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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.

Operationalisation

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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Research Design 101

Everything You Need To Get Started (With Examples)

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

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.

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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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 .

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.

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.

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.

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 .

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This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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

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?

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.

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

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

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

Struggling to find relevant and up-to-date topics for your dissertation? Here is all you need to know if unsure about how to choose dissertation topic.

Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. 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 aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

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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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Research Design: Definition, Types, Characteristics & Study Examples

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A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

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Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

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FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

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• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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

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

When designing the experiment, you decide:

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

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

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

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

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

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

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

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

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

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

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Understanding Research Study Designs

Table of Contents

In order to find the best possible evidence, it helps to understand the basic designs of research studies. The following basic definitions and examples of clinical research designs follow the “ levels of evidence.”

Case Series and Case Reports

Case control studies, cohort studies, randomized controlled studies, double-blind method, meta-analyses, systematic reviews.

These consist either of collections of reports on the treatment of individual patients with the same condition, or of reports on a single patient.

• Case series/reports are used to illustrate an aspect of a condition, the treatment or the adverse reaction to treatment.
• Example : You have a patient that has a condition that you are unfamiliar with. You would search for case reports that could help you decide on a direction of treatment or to assist on a diagnosis.
• Case series/reports have no control group (one to compare outcomes), so they have no statistical validity.
• The benefits of case series/reports are that they are easy to understand and can be written up in a very short period of time.

Patients who already have a certain condition are compared with people who do not.

• Case control studies are generally designed to estimate the odds (using an odds ratio) of developing the studied condition/disease. They can determine if there is an associational relationship between condition and risk factor
• Example: A study in which colon cancer patients are asked what kinds of food they have eaten in the past and the answers are compared with a selected control group.
• Case control studies are less reliable than either randomized controlled trials or cohort studies.
• A major drawback to case control studies is that one cannot directly obtain absolute risk (i.e. incidence) of a bad outcome.
• The advantages of case control studies are they can be done quickly and are very efficient for conditions/diseases with rare outcomes.

Also called longitudinal studies, involve a case-defined population who presently have a certain exposure and/or receive a particular treatment that are followed over time and compared with another group who are not affected by the exposure under investigation.

• Cohort studies may be either prospective (i.e., exposure factors are identified at the beginning of a study and a defined population is followed into the future), or historical/retrospective (i.e., past medical records for the defined population are used to identify exposure factors).
• Cohort studies are used to establish causation of a disease or to evaluate the outcome/impact of treatment, when randomized controlled clinical trials are not possible.
• Example: One of the more well-know examples of a cohort study is the Framingham Heart Study, which followed generations of residents of Framingham, Massachusetts.
• Cohort studies are not as reliable as randomized controlled studies, since the two groups may differ in ways other than the variable under study.
• Other problems with cohort studies are that they require a large sample size, are inefficient for rare outcomes, and can take long periods of time. 

This is a study in which 1) There are two groups, one treatment group and one control group. The treatment group receives the treatment under investigation, and the control group receives either no treatment (placebo) or standard treatment. 2) Patients are randomly assigned to all groups. 

• Randomized controlled trials are considered the “gold standard” in medical research. They lend themselves best to answering questions about the effectiveness of different therapies or interventions.
• Randomization helps avoid the bias in choice of patients-to-treatment that a physician might be subject to. It also increases the probability that differences between the groups can be attributed to the treatment(s) under study.
• Having a  control group allows for a comparison of treatments – e.g., treatment A produced favorable results 56% of the time versus treatment B in which only 25% of patients had favorable results.
• There are certain types of questions on which randomized controlled studies cannot be done for ethical reasons, for instance, if patients were asked to undertake harmful experiences (like smoking) or denied any treatment beyond a placebo when there are known effective treatments.

A type of randomized controlled clinical trial/study in which neither medical staff/physician nor the patient knows which of several possible treatments/therapies the patient is receiving.

• Example : Studies of treatments that consist essentially of taking pills are very easy to do double blind – the patient takes one of two pills of identical size, shape, and color, and neither the patient nor the physician needs to know which is which.
• A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects, which reduces the risk of bias, it can eliminate or minimize the placebo effect which is a further challenge to the validity of a study.

Meta-analysis is a systematic, objective way to combine data from many studies, usually from randomized controlled clinical trials, and arrive at a pooled estimate of treatment effectiveness and statistical significance.

• Meta-analysis can also combine data from case/control and cohort studies. The advantage to merging these data is that it increases sample size and allows for analyses that would not otherwise be possible.
• They should not be confused with reviews of the literature or systematic reviews. 
• Two problems with meta-analysis are publication bias (studies showing no effect or little effect are often not published and just “filed” away) and the quality of the design of the studies from which data is pulled. This can lead to misleading results when all the data on the subject from “published” literature are summarized.

A systematic review is a comprehensive survey of a topic that takes great care to find all relevant studies of the highest level of evidence, published and unpublished, assess each study, synthesize the findings from individual studies in an unbiased, explicit and reproducible way and present a balanced and impartial summary of the findings with due consideration of any flaws in the evidence. In this way it can be used for the evaluation of either existing or new technologies and practices.   

A systematic review is more rigorous than a traditional literature review and attempts to reduce the influence of bias. In order to do this, a systematic review follows a formal process:

• Clearly formulated research question
• Published & unpublished (conferences, company reports, “file drawer reports”, etc.) literature is carefully searched for relevant research
• Identified research is assessed according to an explicit methodology
• Results of the critical assessment of the individual studies are combined
• Final results are placed in context, addressing such issues are quality of the included studies, impact of bias and the applicability of the findings
• The difference between a systematic review and a meta-analysis is that a systematic review looks at the whole picture (qualitative view), while a meta-analysis looks for the specific statistical picture (quantitative view). 

R esearch Process in the Health Sciences  (35:37 min): Overview of the scientific research process in the health sciences. Follows the seven steps: defining the problem, reviewing the literature, formulating a hypothesis, choosing a research design, collecting data, analyzing the data and interpretation and report writing. Includes a set of additional readings and library resources.

Research Study Designs in the Health Sciences  (29:36 min): An overview of research study designs used by health sciences researchers. Covers case reports/case series, case control studies, cohort studies, correlational studies, cross-sectional studies, experimental studies (including randomized control trials), systematic reviews and meta-analysis.  Additional readings and library resources are also provided.

Research Design | Importance, Types of Research Design Examples

Are you wondering about the concept of research design?

Do you need examples of research design or guidance on its elements and selecting the most suitable type for your study?

You’re in the right place!

This article will provide the information you’re looking for.

• Table of Contents

Research Design

Research design encompasses the overall plan or strategy that a researcher adopts to answer specific research questions or test hypotheses.

It includes the framework of methods and techniques chosen to collect, analyze, and interpret data.

Types of Research Design

Understanding the different types of research design is crucial for researchers as it enables them to develop an effective research methodology that aligns with their research objectives and facilitates timely completion of their studies.

While there are various research design types, the two most commonly utilized by researchers are quantitative and qualitative research methods.

1. Quantitative Research

Quantitative research is characterized by its objectivity and utilization of statistical approaches. It aims to establish cause-and-effect relationships among variables by employing various statistical and computational methods. Surveys, experiments, and observations are commonly used techniques in quantitative research, yielding numerical data that can be analyzed and expressed in numerical form.

Types of quantitative research designs and examples of quantitative research designs

Correlational research design.

Correlational research examines the strength and direction of relationships between variables. This design helps researchers establish connections between two variables without the researcher manipulating or controlling either variable.

For instance, a correlational study might investigate the relationship between the amount of time teenagers spend watching crime shows and their tendencies towards aggressive behavior.

Descriptive Research Design:

This quantitative research design is employed to identify characteristics, frequencies, trends, and categories within a study. It often does not start with a hypothesis and is focused on describing an identified variable.

Descriptive research aims to answer questions about “what,” “when,” “where,” or “how” phenomena occur, without going into the reasons or causes behind them.

For example, a study might examine the income levels of individuals who regularly use nutritional supplements.

This type of research aims to outline the features of a population or issues within the study area. It focuses primarily on answering the “what” of the research problem rather than going into the “why.” Researchers in descriptive or statistical research report facts precisely without attempting to influence variables.

Explanatory Research Design

In explanatory research design, a researcher delves deeper into their theories and ideas on a topic to gain a more thorough understanding. This design is employed when there is limited information available about a phenomenon, aiming to increase understanding of unexplored aspects of a subject. It serves as a foundation for future research.

Exploratory research is undertaken when a researcher encounters a research problem without past data or with limited existing studies. This type of research is often informal and lacks structure, serving as an initial exploration tool that generates hypothetical or theoretical ideas regarding the research problem.

It does not aim to provide definitive solutions but rather lays the groundwork for future research. Exploratory research is flexible and involves investigating various sources such as published secondary data, data from other surveys, and so on.

For instance, a researcher might develop hypotheses to guide future studies on how delaying school start times could positively impact the mental health of teenagers.

Causal Research Design

Causal research design, a subset of explanatory research, seeks to establish cause-and-effect relationships within its data. Unlike experimental research, causal research does not involve manipulating independent variables but rather observes naturally occurring or pre-existing groupings to define cause and effect.

For example, researchers might compare school dropout rates with instances of bullying to investigate potential causal relationships.

Diagnostic Research Design

In diagnostic design, researchers seek to understand the underlying causes of a specific issue or phenomenon, typically aiming to find effective solutions. This type of research involves diagnosing problems and identifying solutions based on thorough analysis. For example, a researcher might analyze customer feedback and reviews to pinpoint areas for improvement in an app.

Experimental Research Design

Experimental research design is utilized to study causal relationships by manipulating one or more independent variables and measuring their impact on one or more dependent variables. For instance, a study might assess the effectiveness of a new influenza vaccine plan by manipulating variables such as dosage or administration method and measuring their effects on vaccination outcomes.

2. Qualitative Research

In contrast, qualitative research takes a subjective and exploratory approach. It focuses on understanding the relationships between collected data and observations. Qualitative research is often conducted through interviews with open-ended questions, allowing participants to express their perspectives in words rather than numerical data.

Types of qualitative research designs and examples of qualitative research designs

• Grounded Theory

Grounded theory is a research design utilized to explore research questions that haven’t been extensively studied before. Also known as an exploratory design, it establishes sequential guidelines, provides inquiry strategies, and enhances the efficiency of data collection and analysis in qualitative research.

For instance, imagine a researcher studying how people adopt a particular app. They gather data through interviews and then analyze it to identify recurring patterns. These patterns are then used to formulate a theory regarding the adoption process of that app.

Thematic Analysis

Thematic analysis, another research design, involves comparing data collected from previous research to uncover common themes in qualitative research. For example, a researcher might analyze an interview transcript to identify recurring themes or topics.

Discourse Analysis

Discourse analysis is a research design focusing on language or social contexts within qualitative data collection. For instance, it might involve identifying the ideological frameworks and viewpoints expressed by authors in a series of policies.

3. Analytical Research

Analytical research uses established facts as a foundation for further investigation. Researchers seek supporting data that strengthens and validates their previous findings while also contributing to the development of new concepts related to the research topic.

Thus, analytical research combines minute details to generate more acceptable hypotheses. The analytical investigation clarifies the validity of a claim.

4. Applied Researc h

Applied research is aimed at addressing current issues faced by society or industrial organizations. It is characterized by non-systematic inquiry, typically conducted by businesses, government bodies, or individuals to solve specific problems or challenges.

5. Fundamental Research

Fundamental research is concerned with formulating theories and generalizations, making it the primary focus of this research type. It aims to discover new facts with broad applications, enhancing existing knowledge in specific fields or industries, and supplementing known ideas and theories.

6. Conclusive Research

Conclusive research, on the other hand, is designed to yield information crucial for reaching conclusions or making decisions, as implied by its name. It typically takes a quantitative approach and requires clearly defined research objectives and data requirements. The findings from conclusive research are specific and have practical applications.

Research Design Elements

Research design elements encompass several crucial components:

• Clear Research Question : Defining a clear research question or hypothesis is essential for clarity and direction.
• Research Methodology Type : Choosing the overall approach for the study is a fundamental aspect of research design.
• Sampling Strategy : Decisions regarding sample size, sampling methods, and criteria for inclusion or exclusion are important. Different research designs require different sampling approaches.
• Study Time Frame : Determining the study’s duration, timelines for data collection and analysis, and follow-up periods are critical considerations.
• Data Collection Methods : This involves gathering data from study participants or sources, including decisions on what data to collect, how to collect it, and the tools or instruments to use.
• Data Analysis Techniques : All research designs necessitate data analysis and interpretation. Decisions about statistical tests or methods, addressing confounding variables or biases, are key in this element.
• Resource : Planning for budget, staffing, and necessary resources is essential for effective study execution.
• Ethical Considerations: Research design must address ethical concerns such as informed consent, confidentiality, and participant protection.

Importance of research design  

A good research design includes these key points:

• Guides decision-making at every study step.
• Identifies major and minor study tasks.
• Enhances research effectiveness and interest with detailed steps.
• Frames research objectives based on experiment design.
• Helps achieve study goals within set time and solve research issues efficiently.
• Improves task completion even with limited resources.
• Ensures research accuracy, reliability, consistency, and legitimacy.

Characteristics of research design  

A well-planned research design is essential for carrying out a scientifically thorough study that produces reliable, neutral, valid, and generalizable results. At the same time, it should provide a certain level of flexibility.

Generalizability

The outcomes of a research design should be applicable to a broader population beyond the sample studied. A generalized approach allows for the study’s findings to be applied accurately to different segments of the population.

• Reliability

Research design should prioritize consistency in measurement across repeated measures and minimize random errors. A reliable research design produces consistent results with minimal chance-related errors.

Maintaining a neutral stance throughout the research process, from assumptions to study setup, is crucial. Researchers must avoid preconceived notions or biases that could influence findings or their interpretation. A good research design addresses potential sources of bias and ensures unbiased and neutral results.

Validity focuses on minimizing systematic errors or nonrandom errors in research. A reliable research design uses measurement tools that enhance the validity of results, ensuring accuracy and relevance.

Flexibility

Research design should allow for adaptability and adjustments based on collected data and study outcomes. Flexibility enables researchers to refine their approach and enhance the study’s effectiveness as it progresses.

How to Develop a research design?

The following provides guidance on developing a research design:

Step 1: Identify the Problem Statement

Choose a novel topic within your research field and clearly define the problem statement.

Step 2: Identify the Research Gap

Collect existing data and conduct an extensive literature review to identify gaps in current research.

This step provides insight into research methods, data collection, analysis techniques, and tools needed for your study.

Step 3: Develop the Research Hypothesis and Objectives

The next step is to formulate a strong research hypothesis, which plays a crucial role in guiding the remainder of your research process. Crafting a research hypothesis involves various strategies, such as evaluating data and conducting analysis.

If you’re struggling for ideas, consider listing potential objectives and then narrowing them down to focus on the most essential or critical ones.

Your research objectives can then be developed based on your hypothesis.

Step 4: Design the Research Methodology

When developing your research methodology, take into account several factors such as the type of study, sample location, sampling techniques, sample size, experimental setup, experimental procedures, software, and tools to be utilized.

By carefully considering these elements, you can craft a good research methodology that effectively addresses your research objectives and ensures the completion of your research work.

Step 5: Data analysis and results dissemination

It’s time to initiate the data analysis process, which can involve various techniques such as descriptive statistics, t-tests, and regression analysis. The initial step in this analysis phase is to determine the most appropriate method for your specific data.

Descriptive statistics are beneficial for summarizing data, while t-tests are effective for comparing means between two groups, and regression analysis aids in exploring relationships between variables.

Once the suitable analysis method is identified, you can proceed with analyzing the data. Subsequently, ensure to present your findings clearly and provide appropriate interpretations. Finally, document your findings in a research paper or thesis, accompanied by relevant discussions, and ensure that they align with your research objectives.

Benefits of Research Design

• A strong research design increases research efficiency by enabling researchers to choose appropriate designs, conduct statistical analyses effectively, and save time by outlining necessary data and data collection methods.
• Research design provides clear direction by guiding the choice of objectives, helping researchers focus on specific research questions or hypotheses.
• Proper research design allows researchers to control variables, identify confounding factors, and use randomization to minimize bias, enhancing the reliability of findings.
• Research designs enable replication, confirming study findings and ensuring results are not due to chance or external factors, thus reducing bias and errors.
• Research design reduces inaccuracies and ensures research reliability, maintaining consistent results over time, across different samples, and under varying conditions.
• Research design ensures the validity of research, ensuring results accurately reflect the phenomenon under investigation.

A well-chosen and executed research design facilitates high-quality research, meaningful conclusions, and contributes to knowledge advancement in the respective field.

A carefully planned research design improves the originality, reliability, and validity of your research results. It guides the researcher in the correct path without straying from the objectives. It’s crucial to note that a weak research design can lead to significant setbacks in terms of time, resources, and finances for the entire research project.

Other articles

Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.

Citation Styles

• APA Reference Page
• MLA Citations
• Chicago Style Format
• “et al.” in APA, MLA, and Chicago Style
• Do All References in a Reference List Need to Be Cited in Text?

Comparision

• Basic and Applied Research
• Cross-Sectional vs Longitudinal Studies
• Survey vs Questionnaire
• Open Ended vs Closed Ended Questions
• Experimental and Non-Experimental Research
• Inductive vs Deductive Approach
• Null and Alternative Hypothesis
• Reliability vs Validity
• Population vs Sample
• Conceptual Framework and Theoretical Framework
• Bibliography and Reference
• Stratified vs Cluster Sampling
• Sampling Error vs Sampling Bias
• Internal Validity vs External Validity
• Full-Scale, Laboratory-Scale and Pilot-Scale Studies
• Plagiarism and Paraphrasing
• Research Methodology Vs. Research Method
• Mediator and Moderator
• Type I vs Type II error
• Descriptive and Inferential Statistics
• Microsoft Excel and SPSS
• Parametric and Non-Parametric Test
• Independent vs. Dependent Variable – MIM Learnovate
• Research Article and Research Paper
• Proposition and Hypothesis
• Principal Component Analysis and Partial Least Squares
• Academic Research vs Industry Research
• Clinical Research vs Lab Research
• Research Lab and Hospital Lab
• Thesis Statement and Research Question
• Quantitative Researchers vs. Quantitative Traders
• Premise, Hypothesis and Supposition
• Survey Vs Experiment
• Hypothesis and Theory
• Independent vs. Dependent Variable
• APA vs. MLA
• Ghost Authorship vs. Gift Authorship
• Research Methods
• Quantitative Research
• Qualitative Research
• Case Study Research
• Survey Research
• Conclusive Research
• Descriptive Research
• Cross-Sectional Research
• Theoretical Framework
• Conceptual Framework
• Triangulation
• Quasi-Experimental Design
• Mixed Method
• Correlational Research
• Randomized Controlled Trial
• Stratified Sampling
• Ethnography
• Ghost Authorship
• Secondary Data Collection
• Primary Data Collection
• Ex-Post-Facto
•   Dissertation Topic
• Thesis Statement
• Research Proposal
• Research Questions
• Research Problem
• Research Gap
• Types of Research Gaps
• Operationalization of Variables
• Literature Review
• Research Hypothesis
• Questionnaire
• Measurement of Scale
• Sampling Techniques
• Acknowledgements
• PLS-SEM model
• Principal Components Analysis
• Multivariate Analysis
• Friedman Test
• Chi-Square Test (Χ²)
• Effect Size
• Critical Values in Statistics
• Statistical Analysis
• Calculate the Sample Size for Randomized Controlled Trials
• Covariate in Statistics
• Avoid Common Mistakes in Statistics
• Standard Deviation
• Derivatives & Formulas
• Build a PLS-SEM model using AMOS
• Principal Components Analysis using SPSS
• Statistical Tools
• One-tailed and Two-tailed Test

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• Study Protocol
• Open access
• Published: 17 April 2024

Study protocol: exercise training for treating major depressive disorder in multiple sclerosis

• Robert W. Motl 1 ,
• Charles H. Bombardier 2 ,
• Jennifer Duffecy 3 ,
• Brooks Hibner 1 ,
• Alison Wathen 1 ,
• Michael Carrithers 4 &
• Gary Cutter 5  

BMC Neurology volume  24 , Article number:  131 ( 2024 ) Cite this article

Metrics details

Major depressive disorder (MDD) is prevalent, yet sub-optimally treated among persons with multiple sclerosis (MS). We propose that exercise training may be a promising approach for treating depression in persons with MS who have MDD. Our primary hypothesis predicts a reduction in depression severity immediately after an exercise training intervention compared with minimal change in an attention control condition, and the reduction will be maintained during a follow-up period.

This study involves a parallel-group, assessor-blinded RCT that examines the effect of a 4-month home-based exercise training intervention on depression severity in a sample of persons with MS who have MDD based on the MINI International Neuropsychiatric Interview. The primary outcomes of depression severity are the Patient Health Questionnaire-9 and Hamilton Depression Rating Scale. Participants ( N  = 146) will be recruited from within 200 miles of the University of Illinois at Chicago and randomized (1:1) into either a home-based exercise training condition or control condition with concealed allocation. The exercise training and social-contact, attention control (i.e., stretching) conditions will be delivered remotely over a 4-month period and supported through eight, 1:1 Zoom-based behavioral coaching sessions guided by social-cognitive theory and conducted by persons who are uninvolved in screening, recruitment, random assignment, and outcome assessment. We will collect outcome data at 0, 4 and 8 months using treatment-blinded assessors, and data analyses will involve intent-to-treat principles.

If successful, the proposed study will provide the first Class I evidence supporting a home-based exercise training program for treating MDD in persons with MS. This is critical as exercise training would likely have positive secondary effects on symptoms, cognition, and quality of life, and provide a powerful, behavioral approach for managing the many negative outcomes of MDD in MS. The program in the proposed research is accessible and scalable for broad treatment of depression in MS, and provides the potential for integration in the clinical management of MS.

Trial registration

The trial was registered on September 10, 2021 at clinicaltrials.gov with the identifier NCT05051618. The registration occurred before we initiated recruitment on June 2, 2023

Peer Review reports

Introduction

Multiple sclerosis (MS) is an immune-mediated, neurodegenerative disease of the central nervous system (CNS). There are an estimated one million adults living with MS in the United States [ 1 ]. This disease is characterized by demyelination and transection of axons and loss of neurons in the CNS [ 2 ]. The extent and location of CNS damage results in consequences including motor and cognitive dysfunction, fatigue, and major depressive disorder (MDD) [ 3 ].

MDD is characterized by persistently depressed mood or loss of interest in usual activities plus the presence of at least 5 of 9 symptoms that cause significant impairment in daily life [ 4 ]. The prevalence of MDD in persons with MS is nearly 1.7 times higher than the general population [ 5 ]. One recent systematic review reported the prevalence of MDD among persons with MS as 23.7% [ 6 ] and this translates into an estimated 250,000 people living with MS and MDD in the United States.

MDD has widespread, negative effects on the lives of people with MS [ 3 ]. The presence of MDD is associated with worsening of other symptoms such as fatigue, poorer neuropsychological functioning, and lower health-related quality of life (HRQOL) [ 3 ].

The prevalence and burden of MDD in MS underscore the critical importance of efficacious antidepressant treatments, yet such treatments are sorely lacking in MS. For example, the American Academy of Neurology concluded that there is insufficient evidence from randomized controlled trials (RCTs) for recommending antidepressants for treating MDD in MS [ 7 ]. One meta-analysis [ 8 ] of RCTs concluded that “CBT can be an effective intervention for reducing moderate depression, over the short term in patients with MS.” Yet, nearly 50% of participants do not benefit from CBT [ 9 ].

Exercise training is a promising therapy for improving depressive symptomology and managing MDD in MS [ 10 ]. Exercise training has yielded a moderate-to-large antidepressant effect in persons from the general population who have MDD [ 11 , 12 , 13 , 14 ]. Exercise training further has improved depressive symptomology in MS [ 15 , 16 , 17 ], and those meta-analyses offer critical insights for informing the exercise training parameters for treating MDD. The first meta-analysis indicated that both aerobic and resistance exercise training can yield a reduction in depressive symptoms for people with MS [ 15 ]. The second meta-analysis quantified the effect of exercise on depression in adults with neurologic disorders, including MS [ 16 ], and noted that interventions meeting physical activity guidelines yielded a reduction in depression that was two-times larger than interventions that did not meet physical activity guidelines. The third meta-analysis examined variables that moderate the effects of exercise on depressive symptoms among people with MS [ 17 ], and there was a dose–response effect for frequency (days/week) of exercise on reductions in depressive symptoms with the largest effect occurring for three days/week of exercise training.

The aforementioned meta-analyses identified four major limitations of previous research on exercise training for treating depression in MS [ 15 , 16 , 17 ]. The most pressing limitation is that the samples of persons with MS were not pre-screened for MDD [ 15 , 16 , 17 ]. Another limitation is that the exercise training programs were administered in supervised, center-based settings that present barriers associated with accessibility (e.g., distance, transportation, and costs) that likely influence adoption and maintenance of exercise behavior. An additional limitation is the lack of standardization of the exercise training prescription included in RCTs. The final limitation is the lack of follow-up regarding the durability of changes in depressive symptoms following exercise training.

We designed a RCT that is based on sound scientific rationale established through critical review and analysis of the relevant literature [ 10 , 15 , 16 , 17 ], and further capitalizes on our experiences with home-based delivery of exercise training programs in MS [ 18 , 19 , 20 , 21 , 22 ]. To that end, we propose a parallel group, RCT for examining the efficacy of a home-based, exercise training program informed by prescriptive guidelines [ 23 , 24 ] and guided by social cognitive theory (SCT)-based remote behavior coaching compared with a social-contact, attention control condition (i.e., stretching) for yielding immediate and sustained reductions in the severity of depressive symptoms among persons with MS who have MDD.

Methods/design

There is only one protocol version and it will follow the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines.

Aims, design, and setting of the study

The primary aim examines the efficacy of a 4-month, home-based aerobic and resistance exercise training intervention compared with a 4-month, home-based stretching and flexibility intervention (i.e., social contact, attention control condition) for immediate and sustained (i.e., 4-months post-intervention) reductions in depression severity among persons with MS who have impaired MDD.

The secondary aim examines the efficacy of the exercise training intervention compared with the control condition for immediate and sustained improvements in fatigue, cognition, and HRQOL among persons with MS who have impaired MDD.

The tertiary aim involves a manipulation check and examines the efficacy of the exercise training intervention compared with control condition for immediate and sustained improvements in exercise behavior, physical activity, aerobic fitness, and muscle strength for persons with MS who have impaired MDD.

The study aims will be tested using a parallel-group, assessor-blinded RCT design. This study does not include a data safety monitoring board, but there is a data safety monitoring plan and safety monitor for oversight.

Participants

Recruitment.

We will recruit participants residing within 200 miles of the University of Illinois Chicago (UIC) campus located in Chicago, IL USA through distribution of study materials (flyers, business and post cards, and advertisements) among the Greater Illinois and other regional Chapters of the National MS Society; North American Research Committee on Multiple Sclerosis and iCONQUER MS Registries; waiting rooms of 10 + local MS Centers and Neurology offices; local community centers, churches, libraries, and physical therapy clinics; community events and MS support group meetings; focal study website ( https://metsforms.ahs.uic.edu ); UIC and professional listservs; and social media.

Inclusion/exclusion

We will assess inclusion/exclusion during a scripted phone screening by the project coordinator, and this will involve a two-stage process with the inclusion and exclusion criteria listed in Table  1 .

We have experienced low attrition (10%) in our previous RCTs of the exercise training protocol in this proposal [ 19 ], and we note recent data from a meta-analysis suggesting an attrition rate of ~ 10% across 40 RCTs of exercise training in MS [ 33 ]. We believe attrition could be higher in this RCT based on depressive symptomology in MDD resulting in poor motivation and adherence for exercise engagement. We conservatively planned for a higher attrition rate of 20% for the proposed RCT, and recognize that retention might be a challenge, although we are including SCT-based content and 1:1 zoom-based behavioral coaching for maximizing retention and adherence [ 34 ] as is appropriate for persons with MS who have elevated depressive symptoms [ 35 ].

Power analysis and sample size

The power analysis was conducted in G*Power, Version 3.1 using F test for Test family and ANOVA: Repeated measures, within-between interaction for Statistical test. We estimated the sample necessary for detecting a Condition (2 levels of between-subjects factor: Intervention vs. Control) × Time (2 levels of within-subjects factor: 0 and 4 months) interaction on the primary outcomes of depression severity (i.e., 9-item Patient Health Questionnaire (PHQ-9; [ 36 ]) and Hamilton Depression Rating Scale (HDRS-17; [ 37 ]). We did not include 3 time-points as this assumes linear change across all 3 time points in G*Power 3.1, and we expected change between 0 and 4 months for the intervention condition, followed by stability between 4 and 8 months. The effect size (Cohen’s f  = 0.18) was from our previous meta-analyses [ 15 ] regarding the effect of exercise training on depressive symptoms in persons with MS. The power analysis included assumptions of reliability for the within-subjects factor of ICC = 0.50, two-tailed α  = 0.025, and β  = 0.05 (i.e., 95% power); the α  = 0.025 was selected based on two primary outcomes. The power analysis indicated the minimal total sample size for testing the Time × Condition interaction of 122 participants (61 per group), and we anticipate a dropout rate of ~ 20% resulting in a projected recruitment of 146 participants.

The primary outcomes are the PHQ-9 [ 36 ] and HDRS-17 [ 37 ] as measures of depression symptom severity appropriate for MDD, whereas the secondary outcomes include measures of fatigue, cognitive performance, and HRQOL. The tertiary outcomes are exercise behavior, accelerometry as a device-based measure of free-living PA, and aerobic and muscle fitness. All outcomes will be assessed at baseline (0 months), immediate follow-up (4 months), and long-term follow-up (8 months) by treatment-blinded assessors. The assessors will not be involved in random assignment or delivery of the conditions, and will not directly communicate with the behavior coaches about participants. Participants themselves will be instructed not to discuss exercise routines with assessors, and why it might bias the evaluators.

Primary outcome measures

We include two outcomes for depression severity, as one is self-reported (primary) and the other is a semi-structured, interviewer-rated measure (secondary). The logic is that the self-report change in depression severity should be confirmed with the semi-structured, interviewer-rated change, as change in the former is more likely, but could represent a self-report bias associated with participants not being blinded regarding treatment condition.

Self-reported depression severity

The PHQ-9 is a brief, patient-reported depression severity measure [ 36 ]. The PHQ-9 is unidimensional [ 38 ], has good test–retest reliability [ 39 ], and has validated thresholds of mild, moderate, moderately severe and severe depression [ 36 ]. The PHQ-9 accurately discriminates differential treatment response among groups independently judged to have persistent MDD, partial remission, and full remission [ 39 ]. The PHQ-9 has a valid threshold for determining depression remission (less than 5) [ 39 ], and an established threshold for minimal clinically significant difference for individual change (5 points on 0–27 point scale) [ 39 ].

Interviewer-rated depression severity

The 6-item Maier subscale [ 40 ] of the HDRS-17 [ 37 ] is a semi-structured, interviewer-rated measure that is administered by treatment-blinded assessors. The Maier subscale was developed using Rasch analyses and provides a unidimensional subscale that has equivalent or greater sensitivity to treatment effects compared with the HDRS-17 [ 41 , 42 ]. The Maier subscale has been recommended specifically for depression treatment trials in patients with medical comorbidities because the measure includes no somatic items [ 42 ]. The Maier has a valid cutoff for remission (4 or less) [ 42 ].

Secondary outcome measures

We will include the secondary end-points of fatigue [ 43 ], cognitive performance [ 44 ], and HRQOL [ 45 ], as changes in depression are often accompanied by changes in fatigue, neuropsychological function, and HRQOL [ 3 ]. These outcomes will anchor depression changes with other clinical end-points of substantial relevance for persons with MS who have MDD.

The perception of fatigue severity will be measured using the Fatigue Severity Scale (FSS) [ 43 ]. The FSS has 9 items rated on a 7-point scale regarding the severity of fatigue symptoms during the past 7 days. The item scores are averaged into a measure of fatigue severity that ranges between 1 and 7. FSS scores of 4 or above are indicative of severe MS-related fatigue [ 43 ], and the MDC for the FSS is 1.9 points [ 46 ]. There is evidence for the internal consistency, test–retest reliability, and validity of FSS scores as a measure of fatigue severity in MS [ 43 ].

Cognitive performance

Cognitive performance is a secondary outcome that will be assessed using the Brief International Cognitive Assessment for MS (BICAMS) [ 44 ]. The BICAMS battery includes the Symbol Digit Modalities Test (SDMT), first five learning trials of the California Verbal Learning Test-II (CVLT-II), and first three learning trials of the Brief Visuospatial Memory Test-Revised (BVMT-R) for measuring information processing speed, verbal learning and memory, and visuospatial learning and memory, respectively [ 44 , 47 ]. The SDMT involves pairing 9 abstract geometric symbols with single digit numbers in a key, and orally stating the correct numbers for unpaired symbols as rapidly as possible for 90 s. The primary outcome of the SDMT is the number of correct responses provided in 90 s (i.e., raw score). The CVLT-II involves an examiner reading aloud a list of 16 words (four items belonging to four categories such as vegetables, animals, furniture, modes of transportation) that are randomly arranged; this is done five times in the same order at a rate of approximately one word per second. Participants recall as many items as possible, in any order, following each reading of list. The primary outcome of the CVLT-II is the total number of correct words identified over the five trials (i.e., raw score). The BVMT-R involves three trials of the examiner presenting a 2 × 3 array of abstract geometric figures approximately 15 inches in front of the participant for 10 s. The array is then removed and participants draw the array as precisely as possible with the figures in the correct location. Each drawing is scored based on accurately portraying each figure and its correct location using a 0–2 scale. The primary outcome of the BVMT-R is the total raw score across the three trials. There are benchmark scores for the cognitive tests included in the BICAMS that are associated with specific degrees of impairment in work status [ 48 ].

The 29-item Multiple Sclerosis Impact Scale (MSIS-29) [ 45 ] provides a disease-specific measure of physical (20 items) and psychological (nine items) HRQOL. The scores range between 0 and 100 with lower MSIS-29 scores representing higher HRQOL. There is evidence for the reliability and validity of the MSIS-29 in samples of persons with MS [ 45 , 49 ].

Tertiary outcome measures

We will measure change in exercise behavior using the Godin Leisure-Time Exercise Questionnaire (GLTEQ) [ 50 ] and minutes/day of moderate-to-vigorous physical activity (MVPA) from accelerometry as a measure of free-living physical activity. We will measure aerobic capacity and muscle strength using accepted measures and protocols in MS [ 51 ]; this permits an additional check on the manipulation of performing the GEMS exercise-training protocol.

Self-reported exercise behavior

The GLTEQ measures the frequency of strenuous, moderate, and mild physical activity performed for periods of 15 min or more over a 7-day period [ 50 , 52 ], and it will be scored as the Health Contribution Score (HCS) [ 28 ]. The HCS only includes strenuous and moderate physical activity. The HCS is computed by multiplying the frequencies of strenuous and moderate activities by 9 and 5 METs, respectively, and then summing the weighted scores. The HCS can be converted into one of three categories, namely, insufficiently active (i.e., score < 14 units), moderately active (i.e., score between 14 and 23 units), and active (i.e., score ≥ 24 units).

Device-measured free-living physical activity

The ActiGraph model GT3X + accelerometer (Actigraph Corporation, FL) worn during a seven-day period will provide a measure of free-living physical activity as minutes/day of MVPA. The ActiGraph accelerometer will be placed on an elastic belt that is worn snuggly around the waist over the non-dominant hip during the waking hours of a seven-day period. The data from the ActiGraph accelerometer will be downloaded and processed using the low frequency extension (i.e., filter for increasing the devices sensitivity) into one-minute epochs using ActiLife software (Actigraph Corporation, FL), and then scored for wear time and minutes/day of MVPA using MS-specific cut-points [ 53 ]. Only data from valid days (wear time ≥ 600 min) will be included in the analyses [ 53 ] and this will be confirmed with the compliance log. We will average data over two or more valid days for the outcome of minutes/day of MVPA, as this provides a reliable estimate of free-living physical activity behavior over a seven-day period [ 53 ]. Other measures such as steps/day and minutes/day spent in light physical activity and sedentary behavior [ 54 ] can be generated as additional end-points for understanding change in free-living physical activity.

Aerobic capacity

Cardiorespiratory fitness will be operationalized as peak oxygen consumption (VO 2peak ) and peak power output (watts or W) derived from a maximal, incremental exercise test on an electronically-braked, computer-driven cycle ergometer (Lode BV, Groningen, The Netherlands) and a calibrated open-circuit spirometry system (TrueOne, Parvo Medics, Sandy, UT) for analyzing expired gases [ 55 , 56 ]. The incremental exercise test initially involves a brief, 3-min warm-up at 0 W. The initial work rate for the incremental exercise test is 0 W, and the work rate continuously increases at a rate of 15 W/min (0.25 W/sec) until participants reach maximal exertion defined as volitional fatigue. Oxygen consumption (VO 2 ), respiratory exchange ratio (RER), and W are measured continuously by the open-circuit spirometry system and expressed as 20-s averages. Heart rate (HR) is displayed using a Polar HR monitor (Polar Electro Oy, Finland), and HR and rating of perceived exertion (RPE) are recorded every minute. VO 2peak  is expressed in ml kg −1  min −1  and peak power output is expressed in W based on the highest recorded 20-s values when two or more of the following criteria are satisfied: (1) VO 2  plateau with increasing W; (2) RER ≥ 1.10; (3) peak HR within 10 beats per minute of age-predicted maximum (i.e., ~ 1 SD); or (4) peak RPE ≥ 17 [ 55 , 56 ].

Muscle strength

Bilateral, isometric knee extensor (KE) and knee flexor (KF) peak torque will be measured using an isokinetic dynamometer (Biodex System 3 Dynamometer, Shirley, NY) [ 51 , 57 ]. Participants will be seated on the dynamometer consistent with the manufacturer's recommendations. Isometric torque will be assessed at 3 joint angles of 45°, 60° and 75°. Per joint angle, participants perform three, 5-s maximal knee extensions and one, 5-s maximal knee flexion. There is a rest period of 5-s between contractions within a set, and the rest period is 1 min between sets. The highest recorded peak torque for the stronger leg, regardless of joint angle, represents KE and KF isometric strength (N·m) [ 51 , 57 ].

Random assignment

After collection of baseline data, participants will be randomly assigned into either the exercise training condition or the control condition using a computerized process based on a random numbers sequence, and group allocation will be concealed. Participants will not be informed directly that the exercise training condition represents the experimental treatment condition and the stretching condition (i.e., attention and social contact control condition) represents the control condition, as both conditions are based on guidelines and likely have benefits in MS. To do this, the study will be advertised as comparing two different exercise approaches for managing consequences of MS and improving health indicators among persons with MS. We will measure treatment credibility after the first assigned treatment session using an adaptation of the Reaction to Treatment Questionnaire (RTQ) [ 58 ].

Intervention condition – home-based aerobic and resistance exercise training

The proposed trial will deliver the Guidelines for Exercise in MS (GEMS) program, as fully described in our previous research [ 18 , 19 , 20 , 21 , 22 ], within a remotely coached/guided, home-based setting using telerehabilitation (i.e., Zoom). The schematic of the main program components is provided in Fig.  1 and the components are summarized in Table  2 . The intervention condition consists of six main components: (1) three different progressive trajectories of aerobic/resistance exercise prescriptions for individualization (Orange, Blue, and Green; Table  3 ) that are based on current guidelines for adults with MS who have mild-to-moderate disability (i.e., defined as EDSS 0–7) [ 23 , 24 ], (2) appropriate exercise equipment including a CW-300 pedometer (NEW-LIFESTYLES, INC., Lee’s Summit, MO) and set of elastic resistance bands (Black Mountain Products, McHenry, IL), (3) one-on-one coaching, (4) action-planning via calendars, (5) log books for self-monitoring, and (6) SCT-based newsletters. Of note, the current exercise guidelines specify 30 + minutes of moderate-intensity aerobic exercise 3 time per week and resistance training targeting major muscle groups 3 times per week [ 23 , 24 ]. Walking is the aerobic exercise modality based on it being the most commonly reported mode of exercise among people with mild MS [ 59 ] and the intensity walking is controlled based on a step rate of 100 steps per minute as this corresponds with moderate-intensity exercise in persons with MS [ 60 ]. The resistance training stimulus consists of 1–2 sets involving 10–15 repetitions of 5–10 exercises that target the lower body, upper body, and core muscle groups. The specific lower body exercises are the chair raise, calf raise, knee flexion, knee extension, and the lunge; the specific upper body resistance exercises are the shoulder row, shoulder raise, elbow flexion, and elbow extension; and the specific core exercise is the abdominal curl. The one-on-one coaching (i.e. weeks 1, 2, 3, 4, 5, 7, 11, and 15) focuses on three main components: (1) exercise training guidance and oversight, (2) discussion of the behavioral strategies of action planning and self-monitoring, and (3) presentation and discussion of newsletters based on SCT constructs (i.e., outcome expectations, self-monitoring, goal-setting, self-efficacy, barriers, and facilitators) for optimizing adherence and compliance (Table  4 ) [ 18 , 19 , 20 , 21 , 22 ]. We further provide all participants with an NMSS educational packet “Minimizing your risk for falls: A guide for people with MS”, and a study-specific instruction sheet on fall prevention. Participants are instructed to document any falls and other concerns or adverse events in the exercise adherence log and report these during one-on-one coaching, and all adverse events will be documented and reported per UIC IRB guidelines.

Outline of the Guideline for Exercise in Multiple Sclerosis (GEMS) program

Control condition – home-based stretching and flexibility program

This program has been described in our previous research [ 22 ] and was developed based on a RCT of exercise training for improving mobility in MS [ 51 ] and two RCTs of exercise training for cognitive dysfunction in MS [ 61 , 62 ]. The program itself has identical components as the GEMS program for aerobic and resistance exercise training, but focuses on stretching for improving flexibility and range of motion as important components of fitness. The program itself is based on Stretching for People with MS: An Illustrated Manuel from the National MS Society (Table  5 ), as this is MS specific and enhances the credibility of the control condition. Participants will be provided with a yoga pad (i.e., exercise equipment) and a manual, log-book, calendar, and prescription for the stretching program. This program includes newsletters focusing on SCT for behavior change, and video-chats with behavioral coaches that provide motivation and social accountability. The video-chats occur on the same timeline and frequency as the GEMS exercise training program in the intervention condition, but focus on the SCT constructs applied for stretching. We further monitor safety and compliance as done in the intervention condition, and provide resources and instruction on safety. Importantly, this condition accounts for the possible influences of social-contact and attention associated with the GEMS program on the study outcomes, and this represents a major advancement over waitlist control and standard of care conditions in previous RCTs of exercise training and depression in MS [ 10 , 15 , 16 , 17 ].

The study procedure is administered by a project coordinator with oversight by the PI and Co-Is, and monitored through a fidelity monitoring plan (Table  6 ). As done in our previous research [ 18 , 19 , 20 , 21 , 22 ], the project coordinator will contact interested participants via telephone, describe the study and its requirements, and then conduct the screening for inclusion/exclusion criteria. The project coordinator will then distribute the informed consent document electronically among participants who meet inclusion criteria further information about the study. This will be followed by a telephone call that ensures participants received the document and understand the study and research procedures. The project coordinator will further work with participants in obtaining physician approval for participation and verification of MS diagnosis as a final step in enrollment.

The project coordinator will schedule baseline data collection, and provide written and verbal instructions regarding the baseline testing procedures. The project coordinator will send the participant document with directions and parking information, and contact the participant electronically and through telephone 24-h before the appointment as a reminder. Upon arrival, the project coordinator will review the study procedures with the participant, obtain written informed consent, and then initiate the baseline data collection.

The baseline data collection will be undertaken by treatment-blinded researchers who will start with a PAR-Q for ensuring safety and then administer measures of depression severity (i.e., primary outcomes) followed by the BICAMS and measures of fatigue and HRQOL (i.e., secondary outcomes). The participant will then undertake the maximal exercise test and muscle strength testing with a 15-min break between the measures of fitness. The competition of those measures will take ~ 120 min based on our previous experiences.

The treatment-blinded researchers will provide the participant with a packet containing an accelerometer along with GLTEQ. This packet will include instructions regarding the importance of wearing the accelerometer as instructed every day during the seven-day period, and provide a pre-stamped and pre-addressed envelope for return postal service. The participants will wear the accelerometer for a seven-day period and then complete the GLTEQ. The project coordinator will send brief, scripted e-mails for reminding participants about wearing the accelerometer in the middle of the seven-day period. This will be followed by a telephone call verifying that participants wore the accelerometer daily during the seven-day period and returned it along with the GLTEQ through the United States Postal Service.

Of note, demographic and disease-related characteristics will be collected from participant interviews and verification forms from the treating Neurologist, respectively. The patients will further provide a list of current medications and ongoing treatments for MDD and other symptoms of MS.

Once the baseline assessment is completed, participants will be randomly assigned into either the intervention or control conditions using a random numbers sequence with concealed allocation. The project coordinator will receive information on allocation, record it in a database, and communicate the condition of assignment with the participant and behavioral coaches. Importantly, several strategies will be adopted for maintaining blinded conditions. The behavioral coaches and other study staff are located in a separate lab space from where the treatment-blinded researchers administer outcomes. The behavioral coaches will emphsize among participants the importance of not revealing what type of exercise is being undertaken when interactiong with outcome assessors. The study staff will remind participants about not revealing the type of exercise being undertaken before the outset of follow-up outcome assessments.

The intervention/control conditions will be delivered by behavioral coaches who are univolved in outcome assessments in 12, partially overlapping waves of ~ 12 participants per wave, and the conditions will be delivered across a 4-month period. This use of waves will afford additional time for behavioral coaching during the one-on-one chat sessions than if enrolling 146 in one recruitment wave. This should permit greater penetration of the study materials. Participants will be asked to contact the project coordinator via the dedicated toll-free telephone number or e-mail in the occurrence of an adverse event or any other problem; this information will further be collected during video chats with behavioral coaches. The project coordinator will administer the PHQ-9 on the same weeks as the behavioral chats for ongoing monitoring of the mood status of participants.

The participants will complete the same measurement procedures immediately (i.e., immediate follow-up; 4 months) and 4-months (i.e., long-term follow-up; 8 months) after initiating the intervention/control conditions. There will be no behavioral coaching session during the long-term follow-up period for examining sustainability.

Participants will receive $100 USD remuneration for completing the measures per assessment period, including baseline, for a total of $300 USD. We will collect formative feedback using a Qualtrics survey for identifying opportunities for intervention improvement and refinement; this will be undertaken by participants after completion of the study.

Data analyses

The data analyses will be overseen by a biostatistician and follow intent-to-treat (ITT) principles (i.e., include all persons regardless of dropout). We will perform exploratory data analyses only among those who complete immediate and long-term follow-up testing (i.e., completer’s or per protocol analysis). We will check the data for errors and outliers, and lock the data set before analyses. The analytic plan will account for potential confounders of the intervention effect on the outcomes. The confounders may include MS duration, BMI, age, sex, disease-modifying therapy, and relapse rate. We will include any of those variables and others that differ between conditions as covariates in the following analyses.

Data analysis – aim 1

The first analysis tests the hypothesis that those who are randomly assigned into the intervention condition (i.e., exercise training) will demonstrate (a) reductions from baseline in depression severity that (b) are sustained over 4-months of follow-up compared with those in the control condition (i.e., stretching). The hypothesis will be tested using a linear mixed model in JMP Pro 16.0. The linear mixed model will include condition and time as fixed effects, and subject nested within condition as a random effect using unbounded variance components and the REML method ( https://www.jmp.com/content/dam/jmp/documents/en/academic/learning-library/08-repeated-measures-analysis-(mixed-model).pdf ). The hypothesized interaction term will be decomposed with follow-up tests, and differences in comparison of mean scores will be expressed as Cohen’s d with standard guidelines for interpretation. The final models for PHQ-9 and HDRS-17 scores will be adjusted for covariates. The overall Type I error will be controlled based on an adjustment of alpha (two-tailed α  = 0.025) given the two primary outcomes in Aim 1.

Data analysis – aim 2

The second set of analyses test the hypotheses that those who are randomly assigned into the intervention condition (i.e., exercise training) will report (a) improvements from baseline in fatigue, cognitive performance, and QOL that (b) are sustained over 4-months of follow-up compared with those in the control condition (i.e., stretching). Those hypotheses will be tested with the same modeling approach described for Aim 1. The overall Type I error will be controlled using a step-down procedure testing first fatigue, followed by domains of cognitive performance (SDMT, CVLT-II, and then BVMT-R), and lastly HRQOL [ 63 ].

Data analysis – aim 3

The third set of analyses test the hypotheses that those who are randomly assigned into the intervention condition (i.e., exercise training) will report (a) improvements from baseline in exercise behavior, free-living PA, and aerobic capacity and muscle strength that (b) are sustained over 4-months of follow-up compared with those in the control condition (i.e., stretching). Those hypotheses will be tested with the same modeling approach described for Aim 1. The overall Type I error will be controlled using a step-down procedure testing first exercise behavior and free-living PA, followed by aerobic capacity and muscle strength as outcomes [ 63 ].

Current trial status

As of February 13, 2024 and reported in our quarterly report for the funder, we have enrolled 18 persons into the trial, and these persons have been equally randomized into the intervention and control conditions (9 per condition). There were 6 other persons scheduled for baseline testing and ready for randomization.

We are proposing a Phase-II RCT of exercise training for treating depression severity in persons with MS who have MDD. If successful based on statistically significant and clinically meaningful improvements in depression symptom outcomes (e.g., ½ SD improvement for exercise compared with control) [ 64 ] as well as retention exceeding 20% (primary decision rules), we will proceed with the design of a Phase-III clinical trial of exercise training compared with CBT alone and combined with exercise training for treating depression severity in persons with MS who have MDD. We propose the addition of CBT as it has been considered a “possibly efficacious” treatment for depression in MS [ 7 , 8 ] and can be delivered remotely [ 9 ]. This is a logical next step, as the data gathered herein would power such a clinical trial and provide necessary experiences for a presumed larger trial. We further have experience in the conduct of Phase-III trials of exercise and physical activity in MS [ 21 , 65 ], and our ongoing PCORI trial provides a benchmark for conducting a Phase-III clinical trial of exercise training compared with CBT for managing depression severity in persons with MS who have MDD. Such a Phase-III clinical trial would provide definitive evidence for transition into clinical care and practice of persons with MS who have MDD, perhaps serve as a benchmark for studying exercise training as a treatment of other outcomes in persons with MS – this is a major stumbling block in all MS research involving exercise training [ 66 ], including depressive symptoms in MDD [ 10 ].

We may experience problems with the participants adhering with the intervention and control conditions based on the lack of interest/pleasure in activities, sadness, tiredness/fatigue, or physical problems (e.g., pain) as part of MDD. We are minimizing this by using SCT-based content and strategies and 1:1 remote behavioral coaching for maximizing adherence with both conditions. We further are managing this by enrolling a smaller number of persons (n ~ 12) over 12, partially overlapping recruitment waves (i.e., 12 waves of ~ 12 participants per wave), and thereby having the behavior coaches devote a greater amount of time with the participants during the one-on-one chat sessions. This should permit greater penetration of the study materials and a larger change in behavior for both conditions. The power analysis was based on meta-analyses for the effect of exercise interventions on depressive symptoms in samples that were not prescreened for MDD, and the preliminary data might not represent the treatment effect for those with MDD. Of note, our secondary analysis of previously published data suggested that effect of a physical activity intervention on depressive symptoms was stronger in those with elevated scores [ 67 ], and this would suggest that our power analysis and sample size should be appropriate for detecting an intervention effect on depression in those with MDD. There may be some attrition during the 4-month follow-up period wherein there is no planned coaching/contact, but this has been minimal in our previous [ 68 ] and ongoing [ 21 ] trials using the sample general approach; this is expected as the conditions are designed around teaching people skills, techniques, and strategies for sustainable behavior change.

If successful, the proposed study will provide the first and only Class I evidence for a home-based exercise training program as a treatment of depression in persons with MS who have MDD. This is critical as exercise training would likely have secondary effects on symptoms, cognition, and HRQOL, and provide a powerful, behavioral approach for managing the many negative outcomes of MDD in MS. The program in the proposed research is accessible and scalable for broad-scale treatment of depression in MS, and provides the potential for integration in the clinical management of this disease.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Adverse Events

Brief International Cognitive Assessment for MS

Brief Visuospatial Memory Test-Revised

California Verbal Learning Test-II

Central Nervous System

Cognitive Behavioral Therapy

Co-Investigators

Comparative Effectiveness Research

Diagnostic Manual of Mental Disorders V

Disease Modifying Therapies

Effect Size

Exercise Neuroscience Research Laboratory

Fatigue Severity Scale

Guidelines for Exercise in Multiple Sclerosis

Godin Leisure-Time Exercise Questionnaire

Hamilton Depression Rating Scale

Health Contribution Score

Health-Related Quality of Life

Institutional Review Board

Knee Extensor

Knee Flexor

Major Depressive Disorder

Modified Fatigue Impact Scale

Multiple Sclerosis

Multiple Sclerosis Impact Scale

National MS Society

Oxygen Consumption

Patient-Determined Disease Steps

Patient Health Questionnaire-9

Peak Oxygen Consumption

Peak Power Output

Physical Activity

Physical Activity Readiness Questionnaire

Principal Investigator

Randomized Controlled Trial

Rating of Perceived Exertion

Relapsing-Remitting MS

Respiratory Exchange Ratio

Serious Adverse Events

Social-Cognitive Theory

Symbol Digit Modalities Test

Telephone Interview for Cognitive Status

United States Dollar

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Acknowledgements

The authors recognize the critical role of the behavioral coaches in the delivery of the intervention and control conditions.

This project was funded by a grant from the Congressionally Directed Medical Research Programs – Multiple Sclerosis Research Program (W81XWH2110952). The study sponsor had no role in study design.

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Robert W. Motl, Brooks Hibner & Alison Wathen

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The manuscript was drafted by RWM. All other authors read and critically evaluated the manuscript, and approved it before submission. The PI of the study is RWM. The project coordinator is AW. Data management and analysis is guided by GC. CHB, JD, and BH provide ongoing insights on study management and quality control. CHB and JD provide guidance on all cases of suicidal risk. BH provides oversite of intervention delivery and outcome assessments. MC is the study neurologist and safety officer.

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Correspondence to Robert W. Motl .

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Motl, R.W., Bombardier, C.H., Duffecy, J. et al. Study protocol: exercise training for treating major depressive disorder in multiple sclerosis. BMC Neurol 24 , 131 (2024). https://doi.org/10.1186/s12883-024-03634-y

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Ai ux-design tools are not ready for primetime: status update.

April 12, 2024 2024-04-12

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AI tools won’t be replacing UX designers any time soon. Currently available LLM-based tools are not shortcutting steps in the design process just yet.

In This Article:

Designers use ai, just not for design, existing ai tools for ux design are lacking, examples of ai tools for ux design, advice for ux designers using ai.

There’s so much marketing hype around AI that it’s hard to know what tools and strategies can help UX right now.

To find specific, useful ways in which practitioners integrate AI into their work, we conducted in-depth interviews in early 2024 with UX practitioners (researchers, designers, and managers).

We asked our participants to describe and show us how they currently use AI in their work. We focused on the uses of generative AI and of other tools branded as AI for the study. Our study participants were early adopters, were big proponents of AI, and had been using a variety of AI tools in their work for a while.

Among the practitioners we spoke with, UX designers were most limited in their use of AI in their work. While many AI-based design products exist, we did not identify any design-specific AI tools in active use by UX designers — or any that our participants would recommend to others.

UX designers actively engaged with text-based AI tools such as ChatGPT for brainstorming and ideation tasks, but we found zero design-specific AI tools in serious use by the professional UX designers we spoke with .

AI tools are capable of filling skill gaps, allowing practitioners to continue their work without needing to engage with a specialized team member or stakeholder. For example, a UX designer may turn to a text-based AI tool to generate UX copy for a prototype instead of reaching out to a copywriter and waiting for a response.

AI tools are also helpful as a brainstorming partner, providing suggestions for feature names, research approaches, or anything else that doesn’t require absolute precision or trust. We found that UX designers turn to AI for many nondesign tasks: writing emails, structuring communication, organizing and breaking down difficult tasks into manageable pieces.

We combined our findings from interviews with UX designers with our own evaluations of current AI-based UX-design tools. Our evaluation agreed with the impressions of the UX practitioners we interviewed: existing AI tools that are advertised for design are lacking, and the vast majority are not ready to be used in design workflows.

The UX designers we spoke with said they were not actively using design-specific AI tools (such as Figma Plugins). Our expert review of popular plugins and AI tools (examples included below) also found that they didn’t add much value to the design process.

Issues around these new tools often became magnified when deployed across medium-to-large scale organizations. One participant was particularly disappointed by the lack of customer support and guidance for use when he attempted to set up his agency in Midjourney. He also worried about ethical and legal risk if his team used AI-generated content in designs, due to potential copyright issues.

“There are folks over the past few decades have generated content that these tools are piggybacking on. Is it right to use that without attribution without licensing and royalty fees? We're thinking about the ethical use of it.”

Right now, AI tools, especially in the context of UX design, are a solution in search of a problem. There are many problems and points of friction that cause UX designers to be inefficient and unproductive in their work, but working to develop solutions to those problems wouldn’t necessarily lead you to AI.

AI developers hope to insert AI into the design process, applying it to existing design challenges and inefficiencies. But, for a tool to be useful enough to integrate into a workflow consistently, the interaction with that tool must be consistent.

Unfortunately, AI-based tools are not deterministic at this point. For example, a single ChatGPT 4 prompt for an example of front-end design featuring Google’s Material Design components resulted in three drastically different designs. While this variety in output is valuable for ideation, it isn’t so helpful for the end-to-end, replicable AI-powered design solutions that some products promise.

It remains to be seen whether AI’s opaque and unreliable nature is an inherent characteristic of the technology or could be overcome with future products. Current AI chatbots based on large language models are probabilistic, relying on a statistical dice roll to determine the sequence of words that make up the response to a prompt.

The current state of AI tools, text-based or otherwise, presents an inconsistent, unreliable, and low-value attempt to solve existing design problems. As prompt engineering, community knowledge, and implementation of AI tools progresses, we may see an improvement in the control and precision that these tools allow, potentially crossing the threshold in reliability and trustworthiness for professional workflows.

In our assessment of various AI-based tools for design, we found that most produced basic results that don't add much value to the design process in their current states.

Below we discuss three such tools. These were frequently mentioned by our study participants or in design communities.

Wireframe Designer: A Figma Plugin

AI-based wireframing tools often generated generic layouts that weren’t helpful or creative even with very specific prompts.

The free version of the Figma plugin Wireframe Designer by Chenmu Wu generates low-fidelity wireframes when the design needs and context are met. To test the limits of this tool, we generated screens using a variety of prompts varying in length and detail.

We noted that, even when ample contextual information was provided about design needs and target users, the plugin generated generic, seemingly templatized outputs.

Even when we tried a variety of prompts corresponding to different contexts and business needs, the generated outputs were fairly similar, with the main difference being the placeholder text pulled from the prompt. The Wireframe Designer plugin also didn’t offer a straightforward method for generating several variations from a single prompt, making it harder to get a variety of ideas.

Uizard: An AI-Assisted Design Tool

Uizard is an AI-assisted design tool that integrates AI wireframing, image generation, theme generation, and UI-copy assistance.

Unlike the Wireframe Designer plugin, Uizard requires text prompts to be 300 characters or less. While still basic in its output, Uizard’s screen generator offered more variety and options in comparison to the Wireframe Designer Figma plugin.

When using Uizard’s Autodesigner , the generated screens aligned better with our prompts than those created by the Wireframe Designer. However, the AI-based outputs still varied significantly in quality. Even though Uizard seemed more promising as an AI-assisted prototyping-and-design application, its interface was clunkier than that of more-popular design tools like Figma.

UX Pilot: AI-Driven Toolkit

UX Pilot is a subscription-based, AI-driven toolkit designed to help designers create color schemes, wireframes, and questions for user and stakeholder interviews, as well as plan and analyze workshops.

WHile this toolkit shows some promise for aiding in UX research, it is less helpful for design-specific tasks. For instance, its Color & Gradient AI Figma plugin generated color schemes that didn’t work well together for interface design.

This plugin was good for ideating color schemes quickly, but it did require that the designer understand some color theory when narrowing down color choices.

While our study did not yield specific tool recommendations for designers, it did generate plenty of good advice for UX designers trying to navigate emerging AI tools.

Familiarize yourself with the potential benefits of AI for ideation, brainstorming, and copywriting tasks. Artificial-intelligence tools are currently most effective with text-related tasks and, with effective prompts, can serve as beneficial partners in ideation and brainstorming exercises. UX writing and copywriting are also strong opportunities for AI to streamline work.

Lean on existing methods for design tasks. Current AI offerings cannot meaningfully enhance those visual and design-heavy workflows. Currently, the work necessary to get a high-quality result from an AI design tool is too time-consuming to justify its use. Stick to tried and true methods for visual design, wireframing, and prototyping.

Do not use AI to generate user data, user-research findings, or user personas. Using AI to generate artificial research data is unethical, deceitful, and potentially harmful to a final product and your professional reputation.

Don’t panic about missing the boat on AI. Developers and companies are still figuring out whether AI can meaningfully improve the experience of being a UX designer, and there aren’t any “killer apps” right now. Take time to play and tinker with AI tools to stay current, but don’t expect them to return much value at the moment.

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Carey TS, Sanders GD, Viswanathan M, et al. Framework for Considering Study Designs for Future Research Needs [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2012 Mar. (Methods Future Research Needs Reports, No. 8.)

Framework for Considering Study Designs for Future Research Needs [Internet].

Examples of study design considerations.

The examples that follow illustrate the team’s thinking about the content and format of demonstrating study design considerations within FRN reports. The first three are examples of a single FRN; the fourth example is a discussion of sample size issues. The examples are derived from the first series of EPC FRN reports; the needs have been reformatted to reflect the recommended presentation described in this paper. A tabular format is considered most succinct ( Example 2 ; see Table 2 ). As discussed above, the descriptors of the study design considerations should be brief. Only one to three (rarely more) of the most feasible study designs should be presented; we believe it is redundant and potentially confusing to present iterations of why, for example, a cross-sectional study design is inappropriate as a means of filling an evidence gap related to treatment effectiveness. For some gaps, only an RCT might suffice and, therefore, only one study design presentation is appropriate. As discussed previously, the authors of the FRN report should encourage creativity and emphasize that these considerations are meant to be illustrative, not prescriptive. Further, advances in analytic methods may enable alternative study designs not anticipated by the FRN team. Additionally, the FRN project team should consider and discuss the lessons learned from studies included in the CER. A discussion of methodological weaknesses that limit the strength of available evidence could be used to support a suggestion that would prevent repeating previous mistakes. Methodological evidence gaps, if apparent, should also be addressed in the study design considerations.

Comparison of study designs.

Example 1. Narrative/Bulleted Text

Content area: Fixation of fractured hip: “Do certain procedures (e.g., internal fixation) work better than others for frail elder patients?” 8

Randomized Trial

• Advantages of study design for producing a valid result : A well-done RCT will produce the most convincing results and, if inclusion/exclusion criteria and setting are realistic, should be fairly generalizable.
• Resource use, size, and duration : An RCT has to be large, because the question compares active treatments and the effect size may be small and easily swamped by other causes of morbidity and mortality in this population. Duration depends on whether the trial focuses on peri-procedural complications and short-term outcomes or on the longer-term durability of different treatment options. In either case, the resource requirements will be large or very large, given that the effect size between the treatments might be modest.
• Ethical issues : As long as equipoise exists among the treatment options, ethical issues regarding enrollment should not be present. However, if the study includes patients with dementia, consent issues may occur.
• Availability of data or ability to recruit : Recruitment may be slow, because this is a subpopulation of the population of hip fracture patients, and it may be difficult to reach large numbers.

Prospective Cohort Study

• Advantages of study design for producing a valid result : Although concern for selection bias and unmeasured confounders will always exist, the prospective design allows data for the most relevant known confounders to be collected and controlled for. Therefore, while the results will not be as definitive as an RCT, they could be informative.
• Resource use, size, and duration : This type of study still requires a large size because of potentially small treatment effects, but it would likely be less expensive than an RCT.
• Ethical, legal, and social issues : The main ethical issue is consent in the case of patients with dementia; however, because choice of treatment is not involved, it may be of less concern.
• Availability of data or ability to recruit : Recruiting patients for this design should be easier than for an RCT.

Retrospective Cohort Study

• Advantages of study design for producing a valid result : Significant risk of selection bias exists, and there is less ability to control for confounders than in a prospective cohort study because key variables may not be collected. However, this design could be sufficient for hypothesis generation that could then be used to design a more focused RCT.
• Resource use, size, and duration : A retrospective cohort study design has the potential to be considerably faster and less expensive than either an RCT or a prospective cohort study.
• Ethical, legal, and social issues : Confidentiality and Health Insurance Portability and Account (HIPAA) † issues may arise when diverse databases are linked without specific patient consent.
• Availability of data or ability to recruit : Recruiting is very feasible; the main concern is selection bias, depending on the source of the secondary data, and missing variables. Negotiations with the holders of the secondary data may take significant time.

Example 2. Table

Content area: Elective Cesarean section compared with planned vaginal delivery in healthy women. “What is the comparative effectiveness of planned Cesarean delivery versus planned vaginal delivery on maternal and neonatal outcomes?” 9

Example 3. Process or Methods Considerations

Content area: Treatment of prostate carcinoma. “Facilitate future research on potential biomarkers to identity patients whose disease is likely to be aggressive.” 10

Context: Although many efforts have been made to predict which patients with localized prostate cancer have aggressive disease, existing tools are inadequate to predict which patient to treat with any high degree of accuracy. With the emergence of biomarkers in other diseases, such as breast cancer, that have both prognostic and predictive power, the search continues to identify biomarkers that can predict which patients with prostate cancer face a poorer prognosis and may benefit to a greater degree from immediate treatment. Although a number of biomarkers have been explored to date with limited success, biomarkers continue to have a potentially important role.

Proposed research design: Establish biospecimen repositories with clinical data on diagnosis, treatment, and follow-up.

Study design considerations:

• Advantages of study design for producing a valid result : Biospecimen repositories create the resources needed to test the use of novel biomarkers in the future, while providing long-term data on outcomes that would take a long time to collect. Such repositories are being established for other studies, such as the ProtecT trial in the United Kingdom. In addition, given the differences in treatment regimens, populations, and possibly outcomes across studies, biospecimens from different trials might help address alternative hypotheses. The National Cancer Institute is establishing methods for each step of the process for creating and maintaining biospecimen repositories.
• Resource use, size, and duration : Although expensive to create and maintain, additional repositories will allow more biomarker testing, particularly because tissue specimens are finite. The administrative complexity of tracking specimens and their use is substantial, and ongoing infrastructural funding is essential.
• Ability to recruit : At the time of biopsy or surgery, patients could be consented for participation. Given that tissue is obtained as part of the procedure, this should be straightforward.
• Ethical, legal, and social issues : Biorepositories require extensive documentation of their policies regarding tracking and use of specimens. The proposed revisions to the Federal Department of Health and Human Services (HHS) Common Rule may partially address these issues. ‡ Significant planning will be needed.

Example 4. Sample Size Calculations To Inform Feasibility of Future RCTs

Content area: Coronary artery stenting compared with coronary artery bypass surgery.

Percutaneous coronary interventions (PCI) with or without stents and coronary artery bypass graft surgery (CABG) are clinically relevant treatment options for many patients with coronary artery disease (CAD). In assessing this topic, it was deemed that an important gap pertained to the comparative effectiveness and safety of the interventions in the elderly (aged 75 or older). 11

But exactly what merits further study? A focused value-of-information analysis helped clarify the group of parameters that would most inform the decision of choosing between PCI and CABG in the elderly. The analysis suggested that the relative safety of the interventions (i.e., relative effects on post-intervention complications) was more important than, for example, the absolute frequency of adverse events in the postintervention. As per descriptions above, a possible design to address relative effects of treatments is an RCT.

Is it realistic to consider a new RCT to compare PCI versus CABG? One can perform high-level sample size calculations. The biggest trials in the field enrolled approximately 2,500 patients, which serves as an indication of a large feasible RCT. Figure 2 shows power attained over a range of sample sizes for various control rate values over a mean followup of 3 or 5 years (see legend for details). Power increases with sample size, with control rate, and with length of followup. Over 5 years of followup, a study of approximately 2,500 patients would attain 80 to 90 percent power to find a relative effect of 0.80 only if it chooses an outcome that has at least a 30 percent control rate. This means a composite outcome . To get average followup duration of approximately 5 years, a trial would have to go on for 6 to 8 years at least (see legend).

Power calculations for superiority RCTs for various 5-year primary event rates in the comparator arm. Plotted are power calculations for six different 5-year primary event rates in the comparator arm (5%, 10%, 15%, 20%, 30%, and 40%, as shown next to (more...)

Therefore, de novo RCTs are feasible but would likely require resources comparable to recent large RCTs. The above calculations are generic and thus apply to any subset of patients with heart disease. For example, in middle-aged patients with two vessel disease, one would have to define a composite outcome of death or myocardial infarction or other cardiac events to attain a high event control rate and, thus, high power to detect a significant difference. By contrast, in the subpopulation of elderly patients (e.g., older than 75 years), where mortality rates can be high enough, one may be able to attain high statistical power for the outcome of death alone.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy and Security Rules

The Common Rule is located at 45 CFR part 46, subpart A, and described at: http://www ​.hhs.gov/ohrp ​/humansubjects/commonrule/index.html .

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