion that a phenomenological study should utilize at least six participants, this study utilized semi-structured interviews with 10 EFL learners to explore why and how they have experienced the motivation to learn English throughout their lives. The methodology of horizontalization was used to break the interview protocols into individual units of meaning before analyzing these units to extract the overarching themes (Moustakas, 1994). These themes were then interpreted into a detailed description of LLM as experienced by EFL students in this context. Finally, the resulting description was analyzed to discover how these learners’ lived experiences with LLM conformed with and/or diverged from current theories of LLM.
• The purpose of this qualitative, embedded, multiple case study was to examine how both parent-child attachment relationships are impacted by the quality of the paternal and maternal caregiver-child interactions that occur throughout a maternal deployment, within the context of dual-military couples. In order to examine this phenomenon, an embedded, multiple case study was conducted, utilizing an attachment systems metatheory perspective. The study included four dual-military couples who experienced a maternal deployment to Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF) when they had at least one child between 8 weeks-old to 5 years-old.  Each member of the couple participated in an individual, semi-structured interview with the researcher and completed the Parenting Relationship Questionnaire (PRQ). “The PRQ is designed to capture a parent’s perspective on the parent-child relationship” (Pearson, 2012, para. 1) and was used within the proposed study for this purpose. The PRQ was utilized to triangulate the data (Bekhet & Zauszniewski, 2012) as well as to provide some additional information on the parents’ perspective of the quality of the parent-child attachment relationship in regards to communication, discipline, parenting confidence, relationship satisfaction, and time spent together (Pearson, 2012). The researcher utilized the semi-structured interview to collect information regarding the parents' perspectives of the quality of their parental caregiver behaviors during the deployment cycle, the mother's parent-child interactions while deployed, the behavior of the child or children at time of reunification, and the strategies or behaviors the parents believe may have contributed to their child's behavior at the time of reunification. The results of this study may be utilized by the military, and by civilian providers, to develop proactive and preventive measures that both providers and parents can implement, to address any potential adverse effects on the parent-child attachment relationship, identified through the proposed study. The results of this study may also be utilized to further refine and understand the integration of attachment theory and systems theory, in both clinical and research settings, within the field of marriage and family therapy.

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Qualitative vs. Quantitative Data: 7 Key Differences

Qualitative data is information you can describe with words rather than numbers. 

Quantitative data is information represented in a measurable way using numbers. 

One type of data isn’t better than the other. 

To conduct thorough research, you need both. But knowing the difference between them is important if you want to harness the full power of both qualitative and quantitative data. 

In this post, we’ll explore seven key differences between these two types of data. 

#1. The Type of Data

The single biggest difference between quantitative and qualitative data is that one deals with numbers, and the other deals with concepts and ideas. 

The words “qualitative” and “quantitative” are really similar, which can make it hard to keep track of which one is which. I like to think of them this way: 

• Quantitative = quantity = numbers-related data
• Qualitative = quality = descriptive data

Qualitative data—the descriptive one—usually involves written or spoken words, images, or even objects. It’s collected in all sorts of ways: video recordings, interviews, open-ended survey responses, and field notes, for example. 

I like how researcher James W. Crick defines qualitative research in a 2021 issue of the Journal of Strategic Marketing : “Qualitative research is designed to generate in-depth and subjective findings to build theory.”

In other words, qualitative research helps you learn more about a topic—usually from a primary, or firsthand, source—so you can form ideas about what it means. This type of data is often rich in detail, and its interpretation can vary depending on who’s analyzing it. 

Here’s what I mean: if you ask five different people to observe how 60 kittens behave when presented with a hamster wheel, you’ll get five different versions of the same event. 

Quantitative data, on the other hand, is all about numbers and statistics. There’s no wiggle room when it comes to interpretation. In our kitten scenario, quantitative data might show us that of the 60 kittens presented with a hamster wheel, 40 pawed at it, 5 jumped inside and started spinning, and 15 ignored it completely.

There’s no ifs, ands, or buts about the numbers. They just are. 

#2. When to Use Each Type of Data

You should use both quantitative and quantitative data to make decisions for your business. 

Quantitative data helps you get to the what . Qualitative data unearths the why .

Quantitative data collects surface information, like numbers. Qualitative data dives deep beneath these same numbers and fleshes out the nuances there. 

Research projects can often benefit from both types of data, which is why you’ll see the term “mixed-method” research in peer-reviewed journals. The term “mixed-method” refers to using both quantitative and qualitative methods in a study. 

So, maybe you’re diving into original research. Or maybe you’re looking at other peoples’ studies to make an important business decision. In either case, you can use both quantitative and qualitative data to guide you.

Imagine you want to start a company that makes hamster wheels for cats. You run that kitten experiment, only to learn that most kittens aren’t all that interested in the hamster wheel. That’s what your quantitative data seems to say. Of the 60 kittens who participated in the study, only 5 hopped into the wheel. 

But 40 of the kittens pawed at the wheel. According to your quantitative data, these 40 kittens touched the wheel but did not get inside. 

This is where your qualitative data comes into play. Why did these 40 kittens touch the wheel but stop exploring it? You turn to the researchers’ observations. Since there were five different researchers, you have five sets of detailed notes to study. 

From these observations, you learn that many of the kittens seemed frightened when the wheel moved after they pawed it. They grew suspicious of the structure, meowing and circling it, agitated.

One researcher noted that the kittens seemed desperate to enjoy the wheel, but they didn’t seem to feel it was safe. 

So your idea isn’t a flop, exactly. 

It just needs tweaking. 

According to your quantitative data, 75% of the kittens studied either touched or actively participated in the hamster wheel. Your qualitative data suggests more kittens would have jumped into the wheel if it hadn’t moved so easily when they pawed at it. 

You decide to make your kitten wheel sturdier and try the whole test again with a new set of kittens. Hopefully, this time a higher percentage of your feline participants will hop in and enjoy the fun. 

This is a very simplistic and fictional example of how a mixed-method approach can help you make important choices for your business. 

#3. Data You Have Access To

When you can swing it, you should look at both qualitative and quantitative data before you make any big decisions. 

But this is where we come to another big difference between quantitative vs. qualitative data: it’s a lot easier to source qualitative data than quantitative data. 

Why? Because it’s easy to run a survey, host a focus group, or conduct a round of interviews. All you have to do is hop on SurveyMonkey or Zoom and you’re on your way to gathering original qualitative data. 

And yes, you can get some quantitative data here. If you run a survey and 45 customers respond, you can collect demographic data and yes/no answers for that pool of 45 respondents.

But this is a relatively small sample size. (More on why this matters in a moment.) 

To tell you anything meaningful, quantitative data must achieve statistical significance. 

If it’s been a while since your college statistics class, here’s a refresh: statistical significance is a measuring stick. It tells you whether the results you get are due to a specific cause or if they can be attributed to random chance. 

To achieve statistical significance in a study, you have to be really careful to set the study up the right way and with a meaningful sample size.

This doesn’t mean it’s impossible to get quantitative data. But unless you have someone on your team who knows all about null hypotheses and p-values and statistical analysis, you might need to outsource quantitative research. 

Plenty of businesses do this, but it’s pricey. 

When you’re just starting out or you’re strapped for cash, qualitative data can get you valuable information—quickly and without gouging your wallet. 

#4. Big vs. Small Sample Size

Another reason qualitative data is more accessible? It requires a smaller sample size to achieve meaningful results. 

Even one person’s perspective brings value to a research project—ever heard of a case study?

The sweet spot depends on the purpose of the study, but for qualitative market research, somewhere between 10-40 respondents is a good number. 

Any more than that and you risk reaching saturation. That’s when you keep getting results that echo each other and add nothing new to the research.

Quantitative data needs enough respondents to reach statistical significance without veering into saturation territory. 

The ideal sample size number is usually higher than it is for qualitative data. But as with qualitative data, there’s no single, magic number. It all depends on statistical values like confidence level, population size, and margin of error.

Because it often requires a larger sample size, quantitative research can be more difficult for the average person to do on their own. 

#5. Methods of Analysis

Running a study is just the first part of conducting qualitative and quantitative research. 

After you’ve collected data, you have to study it. Find themes, patterns, consistencies, inconsistencies. Interpret and organize the numbers or survey responses or interview recordings. Tidy it all up into something you can draw conclusions from and apply to various situations. 

This is called data analysis, and it’s done in completely different ways for qualitative vs. quantitative data. 

For qualitative data, analysis includes: 

• Data prep: Make all your qualitative data easy to access and read. This could mean organizing survey results by date, or transcribing interviews, or putting photographs into a slideshow format. 
• Coding: No, not that kind. Think color coding, like you did for your notes in school. Assign colors or codes to specific attributes that make sense for your study—green for positive emotions, for instance, and red for angry emotions. Then code each of your responses. 
• Thematic analysis: Organize your codes into themes and sub-themes, looking for the meaning—and relationships—within each one. 
• Content analysis: Quantify the number of times certain words or concepts appear in your data. If this sounds suspiciously like quantitative research to you, it is. Sort of. It’s looking at qualitative data with a quantitative eye to identify any recurring themes or patterns. 
• Narrative analysis: Look for similar stories and experiences and group them together. Study them and draw inferences from what they say.
• Interpret and document: As you organize and analyze your qualitative data, decide what the findings mean for you and your project.

You can often do qualitative data analysis manually or with tools like NVivo and ATLAS.ti. These tools help you organize, code, and analyze your subjective qualitative data. 

Quantitative data analysis is a lot less subjective. Here’s how it generally goes: 

• Data cleaning: Remove all inconsistencies and inaccuracies from your data. Check for duplicates, incorrect formatting (mistakenly writing a 1.00 value as 10.1, for example), and incomplete numbers. 
• Summarize data with descriptive statistics: Use mean, median, mode, range, and standard deviation to summarize your data. 
• Interpret the data with inferential statistics: This is where it gets more complicated. Instead of simply summarizing stats, you’ll now use complicated mathematical and statistical formulas and tests—t-tests, chi-square tests, analysis of variance (ANOVA), and correlation, for starters—to assign meaning to your data. 

Researchers generally use sophisticated data analysis tools like RapidMiner and Tableau to help them do this work. 

#6. Flexibility 

Quantitative research tends to be less flexible than qualitative research. It relies on structured data collection methods, which researchers must set up well before the study begins.

This rigid structure is part of what makes quantitative data so reliable. But the downside here is that once you start the study, it’s hard to change anything without negatively affecting the results. If something unexpected comes up—or if new questions arise—researchers can’t easily change the scope of the study. 

Qualitative research is a lot more flexible. This is why qualitative data can go deeper than quantitative data. If you’re interviewing someone and an interesting, unexpected topic comes up, you can immediately explore it.

Other qualitative research methods offer flexibility, too. Most big survey software brands allow you to build flexible surveys using branching and skip logic. These features let you customize which questions respondents see based on the answers they give.  

This flexibility is unheard of in quantitative research. But even though it’s as flexible as an Olympic gymnast, qualitative data can be less reliable—and harder to validate. 

#7. Reliability and Validity

Quantitative data is more reliable than qualitative data. Numbers can’t be massaged to fit a certain bias. If you replicate the study—in other words, run the exact same quantitative study two or more times—you should get nearly identical results each time. The same goes if another set of researchers runs the same study using the same methods.

This is what gives quantitative data that reliability factor. 

There are a few key benefits here. First, reliable data means you can confidently make generalizations that apply to a larger population. It also means the data is valid and accurately measures whatever it is you’re trying to measure. 

And finally, reliable data is trustworthy. Big industries like healthcare, marketing, and education frequently use quantitative data to make life-or-death decisions. The more reliable and trustworthy the data, the more confident these decision-makers can be when it’s time to make critical choices. 

Unlike quantitative data, qualitative data isn’t overtly reliable. It’s not easy to replicate. If you send out the same qualitative survey on two separate occasions, you’ll get a new mix of responses. Your interpretations of the data might look different, too. 

There’s still incredible value in qualitative data, of course—and there are ways to make sure the data is valid. These include: 

• Member checking: Circling back with survey, interview, or focus group respondents to make sure you accurately summarized and interpreted their feedback. 
• Triangulation: Using multiple data sources, methods, or researchers to cross-check and corroborate findings.
• Peer debriefing: Showing the data to peers—other researchers—so they can review the research process and its findings and provide feedback on both. 

Whether you’re dealing with qualitative or quantitative data, transparency, accuracy, and validity are crucial. Focus on sourcing (or conducting) quantitative research that’s easy to replicate and qualitative research that’s been peer-reviewed.

With rock-solid data like this, you can make critical business decisions with confidence.

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What is quantitative research?

2020-06-30 Market Research

Quantitative research is the most common research framework in the social sciences. While other research frameworks, such as qualitative research, can be subjective, quantitative research is more objective. It studies processes through numbers, and often, the goal of the research is to produce statistics. The information researchers collect using quantitative methods allows them to perform analyses that can be simple or complex, such as showing the connections made between data, calculating averages or percentages, or using inferential statistics to make generalizations about a large population. Researchers use specific methodologies, such as experiments, questionnaires, and structured observations, to collect the data they use for quantitative research.

Let’s take a closer look at quantitative research methods and data analysis to understand what quantitative research is and how you can use it.

What is the purpose of quantitative research?

Quantitative research aims to help scientists, marketers, and researchers better understand society and people. It often involves the exploration or examination of events or occurrences that affect individuals. An example of quantitative research could involve a hospital that conducts surveys after patients have been admitted and discharged. The surveys’ purpose could be to discover how much time doctors who work at the hospital actually spend with their patients.

Surveys could ask patients research questions about how long their visits with a doctor lasted and how long they spent waiting to be seen. The surveys could also ask patients to rank their overall satisfaction with the care they received on a rating scale from one to 10. The surveys’ goal might be to discover if doctors are spending enough time with patients and how that correlates with the patients’ overall care. Using the data collected in the surveys, the hospital can estimate how much time each physician spends with patients. The hospital may also see a connection between higher patient satisfaction — such as patients giving scores of eight, nine or 10 — and increased patient-physician interaction or shorter wait times.

Types of quantitative research methods and techniques

Quantitative research design describes how a researcher arranges a study in an attempt to control the variables. At one end of the spectrum is a method to observe and describe data rather than control or manipulate variables. At the opposite end are methods that aim to control variables and establish clear connections between them. The method you might use depends on your overall goals and what you hope to get from your research.

1. Descriptive research

Descriptive research describes situations, circumstances, or variables. The focus of descriptive statistics is the “what” instead of “why.” Usually, descriptive research involves a fair amount of observation. A researcher might ask children to describe how they spent their summer vacation or watch a teacher during a class to see how they explain concepts to students.

When using descriptive research, a scientist isn’t likely to start from a hypothesis. If they develop one at all, it will be after they have collected data. They can then use the data to test the hypothesis through synthesis and analysis. As they collect data, they need to be careful about the variables they measure and the items they study. Descriptive research typically doesn’t end with an explanation of the cause and effect between variables.

Some examples of descriptive research include:

• A description of teenagers’ alcohol habits:  Researchers can issue surveys asking teens how much they drink, when they drink, and who they drink with. They can conduct the surveys over a period of years to see how teenage alcohol consumption changes with time.
• A description of how people in assisted-living facilities spend their time:  Researchers can conduct surveys asking people who live or work at assisted-living facilities how much time they spend on certain activities. A researcher might also visit a facility to observe residents and workers, timing their activities.
• A description of how the housing market in a particular city has changed over the years:  A researcher can collect data on housing prices, sales volume, and time-on-the-market to see how real estate in a city has changed over a defined period, such as the past 10 years. Since the researcher aims to describe the data, they aren’t looking for factors that could have affected homes’ prices, such as economic recessions or new amenities in the city.
• A description of how opinions on a subject have changed over a period:  Researchers can describe how opinions on a subject, such as climate change or driving while intoxicated, have changed over time. They can collect data by conducting surveys that ask people to rank their feelings or beliefs on a subject on a scale from one to 10. They can follow up with the same people year after year to describe the evolution of opinions.

2. Correlational research

The goal of correlational research is to examine and determine the relationship between multiple variables or data points. Like descriptive research, correlational research doesn’t attempt to identify a reason for the connection between variables or find a cause for the relationship. Instead, the intent is to discover patterns or trends between the variables by making comparisons. A researcher performing correlational research shouldn’t manipulate or adjust the data.

Examples of correlational research include:

• An examination of the relationship between depression and diet:  A researcher might ask people to rate their depression on a scale from one to 10 and provide details about what types of food they eat and how much of each food. For example, a researcher might notice a correlation between eating less than one serving of vegetables or fruit each day and more severe depression. They might notice that people who rate their depression as a one or two tend to eat multiple servings of sweets during the day.
• An examination of the relationship between SAT scores and grades during the first year of college:  A researcher might be curious to see if there is a connection between standardized test scores and grades once a student is in college. They might look at the grades of first-year college students who received scores of 2200 or higher on the SATs to see if there is a relationship.
• An examination of people’s lifestyle habits and the prevalence of certain diseases:  Researchers can ask people about their lifestyle habits, such as how much alcohol they drink daily or weekly or how many cigarettes they smoke, to see if there is a connection between habits and the prevalence of diseases. People who smoke more might have higher lung cancer rates or more respiratory issues than people who don’t smoke.
• An examination of types of classroom exercises and the grades students receive:  A researcher might be interested in discovering if there is a connection between the types of exercises a teacher leads in a classroom setting and the grades students earn on tests. The research might measure how much time students spend listening to lectures, performing group work, or working individually, then compare that information to the grades students receive.

3. Causal-comparative research

Causal-comparative research is also called quasi-experimental research. It has aspects in common with experimentation but can’t be considered a true experiment. The goal of causal-comparative research is to create a cause-and-effect relationship among multiple variables. What sets this type of research apart from true experimentation is that the researchers cannot manipulate the independent variable. Researchers also need to use naturally occurring or pre-existing groups as part of the study and randomly assign individuals to particular groups. Unlike a true experiment, there is no control group during quasi-experimental research.

Examples of quasi-experimental research include:

• Examining the effect taking vitamins has on children’s school attendance:  A researcher might examine the attendance record of a group of elementary school students who take a multi-vitamin each day to see if the students taking the vitamin are more likely to go to school regularly compared to a group of students who don’t take the vitamin.
• Examining the connection between gender and scores on math tests:  A researcher might ask students in the same grade to take a math test, then examine the scores the students received to see if one gender scored higher than the other overall.
• Examining the effect exercise habits have on heart health:  A researcher compares blood pressure levels, cholesterol levels, and resting heart rates of people who exercise daily and people who don’t exercise to see if there is a connection between exercise and heart health.
• Examining the effect going to preschool has on high school graduation rates:  A researcher might look at preschool attendance rates and compare them to graduation rates later on to see if students who started their education earlier were more likely to finish high school.

One thing to keep in mind with quasi-experimental research is that many other variables, not just the ones studied, can impact the results. Students who take multivitamins might also have other things going on in their lives that cause them to go to school more often, for example. People who exercise and have lower cholesterol and blood pressure levels might have other factors, such as a healthier diet or family history, that make them more likely to have better heart health.

4. Experimental research

An experimental research approach relies on the scientific method to manipulate and control variables to determine the cause-and-effect relationship. Experiments can take place in lab settings, but researchers can also conduct them in workshops or classrooms. To be considered a true experiment, research needs to have a control group and control over all possible variables except the independent variable, which the researchers manipulate. Participants in the experiment are assigned to a random group rather than self-selected, as they usually are in a quasi-experimental research method.

Examples of experimental research include:

• Examining the effect of a new medication on chronic illness:  Researchers divide patients with the same illness into three groups. One group receives no treatment, one receives a placebo, and the third receives a new medication. At the end of the experiment, the researchers assess the patients to determine if the new medication was more effective than a placebo or no treatment at all.
• Examining the effect of personalized instruction on students’ grades:  Researchers divide a class of students into two. Both groups receive in-class instruction. One group also gets an hour of one-on-one tutoring three times a week. At the end of the trial, the students take a test. The researchers examine the students’ grades to see if those who received tutoring performed better than those who didn’t.
• Examining the effect of race or gender on crime:  Researchers hire actors to pretend to commit a crime in broad daylight, such as stealing a bike or breaking into a car. The experiment’s goal is to determine if bystanders react differently based on the gender or race of the actor.

Data collection methodologies

Data collection is a critical part of any research study. When you perform quantitative research, you use one or more methods to gather your data.

1. Surveys or questionnaires

A survey or questionnaire asks participants questions to help researchers collect the data they need. Survey questions can be open-ended, such as, “How do you feel after drinking a cup of coffee?” or closed, such as, “How many cups of coffee do you drink daily?: 0-1, 2-3, 4 or more.” Quantitative research surveys usually use closed questions that provide a numerical value as an answer.

Your sampling methods, or how you choose the  people you will survey , are a key part of data collection. You might want to collect surveys from people who fit a certain demographic description or a wider subset of the population. How frequently you collect survey information is also essential. Two general methods of conducting surveys exist:

• Cross-sectional surveys  gather data from multiple demographic groups at the same point in time. The method lets you compare answers across demographics and also lets you track multiple variables.
• Longitudinal surveys  gather data from one demographic group at multiple periods. A longitudinal survey might follow up with participants one month later, one year later, and five years later. This type of survey aims to see how habits can change over time or what impact habits have on a particular group of people over the course of months or years.

2. Interviews

Interviews are similar to questionnaires. Instead of having a participant fill out a paper or digital form, the researcher asks the questions while sitting face-to-face with the participant or while they are on the phone. Interviews can be structured, meaning the researcher asks the same questions, in the same order, to everyone who participates. During an unstructured interview, the researcher might ask questions as they think of them or questions in response to what the participant says.

3. Observation

Observation involves watching people and taking note of their behaviors and habits. It’s commonly used to collect qualitative data but can be used to collect quantitative data. For example, a researcher might observe employees leaving work for the day and count those who leave right at 5 p.m., those who leave a bit early and those who stay late. The researcher might observe employees for several days to see if there are patterns or changes when people leave work.

Advantages of quantitative research

Quantitative research has many advantages for researchers and companies that need to get information on their customers. Some of the benefits of quantitative research include:

• Reliability:  Quantitative research is objective, meaning the variables and data you collect are reliable and accurate. When you ask someone how many cups of coffee they drink every day, you get a direct, objective answer.
• Reproducibility:  Since you are collecting numerical or computational data when you perform quantitative research, it’s easy to reproduce the surveys or interviews when needed. Replication is a key component of a quantitative approach.
• Impartiality:  Numbers and statistics don’t have a bias. There’s no way for a research team to influence the results or otherwise make the results biased when using a quantitative approach.
• Scalability:  You can scale quantitative research up or down as needed without affecting the quality of the surveys or the data’s validity.

Disadvantages of quantitative research

Quantitative research might not be the best option in all cases. Some possible drawbacks of the method include:

• Might not tell the whole story:  The variables you collect through quantitative methods can be superficial or limited. For example, asking people how much coffee they drink doesn’t tell you very much. It can also be the case that other factors you are surveying affect the responses people give.
• Sample sizes can be small:  Small sample sizes can limit the impact research has. Asking 10 people about their coffee drinking habits won’t give you a good idea of how coffee consumption plays out across the country, for example.
• Data can be over-manipulated:  It’s possible for the setting of a research study to be manipulated and controlled to such an extent that it affects the accuracy of the results or for a range of other, unaccounted-for variables to affect the study.

Although there can be drawbacks to quantitative research, a well-designed study will account for those drawbacks and seek to eliminate them. For example, ensuring a large sample size and setting representational population parameters can help increase the accuracy of the results.

Secondary quantitative research methods

Secondary data is data someone else has already collected. Secondary research is sometimes called desk research since it involves collecting, summarizing, synthesizing and analyzing existing data rather than collecting data, often to strengthen the results from primary research. Secondary data is available from various sources, including:

• Universities and colleges
• Public and private libraries
• Government institutions
• Non-government organizations
• Commercial information sources

What’s the difference between quantitative and qualitative research?

Quantitative research and qualitative research are often compared. While quantitative research focuses on objectivity and numerical values, qualitative research is subjective and values descriptions of feelings or situations. Quantitative research focuses more on establishing measurable (quantifiable) data, while qualitative research is more interpretive and focused on qualities or characteristics.

Some of the data collection methods used during qualitative research are the same as those used during quantitative research, but the end goal is different. A qualitative research study might involve observation and surveys. The survey questions will generally be open-ended. Observation will be to see and describe how people approach situations, rather than counting how frequently they do something.

Another way to look at the difference between quantitative and qualitative research is that one often informs the other — in fact,  they can be used together . Businesses typically perform qualitative research when they want to create a hypothesis. Asking people their opinions on a subject can help a business learn more about its customers’ concerns or the opportunities available to it. The company uses the information gathered to form a hypothesis, which it then tests using quantitative research.

Learn More About Cint’s Quantitative Research Tools

Quantitative research can help you learn more about your company’s customers, potential customers, and the overall market. Cint connects brands and businesses to the right audience for quantitative research surveys. To learn more about our platform and what we do to ensure the data we collect is high-quality and accurate, contact us today.

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Quantitative Data: Definition, Examples, Types, Methods, and Analysis

11 min read

35% of startups fail because there is no market need. This is because they haven’t conducted any customer research to determine whether the product they are building is actually what customers want.

To gather the information needed to avoid this, quantitative data is a valuable tool for all startups. This article will examine quantitative data, the difference between quantitative and qualitative data, and how to collect the former.

• Quantitative data, expressed numerically, is crucial for analysis, driving strategic decisions, and understanding consumer behavior and market trends .
• Metrics like DAU, MRR, sales figures, satisfaction scores, and traffic are examples of quantitative data across industries.
• Quantitative data is numeric and measurable, identifying patterns or trends, while qualitative data is descriptive, providing deeper insights and context.
• Nominal data categorizes information without order and labels variables like user roles or subscription types. It is often shown in bar or pie charts .
• Ordinal data categorizes information in a specific order, such as satisfaction ratings or ticket priorities, and is often shown in a bar or stacked bar chart.
• Discrete data is numerical and takes specific values, like daily sign-ups or support tickets , and is often shown in bar or column charts.
• Continuous data can take any numerical value within a range, such as user time on a platform or revenue over time, and is often shown in line graphs or histograms.
• Quantitative data is objective, handles large datasets, and enables easy comparisons, providing clear insights and generalized conclusions in various fields.
• However, quantitative data analysis lacks contextual understanding, requires analytical expertise, and is influenced by data collection quality that may affect result validity.
• Customer feedback surveys , triggered by tools like Userpilot, collect consistent quantitative data, providing reliable numerical insights into customer satisfaction and experiences.
• Product analytics tools track user interactions and feature usage , offering insights into user behavior and improving the user experience.
• Tracking customer support data identifies common issues and areas for improvement , enhances service quality, and helps understand customer needs.
• Implementing A/B tests and other experiments provides quantitative data on feature performance, helping teams make informed decisions to enhance product and user experience.
• Searching platforms like Kaggle or Statista for accurate, reliable datasets enhances product analysis by providing broader context and robust comparison data.
• Statistical analysis uses mathematical techniques to summarize and infer data patterns, helping SaaS companies understand user behavior, evaluate features, and identify engagement trends.
• Trend analysis tracks quantitative data to identify patterns, helping SaaS companies forecast outcomes, understand variations, and plan strategic initiatives effectively.
• Funnel analysis tracks user progression through stages, identifies drop-off points to enhance user experience, and increases conversions for SaaS companies.
• Cohort analysis groups users by attribute and tracks behavior over time to understand retention and engagement.
• Path analysis maps user journeys to identify users’ optimal routes, helping SaaS companies streamline and enhance the user experience.
• Feedback analysis examines responses to close-ended questions to identify user sentiments and areas for improvement.
• If you want to collect quantitative data within your product and analyze it, then learn how Userpilot can help you. Book a demo now !

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What is quantitative data?

Quantitative data is information that can be measured and expressed numerically. It is essential for making data-driven decisions, as it provides a concrete foundation for analysis and evaluation.

In various fields, such as market research , quantitative data helps businesses understand consumer behavior, market trends, and overall performance. Companies can gain insights that drive strategic decisions and improve their products or services by collecting and analyzing numerical data.

Whether conducting a survey, running experiments , or gathering information from other sources, quantitative data analysis is key to uncovering patterns, testing hypotheses, and making informed decisions based on solid evidence.

What are examples of quantitative data?

Quantitative data comes in many forms and is used across various industries to provide measurable and numerical insights. Here are some examples of quantitative data:

• Daily Active Users (DAU) : This metric counts the number of unique users interacting with a product or service daily. It is crucial for understanding user engagement and product usage trends.
• Monthly Recurring Revenue (MRR) : For SaaS businesses, MRR is a vital metric that shows the predictable revenue generated each month from subscriptions. It helps forecast growth and financial planning.
• Sales figures : This includes the total number of products sold or services rendered over a specific period. Sales data helps in evaluating business performance and market demand.
• Customer satisfaction scores : Often collected through surveys , these scores quantify customers’ satisfaction with a product or service.
• Website traffic : Measured in terms of visits, page views, and unique visitors, this quantitative data helps businesses understand their online presence and the effectiveness of their marketing efforts.
• Conversion rates : This metric shows the percentage of users who take a desired action, such as making a purchase or signing up for a newsletter, out of the total number of visitors.
• Churn rate : This represents the percentage of customers who stop using a product or service over time. It’s essential for understanding customer retention .
• Average Revenue Per User (ARPU) : This metric calculates the average revenue generated per user, which helps assess each customer’s value to the business.
• Bounce rate : In web analytics, the bounce rate indicates the percentage of visitors who leave a website after viewing only one page. It’s useful for evaluating the effectiveness of a website’s content and user experience .

Differences between quantitative and qualitative data

Quantitative data and qualitative data are two fundamental types of information used in research and analysis, each serving distinct purposes and represented in different forms.

Quantitative data is numeric and measurable. It allows you to quantify variables and identify patterns or trends that can be generalized. For example, tracking product trends or analyzing charts to understand market movements. Some quantitative data examples include:

• The number of daily active users on a platform.
• Monthly recurring revenue.
• Customer satisfaction scores .
• Website traffic metrics, like page views.

On the other hand, qualitative data is descriptive and subjective, often represented in words and visuals. It aims to explore deeper insights, understand data , and provide context to behaviors and experiences.

Examples of qualitative data include:

• Customer reviews and testimonials.
• Interview responses.
• Social media interactions.
• Observations recorded during user tests .

Different types of quantitative data

Understanding the different types of quantitative data is essential for effective data analysis . These types help categorize and analyze data accurately to derive meaningful insights and make informed decisions.

Nominal data

Nominal data categorizes information without a specific order or ranking. It is used to label variables that do not have a quantitative value.

For instance, in a SaaS platform , user roles can be categorized as ‘admin,’ ‘editor,’ or ‘viewer.’ Subscription types might be classified as ‘free,’ ‘basic,’ ‘premium,’ or ‘enterprise.’

This data type is typically represented using bar charts or pie charts to show the frequency or proportion of each category.

Ordinal data

Ordinal data categorizes information with a specific order or ranking. It is used to label variables that follow a particular sequence.

Examples include:

• Rating customer satisfaction as ‘poor,’ ‘fair,’ ‘good,’ ‘very good,’ or ‘excellent.’
• Ranking support ticket priorities as ‘low,’ ‘medium,’ or ‘high.’
• User feedback ratings on features as ‘1 star’ to ‘5 stars.’

This type of data is typically represented using bar charts or stacked bar charts to illustrate the order and frequency of each category.

Discrete data

Discrete data is numerical values that can only take on specific values and cannot be subdivided meaningfully.

Examples include the number of new sign-ups daily, the count of support tickets received, and the number of active users at a given time.

This type of numerical data is often represented using bar charts or column charts to display the frequency of each value.

Continuous data

Continuous data is numerical information that can take on any numerical value within a range.

In a SaaS context, examples include measuring the amount of time users spend on a platform, the bandwidth usage of an application, and the revenue generated over a specific period. Continuous data, along with interval data, helps identify patterns and trends over time.

Pros of analyzing quantitative data

Analyzing quantitative data offers several advantages, making it a valuable approach in various fields, especially in SaaS. Here are some key benefits:

Provides measurable and verifiable data

Quantitative data is numeric and objective, allowing for precise measurement and verification. This reduces the influence of personal biases and subjectivity in analysis, leading to more reliable and consistent results.

Analyzing customer data using quantitative methods can provide clear insights into user behavior and preferences, helping businesses make data-driven decisions.

Enables analysis of large datasets

Quantitative data analysis can handle large datasets efficiently, enabling the identification of patterns and trends across extensive samples.

This capability makes it possible to draw broad, generalized conclusions that can be applied to larger populations. For example, a company might analyze usage data from thousands of users to understand overall engagement trends and identify areas for improvement .

Allows easy comparison across different groups, time periods, and variables

Quantitative data allows straightforward comparisons across various groups, time periods, and variables. This facilitates the evaluation of changes over time, differences between demographics, and the impact of different factors on outcomes.

For instance, comparing customer satisfaction scores before and after a product update can help assess the effectiveness of the changes and guide future improvements.

Cons of quantitative data analysis

While quantitative data analysis offers many benefits, it also has some drawbacks:

Lacks contextual understanding

Quantitative data can miss the deeper context and nuances of human behavior, focusing solely on numbers without explaining the reasons behind actions. For example, tracking user behavior may show usage patterns but not the motivations or feelings behind them.

Requires analytical expertise

Accurate analysis and interpretation of quantitative data require specialized skills . Without proper expertise, there is a risk of misinterpretation and incorrect conclusions, which can negatively impact decision-making.

Influenced by data collection quality

The reliability of quantitative analysis depends on the data collection methods and the quality of measurement tools. Poor data collection can lead to data discrepancies , affecting the validity of the results. Ensuring consistent, high-quality data collection is essential for accurate analysis.

How to collect data for quantitative research?

Collecting data for quantitative research involves using systematic and structured methods to gather numerical information. Let’s look at a few methods in detail.

Customer feedback surveys

Customer feedback surveys are a key method for collecting quantitative data. Tools like Userpilot can trigger in-app surveys with closed-ended questions to ensure consistent data collection.

Conducting these surveys quarterly or after a specific period helps track changes in customer satisfaction and other important metrics. This approach provides reliable, numerical insights into customer opinions and experiences.

Product usage data

Product analytics tools are essential for tracking user interactions and feature usage. Utilizing these tools allows you to monitor metrics such as user sessions, feature adoption , and user engagement regularly.

This quantitative data provides valuable insights into how users interact with your product, helping you understand their behavior and improve the overall user experience.

Customer support data

Tracking customer support data is crucial for quantitative research. You can record details such as ticket number, issue type, resolution time, and customer feedback by monitoring support tickets.

Organize these tickets into categories, such as feature requests , to identify common problems and areas needing product improvement . This approach helps understand customer needs and enhance overall service quality.

Experiments

Implementing experiments, such as A/B tests , is a powerful method for collecting quantitative data. By comparing the performance of different features or designs, you can gain valuable insights into what works best for your users.

Use the insights gained from these A/B tests and other product experimentation methods to make informed decisions that enhance your product and user experience.

Open-source datasets

Searching for datasets on platforms like Kaggle or Statista can provide valuable information relevant to your research. However, to avoid issues with data discrepancy , ensure these datasets are accurate and reliable before incorporating them into your analysis.

Utilizing accurate open-source datasets can significantly enhance your product analysis by providing a broader context and more robust quantitative data for comparison and insights.

Quantitative data analysis methods for gathering actionable insights

Analyzing quantitative data involves using various methods to extract meaningful and actionable insights. These techniques help understand the data’s patterns, trends, and relationships, enabling informed decision-making and strategic planning .

Statistical analysis

Statistical analysis involves using mathematical techniques to summarize, describe, and infer patterns from data. This method helps validate hypotheses and make data-driven decisions .

For SaaS companies, statistical analysis can be crucial in understanding user behavior , evaluating the effectiveness of new features, and identifying trends in user engagement.

By leveraging statistical techniques, SaaS businesses can derive meaningful insights from their data, allowing them to optimize their products and services based on empirical evidence.

Trend analysis

Trend analysis involves tracking quantitative data points and metrics to identify consistent patterns. Using a tool like Userpilot, SaaS companies can generate detailed trend analysis reports that provide valuable insights into how various metrics evolve.

This method enables SaaS companies to forecast future outcomes, understand seasonal variations, and plan strategic initiatives accordingly. By identifying trends, businesses can anticipate changes, adapt their strategies, and stay ahead of market dynamics.

Funnel analysis

Funnel analysis defines key stages in the user journey and tracks the number of users progressing through each stage.

This method helps SaaS companies identify friction and drop-off points within the funnel. By understanding where users are dropping off, businesses can implement targeted improvements to enhance user experience and increase conversions.

Cohort analysis

Cohort analysis groups users into cohorts based on attributes such as the month of sign-up or acquisition channel and tracks their behavior over time.

This method allows SaaS companies to understand user retention and engagement patterns by comparing how cohorts perform over various periods. By analyzing these patterns, businesses can identify successful strategies and improvement areas.

Path analysis

Path analysis maps user journeys and analyzes the actions taken by users. This method helps SaaS companies identify the “ happy path ” or the optimal route users take to achieve their goals.

By understanding these paths , businesses can streamline the user experience, making it more intuitive and efficient.

Feedback analysis

Feedback analysis involves using questionnaires and examining responses to close-ended questions to identify patterns in customer feedback . This quantitative data helps you to understand common user sentiments, preferences, and areas needing improvement.

Businesses can make informed decisions to enhance their products and services by systematically analyzing feedback.

Collecting quantitative data is important if you want a product that will succeed. Your customers are the only people who can signal your success, so speaking to them and analyzing the quantitative data you collect will help you to produce the best product you can.

If you want help collecting quantitative data and analyzing it, Userpilot can help. Book a demo now to see exactly how it can help.

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Home » Quantitative Data – Types, Methods and Examples

Quantitative Data – Types, Methods and Examples

Table of Contents

Quantitative Data

Definition:

Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis.

Quantitative Data Types

There are two main types of quantitative data: discrete and continuous.

• Discrete data: Discrete data refers to numerical values that can only take on specific, distinct values. This type of data is typically represented as whole numbers and cannot be broken down into smaller units. Examples of discrete data include the number of students in a class, the number of cars in a parking lot, and the number of children in a family.
• Continuous data: Continuous data refers to numerical values that can take on any value within a certain range or interval. This type of data is typically represented as decimal or fractional values and can be broken down into smaller units. Examples of continuous data include measurements of height, weight, temperature, and time.

Quantitative Data Collection Methods

There are several common methods for collecting quantitative data. Some of these methods include:

• Surveys : Surveys involve asking a set of standardized questions to a large number of people. Surveys can be conducted in person, over the phone, via email or online, and can be used to collect data on a wide range of topics.
• Experiments : Experiments involve manipulating one or more variables and observing the effects on a specific outcome. Experiments can be conducted in a controlled laboratory setting or in the real world.
• Observational studies : Observational studies involve observing and collecting data on a specific phenomenon without intervening or manipulating any variables. Observational studies can be conducted in a natural setting or in a laboratory.
• Secondary data analysis : Secondary data analysis involves using existing data that was collected for a different purpose to answer a new research question. This method can be cost-effective and efficient, but it is important to ensure that the data is appropriate for the research question being studied.
• Physiological measures: Physiological measures involve collecting data on biological or physiological processes, such as heart rate, blood pressure, or brain activity.
• Computerized tracking: Computerized tracking involves collecting data automatically from electronic sources, such as social media, online purchases, or website analytics.

Quantitative Data Analysis Methods

There are several methods for analyzing quantitative data, including:

• Descriptive statistics: Descriptive statistics are used to summarize and describe the basic features of the data, such as the mean, median, mode, standard deviation, and range.
• Inferential statistics : Inferential statistics are used to make generalizations about a population based on a sample of data. These methods include hypothesis testing, confidence intervals, and regression analysis.
• Data visualization: Data visualization involves creating charts, graphs, and other visual representations of the data to help identify patterns and trends. Common types of data visualization include histograms, scatterplots, and bar charts.
• Time series analysis: Time series analysis involves analyzing data that is collected over time to identify patterns and trends in the data.
• Multivariate analysis : Multivariate analysis involves analyzing data with multiple variables to identify relationships between the variables.
• Factor analysis : Factor analysis involves identifying underlying factors or dimensions that explain the variation in the data.
• Cluster analysis: Cluster analysis involves identifying groups or clusters of observations that are similar to each other based on multiple variables.

Quantitative Data Formats

Quantitative data can be represented in different formats, depending on the nature of the data and the purpose of the analysis. Here are some common formats:

• Tables : Tables are a common way to present quantitative data, particularly when the data involves multiple variables. Tables can be used to show the frequency or percentage of data in different categories or to display summary statistics.
• Charts and graphs: Charts and graphs are useful for visualizing quantitative data and can be used to highlight patterns and trends in the data. Some common types of charts and graphs include line charts, bar charts, scatterplots, and pie charts.
• Databases : Quantitative data can be stored in databases, which allow for easy sorting, filtering, and analysis of large amounts of data.
• Spreadsheets : Spreadsheets can be used to organize and analyze quantitative data, particularly when the data is relatively small in size. Spreadsheets allow for calculations and data manipulation, as well as the creation of charts and graphs.
• Statistical software : Statistical software, such as SPSS, R, and SAS, can be used to analyze quantitative data. These programs allow for more advanced statistical analyses and data modeling, as well as the creation of charts and graphs.

Quantitative Data Gathering Guide

Here is a basic guide for gathering quantitative data:

• Define the research question: The first step in gathering quantitative data is to clearly define the research question. This will help determine the type of data to be collected, the sample size, and the methods of data analysis.
• Choose the data collection method: Select the appropriate method for collecting data based on the research question and available resources. This could include surveys, experiments, observational studies, or other methods.
• Determine the sample size: Determine the appropriate sample size for the research question. This will depend on the level of precision needed and the variability of the population being studied.
• Develop the data collection instrument: Develop a questionnaire or survey instrument that will be used to collect the data. The instrument should be designed to gather the specific information needed to answer the research question.
• Pilot test the data collection instrument : Before collecting data from the entire sample, pilot test the instrument on a small group to identify any potential problems or issues.
• Collect the data: Collect the data from the selected sample using the chosen data collection method.
• Clean and organize the data : Organize the data into a format that can be easily analyzed. This may involve checking for missing data, outliers, or errors.
• Analyze the data: Analyze the data using appropriate statistical methods. This may involve descriptive statistics, inferential statistics, or other types of analysis.
• Interpret the results: Interpret the results of the analysis in the context of the research question. Identify any patterns, trends, or relationships in the data and draw conclusions based on the findings.
• Communicate the findings: Communicate the findings of the analysis in a clear and concise manner, using appropriate tables, graphs, and other visual aids as necessary. The results should be presented in a way that is accessible to the intended audience.

Examples of Quantitative Data

Here are some examples of quantitative data:

• Height of a person (measured in inches or centimeters)
• Weight of a person (measured in pounds or kilograms)
• Temperature (measured in Fahrenheit or Celsius)
• Age of a person (measured in years)
• Number of cars sold in a month
• Amount of rainfall in a specific area (measured in inches or millimeters)
• Number of hours worked in a week
• GPA (grade point average) of a student
• Sales figures for a product
• Time taken to complete a task.
• Distance traveled (measured in miles or kilometers)
• Speed of an object (measured in miles per hour or kilometers per hour)
• Number of people attending an event
• Price of a product (measured in dollars or other currency)
• Blood pressure (measured in millimeters of mercury)
• Amount of sugar in a food item (measured in grams)
• Test scores (measured on a numerical scale)
• Number of website visitors per day
• Stock prices (measured in dollars)
• Crime rates (measured by the number of crimes per 100,000 people)

Applications of Quantitative Data

Quantitative data has a wide range of applications across various fields, including:

• Scientific research: Quantitative data is used extensively in scientific research to test hypotheses and draw conclusions. For example, in biology, researchers might use quantitative data to measure the growth rate of cells or the effectiveness of a drug treatment.
• Business and economics: Quantitative data is used to analyze business and economic trends, forecast future performance, and make data-driven decisions. For example, a company might use quantitative data to analyze sales figures and customer demographics to determine which products are most popular among which segments of their customer base.
• Education: Quantitative data is used in education to measure student performance, evaluate teaching methods, and identify areas where improvement is needed. For example, a teacher might use quantitative data to track the progress of their students over the course of a semester and adjust their teaching methods accordingly.
• Public policy: Quantitative data is used in public policy to evaluate the effectiveness of policies and programs, identify areas where improvement is needed, and develop evidence-based solutions. For example, a government agency might use quantitative data to evaluate the impact of a social welfare program on poverty rates.
• Healthcare : Quantitative data is used in healthcare to evaluate the effectiveness of medical treatments, track the spread of diseases, and identify risk factors for various health conditions. For example, a doctor might use quantitative data to monitor the blood pressure levels of their patients over time and adjust their treatment plan accordingly.

Purpose of Quantitative Data

The purpose of quantitative data is to provide a numerical representation of a phenomenon or observation. Quantitative data is used to measure and describe the characteristics of a population or sample, and to test hypotheses and draw conclusions based on statistical analysis. Some of the key purposes of quantitative data include:

• Measuring and describing : Quantitative data is used to measure and describe the characteristics of a population or sample, such as age, income, or education level. This allows researchers to better understand the population they are studying.
• Testing hypotheses: Quantitative data is often used to test hypotheses and theories by collecting numerical data and analyzing it using statistical methods. This can help researchers determine whether there is a statistically significant relationship between variables or whether there is support for a particular theory.
• Making predictions : Quantitative data can be used to make predictions about future events or trends based on past data. This is often done through statistical modeling or time series analysis.
• Evaluating programs and policies: Quantitative data is often used to evaluate the effectiveness of programs and policies. This can help policymakers and program managers identify areas where improvements can be made and make evidence-based decisions about future programs and policies.

When to use Quantitative Data

Quantitative data is appropriate to use when you want to collect and analyze numerical data that can be measured and analyzed using statistical methods. Here are some situations where quantitative data is typically used:

• When you want to measure a characteristic or behavior : If you want to measure something like the height or weight of a population or the number of people who smoke, you would use quantitative data to collect this information.
• When you want to compare groups: If you want to compare two or more groups, such as comparing the effectiveness of two different medical treatments, you would use quantitative data to collect and analyze the data.
• When you want to test a hypothesis : If you have a hypothesis or theory that you want to test, you would use quantitative data to collect data that can be analyzed statistically to determine whether your hypothesis is supported by the data.
• When you want to make predictions: If you want to make predictions about future trends or events, such as predicting sales for a new product, you would use quantitative data to collect and analyze data from past trends to make your prediction.
• When you want to evaluate a program or policy : If you want to evaluate the effectiveness of a program or policy, you would use quantitative data to collect data about the program or policy and analyze it statistically to determine whether it has had the intended effect.

Characteristics of Quantitative Data

Quantitative data is characterized by several key features, including:

• Numerical values : Quantitative data consists of numerical values that can be measured and counted. These values are often expressed in terms of units, such as dollars, centimeters, or kilograms.
• Continuous or discrete : Quantitative data can be either continuous or discrete. Continuous data can take on any value within a certain range, while discrete data can only take on certain values.
• Objective: Quantitative data is objective, meaning that it is not influenced by personal biases or opinions. It is based on empirical evidence that can be measured and analyzed using statistical methods.
• Large sample size: Quantitative data is often collected from a large sample size in order to ensure that the results are statistically significant and representative of the population being studied.
• Statistical analysis: Quantitative data is typically analyzed using statistical methods to determine patterns, relationships, and other characteristics of the data. This allows researchers to make more objective conclusions based on empirical evidence.
• Precision : Quantitative data is often very precise, with measurements taken to multiple decimal points or significant figures. This precision allows for more accurate analysis and interpretation of the data.

Advantages of Quantitative Data

Some advantages of quantitative data are:

• Objectivity : Quantitative data is usually objective because it is based on measurable and observable variables. This means that different people who collect the same data will generally get the same results.
• Precision : Quantitative data provides precise measurements of variables. This means that it is easier to make comparisons and draw conclusions from quantitative data.
• Replicability : Since quantitative data is based on objective measurements, it is often easier to replicate research studies using the same or similar data.
• Generalizability : Quantitative data allows researchers to generalize findings to a larger population. This is because quantitative data is often collected using random sampling methods, which help to ensure that the data is representative of the population being studied.
• Statistical analysis : Quantitative data can be analyzed using statistical methods, which allows researchers to test hypotheses and draw conclusions about the relationships between variables.
• Efficiency : Quantitative data can often be collected quickly and efficiently using surveys or other standardized instruments, which makes it a cost-effective way to gather large amounts of data.

Limitations of Quantitative Data

Some Limitations of Quantitative Data are as follows:

• Limited context: Quantitative data does not provide information about the context in which the data was collected. This can make it difficult to understand the meaning behind the numbers.
• Limited depth: Quantitative data is often limited to predetermined variables and questions, which may not capture the complexity of the phenomenon being studied.
• Difficulty in capturing qualitative aspects: Quantitative data is unable to capture the subjective experiences and qualitative aspects of human behavior, such as emotions, attitudes, and motivations.
• Possibility of bias: The collection and interpretation of quantitative data can be influenced by biases, such as sampling bias, measurement bias, or researcher bias.
• Simplification of complex phenomena: Quantitative data may oversimplify complex phenomena by reducing them to numerical measurements and statistical analyses.
• Lack of flexibility: Quantitative data collection methods may not allow for changes or adaptations in the research process, which can limit the ability to respond to unexpected findings or new insights.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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NSF 24-578: 2024 Hispanic Serving Institutions: Equitable Transformation in STEM Education (ETSE)

Institutionally Coordinated  // Please contact the Office of  HSI Initiatives  for more information.

  HSI Initiatives would coordinate the submission for the Institutional Transformation Track (ITT) and HSI Program Resource Hubs (Hubs) . UArizona does not have any active Institutional Transformation Track (ITT) , so there should not be any limit on the Departmental/Division Transformation Track (DDTT) track proposals. UArizona is not eligible for the Emerging Faculty Research Track (EFRT).

Hispanic Serving Institutions (HSI) are an important component of the nation’s higher education ecosystem and play a critical role in realizing the  National Science Board Vision Report  for a more diverse and capable science and engineering workforce. Aligned with this vision and the  NSF Strategic Plan 2022 -2026  the goals of the NSF HSI Program are to: Enhance the quality of undergraduate science, technology, engineering, and mathematics (STEM) education at HSIs. Increase the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM at HSIs. Meeting these goals requires institutions to understand and embrace their students’ strengths, challenges, identities and lived experiences. This can happen in many ways and across many areas of an institution. As such, the IUSE: HSI program provides multiple opportunities to support an institution’s goal to become more student centered, including the  Equitable Transformation in STEM Education (ETSE ) competition. This competition includes the following tracks: Departmental/Division Transformation Track (DDTT)  - New Institutional Transformation Track (ITT) Emerging Faculty Research Track (EFRT)  - New HSI Program Resource Hubs (Hubs) This solicitation will also accept conference proposals and planning proposals, as defined by the  PAPPG . The ETSE competition focuses on (1) institutional transformation projects that support HSIs in their effort to achieve equity in STEM education, and (2) the infrastructure—the HSI-Net network of resource hubs—which supports the overall program goals. Institutions are encouraged to consider how their HSI designation, and their organizational mission align to better support STEM success of all students. The ETSE competition welcomes proposals that look to implement and evaluate promising practices and/or conduct research related to broadening participation or improving recruitment, retention, graduation, and other successful outcomes in STEM undergraduate education. The ETSE solicitation supports projects designed to catalyze change and help HSIs meet students where they are, accounting for their assets and the challenges they may face. Identities and experiences are not determined solely by membership in a single monolithic population of students (e.g., Hispanic, first-generation, commuter, etc.). Consequently, institutions are expected to use institutional data to identify equity gaps, identify areas of need, and unpack the factors that shape students’ individual identities and shared experiences. The perspectives gained from this data should be central to the design of the proposed project. Please see below for specific information about each track. While proposals are focused on mechanisms for transforming undergraduate STEM education, projects should also consider student voices and include mechanisms to aggregate and analyze existing student feedback and collect quantitative and qualitative student data throughout the life of the proposed project.

Limit on Number of Proposals per Organization:

DDTT proposals: Eligible institutions with an active Track 3: Institutional Transformation project (ITP) award from  NSF 22-611 ,  NSF 22-545 , or  NSF 20-599  or an active ITT award from this solicitation must describe how the proposed DDTT project is compatible with the efforts being undertaken by the active award. ITT proposals: Eligible institutions may submit one proposal and may not have an active Track 3 Institutional Transformation Project (ITP) award from,  NSF 22-611 ,  NSF 22-545 , or  NSF 20-599 . Institutions with an active DDTT award from this solicitation must describe how the proposed ITT project is compatible with the departmental/divisional transformation effort being undertaken by the active award. EFRT and Hub proposals: No Restrictions

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ORIGINAL RESEARCH article

Qualitative and quantitative analysis of colorimetric response in different uv solar radiation level indicator bracelets based on photochromic substances.

• 1 Photobiological Dermatology Laboratory, Medical Research Center, Department of Dermatology and Medicine, Faculty of Medicine, University of Málaga, Málaga, Spain
• 2 Departament of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain

Background and Objective: We are exposed to solar radiation from early childhood, so learning different photoprotection strategies is fundamental. As a primary prevention tool, knowledge of the solar ultraviolet radiation to which we are exposed is fundamental. There are devices based on the color change of photochromic substances when exposed to UV radiation which are widely used as a tool for children’s play. The aim was to assess both quantitatively and qualitatively the effectiveness of the use of these devices under different conditions of simulated sun exposure.

Materials and Methods: Ten commercial UV-indicating photochromic devices plus three pure photochromic substances were used. Photochromic devices belong to the spirooxazine family of molecules. They were exposed to different rates of UV radiation under a solar UV-visible simulator, and their velocity, degree of response, and color reversal time were analyzed both visually and objectively using a colorimeter based on three-dimensional CIE L*a*b* color space values. Activation response was also evaluated under different UV high-energy visible light wavelengths using different cut-off filters. Finally, the devices were exposed under high UV irradiance (corresponding to UVI of 10) and interposing sunscreens of different sun protection factors in order to evaluate their potentials as indicators for sunscreen re-application.

Results: All the devices and pure substances offered color change almost immediately under simulated solar radiation. From very low irradiances (UVI >3), they saturated color, and it was not possible to observe and measure color change. The time to color reversal took longer than activation (from 60 s to obtain initial values). When devices were exposed to a UVI of 10 with topical sunscreen overlay, color was observed after 1 min irradiation even under SPF of 50+ sunscreen. Photochromic devices were activated under both UVA and high energy visible light.

Conclusion: UV radiation indicators using photochromic substances which, although possible awareness-raising elements, are of limited use as indicators of UV radiation intensity and dosimetry. They are useful elements for detecting the presence/absence of UV radiation and do not distinguish between different UV radiation spectral bands. Moreover, their incorporation as photoprotective elements used by minors could lead to exposures that are not objectively controlled.

Introduction

According to data from the Spanish State Meteorological Agency, there are on average around 3,000 h of potential sun exposure per year in Spain and other southern European countries ( Sánchez-Laulhé Ollero, 2021 ). This, together with social and cultural aspects, contributes to a high lifetime sun exposure ( Aguilera, 2019 ). The lack of awareness of the risks of so much sun exposure is one of the most important health problems today ( Gilaberte and González, 2010 ).

Both radiometers and spectroradiometers are used to determine the solar radiation to which we are daily exposed; these offer measurements such as the UV index (UVI), a measure established by the WHO to determine the levels of solar radiation with erythematic potential to which we may be exposed ( Pérez Ferriols et al., 2014 ; ISO/CIE 17166, 2019 ).

At a social level, knowledge of the current UVI, as a tool for controlling this overexposure, can be discovered on the internet or by various smartphone applications ( Turner et al., 2020 ). Easier tools developed to raise awareness about our exposure to the sun include UV indicator strips, based on photochromic substances, or substances that change color when exposed to UV radiation and reverse in the dark ( Wang et al., 2018 ; Saric-Bosanac et al., 2019 ). Such UV detectors are based on photochromic chemical compounds which can absorb ultraviolet radiation between 280 and 400 nm and change their absorbance in the visible region of the spectrum—mainly red, violet, and blue. Molecules that exhibit photochromic properties, including azobenzene, spiropyran, spirooxazine, naftopyranes, diarylethene, or fulgide structures are the most widespread ( Coelho, 2006 ; Tian and Zhang, 2016 ; Han et al., 2022 ; Kozlenko et al., 2023 ). These substances were introduced 1976 using polysulphone bands for dosimetry studies in medicine, sports, and education and for use in skin stickers, helmets, and hats ( Challoner et al., 1976 ; Davis et al., 1976 ; Downs et al., 2019 ; Downs et al., 2020 ; Henning et al., 2022 ). Research on these substances is mainly focused on promoting awareness of the use of photoprotection tools by quickly observing the changes in UV radiation to which we are exposed at any given time. Spirooxazines are easy-to-handle and low-cost photochromic substances, and are extensively used as UV indicators in wristbands, fabrics, detectors in sunglasses, merchandising items, and even as elements in tattoo inks ( Goudjil, 1996 ; Goudjil, 1998 ; Carrol, 1999 ; Kurz et al., 2020 ). The reversible color change of spirooxazines is observed after a closed spiro form absorbs UV radiation and opens into a merocyanine form ( Tian and Zhang, 2016 ; Han et al., 2022 ). Recently, they have been used as detectors to promote the reapplication of sunscreens, being incorporated into stickers placed on the skin ( Hacker et al., 2019 ; Horsham et al., 2020a ). These could become very useful for teaching children about sun exposure ( Garnacho Saucedo et al., 2020 ), although they could also promote uncontrolled overexposure to the sun if used as toys, which has recently raised doubt about their usefulness in photodermatological forums.

The main objective of this research was the qualitative and quantitative analysis of the color change performance of a set of photochromic devices based on spirooxazines, as well as the analysis of these pure component substances to assess their potential usefulness as UV exposure indicators.

Materials and methods

A comparative experimental analysis between different UV-indicator wristbands, cards, and stickers and pure photochromic substances was carried out. Ten UV-indicator devices and a sample of three photochromic substances in red, purple, and blue, which are the substances that make up the majority of devices in commercial use, were used. The devices were acquired as gift samples, purchased from specific photoprotection education websites, and the pure photochromic substances were purchased from Hali Industries Co. (Changzhou, China). No indications of the compositions of the photochromic substances were shown in the different devices since no specific regulation is applied to these elements in their commercialization ( Han et al., 2022 ). After requesting information from suppliers, only the provider of pure photochromic substances answered us with information about their composition. The pure photochromic substances acquired were 1-photochromic blue dye 6′-(indolin-1-yl)-1,3,3-trimethylspiro[indoline-2,3′-naphtho[2,1-b][1,4]oxazine], 2-photochromic purple-violet dye (1,3,3-trimethyl-6'-(piperidin-1-yl)spiro[indoline-2,3′-naphtho [2,1-b][1,4]oxazine]), and 3-photochromic red dye ((1,3,3-trimethylindolino-6'-(1-piperidinyl) spironaphthooxazine). More information on the compounds of the formula was the use of plyyoxymethyleneamine as a stabilization photochromic dye and styrene maleic anhydremonomethyl-maleate polymer as support material. We received no answer from suppliers in three cases and responses about protection under trade secrecy were given in another three cases. Only in three cases of UV cards and wristbands indicated to us that they use the Technocolor Purple 3—another name for the red and purple pure spirooxacines. One company referred to us to the patents US5581090A and US5914197A regarding the use of spirooxazines. ( Goudjil, 1996 ; Goudjil, 1998 ).

The following indicator element type samples were analyzed with red/purple spirooxazines.

 1. Bracelet of silicone UV test “Euromelanoma”. Seritec

 2. ISDIN UV tester. Isdin

3. Kids Sun Watch. IPS

4. Silicone bracelet UV solar reactive. Zhongyepei. China

5. Smart Sun. Bluemarionge Health

6. UV Test Card. HUXUAN. China

7. Quantadose multi UV wavelength photochromic card

8. UV detection stickers. Sunny Patch

9. Photochromic substance Halli HLPC—1 (Red) Hali Industries Co. Changzhou, China

10. Photochromic substance Halli HLPC—2 (Purple)

11. UVI Evo Advanced Protection. IPS

12. Hönle test UV strip

13. Photochromic substance Halli HLPC—3 (Blue)

UV exposure and color change measurement and observation

This study’s objective was to qualitatively and quantitatively determine the color change response of the devices under different UV exposure conditions. An Oriel 300 W solar simulator (Newport Co. Cleveland, Ohio, United States) was used as the UV-visible light source (290–750 nm). In order to avoid any temperature effect on photochromic responses, the solar simulator included a constantly refrigerated water column in order to avoid any infrared radiation that could increase temperature at the output level (see Figure 1 on the position of the Teflon tubes for the water refrigeration system). Thus, the temperature at the site of exposure was a constant 21–22 °C. The spectral distribution as well as the UV-visible irradiance of the solar simulator was characterized using a Macam SR9910-V7 double monochromator spectroradiometer (Irradian Co., Scotland, United Kingdom) with an Ulbrich integrating sphere ( Figure 1 ), calibrated at the National Institute of Aerospatial Technologies of Spain against a NIST-certified calibrated halogen lamp (1000 W −50 cm distance). The spectral distribution of the full UV-visible radiation (290–750 nm) emitted by the solar simulator is shown in Figure 2 . The maximum irradiance measured at 3 cm from the lamp output was 435 W m -2 in the interval of 290–750 nm and 57.6 W m -2 of total UV in the interval of 290–400. This irradiance corresponds to midday mean summer solar irradiance in latitudes of south Spain (36 o N) ( Figure 2 ). To obtain the different UVI values to which the photochromic devices were exposed, the erythemally weighted irradiance of the light source was characterized from the physical energy units per wavelength weighted by its erythematic potential according to ISO/CIE 17166:2019 ( ISO/CIE 17166, 2019 ). The final values were expressed in terms of UV index (UVI) as explained by the WHO to make solar radiation with a erythematic effect more understandable ( WHO Team, 2003 ). To obtain different UVI values in which samples were exposed, the spectroradiometer sensor was situated gradually at different distances from the light source (from 5.5 cm at UV of 10–40 cm at UVI of 0.25) ( Table 1 ). The color grade of the photochromic devices was determined using a DermaLab Combo Series colorimeter (Cortex Technology ApS., Denmark) associated with calculation software ( Figure 1 ). This device provides color values in three-dimensional space based on the L*a*b system proposed by the Commission Internationale de l'Éclairage (CIE) ( Smith and Guild, 1931 ; van der Wal et al., 2013 ). Basically, since color is a subjective perception and interpretation, color space is widely used because it consistently correlates numerical color values with human visual perception. L* indicates lightness and a* and b* are the chromatic coordinates: a* = red/green coordinates (+a indicates red, −a indicates green) and b* = yellow/blue coordinates (+b indicates yellow, −b indicates blue). In this study, the chromatic coordinate a* (positive values indicating red intensity) was used for most of the red/purple samples, while the chromatic coordinate b* was used for blue photochromic (negative values indicating blue intensity).

Figure 1 . (A) Image of the lighting system with solar simulator, spectral measurement system, and colorimeter. (B) Illumination of photochromic wristbands under solar simulator. (C) Color intensity measurement. (D) Purple color of a photochromic wristband under different UVI levels.

Figure 2 . Spectral characteristics of transmitted UV-visible radiation (290–750 nm) from an Oriel solar simulator using a cut-off filter of 290 nm compared with the sun at midday on a mean summer day in Málaga (Spain). Solar spectrum was measured at midday (14:00 local time) in the second week of June 2022 using a MACAM spectroradiometer of double monochromator combined with an Ulbricht integrating sphere.

Table 1 . Values corresponding to total UV irradiance, corresponding erythematic irradiance, UVI at each of the distances between the emission source, and the place where the photochromic strips are placed. In colors, the UVI levels corresponding to the WHO recommendations are green-low; yellow-medium; orange-high; violet-extreme.

Five types of experiment were carried out to analyze the potential use of devices based on photochromic substances. Each experiment was performed in triplicate, and measurements were repeated twice per device (wristbands, UV detector, stickers, and pure photochromic substances) ( Figure 3 ).

1. Activation time under very high UVI. Each UV device as well as pure photochromic substances were exposed to a constant UV irradiance of 57.6 W m -2 , corresponding to a WHO Very High UVI value of 10 for 120 s, taking color measurements (a* or b* depending the color substance) during the test after 0, 7, 15, 30, 60, and 120 s.

2. Activation level by UVI value. Each device was exposed to different UVI values of 0, 0.25, 0.5, 1, 2, 3, 5, 7, and 10 for 60 s. The different UVI values were obtained by placing the photochromic devices at different distances from the emitting source of the solar simulator ( Table 1 ).

3. Color reversal time. After color saturation under UVI of 10, each wristband was kept in darkness (samples and colorimeter sensor were placed into a closed box) and color measurements were made in same position of each UV device at 0, 15, 30, 60, and 120 s after exposure.

4. Activation level against different sunscreen application with different sun protection factors (SPF). An experiment was conducted to assess the potential of these devices as indicators of when the topical sunscreen should be reapplied. Two photochromic devices were used for the test: 1) a UV test card Huxuan (China) and 2) a Smart Sun band (Bluemarionge Health). For this purpose, a standard methacrylate plate (Schoemberg, Hamburg, Germany), used in the UVA sun protection test according to ISO 24443:2021 ( ISO 24443, 2021 ) as skin simulating support, was placed on each photochromic band, and different sunscreen formulas were tested. These were the standard topical sunscreen P8 (expected sun protection factor of 60) from ISO 24444:2019 ( ISO 24444, 2019 ) and one commercial sunscreen anthelios 50+ dermo-pediatrics gel ester from Laroche Posay (L'Oreal Co. Paris, France). Both products were applied at different concentrations diluted with the product excipient to obtain different mixtures with different SPF levels. They were diluted to 0% (excipient only), 12.5%, 25%, 50%, and 100% and applied on methacrylate plate at a concentration of 1.3 mg cm -2 following as per ISO 24443:2021. In vitro SPF values were 3.5, 5.9, 14.8, 29.6, and 59.2 for P8 formula and SPF of 4.1, 6.9, 17.3, 34.7, and 69.3 for Laroche Posay dermo-pediatrics sunscreen. The color level under different SPF combinations was measured after 60 s under UV irradiance of the solar simulator corresponding to a UVI of 10 (total UV irradiance of 57.6 W m -2 ).

5. Wavelength effect on activation level. In order to analyze the action spectrum of the spirooxazine-based photochromic substances activated by UV and visible light, one red UV detector card device as well as the pure red and blue photochromic substances (four replicas for each) were exposed under a solar simulator to a constant UV irradiance of 57.6 W m -2 (corresponding to a UVI value of 10). Different wavelength exposures were obtained using a set of different cut-off filters 290–450 nm (305, 310, 320, 330, 355, 370, 400, 420, and 450 nm Schott AG, Mainz, Germany). The values represent the approximate 50% transmission wavelength of each cut-off filter. The spectral characteristics of full and filtered UV and blue light by different cut-off filters is shown in Figure 3. In order to obtain the same light doses for all experiments, time exposure depended on the total transmitted light from 290 to 750 nm. Thus, exposure under filter with 50% transmission wavelength of 290 nm was 20 s and was gradually increased depending on the total irradiance measured (290-20 s, 305-21 s, 310-22 s, 320-23 s, 330-25 s, 355-25 s, 370-25 s, 400-28s, 420-30 s, 450-32 s).

Figure 3 . Spectral characteristics of the transmitted UV-visible radiation (290-750nm) from an Oriel solar simulator using a set of different cut-off filters used to determine wavelength activation of photochromic substances.

Activation time

The activation response time of the different devices, photochromic wristbands, UV cards, UV stickers, and pure photochromic substances with respect to maximum value in relative units is represented in Figure 4 . The response of most of the photochromic bands and pure photochromic red/purple substances was very fast, with color saturation of 7–15 s after illuminating the different as well as pure photochromic substances ( Figure 4A ). Only two wristbands needed 30 s to reach their maximum (Isdin Sun watch and Quantadose multi UV). The results were similar for the blue photochromic substances, with very fast activations reaching color saturation b* 7 s after illumination under simulated solar UV ( Figure 4B ).

Figure 4 . Change in a* (green to red) and b* (blue to yellow), relative to the maximum a* and b* values (of the CIE Lab color space) with respect to exposure time under a solar simulated total UV irradiance corresponding to an UVI value of 10. (A) Bracelets, stickers, UV cards, and pure red and purple photochromic substances (color space a* with positive values). (B) Blue UV cards and photochromic substance (color space b* with negative values). Standard deviation of mean values is less than 5% (data not shown).

Color reversion

Color reversion in the dark after irradiation took much longer than color saturation under a high UVI index of 10 ( Figure 5A in relative units with respect to maximum values time 0). An exponential a* color decay pattern was observed for the color reversion of photochromic devices and pure substances. In the case of the pure photochromic substances, both red and purple, the decay was gradual, up to 120 s, while the other photochromic bands and bracelets were near baseline at 60 s. A similar effect was observer for blue devices and pure photochromic substances ( Figure 5B ).

Figure 5 . Change in a* (green to red) and b* (blue to yellow) (in relative units with respect to the initial maximum value of activation of the CIE Lab color space) in the dark after exposing several bracelets to a solar simulated total UV irradiance corresponding to an UVI value of 10. (A) Bracelets, stickers, UV cards, and pure red and purple photochromic substances (color space a* with positive values). (B) Blue UV cards and photochromic substance (color space b* with negative values). Standard deviation of mean values less than 5% (data not shown).

Color activation under different UVI values

The color response of the different devices, photochromic wristbands, UV cards, UV stickers, and pure photochromic was analyzed against exposure to different UVI levels ( Figure 6 in relative units with respect to maximum values). All bands and pure photochromic substances showed color saturation (a* and b* values of the CIE Lab color space) when exposed to a very low UVI of 2–3 ( Figure 6A ). Only wristband 3 gradually increased the color level up to a UVI of 10. When color changes were analyzed by eye observation, the a* and b* color changes detected by the colorimeter were no longer visible to the human eye after 15 s under irradiances corresponding to a UV index of 2 and over ( Figure 7 ).

Figure 6 . Change in a* (green to red) and b* (blue to yellow) (from the CIE Lab color space) with increasing solar simulated total UV irradiance corresponding to an UVI increment from 0 to 10. (A) Bracelets, stickers, UV cards, and pure red and purple photochromic substances (color space a* with positive values). (B) Blue UV cards and photochromic substance (color space b* with negative values). Standard deviation of mean values is less than 5% (data not shown).

Figure 7 . Photograph of the photochromic substances and two examples of UV detector devices after 15 s of exposure under different UV-visible irradiance corresponding to UVI from 1 to 10. M1 corresponds to a Kid Sun Watch (IPS), M2: UV Test Card (Huxuan), M3 Red pure photochromic substance, M4: UVI Evo Advance Protection (IPS) and M5: Pure blue photochromic substance.

The time necessary for activation of the two examples of photochromic substances as well as a red (Sunny patch photoprotection sticker) and blue (Hönle UV test strip) under different UV-visible irradiances corresponding to different UVI (from 1 to 10) is shown in Figures 8A–D . In all cases a gradual increase in color can be observed following a potential model of longer saturation times under lower UV-visible irradiance corresponding to lower UVI values. UVI over 5 required around 10 s for saturation. In the case of lower irradiances, saturation took 30 s. Similar results were observed in the case of blue devices. Differences observed in the velocity of saturation reflects the difficulty in determining a fixed time for using the photochromic devices as dosimeters, especially by simple observation as observed in the devices’ photos.

Figure 8 . Time necessary for color activation measured in terms of a* (green to red) and b* (blue to yellow) (from the CIE Lab color space) in terms of relative values with respect to the maximum value, with increasing solar simulated total UV-visible irradiance corresponding to an UVI increment from 0 to 10. (A) Red photochromic substances (color space a* with positive values). (B) Sunny patch photoprotection sticker (red photochromic), (C) pure blue photochromic substance, and (D) Hönle UV test strip blue.

Activation under different sun protection factor sunscreens

To analyze spirooxazine-based photochromic substances as detectors for sunscreen reapplication, a photochromic detection band, the UV test card Huxuan red, a Sunny Patch photoprotection sticker (used as a sunscreen re-application photochromic device), and the pure red photochromic substance were used. Figure 9 shows the color changes of photochromic devices under PMMA plates with different SPF measured after 60 s under 57.6 W m -2 of UV, corresponding to a UVI of 10. In all cases, the increase in SPF produced a gradual exponential decrease in color level a* from the SPF of 0, or untreated plate which saturated the UV band, sticker, and pure red photochromic product with color. Although there was a gradual decrease in the degree of color, pink/red was visible to the naked eye, and, from SPF 17 for the standard P8 and 20 for the pediatric sunscreen, no further decrease was apparent to the naked eye. It could also be observed that even at protection factors 60 and 69 (high SPF of both sunscreens), after 1 min of exposure there was a UVI of 10. Only the Sunny patch photoprotection sticker showed no color change after 1 min under a UVI of 10. However, a value of a* color of 15 was again observed under SPF 30 under P8 sunscreen standard and, in cases of dermo-pediatrics SPF 50+ sunscreen, very low values of a* color were observed from SPF 17. Results indicate that photochromic substances are very sensitive to UV, and it was possible to observe color changes under SPF values sufficient for good photoprotection for skin that could lead to unnecessary reapplication of sunscreens in normal sun exposure.

Figure 9 . Change in a* color values (of the CIE Lab color space) in the dark after exposing one UV detector test card and the red pure photochromic substance to different sun protection factors (SPF) after application of (A) P8 reference of the ISO 2444:2019 and (B) a pediatric SPF 50+ commercial sunscreen. The exposure time was 1 min under an irradiance of total solar simulated UV radiation of 45.7 W m -2 , corresponding to an UVI of 10 after ponderation with respect to erythema action spectrum. The dashed lines indicate the color level when the pure photochromic substance or each device was non-irradiated (similar to that obtained when irradiated under highest SPF).

Finally, in order to analyze how the exclusion of parts of the UV-visible spectrum affects the activation of photochromic substances, some red and blue devices and pure substances were irradiated under a series of long-pass filters with a range of cut-off wavelengths; the results are shown in Figures 10 – 12 . Both the pure red and red UV card as well as the pure photochromic substance as examples were analyzed, and the color reached after 20–25 s under cut-off wavelengths under 370 nm and did not vary ( Figures 10 , 11 ). The maximum color of the pigment activation was visually observed under the different cut-off filters with respect to the half of the irradiated surface not covered by the filters, as shown in the device photos. The color level after activation was gradually decreased for cut-off wavelengths of 370–400 nm, and exposure over 450 nm still showed a faint but significant color level of the photochromic substances after just 32 s of irradiation. Thus, spirooxazines as photochromic substances can be activated not only by UV radiation but also by high-energy visible radiation. Similar results were observed for blue devices ( Figures 10 – 12 ).

Figure 10 . Photographs as well as the figure of the change in a* color values (of the CIE Lab color space) after exposure of 30–35 seg under total UV-visible radiation corresponding to an UVI of 10 across different cut-off filters. Each wavelength indicates the approximate 50% transmission wavelength of each cut-off filter. Substances, included in a UV transparent plastic bag, were directly exposed to the solar simulator, and half of the surface was covered by the different cut-off filters. Non-irradiated half of the substance was made covering with a black PMMA piece.

Figure 11 . Photographs as well as the figure of the change in a* color values (of the CIE Lab color space) after exposure of 30–35 seg under total UV-visible radiation corresponding to an UVI of 10 across different cut-off filters. Each wavelength indicates the approximate 50% transmission wavelength of each cut-off filter. UV detectors were directly exposed to the solar simulator and half of the surface was covered by the different cut-off filters. Non-irradiated half of the UV detector was made covered with a black PMMA piece.

Figure 12 . Photographs as well as the figure of the change in b* color values (of the CIE Lab color space) after exposure of 30–35 seg under total UV-visible radiation corresponding to an UVI of 10 across different cut-off filters. Each wavelength indicates the approximate 50% transmission wavelength of each cut-off filter. Substances, included into a UV transparent plastic bag, were directly exposed to the solar simulator and half of the surface was covered by the different cut-off filters. The non-irradiated half of the substance was made covered with a black PMMA piece.

Protection against excessive exposure to solar UV radiation should begin in childhood due to high exposure. Both because of our social learning environment and the physiological need for vitamin D synthesis, sun exposure in children is high from an early age. It is in this learning process that tools such as UV radiation indicator bracelets, like those analyzed in this study, could be particularly useful in establishing good habits ( Boldeman et al., 2004 ; Blázquez-Sánchez et al., 2021 ; Huh et al., 2021 ; Thoonen et al., 2021 ).

Previous results of this research group have shown experience with the use of bio-dosimeters ( Biologically weighting UV dosimetry VioSpor, 2023 ; Horsham et al., 2020b ), which at the research level have demonstrated great potential as biosensors. We have used these to characterize the daily exposures of athletes in Olympic competition ( Gutiérrez-Manzanedo et al., 2023 ) or for occupational exposures of personnel at risk due to high sun exposure (physical education teachers, lifeguards, construction workers, and street cleaners) ( Castro-Maqueda et al., 2019 ; de Troya Martín et al., 2021 ). However, in this study we have attempted to address the potential of spirooxazine films, which are widely used as UV detectors and as dosimeters ( Thieden et al., 2000 ; Henning et al., 2022 ) due to their ease of use, reversibility of measurement, and low cost. Other bands of photochromic substances have been designed to indicate when photoprotection should be reapplied and even allow the application of the cream on the bracelet to indicate when the sunscreen is no longer effective ( Horsham et al., 2020b ). The results obtained in this work confirm these facts, as these bracelets are good indicators of UV presence/absence but do not offer potential utility as radiation level sensors or dosimeters. Horsham et al. ( 2020c ) demonstrated that the use of these bracelets (presence of red color change) led to attitudinal changes in a population of 663 adolescents participating in a study of photoprotection and sun exposure behavior during an outdoor festival on the Gold Coast in Australia. These indicator bracelets prompted the use of sunglasses and topical sunscreen, although the decrease in sunburn was not significant.

The positive aspect of these widely used photochromic substances is that they require a very short activation time under the sun to show the assumed amount of radiation to which we would be exposed, while in total darkness, which would be ideal for their return to the pre-exposure state, it would take no more than 1 min for most of the substances to reverse, as demonstrated in the present work.

A limitation of the UVI indicator bracelets is their response to the light intensity to which we are exposed. From colorimetric analysis as well as from visual observation of the photochromic substances of different red and blue devices, no gradual increase in color depending on light intensity could be observed. In terms of total solar simulated UV radiation corresponding to UVI values of 2, the photochromic substances are saturated in almost all the devices and pure photochromic substances. This UVI of 2 corresponds to maximum values for winter days at our latitude. Therefore, we have an indicator of the level of exposure to UV radiation that offers the same color value at 10:00 intensities on a summer day (UVI of 2) as at 14:00 (UVI of 9–10) ( Aguilera et al., 2020 ). When a deeper analysis regarding the potential as UV detectors of some example devices and pure red and blue photochromic substances was performed, the color activation under different cut-off filters again showed an important conclusion ( Figures 10 – 12) . All devices and pure substances showed similar color activation when UV radiation was cut off at 370 nm and under, with color saturation reached after activation in less than 25 s and with a lower level but significant activation when solar radiation was cut off over 400 nm. No significant added effect was found when UVB was included with UVA in the cut-off filter of 290 nm. When photochromic devices were exposed to only UVB (exposure under 5.8 W m -2 of a narrow Band UVB phototherapy lamp (Philips PL-S 9 W/01, Philips, Netherlands) with a peak at 311 nm), only faint activation was observed after 120 s (data not shown). A full color change should have occurred if the devices were responding to erythemal irradiance since this exposure has a UVI >10. Thus, the activation under solar simulated UV-visible radiation was mainly due to UVA radiation. It is therefore difficult to accurately correlate color changes with UVI as many suppliers indicate that UVI (related to erythemal wavelengths) depends mainly on UVB radiation. It is clear that when solar radiation increases to higher UVI values, solar UVA irradiance also increases, and we could correlate the activation of photochromic devices with respect to increased total UV radiation and, therefore, the UVI. However, the atmospheric conditions that affect UVI (clouds, ozone, aerosols) cannot be objectively quantified with photochromic devices because solar spectral characteristics change in these atmospheric conditions. Since photochromic-based devices respond to UV long wavelengths, and these longer wavelengths reach earth surface at much higher irradiances, the devices would be more informative as total UV dosimeters (mainly UVA radiation) than as UVI detectors.

A similar limitation applies to the use of spirooxazine-based photochromic substances as detectors for the reapplication of sunscreen ( Hacker et al., 2019 ; Horsham et al., 2020a ). A priori , a color change of the sticker placed on the skin and covered by sunscreen could give us an idea of when to re-apply the sunscreen. We found that when sunscreen with SPF over 60 was applied over the photochromic devices, the color was not altered over 1 min of exposure. However, when the same sunscreen was diluted with excipient in order to reach SPF of 30 and lower, color changes could be observed very quickly. This means that a user, only by simply observing the color of the photochromic device, can make a decision to reapply sunscreen even when it offers an SPF of 30 and does not really require reapplication. Again, the photochromic substances are very sensitive to UV, even when there is enough sunscreen on the skin and a user could decide to unnecessarily reapply it. Sunscreens with very high SPF ensure a long time of photoprotection on the skin. We should (based on color changes even at time interval of few minutes) follow international recommendations of reapplication every 2 h or when skin conditions have been altered (swimming, sport, skin friction) as the best way to protect against solar overexposure.

The photochromic responses of spirooxazine to temperature could also lead to color changes that not only depend on UV presence ( di Nunzio et al., 2008 ). Thus, all our experiments have been done under controlled temperature conditions of around 21–22 °C; however, in normal use conditions, this temperature control does not exist, especially under elevated temperature exposure in summer. This could affect final readings of photochromic color change not only due to UV radiation.

In conclusion, wristbands, UV stickers, and UV detection cards based on photochromic substances as spirooxazine can serve as fast indicators of the presence/absence of UV, which, when translated into educational terms, could be useful for raising awareness among children of the importance of sun protection. Other uses such as UV level indication or objective sunscreen reapplication indication are limited, given our results. In addition, the lack of information associated with the use of such devices offered as promotional items increases the lack of objective control of UV exposure; therefore, the use of these color-changing devices should be limited to certain uses and not be used on a generalized basis.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors without undue reservation.

Author contributions

JA: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing–original draft. DC-G: Data curation, Investigation, Methodology, Validation, Writing–review and editing. CS-R: Data curation, Investigation, Methodology, Writing–review and editing. SG: Supervision, Validation, Writing–review and editing. RB-G: Supervision, Validation, Writing–review and editing. EH-C: Supervision, Validation, Writing–review and editing. MdG: Investigation, Project administration, Supervision, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research is part of the project funded by the State Programs of Generation of Knowledge and Scientific and Technological Strengthening of the System from the Spanish Ministry of Science and Innovation, Grant/Award Number: PID 2020- 117224RB-100. This work is part of the research of the Institute of Biomedicine of Málaga (IBIMA) and the Junta de Andalucía working group CTS-162.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: indicator bracelets, photochromic substances, photoprotection, ultraviolet radiation, children game

Citation: Aguilera J, Cárdenas-González D, Sánchez-Roldán C, González S, Bosch-García R, Herrera-Ceballos E and de Gálvez MV (2024) Qualitative and quantitative analysis of colorimetric response in different UV solar radiation level indicator bracelets based on photochromic substances. Front. Photobiol. 2:1281474. doi: 10.3389/fphbi.2024.1281474

Received: 22 August 2023; Accepted: 05 July 2024; Published: 08 August 2024.

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Copyright © 2024 Aguilera, Cárdenas-González, Sánchez-Roldán, González, Bosch-García, Herrera-Ceballos and de Gálvez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: J. Aguilera, [email protected]

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