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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Research design for qualitative and quantitative studies

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

  • Research design types for quantitative studies
  • Video explainer : quantitative research design
  • Research design types for qualitative studies
  • Video explainer : qualitative research design
  • How to choose a research design
  • Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

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Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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type of design for research

Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

The four most common quantitative research design types are descriptive, correlational, experimental and quasi-experimental.

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

type of design for research

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

Case study design often involves investigating an individual to gain an in-depth understanding of their experiences, behaviours or outcomes.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

type of design for research

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

  • Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
  • Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
  • Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
  • When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

type of design for research

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Wei Leong YONG

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Solly Khan

Thanks this was quite valuable to clarify such an important concept.


Thanks for this simplified explanations. it is quite very helpful.


This was really helpful. thanks


Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

Sam Msongole

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

Robyn Pritchard

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks


how to cite this page


Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .


how can I put this blog as my reference(APA style) in bibliography part?

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

  • Your overall aims and approach
  • The type of research design you’ll use
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

  • Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

  • How much time do you have to collect data and write up the research?
  • Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
  • Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
  • Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.


Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

  • How many participants do you need for an adequate sample size?
  • What inclusion and exclusion criteria will you use to identify eligible participants?
  • How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

  • The distribution of the data (e.g., the frequency of each score on a test)
  • The central tendency of the data (e.g., the mean to describe the average score)
  • The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

  • Make estimates about the population based on your sample data.
  • Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation 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, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

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

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.

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Organizing Your Social Sciences Research Paper

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Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

  • Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
  • Review and synthesize previously published literature associated with the research problem,
  • Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
  • Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
  • Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

  • This is a collaborative and adaptive research design that lends itself to use in work or community situations.
  • Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
  • When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
  • Action research studies often have direct and obvious relevance to improving practice and advocating for change.
  • There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

  • It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
  • Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
  • Personal over-involvement of the researcher may bias research results.
  • The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
  • Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

  • Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
  • A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
  • Design can extend experience or add strength to what is already known through previous research.
  • Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
  • The design can provide detailed descriptions of specific and rare cases.
  • A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
  • Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
  • Design does not facilitate assessment of cause and effect relationships.
  • Vital information may be missing, making the case hard to interpret.
  • The case may not be representative or typical of the larger problem being investigated.
  • If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

  • Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
  • Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
  • Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
  • Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
  • Replication is possible.
  • There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
  • Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
  • Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
  • If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

  • Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
  • Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
  • The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
  • Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
  • Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
  • Either original data or secondary data can be used in this design.
  • In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
  • Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
  • Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

  • Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
  • Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
  • Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
  • Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
  • Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
  • Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
  • Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
  • Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
  • Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
  • Studies cannot be utilized to establish cause and effect relationships.
  • This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
  • There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

  • The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
  • Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
  • If the limitations are understood, they can be a useful tool in developing a more focused study.
  • Descriptive studies can yield rich data that lead to important recommendations in practice.
  • Appoach collects a large amount of data for detailed analysis.
  • The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
  • Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
  • The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

  • Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
  • Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
  • Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
  • Approach provides the highest level of evidence for single studies.
  • The design is artificial, and results may not generalize well to the real world.
  • The artificial settings of experiments may alter the behaviors or responses of participants.
  • Experimental designs can be costly if special equipment or facilities are needed.
  • Some research problems cannot be studied using an experiment because of ethical or technical reasons.
  • Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

  • Familiarity with basic details, settings, and concerns.
  • Well grounded picture of the situation being developed.
  • Generation of new ideas and assumptions.
  • Development of tentative theories or hypotheses.
  • Determination about whether a study is feasible in the future.
  • Issues get refined for more systematic investigation and formulation of new research questions.
  • Direction for future research and techniques get developed.
  • Design is a useful approach for gaining background information on a particular topic.
  • Exploratory research is flexible and can address research questions of all types (what, why, how).
  • Provides an opportunity to define new terms and clarify existing concepts.
  • Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
  • In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
  • Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
  • The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
  • The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
  • Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

  • Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
  • The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
  • Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
  • Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
  • Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

  • A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
  • Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
  • The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
  • Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
  • Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
  • The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

  • The historical research design is unobtrusive; the act of research does not affect the results of the study.
  • The historical approach is well suited for trend analysis.
  • Historical records can add important contextual background required to more fully understand and interpret a research problem.
  • There is often no possibility of researcher-subject interaction that could affect the findings.
  • Historical sources can be used over and over to study different research problems or to replicate a previous study.
  • The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
  • Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
  • Interpreting historical sources can be very time consuming.
  • The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
  • Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
  • Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
  • It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

  • Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
  • Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
  • The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
  • Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
  • The data collection method may change over time.
  • Maintaining the integrity of the original sample can be difficult over an extended period of time.
  • It can be difficult to show more than one variable at a time.
  • This design often needs qualitative research data to explain fluctuations in the results.
  • A longitudinal research design assumes present trends will continue unchanged.
  • It can take a long period of time to gather results.
  • There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

  • Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
  • A well-reasoned and well-documented justification for identification and selection of the studies;
  • Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
  • Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
  • Justification of the techniques used to evaluate the studies.
  • Can be an effective strategy for determining gaps in the literature.
  • Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
  • Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
  • Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
  • Can be used to generate new hypotheses or highlight research problems for future studies.
  • Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
  • A large sample size can yield reliable, but not necessarily valid, results.
  • A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
  • Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

  • Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
  • Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
  • A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
  • The strengths of one method can be used to overcome the inherent weaknesses of another method.
  • Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
  • May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
  • Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
  • A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
  • Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
  • Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
  • Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
  • Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
  • Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

  • Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
  • The researcher is able to collect in-depth information about a particular behavior.
  • Can reveal interrelationships among multifaceted dimensions of group interactions.
  • You can generalize your results to real life situations.
  • Observational research is useful for discovering what variables may be important before applying other methods like experiments.
  • Observation research designs account for the complexity of group behaviors.
  • Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
  • In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
  • There can be problems with bias as the researcher may only "see what they want to see."
  • There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
  • Sources or subjects may not all be equally credible.
  • Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

  • Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
  • Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
  • Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
  • Can provide a basis for applying ethical decision-making to practice.
  • Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
  • Brings clarity to general guiding practices and principles of an individual or group.
  • Philosophy informs methodology.
  • Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
  • Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
  • Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
  • Limited application to specific research problems [answering the "So What?" question in social science research].
  • Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
  • While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
  • There are limitations in the use of metaphor as a vehicle of philosophical analysis.
  • There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

  • The researcher has a limitless option when it comes to sample size and the sampling schedule.
  • Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
  • This is a useful design for exploratory studies.
  • There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
  • Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
  • The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
  • The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
  • Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

  • A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
  • The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
  • They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
  • Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
  • The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
  • Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
  • The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
  • Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
  • Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
  • The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
  • The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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The Four Types of Research Design — Everything You Need to Know

Jenny Romanchuk

Updated: December 11, 2023

Published: January 18, 2023

When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.

research design used to test different beauty products

In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.

Table of Contents

What is research design?

The four types of research design, research design examples.

Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.

Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.

Marketers use different types of research design when conducting research .

There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.

Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.

Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.

type of design for research

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An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).

In other words, the researcher wants to test a causal relationship between two or more variables.

In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.

Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.

The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.

One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.

Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).


A correlational design examines the relationship between two or more variables without intervening in the process.

Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.

For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.

Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.

Best for: Understanding the extent to which two or more variables are associated with each other in the real world.


Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.

Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.

SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.

For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

different types of research design: descriptive research example.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

research design example, tweezerman

Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Research Design

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:


This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.


This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.


Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

  • Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
  • Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
  • Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
  • Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
  • Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
  • Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
  • Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

  • Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
  • Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
  • Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
  • Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
  • Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

  • When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
  • When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
  • When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
  • When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
  • When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

  • Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
  • Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
  • Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
  • Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
  • Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
  • Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
  • Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
  • Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
  • Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

  • Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
  • Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
  • Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
  • Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
  • Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
  • Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

  • Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
  • Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
  • Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
  • Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
  • Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
  • Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
  • Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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Types of clinical trial designs: The essentials

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On average, it takes 10.5 years for a drug to advance from phase 1 clinical trial to regulatory approval and market access. Between 2010 and 2020, the overall likelihood of approval was 7.9%. These figures underline the importance of the choice of the clinical trial design type. As the biotech sector continues to grow, driven by rapid advancements in technology and an increasing focus on personalized medicine, the variety of clinical trial designs is expanding.

As the demand for more innovative and efficient trials grows, the industry faces challenges such as increasing the diversity of trial participants and managing the high costs and complexities associated with advanced trial designs. The adaptation of clinical trials to include remote technologies and decentralized approaches is also a pivotal trend helping to broaden participant inclusion and streamline processes. In this article, we delve into the essentials of clinical trial designs and emerging models.

Table of contents

Overview of the different types of clinical trial designs, randomized controlled trials.

Randomized Controlled Trials (RCTs) are considered the gold standard in clinical research due to their ability to minimize bias through randomization, allocation concealment, and blinding. Allocation concealment is a technique preventing selection bias by concealing the treatment from those assigning it to the participants until the moment of assignment. In other words, the clinicians do not know in advance what treatment the next participant will receive. This prevents clinicians from consciously or unconsciously influencing which participants will be assigned to a group based on their characteristics. Blinding is the practice of keeping participants, healthcare providers, and sometime the people in charge of analyzing the data unaware of the treatment each participant received. This avoids expectation bias during the interpretation of the trial.

RCTs are also known as parallel group designs, in which the participants are randomly assigned to either an experimental group or a control group. As Dan Goldstaub, co-founder of PhaseV, a company developing a platform for adaptive clinical trials, puts it: “Its purpose is to determine the effectiveness of a new treatment or intervention by comparing it with a control group that does not receive the treatment or receives a standard treatment.”

To put it in a nutshell, RCTs can provide high-quality evidence due to the random assignment of treatments, which helps eliminate bias. However, this comes with high costs and ethical dilemmas associated with withholding potentially better treatments from control groups. Moreover, the design and execution of RCTs can be complex and time-consuming, making them less feasible for urgent medical research needs.

Crossover trials

In this clinical trial design type, participants receive multiple treatments sequentially, serving as their own controls. According to Meri Beckwith, co-founder of Lindus Health, a clinical research organization, this enhances statistical power and reduces sample size needs but is only suitable for stable conditions and treatments with quick washout periods to avoid carryover effects.

Crossover trials allow participants to receive more than one intervention, with periods of no treatment or standard treatment in between. This type of clinical trial design is beneficial for comparing the effects of multiple treatments within the same group of participants, as each participant acts as their control, which can enhance the statistical power of the trial. However, crossover trials are not suitable for treatments with long-lasting effects such as chemotherapy, or for conditions that fluctuate over time like multiple sclerosis characterized by periods of relapse, as these factors can complicate the separation of treatment effects​​.

Factorial designs

These trials test multiple treatments and their interactions simultaneously. “This can provide comprehensive data but requires larger sample sizes to detect interaction effects,” said Goldstaub.

Factorial designs allow for the assessment of each intervention independently and in combination. Participants are randomly assigned to different treatment combinations, to test multiple interventions simultaneously and explore interactions between them. This design can significantly reduce the resources and time required as compared to conducting multiple separate trials. 

According to Goldstaub, the main challenge regarding these designs is that the statistical analysis of interactions between treatments can be complex, requiring advanced expertise and potentially complicating the interpretation of results.

Adaptive designs

These designs include predetermined points where the accumulating data from the trial is leveraged to adjust the trial design, without compromising the integrity of the study. Adaptive design improves the efficiency and flexibility of a trial, supporting more ethical trials and better use of resources, time, and money.

Adaptive designs offer the flexibility to modify trial parameters based on interim data analysis. This adaptability can lead to more efficient and potentially shorter trials by focusing resources on promising treatments and discontinuing ineffective ones. However, the implementation of adaptive designs requires advanced statistical expertise and thorough planning to ensure that modifications do not undermine the integrity of the trial​.

While the flexibility of these designs can enhance the ethical aspect of trials by minimizing participant exposure to ineffective treatments, it introduces statistical complexities and necessitates rigorous oversight to maintain the integrity of the trial.

Single-arm trials

In single-arm trials, all participants receive the experimental therapy and results may be compared to historical data or external controls instead of a concurrent control group. These trials are often used in situations where it is unethical or impractical to include a control group, such as with rare diseases or when the expected outcome without treatment is poor. These trials can provide preliminary data on the efficacy of a treatment, but they generally offer weaker evidence than randomized controlled trials due to the lack of a comparator group​.

“Single-arm trials are useful when it’s not practical or ethical to have a control group, such as with rare diseases or very novel therapies, and can also be quicker and less expensive to conduct than randomized controlled trials. With that, the results can be less definitive without a comparison group, leading to potential biases and limited ability to infer causality,” said Goldstaub.

Cluster randomized trials

Cluster randomized trials are conducted when interventions are best implemented at a group level, such as in different hospitals or communities, rather than targeting individual participants. This design helps to avoid contamination effects between groups but requires careful handling of data due to potential variations within each cluster. 

In clinical trials, contamination effects refer to instances where participants in different groups inadvertently share or are exposed to treatments or interventions intended for another group. This can occur if participants interact with each other or if interventions are not strictly contained within the designated groups.

According to Goldstaub, cluster randomized trials can reduce the risk of treatment effects transferring between individuals. “In these designs, the variability within clusters can affect the statistical power, often requiring larger sample sizes and costs. In addition, these trials are logistically complex, as managing and implementing interventions across multiple groups or locations can be challenging.”

An example of a cluster randomized trial could be set up for a gene therapy for Duchenne muscular dystrophy (DMD) . The clusters would be different pediatric hospitals or specialized treatment centers. These clusters would be randomly assigned either to the intervention group which would administer the novel treatment, or the control group that would keep going with standard care. The measurement of of the outcome could be focused on muscle function improvement through standardized tests. 

Basket trials

Basket trials test how a single intervention performs across multiple diseases or conditions, which can reveal the broader applicability of a treatment but necessitate complex statistical strategies to manage diverse patient responses​. Basket trials are prominently used in oncology due to the diversity of cancers with common genetic mutations. 

This design allows researchers to evaluate the therapeutic impact of targeting a specific genetic aberration irrespective of the cancer’s location in the body. The central idea is to “basket” together patients who have different types of cancer but share a specific molecular signature that the drug in question is designed to target.

Patients are selected based on the presence of specific biomarkers or genetic mutations rather than the type of cancer. Advanced genomic profiling is used to identify eligible patients. All patients receive the same investigational treatment aimed at targeting the mutation, regardless of their cancer type.

The main outcome is the response rate to the treatment across all included cancer types, evaluated through metrics like tumor shrinkage, progression-free survival, or overall survival. Researchers also observe how the drug’s effectiveness may vary between different tumor types.

Basket trials are often adaptive, meaning that the trial design can be modified in response to interim results. For example, if a particular subgroup shows no benefit, it can be closed, while resources are redirected to more promising groups.

A hypothetical example could be a basket trial investigating a new inhibitor targeting the BRAF V600E mutation found in melanoma, colorectal cancer, and thyroid cancer. Despite the different tissue origins, the presence of this specific mutation might suggest a common pathway for drug action. Patients from these different cancer types would be grouped together to assess the drug’s efficacy, providing valuable insights into the mutation’s role across different tumors and the broader applicability of the drug.

How do specific and advanced clinical trial designs compare to traditional parallel-group designs?

Clinical trial designs such as crossover, factorial, and adaptive are taking more space in achieving clinical endpoints, offering distinct advantages over traditional parallel-group designs.

In parallel-group designs participants are randomly assigned to different groups, each receiving a different treatment, and the results from each group are compared at the end of the trial. This simple and proven approach requires a larger sample size to achieve the clinical endpoints. 

According to Beckwith, that is the reason why recently there has been a push to avoid RCTs. “I believe this is slightly misguided, as randomization is almost ‘magic’ in the way it simultaneously controls for all possible confounding variables, assuming an adequate sample size. Alternatively, researchers would need to predict and control for all possible confounding variables when conducting a retrospective non-randomized analysis, which can be very difficult.”

While parallel-group designs have proven their efficiency, innovative trials still have a lot to offer.

Goldstaub notes that in crossover trials, because each participant serves as their own control, variability among participants is reduced, potentially increasing the sensitivity to detect treatment effects. “Fewer participants may be needed to achieve similar statistical power as parallel-group designs, as each participant provides multiple data points across the different treatment conditions.”

Adaptive design enables leveraging the accumulated trial data for trial modifications, including changing the number of participants, adjusting dosages, stopping ineffective treatment arms, or reallocating resources to more promising ones. According to Goldstaub, the flexibility to make changes during the trial based on accumulating evidence can lead to more efficient trials, potentially reaching conclusions faster, and with fewer resources. In addition, these trials allow us to focus on more promising treatments, and reduce patient exposure to less effective or harmful treatments.

”Adaptative designs can and should be more widely adopted, as they combine advantages of both approaches; randomization controlling for unknown confounding variables, while minimizing the number of patients who receive a placebo.”

In 2019, the FDA released the guidance for adaptive trials which specifies some of the statistical aspects and interpretations of the results. In adaptive clinical trials, the type, method, and logic of adaptation are defined apriori. “We don’t know how the trial is going to play out, but we define how the trial should adapt to the different scenarios. For that, hundreds of thousands of simulations are submitted. Indeed, it is harder to design and interpret adaptive trials, but in recent years harnessing software and machine learning has made it easier to understand and more intuitive,” said Goldstaub.

Advanced clinical trial designs, such as basket trials, offer significant advantages but also present distinct challenges. Goldstaub notes the main challenge is statistical complexity. 

“Innovative designs often require more sophisticated statistical techniques and the use of technology, which can increase the complexity of the trial and the expertise required to manage it. In addition, gaining approval from regulatory bodies can be challenging due to the novel approaches these trials take. Ensuring ethical standards, especially in basket and platform trials, is crucial. These challenges should be managed through meticulous design, execution, and regulatory compliance, utilizing the advanced technology and expertise available.”

How to make a decision regarding the type of clinical trial designs?

When selecting a clinical trial design type, Beckwith thinks it comes to five crucial considerations:

  • Objective of the study: The choice of design depends on the primary research questions. For instance, whether the trial aims to compare two treatments directly, investigate multiple treatments simultaneously, or adjust the study based on interim results influences the selection of parallel, factorial, or adaptive designs, respectively.
  • Disease characteristics: The nature and stage of the disease being studied also impact design choice. Chronic diseases may be suitable for crossover designs, whereas acute conditions are not.
  • Patient population: The availability and characteristics of the patient population, including disease prevalence and severity, can dictate the feasibility of enrolling sufficient participants for different types of clinical trial designs.
  • Regulatory considerations: Approval from regulatory bodies might favor certain types of clinical trial designs over others based on historical precedence, perceived rigor, or comprehensive data requirements.
  • Resource availability: Budget, time constraints, and available expertise can also influence the choice of design, as some designs may require more complex management and analysis. For instance, adaptive designs typically employ more sophisticated statistical techniques to allow for mid-trial adjustments based on accrued data. This requires advanced planning and simulation to ensure the integrity of the data and prevent biases.

Where do we stand in clinical trials and where are we heading?

Recent innovations in clinical trial designs are significantly influenced by the integration of real-world data (RWD) and the use of synthetic control arms (SCAs).

Use of real-world data and synthetic controls

“There are few recent advancements such as incorporating historical data into clinical trials – synthetic controls and priors in Bayesian trials . Adaptive trials in biosimilars development; more advanced adaptive enrichment trials; more complex platform trials,” said Goldstaub

RWD is increasingly used to enhance clinical trials by providing insights that help optimize care distribution and improve protocol designs. One innovative application of RWD is the creation of SCAs . SCAs use data from past trials or real-world sources to simulate the outcomes of control groups. This approach can reduce the need for enrolling large numbers of patients in control groups, decrease study costs, and speed up the trial process. The ability to leverage SCAs is supported by advancements in data analytics and increasing regulatory openness to these methods. These factors are pushing the boundaries of traditional clinical trial designs and opening up new possibilities for testing and development.

“An increase in the availability of data is allowing researchers to construct synthetic control groups, which can avoid patients being allocated to a placebo arm. However, there are significant challenges in that it is difficult to control for all possible confounding variables.”

Emerging trends: Patient-centric clinical trial designs and decentralized technologies

Jytte van Huijstee, director of clinical trial operations at myTomorrows, a company facilitating the connection between patients and clinical trial stakeholders, highlights the shift towards more patient-centric clinical trials. This approach focuses on designing trials that are more considerate of the patient’s needs and convenience, which can enhance patient recruitment and retention.

Decentralized trials, which use technologies like mobile health apps and remote monitoring, play a crucial role in this trend. These technologies facilitate trials that are less disruptive to participants’ daily lives and can gather data more continuously and in real time. This shift supports better patient engagement but also broadens the scope of data collection, enhancing the quality and applicability of trial results.

“Sponsors are increasingly looking for ways to design more “patient-centric” clinical trials. This includes incorporating patient feedback in critical design elements such as the frequency, timing, and format.”

A slow shift toward novel clinical trial designs

“Regulators and industry are slowly beginning to embrace adaptive clinical trial designs. This is a good thing, but unfortunately, as with many developments in clinical research, industry adoption is sometimes lagging even behind the regulatory guidance,” said Beckwith.

According to Beckwith, sponsors are still often hesitant to propose adaptive designs even if regulators have signaled their acceptance. Ultimately, if an adaptive clinical trial is well designed, and blinding can be maintained there is no potential downside, and there is potential to significantly save cost, shorten timelines and protect patient wellbeing.

 “I firmly believe that adaptive trials hold the potential to dramatically increase the efficiency and effectiveness of drug development. Along these lines, adaptive enrichment can be a cornerstone in the precision medicine transformation”

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AlphaFold 3 predicts the structure and interactions of all of life’s molecules

May 08, 2024

[[read-time]] min read

Introducing AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. By accurately predicting the structure of proteins, DNA, RNA, ligands and more, and how they interact, we hope it will transform our understanding of the biological world and drug discovery.

Colorful protein structure against an abstract gradient background.

Inside every plant, animal and human cell are billions of molecular machines. They’re made up of proteins, DNA and other molecules, but no single piece works on its own. Only by seeing how they interact together, across millions of types of combinations, can we start to truly understand life’s processes.

In a paper published in Nature , we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.

We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server , an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.

Our new model builds on the foundations of AlphaFold 2, which in 2020 made a fundamental breakthrough in protein structure prediction . So far, millions of researchers globally have used AlphaFold 2 to make discoveries in areas including malaria vaccines, cancer treatments and enzyme design. AlphaFold has been cited more than 20,000 times and its scientific impact recognized through many prizes, most recently the Breakthrough Prize in Life Sciences . AlphaFold 3 takes us beyond proteins to a broad spectrum of biomolecules. This leap could unlock more transformative science, from developing biorenewable materials and more resilient crops, to accelerating drug design and genomics research.

7PNM - Spike protein of a common cold virus (Coronavirus OC43): AlphaFold 3’s structural prediction for a spike protein (blue) of a cold virus as it interacts with antibodies (turquoise) and simple sugars (yellow), accurately matches the true structure (gray). The animation shows the protein interacting with an antibody, then a sugar. Advancing our knowledge of such immune-system processes helps better understand coronaviruses, including COVID-19, raising possibilities for improved treatments.

How AlphaFold 3 reveals life’s molecules

Given an input list of molecules, AlphaFold 3 generates their joint 3D structure, revealing how they all fit together. It models large biomolecules such as proteins, DNA and RNA, as well as small molecules, also known as ligands — a category encompassing many drugs. Furthermore, AlphaFold 3 can model chemical modifications to these molecules which control the healthy functioning of cells, that when disrupted can lead to disease.

AlphaFold 3’s capabilities come from its next-generation architecture and training that now covers all of life’s molecules. At the core of the model is an improved version of our Evoformer module — a deep learning architecture that underpinned AlphaFold 2’s incredible performance. After processing the inputs, AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators. The diffusion process starts with a cloud of atoms, and over many steps converges on its final, most accurate molecular structure.

AlphaFold 3’s predictions of molecular interactions surpass the accuracy of all existing systems. As a single model that computes entire molecular complexes in a holistic way, it’s uniquely able to unify scientific insights.

7R6R - DNA binding protein: AlphaFold 3’s prediction for a molecular complex featuring a protein (blue) bound to a double helix of DNA (pink) is a near-perfect match to the true molecular structure discovered through painstaking experiments (gray).

Leading drug discovery at Isomorphic Labs

AlphaFold 3 creates capabilities for drug design with predictions for molecules commonly used in drugs, such as ligands and antibodies, that bind to proteins to change how they interact in human health and disease.

AlphaFold 3 achieves unprecedented accuracy in predicting drug-like interactions, including the binding of proteins with ligands and antibodies with their target proteins. AlphaFold 3 is 50% more accurate than the best traditional methods on the PoseBusters benchmark without needing the input of any structural information, making AlphaFold 3 the first AI system to surpass physics-based tools for biomolecular structure prediction. The ability to predict antibody-protein binding is critical to understanding aspects of the human immune response and the design of new antibodies — a growing class of therapeutics.

Using AlphaFold 3 in combination with a complementary suite of in-house AI models, Isomorphic Labs is working on drug design for internal projects as well as with pharmaceutical partners. Isomorphic Labs is using AlphaFold 3 to accelerate and improve the success of drug design — by helping understand how to approach new disease targets, and developing novel ways to pursue existing ones that were previously out of reach.

AlphaFold Server: A free and easy-to-use research tool

8AW3 - RNA modifying protein: AlphaFold 3’s prediction for a molecular complex featuring a protein (blue), a strand of RNA (purple), and two ions (yellow) closely matches the true structure (gray). This complex is involved with the creation of other proteins — a cellular process fundamental to life and health.

Google DeepMind’s newly launched AlphaFold Server is the most accurate tool in the world for predicting how proteins interact with other molecules throughout the cell. It is a free platform that scientists around the world can use for non-commercial research. With just a few clicks, biologists can harness the power of AlphaFold 3 to model structures composed of proteins, DNA, RNA and a selection of ligands, ions and chemical modifications.

AlphaFold Server helps scientists make novel hypotheses to test in the lab, speeding up workflows and enabling further innovation. Our platform gives researchers an accessible way to generate predictions, regardless of their access to computational resources or their expertise in machine learning.

Experimental protein-structure prediction can take about the length of a PhD and cost hundreds of thousands of dollars. Our previous model, AlphaFold 2, has been used to predict hundreds of millions of structures, which would have taken hundreds of millions of researcher-years at the current rate of experimental structural biology.

Demo video showing the capabilities of the server.

Sharing the power of AlphaFold 3 responsibly

With each AlphaFold release, we’ve sought to understand the broad impact of the technology , working together with the research and safety community. We take a science-led approach and have conducted extensive assessments to mitigate potential risks and share the widespread benefits to biology and humanity.

Building on the external consultations we carried out for AlphaFold 2, we’ve now engaged with more than 50 domain experts, in addition to specialist third parties, across biosecurity, research and industry, to understand the capabilities of successive AlphaFold models and any potential risks. We also participated in community-wide forums and discussions ahead of AlphaFold 3’s launch.

AlphaFold Server reflects our ongoing commitment to share the benefits of AlphaFold, including our free database of 200 million protein structures. We’ll also be expanding our free AlphaFold education online course with EMBL-EBI and partnerships with organizations in the Global South to equip scientists with the tools they need to accelerate adoption and research, including on underfunded areas such as neglected diseases and food security. We’ll continue to work with the scientific community and policy makers to develop and deploy AI technologies responsibly.

Opening up the future of AI-powered cell biology

7BBV - Enzyme: AlphaFold 3’s prediction for a molecular complex featuring an enzyme protein (blue), an ion (yellow sphere) and simple sugars (yellow), along with the true structure (gray). This enzyme is found in a soil-borne fungus (Verticillium dahliae) that damages a wide range of plants. Insights into how this enzyme interacts with plant cells could help researchers develop healthier, more resilient crops.

AlphaFold 3 brings the biological world into high definition. It allows scientists to see cellular systems in all their complexity, across structures, interactions and modifications. This new window on the molecules of life reveals how they’re all connected and helps understand how those connections affect biological functions — such as the actions of drugs, the production of hormones and the health-preserving process of DNA repair.

The impacts of AlphaFold 3 and our free AlphaFold Server will be realized through how they empower scientists to accelerate discovery across open questions in biology and new lines of research. We’re just beginning to tap into AlphaFold 3’s potential and can’t wait to see what the future holds.

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Creating a resilient and positive workplace through design, a canadian reit’s “dream” office, designing for neurodiversity and inclusion, the role of smart furniture in achieving healthier office spaces, chair of the month.

Katie Sargent

Evolution Design showcases Viennese Design for research and development client PULS Vario ‘s expanded offices .

Project overview:.

  • Design Firm: Evolution Design
  • Client: PULS Vario
  • Completion Date: 2024
  • Location: Vienna, Austria
  • Size: 2000 sq m (21,527 sq ft)

Designed specifically to stimulate innovative thinking, the expanded offices of PULS Vario are a showcase of a bold use of colors in workplace design.

The Vienna-based research and development company specializes in tailor-made power supply solutions and product development. In direct response to the client’s requirement to establish a dynamic and versatile work environment that would stimulate innovation, increase creativity, and improve productivity for the team of 60 engineers, Evolution Design crafted a design concept that employs a striking color palette.

type of design for research

The interior concept follows four stages of the innovation process directly reflected in the physical space: dialogue, create, share, and retreat , creating a highly functional workplace bathed in natural light and rich hues that fully supports employee choice.

Two spiral staircases, seamlessly connecting two floors, facilitate easy access between the different workshop zones, high-tech labs, prototyping areas, a broadcast studio, individual workspaces, and a magnetic hub for exchange and interaction designed in a genuine Viennese café style.

Thanks to a blend of soft textures, natural materials, ambient lighting, and comfortable furniture, this work environment feels more like home than a traditional office, while also incorporating innovative working methods and technology to achieve a truly hybrid workspace .

type of design for research

Project Planning

Just as in the project’s initial phase back in 2018, both the company’s leadership and the entire team of engineers and developers were intricately involved in the planning and execution of the project. Evolution Design places significant emphasis on fostering close collaboration with clients. In this case, the project thrived thanks to the strong partnership that was established between the design team and the entire PULS Vario team. By engaging engineers and developers and fostering open communication channels, the architects seamlessly aligned the interior concept with PULS’s objectives, culminating in a successful implementation.

Among the notable achievements was the integration of Austrian and Viennese cultural references into the interior design.

This was accomplished through a vibrant color scheme in the meeting rooms, drawing inspiration from the region’s distinctive features. For example, deep blues and subtle grays reminiscent of alpine flowers such as gentian and edelweiss were employed to evoke the tranquil ambiance of mountain landscapes. Meanwhile, the choice of poppy red was inspired by the vibrant poppy blossoms adorning fields outside Vienna. Furthermore, the crawling vines adorning the pergola in the focus work area pay homage to Austria’s rich winemaking tradition.

type of design for research

Project Details

The health and wellness dimensions of this project are fundamental components woven into the overarching design philosophy, aligned with the previously mentioned four stages of the innovation process: retreat, dialogue, create, and share. For instance, through the establishment of retreat areas such as dedicated spaces for focused work, employees are provided with opportunities for reflection, time for individual research and deep thinking, and concentrated work without interruptions – an intentional response to the understanding that chronic stress can lead to illness. Creating customized spaces for different activities, like breakout areas, casual meeting spots, formal meeting rooms, AV booths, and brainstorming areas, is crucial in empowering people to perform optimally in their roles.

Another standout feature of the project is the integration of branding within the interiors. Consistent with the design direction set in 2018, the interiors showcase personalized wall graphics showcasing the client’s flagship power supply products.

type of design for research

Key Products  

Arco Ease, chair Arco Sketch work middle, chair Bene Timba, stool Cascando Trunk, space divider / whiteboard Four Design Four Real Flake, table Four Design FourSure 77, stackable chair Freifrau Ona, chair / bar stool Johanson Design Stroll 65 with wheels, stool Johanson Design Pelikan 03 Johanson Design Peak 72, table Pedrali Blume, chair Pedrali Inox 4402, table (laminate with satin brass edge) Pedrali Modus MDL, modular sofa Sancal Tea, high chair Sedus Turn around, high chair Wilkhahn Timetable, foldable conference table

type of design for research

BuzziSpace BuzziPleat + Trio Globe Light, pendant light BuzziSpace BuzziDome, pendant light Flos IC Lights S1 Messing, pendant & wall lights Karboxx /Quadrifoglio Group Triangle / Lightsound, pendant light Lodes A-Tube Nano, pendant lights LZF Cuad, pendant light LZF Cosmos, standing light Prolicht Glorious, pendant light Secto Design Owalo 7000, pendant light Toscot Novecento Aste, pendant lights (light bulbs)

Camira Blazer (Cuz1N), curtain Object Carpet Poodle 1421, deep pile carpet

type of design for research

Room in Room

Cap Solutions Mike XL, AV Pod

Floor Finishes

Aparici Stracciatella, porcelain stoneware tiles Interface Touch & Tones 103, carpet Weitzer Parkett WP Quadra, parquett

Wall Finishes

Tarimatec Aris Cadence, wall panels Aistec Ecopanel Origami, acoustic wall panels Impact Acoustic Fon, wall panels

type of design for research

Overall Project Results

Overall, PULS Vario offices serve as an inspiring and nurturing work environment, dedicated to fostering an innovative mindset, building informal team spirit, and promoting the growth of this innovation-driven engineering company.

type of design for research


Leo Schulmeister, joinery

Schreinerei Ganslmeier, joinery

Trowal, metal work  

Bürofreunde, furniture supplier

Lichtprojekt, lighting consultant and supplier

Lindner Group, partitions

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Design Team

Claudia Berkefeld, Katarzyna Kosciuk, Natalia Maciejowska, Dariusz Florczak, Balazs Götz


Andreas Buchberger ©Evolution Design

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Illustration of well types; ( a ) Vertical well, ( b ) Cluster well, ( c ) Horizontal Well, and ( d ) Horizontal L-Shaped well with a vertical well forming a U-Shaped well.

In the development of CBM wells, L-shaped, U-shaped and multi-branch horizontal wells are usually used for new exploration and development blocks (defined here as new fields or area blocks in the oil and gas industry) 4 , 5 , 6 . However, complex formation structure, and small faults development have made it an extremely challenging task to achieve high output from newly developed CBM wells 7 . For instance, U-shaped wells (a well type in which a vertical well and a horizontal well are connected in the same target layer) face huge difficulties in accurate docking along the coal seam and have limited benefits in the presence of multiple faults in the horizontal Section 8 . Similarly, the applicability of multi-branch horizontal wells is poor, especially in complex stratigraphic structures and fault development of the block 9 .

On the other hand, L‑shaped horizontal wells are often adopted as the main type of wells for exploring and developing CBM in new blocks. The L-shaped horizontal wells exhibit uncomplicated drilling prerequisites, demonstrate a low probability of wellbore collapse or obstruction, and facilitate subsequent access for maintenance of the initial wellbore 10 . However, the drilling process of these wells are not free of challenges. L-shaped wells have a high requirement for wellbore trajectory control, and they are usually difficult to achieve one-time “soft landing” and ultra-long horizontal segment footage 10 . In addition, drainage equipment and method are another key restriction for the promotion and application of this type of well 11 . For example, reported completion data from several exploration wells indicated that the drilling ratio along the coal seam of the actual trajectory is less than 60%. The drilling cycle is nearly two months, and gas production is low 11 . Table 1 illustrates a tabulated analysis of the applicability and challenges associated with different well types in exploration blocks characterized by complex geological formations and the presence of micro-faults.

Various methods have been used to improve the drilling ratio, by improving the trajectory control. These methods, shown in Table 2 , include: geological guidance technology of adjacent well data, electromagnetic waves, natural gamma measurement, and three-dimensional seismic exploration technology. However, each method has its own limitations, such as high costs, difficulty in obtaining gamma values in specific directions, and signal loss when applied to drilling in complex formations 12 , 13 .

This study delves into trajectory control methods for Horizontal wells within Coalbed Methane (CBM) exploration and development blocks. The approach involves the utilization of pilot holes to determine the characteristics of the target coal seam and the surrounding upper and lower rock layers based on the magnitude of gamma values. This information serves as a predictive identification of marker layers, allowing real-time control and adjustment of the drilling trajectory within the target coal seam. This methodology enables the identification of whether the drilling trajectory is presently positioned within the target coal seam, the roof rock layer, or the floor rock layer. Additionally, a two-dimensional resonance exploration technology is employed for geological structure and fault detection prior to drilling, enabling pre-drilling trajectory optimization. Furthermore, azimuth gamma logging technology is utilized for real-time monitoring and correction of the drilling trajectory's horizontal positioning during the drilling process. Using L-shaped Short-Radius Well-Q in Block-W of the Qinshui Basin as a case study, a comprehensive assessment of the combined effectiveness of these three methods is conducted. Simultaneously, the research delves into the technical mechanisms and applicability analysis. This exploration of the technical mechanisms aims to enhance the understanding of the functions of these methods, their application conditions, and the analysis and utilization of their technical effects.

Trajectory control methodology

Pilot hole drilling, construction background and reasons.

The area formation structure and faults nature could be obtained by two-dimensional seismic data. Seismic surveys and exploratory drilling in the area could provide a good indication on the coal seam actual depth, coal seam distribution, layers, belts and interbeds. For the geological conditions of developing new blocks, such as less drilling data, less seismic exploration data, complex formation structure and micro-fault development, etc., before drilling, it is imperative to obtain the key parameters of the target coal seam, including its lithology, gas-bearing capacity, gamma value, etc., along with those of the rock layers above and below it. This will allow for the determination of the precise horizon of the coal seam and provide technical support for real-time monitoring and well trajectory control along the target coal seam. To achieve this, it is necessary to design and implement a pilot hole drilling program to obtain the characteristic parameters of the target coal seam and the surrounding strata 14 , 15 .

Pilot hole construction design

Once the goal of layer identification is achieved, the next step is to backfill and sidetrack the pilot hole to open branches and land according to the actual occurrence of the coal seam. To ensure the effectiveness of the pilot hole guidance in subsequent construction, it is advisable to minimize the distance between the coal-seem top point (the point where the drilling trajectory first drills into the target coal seam) and the landing point by increasing the well angle of inclination. Conversely, in order to enhance the construction efficiency of the pilot hole, it is preferable to keep the depth of the pilot hole to a minimum, which is indicated by a small well angle of inclination (70 degrees). Figure  2 illustrates this concept.

figure 2

Optimization of pilot hole scheme.

Taking into account the underlying reasons and background for constructing a pilot hole, as well as the difficulty of side-tracking and the efficiency of construction, a comprehensive plan has been developed. The plan involves drilling the pilot hole at a steady angle of approximately 70° until the bottom of the target coal seam is reached.

  • Two-dimensional resonance exploration

Resonance exploration mechanism

The seismic wave frequency resonance exploration technology is a novel geophysical exploration method that utilizes the frequency resonance principle prevalent in nature to investigate underground geological formations 16 , 17 , 18 , 19 . This technique enables the acquisition of geometric attributes of subsurface structures, such as fractures and faults. Figure  3 illustrates a typical resonance diagram of a seismic wave.

figure 3

( a ) Typical resonance curve of seismic wave ( b ) self-excite resonance to vibration.

Resonance exploration technology boasts numerous advantages, including high sensitivity to density changes, exceptional vertical and horizontal resolution, and an exploration depth of up to 5000 m. Additionally, this technology can be acquired and processed passively, making it an economical and straightforward exploration method 20 .

Analysis of technical applicability

At this stage, the analysis of the existing two-dimensional seismic data in the exploration block would indicate the geological structure of the target coal seam in the block. In addition, it will reveal fault’s locations beside faults development status. The pilot hole drilling can accurately obtain the actual depth of the target coal seam and the characteristic parameter values of the target layer, as well as the roof and floor, but conventional means cannot predict structural conditions such as the development of micro faults in the horizontal section of the drilling along the designated direction. This increases the difficulty of well trajectory control and makes it challenging to ensure the coal seam drilling ratio. However, the two-dimensional resonance exploration technology can be used to infer the development of small faults in the horizontal section drilled along the specified direction by interpreting the resonance image. This enables the optimization of the well trajectory in advance to control the actual drilling trajectory and improve the drilling rate of the target coal seam.

Azimuth gamma control technology

Working principle of azimuth gamma.

The azimuth gamma logging tool is utilized to measure the width of gamma ray energy level 21 , 22 , 23 . The scintillation counter captures gamma rays from the stratum, and azimuth gamma logging while drilling offers unique advantages 24 , 25 . Firstly, it enables real-time calculation of the strata's apparent dip angle. It is convenient to calculate the apparent dip angle of the strata by utilizing the azimuth gamma data. The apparent dip angle at the current position can be obtained as long as it is required to cross an interface. The formula for calculating the apparent dip angle using the azimuth gamma 26 is as follows:

where α is the apparent strata dip; D is the well diameter; Δd is the distance between the upper and lower gamma value change points; β is the well deviation angle.

Second, measuring the natural gamma value in a specific direction. By transmitting up and down gamma data in real-time, it becomes possible to accurately determine the positions of different formation interfaces 27 , 28 . This information can then be used to ensure that the trajectory of the control well is precisely aligned with the target coal seam after drilling is complete. The specific process involved is illustrated in Fig.  4 .

figure 4

Trajectory control based on azimuth-while-drilling gamma logging. ( a ) Coal seam drilled out from the roof. ( b ) Coal seam drilled out from the floor.

The drilling process in the horizontal section along the coal seam is susceptible to deviate from the target due to increased drilling pressure or the impact of the formation structure. The strata above and below the coal seam are usually mudstone or carbonaceous mudstone. When using azimuth gamma logging during drilling, the upper gamma value first increases, followed by the lower gamma value, indicating that the drilling has exited the coal seam roof at point C in Fig.  4 a. When the upper and lower gamma values become similar, it suggests that the drilling has left the layer, as shown at point D in Fig.  4 a. To correct the inclined drilling control track deviation, the trajectory correction process is initiated when drilling to point C using azimuth gamma measurement, as demonstrated at point E in Fig.  4 a. Similarly, when the lower gamma value increases first and the upper gamma value increases later, it indicates that the drilling trajectory is exiting the coal seam floor at point C1 in Fig.  4 b. When the upper and lower gamma values become similar, the drilling has left the layer, as shown at point D1 in Fig.  4 b. To correct the incremental drilling control track deviation, the trajectory correction process is initiated when drilling to point C1, as illustrated at point E1 in Fig.  4 b.

In terms of technical applicability, conventional natural single gamma logging technology cannot accurately determine the bit's position once it leaves the coal seam, making it challenging to provide precise corrective measures. This issue is particularly problematic wherever the geological structure of the target coal seam is complex, micro faults are developed, and the coal seam is thin. To ensure the penetration ratio of the target coal seam and ensure the safety of underground construction, azimuth gamma logging while drilling technology can be utilized. This technology allows for the real-time monitoring of the current drilling horizon and provides effective guidance during construction. As a result, the drill bit can efficiently drill into the coal seam, maximizing the penetration ratio of the target coal seam.

Technical applicability analysis

In the second drilling operation, if the targeted coal seam is complex due to its thinness or the presence of micro-faults, it will be very challenging to accurately determine the position of the drilling bit after it exits the coal seam. Therefore, it will be necessary to use azimuth gamma logging while drilling. This technology enables the real-time monitoring of the drilling bit's current horizon, guiding the construction process and ensuring that the bit drills to the maximum extent possible within the coal seam.

Trajectory control technology and case study

Geological setting.

In this study, the short radius, well-Q in Block-W of the Qinshui Basin is taken as an example. Based on the most recent exploration wells drilled in Block-W of Qinshui Basin, the geological horizons have been revealed. The strata in the block, from bottom to top, consist of Paleozoic Ordovician, Carboniferous, Permian, Mesozoic Triassic, Jurassic, and Cenozoic Quaternary. The stratum near Well-Q has a general inclination from northeast to northwest, and Coal Seam no.15 is the development target stratum. The coal seam is located in the lower part of the Taiyuan Formation and has a simple structure. It is a thick coal seam that is stable and easy to drill throughout the area and generally contains 0–2 layers of dirt shale. The effective thickness of the coal seam ranges from 0 to 5.30 m, with an average of 3.39 m. It is thicker in the east and thinner in the west. However, there is one exploration well in the block that did not drill into Coal Seam no.15, possibly due to fault interference resulting in the loss of the coal seam. The coal seam deposit depth ranges from 728 to 2002 m, with an average of 1479 m. The depth is shallow in the southeast of the block and gradually deepens towards the northwest. Due to the influence of the stratum tendency (Stratum dip), the depth of the coal seam reaches over 1500 m in the west 14 . The roof lithology of the coal seam mostly consists of sandy mudstone, mudstone, siltstone, and fine sandstone, while the floor is mostly sandy mudstone, mudstone, and siltstone.

Wellbore structure

Designing an optimized wellbore structure can greatly improve drilling efficiency and safety by reducing annular pressure loss and back pressure (the drilling tool back pressure phenomenon), especially for long well sections. In the case of Well-Q, the wellbore structure was designed with a three-opening sections to ensure gas production of the coal seam during subsequent fracturing development. The first section seals the formation prone to collapse and leakage in the upper part of the primary casing, creating a safe drilling environment for the second well section. The second section seals sandstone, mudstone, and sandy mudstone intervals at the upper part of the coal seam, with the second well section casing obliquely drilled to a depth of no less than 3 m from the target coal seam no.15.

The third section extends along coal seam no.15 and runs casing to form a stable gas production channel to prevent coal seam collapse in the horizontal section due to the influence of multiple factors such as fracturing in the later stage. Prior to drilling the second well section of the main borehole, pilot hole drilling was carried out to obtain relevant geological parameter information of the target coal seam and the adjacent marker bed. Specific design parameters and requirements are as follows:

In the first well section, a ø 346.1 mm drill bit was used to drill into the stable bedrock for 30 m. J55 grade steel ø 273.1 mm surface casing was then lowered and cementing cement slurry returned to the surface.

In the second well section, a ø 241.3 mm drill bit was used to drill to the roof of the target no.15 coal seam and then the drilling was stopped. The landing point was determined based on the lithology of the roof of the coal seam and the actual drilling process. N80 grade steel ø 193.7 mm technical casing was run to 3–5 m above the roof of the coal seam. Through variable density cementing process, high-density cement slurry was used to return to 300 m above the roof of Coal Seam no.15, while low-density cement slurry returned to the surface.

The third well section was drilled with a ø 171.5 mm drill bit. After entering the target coal seam no.15, the drilling followed the coal seam. Upon reaching the designed well depth, P110 grade steel ø 139.7 mm production casing was run, and the well was completed without cementing.

The pilot hole was drilled with a ø215.9 mm bit, and the inclination angle stabilizing drilling crossed the floor of the target coal seam for tens of meters. Subsequently, the bit was backfilled with pure cement slurry to the side drilling depth of the second well section. The specific wellbore structure is shown in Fig.  5 .

figure 5

Well structure.

Case study: well-Q design optimization

Using Well-Q as a case study, the pilot hole trajectory design included the following: straight well section, kicking-off section, and stabilizing section. The stabilizing drilling passes through the floor of Coal Seam no.15 for approximately 30 m at an inclination angle of 70° to ensure accurate measurement of the gamma value, gas measurement value, and other characteristic parameters of the target coal seam bottom and floor using a simple gesturing instrument. The pilot hole is sealed by backfilling it with 42.5 grade Portland cement up to the well section with an inclination of about 25°, and the cement slurry has a specific gravity of 1.6–1.7 g/cm3. As the well deviation angle increases, the azimuth angle of directional and composite drilling becomes more stable, particularly when the well deviation angle exceeds 25°, resulting in a smaller azimuth drift 29 . This stability is beneficial for the subsequent inclined side-tracking in the main wellbore's second well section. The pilot hole and main borehole design trajectories are shown in Fig.  6 .

figure 6

Design trajectory of pilot hole and main hole.

Significant data has been obtained through the pilot hole design and the actual drilling of Well-Q. This dataset is pivotal for precise trajectory control in Coalbed Methane (CBM) exploration. The acquisition process relies on several methods, including real-time drilling natural gamma logging for gamma values of marker layers, and downhole gas logging for coal seam gas characteristics. The examination of cuttings recorded in real-time during drilling operations further aids in the identification and differentiation of these marker layers.

The critical information gleaned encompasses the identification of the K2 marker bed, the longitudinal stratification of the target no.15 coal seam, as well as the lithological composition, gamma values, and gas-bearing attributes of the upper and lower rock layers. These specific parameters are thoughtfully presented in Fig.  7 , establishing a robust foundation for the meticulous control of trajectory and the rational design of the landing point within the target coal seam. This dataset also serves as a valuable point of reference, ensuring the seamless execution of the horizontal drilling phase within the coal seam. Consequently, these findings play a pivotal role in enhancing drilling efficiency, ultimately culminating in the realization of efficient drilling objectives.

figure 7

Characteristic parameters and lithology map of the marker layer, target, top, bottom layer.

The effect of two-dimensional resonance method

The horizontal section's overall drilling azimuth in the target coal seam is 200°. To identify minor faults in the coal seam azimuth direction, measurement points are arranged every 10 m from the landing point A to the final target point B along the 200° azimuth direction. Additionally, one exploration point is set every 20 m across the azimuth line perpendicular to the landing point A and 200° azimuth direction. Furthermore, exploration points are arranged 300 m along both sides of the landing point. Figure  8 shows the specific layout of the exploration points, where Line (L1) represents the 711 m long horizontal well section of the target coal seam in the 200° azimuth direction. Meanwhile, Line (L2) represents the 600 m long vertical section between the landing point A and L1. The obtained data from these exploration points are crucial in detecting potential faults and ensuring smooth drilling of the horizontal section of the coal seam. ultimately leading to improved drilling ratios and more efficient drilling.

figure 8

Two-dimensional resonance exploration layout points.

Figure  9 shows the seismic frequency resonance inversion profile. The trajectory of the designed horizontal section coincides with the ground position of L1, with the no.4700 measuring point located at the ground projection position of the A target point, and the no.4000 measuring point located at the ground projection position of the B target point. Based on the interpretation of seismic frequency resonance line L1 profile, it is observed that the burial depth of the coal seam on the horizontal well section from target A to target B of the no.15 coal seam in the direction of 200° azimuth is shallow in the northeast and deep in the southwest. The overall trend of the burial depth of the coal seam indicates a shallow-to-deep trend. Furthermore, three small faults are expected to be encountered while drilling along this azimuth direction, located at no.4700, no.4280 and no.4096 measuring points, respectively, with a fault distance of approximately 5–10 m.

figure 9

Design of horizontal section trajectory resonance exploration inversion profile.

The contour map of fault points found in the horizontal section is displayed in Fig.  10 . This map serves as a useful tool in guiding the vertical depth control of the horizontal section track.

figure 10

Contour map of fault points in the horizontal section.

To ensure that the drilling trajectory is within the target coal seam and to prevent any reduction in drilling ratio caused by the faults, it is necessary to optimize the well trajectory prior to drilling. Each fault point must be considered as a target point and their relative coordinate positions are presented in Table 3 .

Resonance exploration data is utilized to adjust the trajectory parameters every 10 to 20 m during the actual drilling process. This is before exploring the coal seam behind the fault following reasonable adjustment of the parameters. This method is simple and minimizes the length of the non-coal section during the coal chasing process after drilling through the fault. Based on the coordinate position of each target point, the design of the directional trajectory for the third well section is optimized, as shown in Fig.  11 .

figure 11

optimized well trajectory for drilling reservoir section. ( a ) vertical section, ( b ) horizontal projection section.

The optimized design trajectory should be followed during actual drilling, ensuring that the dogleg degree ≤ 4°/30 m required by the management method for safe operations. Across the fault points F1, F2, and F3, the length of the non-coal section for coal tracking drilling was 56 m, 53 m, and 35 m, respectively. The total non-coal section for actual drilling was approximately 144 m, while achieving a drilling ratio of 80% for the target coal seam with an average thickness of 2.06 m. The entire drilling cycle takes approximately 45 days.

Azimuth gamma application

By analyzing the azimuth gamma data obtained during the drilling of the pilot hole and using the basic parameters of the pilot hole and formula ( 1 ), the apparent dip angle of the stratum near the designed landing point is determined to be α = 6.5°. The parameters of the landing point are shown in Fig.  12 , and the deviation angle of the actual main borehole trajectory of the second well section at the landing point β should be controlled at around 83.5° to ensure that the drilling ratio along the coal seam of the third well section is achieved and to reduce the frequency of directional trajectory adjustment.

figure 12

Parameters of the landing site.

During the drilling of the third horizontal section of Well-Q, a combination of Two-dimensional resonance exploration results and azimuth gamma logging while drilling technology was used to guide rapid coal tracking during the drilling of three faults. The process for each fault was as follows:

F1 Fault: The logging curve in Fig.  13 indicates that the F1 fault caused the drilling track of the 1920–1976 m well section to be drilled out from the coal seam roof. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of mudstone. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ) and fault contour (Fig.  10 ), the coal seam was traced by drilling with deviation correction through the lowering of well deviation. The actual drilling track during the pursuit of coal process is shown in Fig.  14 .

figure 13

Non-coal seam section azimuth gamma logging curve crossing fault F 1 .

figure 14

Actual drilling trajectory of fault F 1 in pursuit coal.

F2 Fault: The logging curve in Fig.  15 shows that the F2 fault caused the drilling trajectory of the 2130–2183 m well section to be drilled out from the coal seam roof. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of mudstone. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ), the back fault block of F2 fault in the direction of drilling trajectory of F2 fault shows a tendency of coal seam incline, so directly using lowering deviation correction drilling to trace the coal seam is not feasible and increases the length of the non-coal seam section. Therefore, the coal seam was pursued by increasing well deviation and rectifying drilling. The actual drilling track during the pursuit of coal process is shown in Fig.  16 .

figure 15

Non-coal seam section azimuth gamma logging curve crossing fault F 2 .

figure 16

Actual drilling trajectory of fault F 2 in pursuit coal.

F3 Fault: The logging curve in Fig.  17 shows that the F3 fault caused the drilling trajectory of the 2315–2350 m well section to be drilled out from the coal seam roof floor. Geological logging revealed that the rock debris returning out of the hole bottom contained a large amount of carbonaceous mudstone. Using formula ( 1 ), the coal point well inclination angle was calculated as 96°. Based on the Two-dimensional resonance exploration inversion (Fig.  9 ) and fault contour (Fig.  10 ), the coal seam was pursued by slowly lowering the well inclination and correcting the deviation. The actual drilling track during the pursuit of coal process is shown in Fig.  18 . The well inclination angle was 91° upon returning back to the coal seam, after which drilling along the coal seam was continued normally.

figure 17

Non-coal seam section azimuth gamma logging curve crossing fault F 3 .

figure 18

Actual drilling trajectory of fault F 3 in pursuit coal.

In conclusion, for the exploration block of CBM, the combined use of pilot hole drilling, two-dimensional resonance exploration technology, and azimuth gamma logging technology has proven effective in controlling the drilling of short-radius horizontal sections along the seam and ensuring the coal seam drilling ratio. Two major points can be drawn from this:

The two-dimensional resonance exploration technology detected the development of micro faults in the horizontal section of the drilling, enabling trajectory optimization before drilling. The azimuth gamma logging while drilling technology monitored the current drill bit drilling horizon in real-time, ensuring timely and accurate well trajectory adjustment.

The comprehensive use of these technologies has led to a 20% improvement in the coal seam drilling ratio and a 25% reduction in drilling cycle time in tested short-radius wells in the new exploration and development block-W in Qinshui Basin. This provides technical experience for low-cost exploration and development of CBM in new blocks.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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The financial support by the Found of the National Key Research and Development Program and Key Special Fund Project (No.2018YFC0808202) are gratefully acknowledged.

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X.L. conceived the study and, together with Z.J., Y.W., and H.M. did the literature search, selected the studies. X.L. and H.L. extracted the relevant information. X.L. synthesised the data. J.G. drawed pictures. X.L.and Z.J.wrote the first drafts of the paper.Y.W.and H.M.critically revised successive drafs of the paper. All authors approved the final drafts of the manuscript. X.L. is the guarantor of the study.

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Liu, X., Jiang, Z., Wang, Y. et al. Research on trajectory control technology for L-shaped horizontal exploration wells in coalbed methane. Sci Rep 14 , 11343 (2024).

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Received : 17 January 2024

Accepted : 24 April 2024

Published : 18 May 2024


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  • Coalbed methane (CBM)
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  • Trajectory control
  • Azimuth gamma logging while drilling (LWD)

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type of design for research


  1. What is Research Design in Qualitative Research

    type of design for research

  2. 25 Types of Research Designs (2024)

    type of design for research

  3. PPT

    type of design for research

  4. Types of Research Design

    type of design for research

  5. Research

    type of design for research

  6. Types of Research Design

    type of design for research


  1. A lowercase type design

  2. all type design & printing Rj Graphics Flex printing Andrsul

  3. What is type design, and what sets it apart from lettering?

  4. Research Design, Research Method: What's the Difference?

  5. p type design #typedesign #logodesigner #typography #illustrator #graphicdesign #typographydesign

  6. Types of Research Design


  1. What Is a Research Design

    A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.

  2. What Is Research Design? 8 Types + Examples

    Experimental Research Design. Experimental research design is used to determine if there is a causal relationship between two or more variables.With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions ...

  3. Research Design

    Step 2: Choose a type of research design. Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research. Types of quantitative research designs. Quantitative designs can be split into four main types.

  4. Types of Research Designs

    This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where ...

  5. The Four Types of Research Design

    In short, a good research design helps us to structure our research. Marketers use different types of research design when conducting research. There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let's take a look at each in more detail.

  6. Study designs: Part 1

    Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...

  7. Understanding Research Study Designs

    Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

  8. Research Design: What is Research Design, Types, Methods, and Examples

    There are various types of research design, each suited to different research questions and objectives: • Quantitative Research: Focuses on numerical data and statistical analysis to quantify relationships and patterns. Common methods include surveys, experiments, and observational studies. • Qualitative Research: Emphasizes understanding ...

  9. What is a Research Design? Definition, Types, Methods and Examples

    Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields: 1.

  10. Research Design

    This type of research design is used when it is not feasible or ethical to conduct a true experiment. Case Study Research Design. Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The ...

  11. What is Research Design? Types, Elements and Examples

    Research design elements include the following: Clear purpose: The research question or hypothesis must be clearly defined and focused. Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types.


    about the role and purpose of research design. We need to understand what research design is and what it is not. We need to know where design fits into the whole research process from framing a question to finally analysing and reporting data. This is the purpose of this chapter. Description and explanation Social researchers ask two ...

  13. Clinical research study designs: The essentials

    Introduction. In clinical research, our aim is to design a study, which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods that can be translated to the "real world" setting. 1 Before choosing a study design, one must establish aims and objectives of the study, and choose an appropriate target population that is most representative of ...

  14. What are the main types of research design?

    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.

  15. How to choose your study design

    While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a ...

  16. 5 Types of Research Design

    This type of research design tries to solve the problems in a structured form divided into three phases- the issue's inception, diagnosis of the issue, and solution for the issue. Explanatory design. In this research design, the researcher explores concepts and ideas on a subject to explore more theories. The main aim of the research is to ...

  17. Types of Research Design in 2024: Perspective and Methodological

    Yin (2014) has a succinct way of differentiating the two: design is logical, while method is logistical. In other words, the design is the plan, the method is how to realize that plan. There are important factors at play when creating a methodology in research. These include ethics, the validity of data, and reliability.

  18. Research Design: What It Is (Plus 20 Types)

    Here are 20 types of research design that you can consider using for your research project: 1. Exploratory research design. One common type of research design is exploratory design. The exploratory research design format is useful when you don't have a clearly defined problem to study. Often, this type of research design is less structured than ...

  19. Research Methodology

    Qualitative Research Methodology. This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

  20. Experimental Design

    Experimental Design. Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results. Experimental design typically includes ...

  21. Types of clinical trial designs: the essentials

    In this clinical trial design type, participants receive multiple treatments sequentially, serving as their own controls. According to Meri Beckwith, co-founder of Lindus Health, a clinical research organization, this enhances statistical power and reduces sample size needs but is only suitable for stable conditions and treatments with quick ...

  22. The expansion of research designs, methodologies, processes ...

    Practical Research is a do-it-yourself, how-to manual for planning and conducting research.. The 13th Edition includes the latest technology-based strategies and tools for research, a greater focus on the ethics of research, new examples, and expanded discussions of action research and participatory designs. Request a desk copy

  23. Google DeepMind and Isomorphic Labs introduce AlphaFold 3 AI model

    Google DeepMind's newly launched AlphaFold Server is the most accurate tool in the world for predicting how proteins interact with other molecules throughout the cell. It is a free platform that scientists around the world can use for non-commercial research. With just a few clicks, biologists can harness the power of AlphaFold 3 to model structures composed of proteins, DNA, RNA and a ...

  24. Types of studies and research design

    Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...

  25. PULS Vario's Dynamic Work Environment by Evolution Design

    Client: PULS Vario. Completion Date: 2024. Location: Vienna, Austria. Size: 2000 sq m (21,527 sq ft) Designed specifically to stimulate innovative thinking, the expanded offices of PULS Vario are a showcase of a bold use of colors in workplace design. The Vienna-based research and development company specializes in tailor-made power supply ...

  26. Staff Members

    208 Raleigh Street CB #3916 Chapel Hill, NC 27515-8890 919-962-1053

  27. Research on trajectory control technology for L-shaped horizontal

    Various methods have been used to improve the drilling ratio, by improving the trajectory control. These methods, shown in Table 2, include: geological guidance technology of adjacent well data ...

  28. Valorization of plant proteins for meat analogues design—a ...

    Several research studies have already performed such as peanut flour being used in replace of soya flour, potato protein, and corn zein are blended to develop meat mimics the gel-like structure, but no clear results have been obtained till now . The bottom-up design of proteins needed to develop meat analogues was imagined in Fig. 8.