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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• 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
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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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 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…

Need a helping hand?

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 .

Psst… there’s more (for free)

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

how to cite this page

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

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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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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.

Prevent plagiarism, run a free check.

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 – 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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How to choose your study design

Affiliation.

• 1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
• PMID: 32479703
• DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

• Case-Control Studies
• Cross-Sectional Studies
• Research Design*

What Are the Types of Study Design?

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• First Online: 24 October 2021

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• Atul Kakar 5 &
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The quality, reliability, dependability, and publishability of a study depend on its design. A clinical study design includes the preparation of trials, ]experiments, and observations in research involving human beings. The various types of study designs are depicted in Fig. 8.1.

“Education without application is just entertainment.”Tim Sanders, American author and speaker (1959-)

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The Beginning – Historical Aspects of Clinical Research, Clinical Research: Definitions, “Anatomy and Physiology,” and the Quest for “Universal Truth”

Introduction to clinical research concepts, essential characteristics of clinical research, overview of clinical research study designs.

Steps of a Research Study: From Research Question to Publication

1 what are the various types of clinical study designs.

The quality, reliability, dependability, and publishability of a study depend on its design. A clinical study design includes the preparation of trials, experiments, and observations in research involving human beings. The various types of study designs are depicted in Fig. 8.1 .

A study can be classified into three major groups: observational, experimental, and meta-analysis

2 What Are the Types of Observational Studies?

Broadly there are two types of observational studies, i.e.:

Descriptive.

Analytical.

3 What Is a Descriptive Study?

This kind of study deals with observing the distribution of a given phenomenon. It generally deals with a time, place, and person distribution [ 1 , 2 ].

The procedures involved in a descriptive study include:

Definition of the population to be studied.

Naming of the illness.

Describing the disease by time, place, and person.

Quantification of the disease outcome.

Comparing this with known parameters.

The advantages of a descriptive study:

Provides information regarding the extent of the disease load.

May suggest a clue to its aetiology.

Provides background data for planning.

Contributes to research by describing the illness in relation to time, place, and persons.

Examples of descriptive studies include:

Case reports 1. Profound neutropenia in a patient with COVID-19.

2. Multiple Renal Abscesses in a Horseshoe Kidney

Case series 1. Gastrointestinal manifestations in COVID-19: A review of 30 cases.

2. Long-Term Follow-Ups of Relapses after Surgery for Astrocytoma

4 What Is Analytical Epidemiology?

The objective of this kind of study is to test a hypothesis and includes specific subjects of interest. There are four distinct types of investigations:

Ecological.

Cross sectional.

Case control.

5 What Are Ecological Studies?

These are observational studies often used to measure the prevalence and incidence of disease, particularly when the disease is rare, and are quite easy to conduct. The other advantage is that they are usually retrospective in nature. In them, there should be only one exposure in the population. An example of such a study would be to compare the prevalence of rheumatoid arthritis in Delhi and Manipur. This data is usually extracted from large databases which may have been used for other purposes and thus are not always reliable. Ecological studies are generally economical and serve as a preliminary point for hypothesis generation [ 2 ].

6 What Is a Case–Control Study?

This compares a population with a certain medical condition with another group of people who do not have the disease but are otherwise similar to the study population.

The basic steps include:

Proper selecting cases and controls.

Matching of cases with controls.

Measuring the exposure.

Analyzing and interpreting the results.

Case–control studies are inexpensive and frequently used kind in epidemiology. Their design allows the study of a rare illness. The preliminary data help to learn what is already known about the association between the risk factors for the disease. The measure of interest is the calculation of the odds ratio. These are also retrospective studies that cannot calculate prevalence and are usually used for rare diseases. They can also be nested within longitudinal studies but given their retrospective nature, can be prone to recall bias [ 3 ].

An example of such a study is the occurrence of cervical cancer in patients who have received Human Papillomavirus vaccine in childhood. Figure 8.2 is an example of a case–control study design and how to calculate the odd’s ratio.

( a , b ) Case–control study and calculation of odds ratio

7 What Is a Cohort Study?

A cohort study is done on a group of people who are followed up over many years—for instance, to determine how often a certain disease occurs. It is performed to obtain evidence to support the existence of an association between a suspected cause and disease (Fig. 8.3 ).

Depicts both a retrospective and a prospective cohort study

Types of cohort study:

Prospective cohorts.

Retrospective cohorts.

Combination of prospective and retrospective cohorts.

Elements of a cohort study:

Collection of study patients.

Procuring data on exposure.

Study of comparison groups.

Review visits.

Final data analysis.

These studies can help in calculating point prevalence or period prevalence. Prospective cohort studies are the ‘gold standard for observational research’.

8 What Are Cross-Sectional Studies?

These are also retrospective and study the prevalence of a disease. They are economical and easy to conduct. An example of a cross-sectional study design would be enrolling participants who are either current alcohol consumers or have never consumed alcohol, and are being assessed whether or not they have liver-related issues. The studies assess both exposure and outcome at a single point in time. Figure 8.4 shows an example of this [ 3 , 4 ].

Cross-sectional study

9 What Is an Experimental Study Design?

This has a similar approach to a cohort study except that it is carried out under direct control of an investigator. The aim is to provide systematic proof of either aetiological or risk factors of the disease the modification of which can control it. Epidemiological and interventional research studies include three elements:

Definition and measure of exposure in two or more groups.

Measure of disease outcome(s) in the same groups.

Statistical comparison made between groups to assess potential relationships between the exposure and outcome, all of which are defined by the researcher.

10 What Is a Randomized Controlled Trial?

This is a study performed to avoid any bias while testing for the efficacy of, e.g., a drug. The study population is randomly divided into two groups, of which one receives the drug under study and the second group receives a placebo and acts as the control group. The experiment may be blinded, which means that any information which may influence the participant is withheld while the trial is ongoing or maybe double blinded in which the information is withheld from both the subject and the investigator [ 5 ].

The basic steps of a randomized control trial (RCT) include:

Writing a protocol.

Selecting a normal and experimental population.

Randomization.

Intervention in the study group and placebo.

Measuring the outcome of interest.

11 Design of a Randomized Control Trial (Fig. 8.5 )

Randomized control trial

12 What Are the Standards of Research and Reporting?

There are many available guidelines on study design, execution, and how it needs to be reported in the final manuscript. This improves the quality of a research paper and allows results to be presented in a systematic manner for a sound conclusion to be drawn. Table 8.1 mentions some important reporting formats and their websites.

13 Conclusions

Formulating a study design is the most important part of the planning stage of clinical research. It is an indispensable part of new drug discovery.

Basic research is also called experimental and done in genetics, biochemistry, and physiology. Studies on drug properties are also included in this.

Clinical studies can be interventional or non-interventional. Interventional studies are done on surgery, chemotherapeutic agents, devices, or drugs.

A rare disease is best investigated by a case–control study and rare exposures by cohort studies.

A retrospective study is based on historical data, which may be obtained from past records. In prospective studies the data are collected after the work has begun.

Observational studies are divided into descriptive and analytical studies.

In cohort studies, two or more groups are selected on the basis of their exposure to a drug or environmental exposure and then followed up for outcome.

The evidence collected from randomized controlled trials is of good quality. They allow a proper evaluation of a drug. More recently adaptive designs allow for greater flexibility and pragmatic randomized trials.

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Literature Reviews: Types of Clinical Study Designs

• Library Basics
• 1. Choose Your Topic
• How to Find Books
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Types of Study Designs

Meta-Analysis A way of combining data from many different research studies. A meta-analysis is a statistical process that combines the findings from individual studies.  Example :  Anxiety outcomes after physical activity interventions: meta-analysis findings .  Conn V.  Nurs Res . 2010 May-Jun;59(3):224-31.

Systematic Review A summary of the clinical literature. A systematic review is a critical assessment and evaluation of all research studies that address a particular clinical issue. The researchers use an organized method of locating, assembling, and evaluating a body of literature on a particular topic using a set of specific criteria. A systematic review typically includes a description of the findings of the collection of research studies. The systematic review may also include a quantitative pooling of data, called a meta-analysis.  Example :  Complementary and alternative medicine use among women with breast cancer: a systematic review.   Wanchai A, Armer JM, Stewart BR. Clin J Oncol Nurs . 2010 Aug;14(4):E45-55.

Randomized Controlled Trial A controlled clinical trial that randomly (by chance) assigns participants to two or more groups. There are various methods to randomize study participants to their groups.  Example :  Meditation or exercise for preventing acute respiratory infection: a randomized controlled trial .  Barrett B, et al.  Ann Fam Med . 2012 Jul-Aug;10(4):337-46.

Cohort Study (Prospective Observational Study) A clinical research study in which people who presently have a certain condition or receive a particular treatment are followed over time and compared with another group of people who are not affected by the condition.  Example : Smokeless tobacco cessation in South Asian communities: a multi-centre prospective cohort study . Croucher R, et al. Addiction. 2012 Dec;107 Suppl 2:45-52.

Case-control Study Case-control studies begin with the outcomes and do not follow people over time. Researchers choose people with a particular result (the cases) and interview the groups or check their records to ascertain what different experiences they had. They compare the odds of having an experience with the outcome to the odds of having an experience without the outcome.  Example :  Non-use of bicycle helmets and risk of fatal head injury: a proportional mortality, case-control study .  Persaud N, et al.  CMAJ . 2012 Nov 20;184(17):E921-3.

Cross-sectional study The observation of a defined population at a single point in time or time interval. Exposure and outcome are determined simultaneously.  Example :  Fasting might not be necessary before lipid screening: a nationally representative cross-sectional study .  Steiner MJ, et al.  Pediatrics . 2011 Sep;128(3):463-70.

Case Reports and Series A report on a series of patients with an outcome of interest. No control group is involved.  Example :  Students mentoring students in a service-learning clinical supervision experience: an educational case report .  Lattanzi JB, et al.  Phys Ther . 2011 Oct;91(10):1513-24.

Ideas, Editorials, Opinions Put forth by experts in the field.  Example : Health and health care for the 21st century: for all the people . Koop CE.  Am J Public Health . 2006 Dec;96(12):2090-2.

Animal Research Studies Studies conducted using animal subjects.  Example : Intranasal leptin reduces appetite and induces weight loss in rats with diet-induced obesity (DIO) .  Schulz C, Paulus K, Jöhren O, Lehnert H.   Endocrinology . 2012 Jan;153(1):143-53.

Test-tube Lab Research "Test tube" experiments conducted in a controlled laboratory setting.

Adapted from Study Designs. In NICHSR Introduction to Health Services Research: a Self-Study Course.  http://www.nlm.nih.gov/nichsr/ihcm/06studies/studies03.html and Glossary of EBM Terms. http://www.cebm.utoronto.ca/glossary/index.htm#top  

Study Design Terminology

Bias - Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.

Case Control Studies - Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.

Causality - The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.

Control Groups - Groups that serve as a standard for comparison in experimental studies. They are similar in relevant characteristics to the experimental group but do not receive the experimental intervention.

Controlled Clinical Trials - Clinical trials involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness. Control measures include placebos, active medicines, no-treatment, dosage forms and regimens, historical comparisons, etc. When randomization using mathematical techniques, such as the use of a random numbers table, is employed to assign patients to test or control treatments, the trials are characterized as Randomized Controlled Trials.

Cost-Benefit Analysis - A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. Cost effectiveness compares alternative ways to achieve a specific set of results.

Cross-Over Studies - Studies comparing two or more treatments or interventions in which the subjects or patients, upon completion of the course of one treatment, are switched to another. In the case of two treatments, A and B, half the subjects are randomly allocated to receive these in the order A, B and half to receive them in the order B, A. A criticism of this design is that effects of the first treatment may carry over into the period when the second is given.

Cross-Sectional Studies - Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.

Double-Blind Method - A method of studying a drug or procedure in which both the subjects and investigators are kept unaware of who is actually getting which specific treatment.

Empirical Research - The study, based on direct observation, use of statistical records, interviews, or experimental methods, of actual practices or the actual impact of practices or policies.

Evaluation Studies - Works consisting of studies determining the effectiveness or utility of processes, personnel, and equipment.

Genome-Wide Association Study - An analysis comparing the allele frequencies of all available (or a whole genome representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.

Intention to Treat Analysis - Strategy for the analysis of Randomized Controlled Trial that compares patients in the groups to which they were originally randomly assigned.

Logistic Models - Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.

Longitudinal Studies - Studies in which variables relating to an individual or group of individuals are assessed over a period of time.

Lost to Follow-Up - Study subjects in cohort studies whose outcomes are unknown e.g., because they could not or did not wish to attend follow-up visits.

Matched-Pair Analysis - A type of analysis in which subjects in a study group and a comparison group are made comparable with respect to extraneous factors by individually pairing study subjects with the comparison group subjects (e.g., age-matched controls).

Meta-Analysis - Works consisting of studies using a quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc. It is often an overview of clinical trials. It is usually called a meta-analysis by the author or sponsoring body and should be differentiated from reviews of literature.

Numbers Needed To Treat - Number of patients who need to be treated in order to prevent one additional bad outcome. It is the inverse of Absolute Risk Reduction.

Odds Ratio - The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.

Patient Selection - Criteria and standards used for the determination of the appropriateness of the inclusion of patients with specific conditions in proposed treatment plans and the criteria used for the inclusion of subjects in various clinical trials and other research protocols.

Predictive Value of Tests - In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

Prospective Studies - Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.

Qualitative Studies - Research that derives data from observation, interviews, or verbal interactions and focuses on the meanings and interpretations of the participants.

Quantitative Studies - Quantitative research is research that uses numerical analysis.

Random Allocation - A process involving chance used in therapeutic trials or other research endeavor for allocating experimental subjects, human or animal, between treatment and control groups, or among treatment groups. It may also apply to experiments on inanimate objects.

Randomized Controlled Trial - Clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.

Reproducibility of Results - The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.

Retrospective Studies - Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.

Sample Size - The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups.

Sensitivity and Specificity - Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition.

Single-Blind Method - A method in which either the observer(s) or the subject(s) is kept ignorant of the group to which the subjects are assigned.

Time Factors - Elements of limited time intervals, contributing to particular results or situations.

Source:  NLM MeSH Database

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6.1 Selecting the appropriate study design

One of the most fundamental issues in determining how to conduct a study is determining the right study design to answer a research question and to achieve the stated aims of a research idea. It is essential to select the appropriate study design as it is crucial in determining the methodology of any research. There are different study designs under three broad categories, namely quantitative, qualitative and mixed methods research, and these have been discussed in chapters 1, 3, 4 and 5. Quantitative research measures quantifiable things while seeking to investigate the relationship between variables and utilises numerical data and statistical analysis to understand the phenomenon. 1 On the other hand, qualitative research aims to explore and develop an in-depth understanding of the concept with an emphasis on the development process. 2 The mixed method combines quantitative and/ or qualitative methods with features of both methods integrated into mixed method research. 3 The question is, how can a researcher decide on a study design type given the numerous options? The choice depends on the following key factors- the research question, resources (timing and funding), data availability and study population, ethical considerations and research expertise. This video clip gives a quick recap of the different research methods and study designs that we presented in chapters 3, 4 and 5.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Study Designs: A Complete Guide

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Study Designs: Basics of Research

Study designs are paramount in research. From generating a scientific question and testing a hypothesis to publishing a scientific paper, research teams need to plan and develop a relevant study design, which can suit their experimental goals and financial strategies.

Designing a study is an exciting process. Since experiments in both physical and life sciences aim to establish a causal relationship between variables, each study design should be adapted to the basics of research accordingly. Of course, the first step for every team is to formulate a clear research problem, based on in-depth literature research. Then, research hypotheses, roles, and experimental methods, such as data collection and analysis, should be established clearly. Most of all, when designing and conducting a study, researchers need to follow safety and ethical regulations – always aiming for patients’ well-being.

Designing a study, however, is also a challenging task. Financial demands, time delays, and ethical issues seem to sabotage medical research. What’s more, when it comes to medicine, safety and efficacy become crucial.

Therefore, when choosing a study design, experts need to be familiar with all basics, specifications, limitations, and benefits of all types of study designs.

Study Designs: Choose the Right One

Before experts proceed with choosing a design and creating a study within medical settings, there are a few research steps and terms that need to be clarified (Peat, 2011)

Descriptive Vs. Experimental Studies:

While each study design can be described as the procedure of employing research methods to recruit participants, administer interventions, and collect data, descriptive and experimental studies should be differentiated.

Descriptive studies refer to situations in which vital research factors, such as gender or age, cannot be modified. In other words, descriptive studies identify behaviors but can’t make predictions or reveal causality. Thus, when employing descriptive studies, researchers become observers and analysts. Descriptive studies simply provide a snapshot of the current situation (Stangor, 2011).

Another powerful method is a correlation. A correlational study can describe, discover or predict the way research variables are connected – without manipulating them.

On the other hand, experimental studies give researchers the unique opportunity to modify and control variables to test research hypotheses. For instance, when implementing experimental studies, experts can modify various aspects of research – let’s say via administering a novice drug treatment across various groups. As a result, experimental studies are perceived as more powerful when compared to descriptive studies, and consequently, they are widely used in medical research.

Qualitative Vs. Quantitate Studies:

Another important difference between studies is their qualitative or quantitative nature (Moffatt, 2006). While quantitative methods rely on conventional data collection and statistical procedures, qualitative studies involve open questions and other in-depth methods, such as interviews with patients.

As mentioned above, quantitative studies help researchers collect data and transform it into statistics. Such studies are well structured and can include large samples. As a result, they are widely used in research and medical practice.

On the other hand, qualitative studies can help researchers gain insights and generate testable hypotheses. They can be used parallel to quantitative studies to explore patients’ feelings, attitudes towards a new treatment, and personal tactics to cope with a disease.

Phases of Clinical Trials

Also, when it comes to medical trials, there are four different phases according to which studies can be divided (Peat, 2011). As a matter of fact, some rare studies may include Phase 0. Note that this differentiation comes from the fact that patients’ safety always comes first.

Phase I studies aim to test the safety aspect of all new medical interventions. Usually, new interventions and drugs are tested on animals and a small group of volunteers. Still, ethics and safety are the main objectives. Note that since animals are used to develop and test new medical treatments, the ethics of animal research may challenge conventional methods and medicine (Festing & Wilkinson, 2007).

Phase II studies include a larger sample of participants and aim to test the efficacy of a treatment. Note that efficacy can be defined as the effect of a treatment in ideal conditions – whether an intervention does more good than harm under ideal options. Here placebo can also be employed. This can be the case only if the employment of a placebo won’t breach ethical and safety issues. In other words, researchers cannot deny a life-saving treatment just to test a placebo control group.

Phases III studies can be conducted after safety and efficacy have been established. Such studies include randomized trials and multicenter studies. In fact, they aim to test the effectiveness and equivalence of the treatment. In other words, Phase III studies aim to test the effect of the treatment in less ideal circumstances and routine practices, which is the so-called effectiveness, and the extent to which the new treatment relates to an existing treatment, which is the so-called equivalence.

Last but not the least, Phase IV studies can also be implemented. They are crucial in medical research as they aim to test any possible rare side effects of a new treatment or intervention in the long-term.

Study Designs: Types

Now, when it’s clear that each study design can only benefit practice, let’s explore some of the most common types of study designs used in medical research. There’s no right and wrong as there’s no such a thing as the one-size-fits-all approach in medicine.

Meta-analysis

Meta-analysis is a study design, which is a powerful research method. It’s based on data collected from different studies. Meta-analysis can also be described as quantitative and epidemiological study design. In fact, a rigorous meta-analysis is a great approach to evidence-based medicine.

Since this design involves the profound analysis of previous studies, a meta-analysis may have the potential to reveal hidden insights and relationships, such as possible health risks related to a new treatment and medical interventions. Not surprisingly, this particular aspect is one of the main advantages of meta-analyses (Peat, 2011).

However, as meta-analysis requires the use of complex data and quantitative review of the literature, failing to identify all existing studies in literature may lead to wrong conclusions and sabotage research.

Systematic Review

The systematic review is another common type of research used in the assessment of literature and studies, which addresses a particular health-related issue (Cochrane Handbook for Systematic Reviews of Interventions).

Systematic reviews can be used to summarize the results of all available medical studies and controlled trials. In fact, they can provide vital information about the effectiveness of an intervention. Note that systematic reviews can include meta-analyses.

Nevertheless, one of the main disadvantages is that, as mentioned above, failing to collect and research complicated data may lead to erroneous conclusions and crash research.

Randomized Controlled Trials

Randomized controlled trials are among the most rigorous study designs implemented in clinical research. These trials are defined as controlled experiments that give researchers the chance to test various interventions in random order. Randomized trials can also be classified as quantitative and comparative studies because research outcomes can be measured and compared easily.

To be more precise – as the name suggests- randomized controlled trials are studies in which subjects are allocated to conditions and interventions at random (Peat, 2011). The method of allocating by chance alone helps experts exclude interfering factors, such as selection bias. Note that double binding can also be used to avoid bias and other prognostics factors.

Since the subjects are allocated to receive different interventions (new treatment, existing treatment, placebo, or no intervention) at random, randomized controlled trials reveal an outstanding advantage when compared to other study designs. By implementing randomized controlled trials in research, experts have the unique opportunity to check efficacy, effectiveness, equivalence, and causation. On top of that, vital cofounders and factors, such as environmental exposure, can also be compared and assessed. What’s more, each subject has an equal chance to be in a control group or treatment, which can help generalizability. Yet, consent is mandatory.

However, one of the main limitations consists in the fact that this study design requires large samples. The size of the sample matters to measure adverse effects and other long-term outcomes that may occur with time. For this purpose, though, Phase IV surveys can be used. In fact, the size of the sample is also crucial in determining statistical significance in medical conditions that can have only minimal improvement (for instance, chronic conditions like asthma).

An example of a double-blinded randomized trial is a study conducted by Idris and colleagues (1993). The team aimed to test the equivalence of treatment with a bronchodilator via a nebulizer and the metered-dose inhaler for acute asthma. Patients (N=35) who were attending two emergence asthma departments were included. 20 patients received treatment by a nebulizer and 15 – by an inhaler and placebo via a nebulizer. The study didn’t find any significant difference between groups but noted quicker effects of treatment delivered via inhalers. However, there are various limitations in this study. One of the biggest disadvantages is the small sample and the lack of measurement of outcomes, such as the number of discharges.

Placebo-Controlled Trials

Placebo-controlled trials are an important aspect of randomized controlled trials. Placebo groups are often beneficial in medicine. Still, a placebo can only be used when researchers are uncertain about which treatment is the best for patients (Peat, 2011). Often Phase II studies implement placebo groups, and only with small samples in the short-term.

Nevertheless, the use of a placebo control group can raise some ethical issues; for instance, in cases when subjects are denied the best treatment. Therefore, as explained above, the use of a placebo can only be justified when experts are uncertain which treatment is the best. Unfortunately, there were cases of patients who had been withheld from receiving a beneficial treatment and left in a placebo group for long periods.

An example of a placebo trial, for instance, was a study on asthma that aimed to test the use of leukotriene receptor agonists against placebo (Ferreira, 2001). Note that the next logical step, if effective and efficacy were revealed, would be to conduct Phase III and Phase IV studies to compare effectiveness.

Pragmatic Trials

Since clinical trials can be divided into two categories – pragmatic and explanatory trials, we should focus on pragmatic trials (Tosh et al., 2011). Pragmatic trials are used to assess the effectiveness of medical interventions in real-life practices. On the other hand, explanatory trials test the effectiveness of an intervention under optimal conditions. To be more precise, pragmatic trials are a variation of the randomized controlled trial designs which means that participants are allocated at random.

A major advantage of this design is the fact that pragmatic designs measure effectiveness, not efficacy, and aim to test if a new treatment is better than current treatment. Complex methods, survival rates, and other factors that are patient-oriented are also a focus of research. These studies employ ‘intention-to-treat’ methods to improve patients’ physical and emotional well-being. In order words, pragmatic trials help experts choose the best treatment available to help patients.

However, let’s not forget that blinding is not always possible in practice, which can be one of the biggest limitations of all pragmatic studies. Often, to avoid drop-out rates, researchers can organize a run-in phase before randomization and give time for the subjects to decide if they want to participate or not. Identifying non-complaint subjects may affect the generalizability of results, but smaller samples may prevent delays and dropouts.

An example of a pragmatic study is the randomized trial designed by Laidlaw and colleagues (1998) to test the effectiveness of second eye cataract surgery, following the first eye. Out of 807 subjects, 208 consented to participate. Questions regarding one’s visual difficulties along with visual tests were given to participants. The team proved that second surgery might lead to some improvements. Still, subjective information should be interpreted carefully.

Cross-over Trials

Cross-over trials are also a powerful study design. Just like with randomized trials, they involve the procedure of researchers allocating subjects randomly. However, the difference here is that subjects receive two or more treatments one after another; so, the randomization refers to the order patients receive each treatment (Peat, 2011). In other words, this design is a repeated measurements design, and subjects cross over treatments during the study. In comparison, in parallel studies subjects stay on the same treatment during the whole trial.

Having one subject going through multiple treatments is, in fact, one of the biggest pluses of this study design. Thus, cross-over trials are powerful methods with each subject serving as their own control group. This also means that big samples are not a must and even fewer subjects can be used to obtain meaningful results. Note that cross-over trials work the best for chronic diseases where there’s no absolute treatment for patients but some slight improvements in life.

Nevertheless, there are some disadvantages to this design. If a subject drops out after the second treatment, their results need to be excluded from all analyses. Also, there might be a ‘carry-over’ effect; this means that subjects whose health has improved after the first treatment carry this improvement in the second phase. To resolve the ‘carry-over’ effect, experts can organize a ‘wash-out’ period, something that should be considered during the design stage of the study. Of course, lack of treatment during the wash-out period should not have a negative effect on patients’ health.

Ellaway and colleagues (1999) organized a cross-over trial to measure the L-carnitine effectiveness in improving functional limitations in girls with Rett syndrome. 35 girls were randomized and received either: 1) eight weeks of L-carnitine, washout of four weeks, and eight weeks of placebo; or 2) eight weeks of placebo, washout of four weeks, and eight weeks of L-carnitine. Note that -carnitine proved to be effective in patients with classical Rett syndrome.

Zelen’s Design

Zelen’s design or randomized consent design is also a randomized design, but randomization occurs before consent. In fact, consent is obtained only from the subjects allocated to an experimental treatment. When it comes to Zelen’s designs, treatment should be invasive and illnesses severe. Note that statistician Marvin Zelen was the first person to suggest this idea, so the design was named after him.

When randomization to a placebo or a standard treatment is not acceptable, Zelen’s designs can be employed. In fact, this helps experts deal with low rates of consent and low recruitment rates in invasive treatments. Subjects who don’t agree with taking part in an experimental treatment still receive the standard treatment, and their results are analyzed as if they were part of the experimental groups (Peat, 2011).

One of the disadvantages of this study design, though, is that due to its nature, experts cannot always control all confounders. On top of that, Zelen’s designs raise some questions: isn’t it unethical to randomize patients before consent? Yet, such designs are a great research method that can be employed in screening.

Comprehensive Cohort Studies

The comprehensive cohort study or prospective cohort study with a randomized sub-cohort is a study design that includes subjects who agree to be randomized and subjects who choose (and insist on) a treatment.

Comprehensive cohort studies are extremely useful in cases when subjects may refuse randomization; for instance, when it comes to radiotherapy or surgery for treating cancer. In contrast with Zelen’s studies, comprehensive cohort studies respect patients’ freedom and choice, which is highly beneficial.

Just like other trials with a preference group, comprehensive cohort studies do not provide definitive information about the effectiveness and the efficacy of a treatment, but supplemental results. Therefore, a further independent randomized controlled trial will be needed to provide clear evidence and to answer generalizability goals.

In fact, one of the biggest limitations of the comprehensive cohort study is related to generalizability: the randomized groups should be big enough to lead to meaningful results (Peat, 2011).

An example of a comprehensive cohort study with patient preference groups is the study conducted by Agertoft and Pedersen (1994). The research team aimed to measure the effects of long-term treatment with inhaled corticosteroids in kids with asthma. The parents of 216 children consented to take inhaled corticosteroids for 3-6 years, and the parents of 62 children preferred their kids to stay on cromoglicate. Results showed that experimental treatment resulted in reduced hospital admission rates.

Non-randomized Clinical Trials

In non-randomized clinical trials, the researcher or the participants themselves decide the group of allocation. Non-randomized clinical trials are used mainly to answer questions and provide additional evidence that randomized trials can’t.

Yet, the information can have greater generalizability, and selection bias is not an issue. In contrast, randomized trials have strict inclusion criteria, which may affect the generalizability of results. On top of that, while subjects in non-randomized trials are more willing to enroll, participants in the randomized trial who are receiving standard treatment after randomization may drop out due to dissatisfaction (Peat, 2011). As a result, selection bias may affect the generalizability of results in randomized trials.

Nevertheless, in non-randomized studies, allocation bias is still an issue. Cofounders may not be controlled and lead the team to wrong interpretations. What’s more, when subjects choose a treatment in which they believe, this positive attitude may affect the study results.

However, do not forget that the information obtained during a non-randomized trial is only supplemental. Therefore, one of the best options – in case the sample is big enough and allows it – is to include randomized and preference groups and compare them separately.

Open Trials

Open trials or open-label trials are study designs in which both researchers and subjects know which treatment the subject receives.

This study design is very useful in Phase I trials. It’s important for patients to be aware that the treatment is experimental, and the study may answer questions only about efficacy (Peat, 2011). Transparency is a must.

However, open trials have a major disadvantage due to their transparent nature: subjects’ optimistic attitudes may lead to bias in a positive direction.

Case-control Studies

Case-control studies are also very informative. In case-control studies, people suffering from a disease are compared to healthy people. Information about exposure factors is then collected and compared (Peat, 2011). Note that there are matched case-control studies in which vital personal characteristics between cases and controls match.

Case-control studies are not as costly as cohort studies, and on top of that, they provide answers quicker than cohort studies. Another advantage is that by implementing a case-control design, researchers have the unique chance to employ various ways to select cases and controls. In fact, the most appropriate way to choose controls is to recruit people who would have been selected as cases – if they had the particular health issue of interest. Also, a good approach is to select controls from the same study base or population of the cases. Note that each case can have more than one control, which will be compared.

As this study design often relies on retrospective data about exposure and other risks, recall of past events can lead to uncontrolled confounding and bias and type I error (false-positive result). Therefore, the results that case-control studies provide are more beneficial in generating hypotheses and ideas, instead of testing actual causation. In other words, case-control studies are great in the initial stages of research, and in fact, all generated ideas can be consequently developed in other studies.

For instance, results from one particular case-control study conducted by Parker and colleagues (1998) can be extremely beneficial in practice. The team tested if there was a connection between neonatal intra-muscular administration of vitamin K and childhood cancer, based on self-reported exposure. Consequently, based on results, another study was conducted using medical records and assessing the national cancer registry for more objective information.

Nested Case-control Studies

Nested case-control studies are studies that can be conducted within a cohort study. It is a powerful study design as cases and controls are chosen from one single cohort. In other words, this approach reduces costs and time when compared to the full cohort approach. Nested case-control studies are extremely beneficial for studying factors, such as biological precursors of an illness.

One of the main advantages of this design is that when a case appears in the cohort, controls who were at risk at the time can be selected. This tactic gives researchers control over any confounding effects that may be established. As mentioned above, nested case-control studies reduce the costs of following up a whole cohort.

Nevertheless, bias and other errors may occur. Therefore, a larger number of controls can be enrolled for each case to improve the statistical efficiency of the study.

An example is the following study. Badawi and colleagues (1998) designed a case-control study to measure risk factors for newborn encephalopathy. The sample base was selected from all births in the metropolitan area of Western Australia between June 1993 and September 1995. The team proved that many causes of encephalopathy start before birth.

Matched Case-control studies

Matched case-control studies are another powerful design. They are used when cases and controls are selected based on matching personal characteristics (like gender). This way vital confounders, such as gender and age, can be eliminated, and as a result, researchers can focus on other exposure factors related to a certain disease. This approach is highly important to determine prognostic factors, which may go unnoticed in the random selection and small studies.

Some factors are strong cofounders in many diseases. However, when the matching variables occur within friends and family, selection bias may occur. Therefore, a better option is to choose matched controls based on other characteristics; for instance, the next person on an electoral registry. Note that an effective sample size counts the number of pairs, not the number of subjects. Also, another disadvantage of this study design is related to generalizability: generalizability may be reduced if controls match cases more than the general population. On top of that, the case has to be excluded from the analysis if control cannot be found. Last but not the least, remember that over-matching is also harmful: it can lead to bias and shift the results towards the null. Over-matching can occur when experts select cases and controls based on many cofounders: age, gender, social background, etc.

Salonen and colleagues (1998) conducted a matched case-control study to test the link between iron stores and non-insulin-dependent diabetes among men (N=1038). Matching characteristics were age, time of examination, smoking, exercising, weight, etc. The follow-up period was four years. The statistical power of the study was increased by having two controls for each case.

Studies with Historical Cohorts

Studies with historical cohorts compare people given the new treatment and people given the standard treatment in the past. These studies can be used for convenience, and they definitely benefit practice.

Nevertheless, studies with historical cohorts are often subject to bias. Lack of factors, such as control of cofounders, changes in inclusion criteria for other treatments, and outcome variables, may interfere with findings.

Halken and colleagues (2004) conducted an interesting study with historical cohorts. Their research aims were to investigate the effectiveness of allergenic avoidance in the prevention of allergic symptoms in infancy. The intervention consisted of avoidance of exposure to tobacco, pets, etc. The infants were followed up within 18 months. Outcomes like dermatitis and wheeze were measured.

Cross-sectional Studies

Cross-sectional studies are another wonderful way to obtain initial information about diseases within a community, including mortality rates and associations between exposure and risks. Data is collected at a single point in time, which provides a snapshot of a disease. A large random sample is obtained from the general population of interest.

One of the biggest advantages is that such studies provide vital information about the burden of disease within the community (Peat, 2011). On top of that, serial cross-sectional studies are a preferred method for measuring health status, health-related behavior, and chronic diseases in a population. Since information is collected via questionnaires, serial cross-sectional studies are cheaper and quicker than cohort studies, for example.

However, a big disadvantage is that, since exposures and outcomes are measured at the same time, researchers can’t determine which comes first. Note that a response over 80% is needed to avoid bias and improve generalizability. In general, smaller samples with high response rates are more useful than larger samples with low response rates. Note that the poor validity of the methods can also affect the results.

An example of a cross-sectional study is the study designed by Kauer and colleagues. The research team aimed to measure the variation and prevalence of asthma symptoms in children (12-14). Various schools in England, Wales, and Scotland were selected.

Ecological Studies

Ecological studies are also a popular study design. They provide statistics on population groups, such as schools and countries. They are extremely useful in describing populations and differences between different groups (such as schools). Research is often based on various factors, such as the prevalence of a disease or mortality rates.

However, ecological studies rely on data that is collected not directly from subjects, but a national census, for example. Therefore, these studies can’t show vital differences between individuals. On top of that, ecological studies have one big disadvantage: since there’s no control of confounders, ecological studies provide a weak study design to assess causation (Peat, 2011).

For instance, Douglas and colleagues (1998) conducted an ecological study investigating if sudden infant death syndrome (SIDS) has a seasonal pattern. They concluded that there’s a seasonal peak in winter, especially in kids under five months

Qualitative Studies

Qualitative studies, as explained earlier, can be described as descriptive studies that use in-depth interviews to collect vital information. Such studies give additional meaning to research, provide a better understanding, and may complete qualitative studies.

Qualitative studies provide information about the patients and carers, which is tailored and patient-oriented. It is extremely helpful in providing additional information and for revealing patients’ needs and feelings towards healthcare. Since patients can share their thoughts on treatments (in studies of effectiveness), qualitative studies can reveal the acceptability of treatments and interventions (Peat, 2011).

Nevertheless, there are some disadvantages. For instance, the generalization of results is not known.

An example of a qualitative study is the work of Butler and colleagues (1998). They conducted semi-structured interviews to assess the effectiveness of opportunistic anti-smoking campaigns, which is an overused practice employed by general practitioners.

Case Reports

Last but not the least, case reports and case series are also vital. They are descriptive designs and are very educational tools. They can be described as a record of interesting cases. As such, case reports provide information about one patient or a small group of patients, and case series – about larger groups of patients.

Unfortunately, hypotheses can’t be tested, and associations can’t be explored. What’s more, the number of cases is often limited.

A curious case report was provided by Ellaway and colleagues (1998). They examined the association of protein-losing enteropathy with cobalamin C-defect in a male infant.

Study Designs: Plan in Advance

Pilot studies.

Pilot studies, as mentioned earlier, are vital in research. Before conducting any study, a pilot study or a preliminary investigation will be needed.

For instance, pilot studies, also called feasibility studies, are beneficial in testing recruiting methods, practicality, sample size, and other specifications of each study design (Peat, 2011). Such studies will prevent changes in protocol and eliminate errors.

Remember that it’s better to conduct a pilot study, instead of changing the design and failing to conduct the actual study in later stages of research.

Methodological Studies

Methodological studies, just like pilot studies, are also paramount in research. They are studies that aim to test if research methods are accurate and repeatable (Peat, 2011).

Methodological studies can help experts test if the employed research instrument can be interchanged with another one. Therefore, studies that assess repeatability and agreement of a research instrument are crucial. In the end, repeatability and avoiding bias are important aspects of research.

Study Designs: Conclusion

To sum up, designing a study is a challenging process. There are many factors that should be considered: from funding to recruiting subjects; research can be tricky. Unfortunately, without a good study design, even the most impressive research idea can fail.

Also, it’s important to understand that each type of study design has various benefits and limitations, and there’s no one-size-fits-all approach in medicine.

In the end, designing a study is worth it: all research studies pile on the existing scientific knowledge with the sole aim to improve patients’ well-being.

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• Published: 09 April 2024

Creating culturally-informed protocols for a stunting intervention using a situated values-based approach ( WeValue InSitu ): a double case study in Indonesia and Senegal

• Annabel J. Chapman 1 ,
• Chike C. Ebido 2 , 3 ,
• Rahel Neh Tening 2 ,
• Yanyan Huang 2 ,
• Ndèye Marème Sougou 4 ,
• Risatianti Kolopaking 5 , 6 ,
• Amadou H. Diallo 7 ,
• Rita Anggorowati 6 , 8 ,
• Fatou B. Dial 9 ,
• Jessica Massonnié 10 , 11 ,
• Mahsa Firoozmand 1 ,
• Cheikh El Hadji Abdoulaye Niang 9 &
• Marie K. Harder 1 , 2  

BMC Public Health volume  24 , Article number:  987 ( 2024 ) Cite this article

Metrics details

International development work involves external partners bringing expertise, resources, and management for local interventions in LMICs, but there is often a gap in understandings of relevant local shared values. There is a widespread need to better design interventions which accommodate relevant elements of local culture, as emphasised by recent discussions in global health research regarding neo-colonialism. One recent innovation is the concept of producing ‘cultural protocols’ to precede and guide community engagement or intervention design, but without suggestions for generating them. This study explores and demonstrates the potential of an approach taken from another field, named WeValue InSitu , to generate local culturally-informed protocols. WeValue InSitu engages stakeholder groups in meaning-making processes which ‘crystallize’ their envelope of local shared values, making them communicable to outsiders.

Our research context is understanding and reducing child stunting, including developing interventions, carried out at the Senegal and Indonesia sites of the UKRI GCRF Action Against Stunting Hub. Each national research team involves eight health disciplines from micro-nutrition to epigenetics, and extensive collection of samples and questionnaires. Local culturally-informed protocols would be generally valuable to pre-inform engagement and intervention designs. Here we explore generating them by immediately following the group WeValue InSitu crystallization process with specialised focus group discussions exploring: what local life practices potentially have significant influence on the environments affecting child stunting, and which cultural elements do they highlight as relevant. The discussions will be framed by the shared values, and reveal linkages to them. In this study, stakeholder groups like fathers, mothers, teachers, market traders, administrators, farmers and health workers were recruited, totalling 83 participants across 20 groups. Themes found relevant for a culturally-informed protocol for locally-acceptable food interventions included: specific gender roles; social hierarchies; health service access challenges; traditional beliefs around malnutrition; and attitudes to accepting outside help. The concept of a grounded culturally-informed protocol, and the use of WeValue InSitu to generate it, has thus been demonstrated here. Future work to scope out the advantages and limitations compared to deductive culture studies, and to using other formative research methods would now be useful.

Peer Review reports

Although progress has been made towards the SDG of ‘Zero Hunger by 2025’, the global rates of malnutrition and stunting are still high [ 1 ]. Over the past 20 years, researchers have implemented interventions to reduce undernutrition, specifically focussing on the first 1000 days of life, from conception to 24 months [ 2 ]. However, due to both differing determinants between countries [ 3 , 4 ] as well as varying contextual factors, it is clear that no single fixed approach or combination of approaches can be relied on when implementing stunting interventions [ 5 , 6 , 7 ]. Furthermore, when external researchers design interventions for local areas in Low- and Middle-Income Countries (LMICs) they can often overlook relevant local cultural factors that consequently act as barriers to intervention uptake and reduce their effectiveness, such as geographical factors and the levels of migration in certain populations [ 8 , 9 ], or social norms or perceptions relating to accepting outside help, and power dynamics related to gender [ 10 , 11 , 12 ]. The inclusion of cultural level factors in behaviour change interventions has been proposed as a requirement for effective interventions [ 13 ]. However, despite the breadth of literature highlighting the negative impacts from failing to do this, the lack of integration or even regard of local culture remains a persistent problem in Global Health Research [ 14 ], possibly hindering progress towards the SDGs. Thus, there is a need for approaches to integrate local cultural elements into intervention design.

This lack of understanding of relevant local culture, social norms and shared values also has ethical implications. The field of Global Health Ethics was predominantly developed in the Global North, in High Income Countries (HICs), embedding values common in those countries such as the prominence of individual autonomy [ 15 , 16 ]. Researchers from HICs carrying out research in LMICs may wrongly assume that values held in the Global North are universal [ 14 ] and disregard some local values, such as those related to family and collective decision making, which are core to many communities in LMICs. It is therefore important for outside researchers to have an understanding of relevant local values, culture and social norms before conducting research in LMICs so as not to impose values that do not align with local culture and inadvertently cause harm or offence [ 16 , 17 ]. The importance of this is compounded by the colonial history that is often present in relationships between research communities in HICs and LMICs, and the fact that the majority of the funding and leading institutions are still located in the Global North [ 18 , 19 ]. Thus, conscious steps must be taken to avoid neo-colonialism in Global Health Research [ 20 ]. From a health-equity perspective, it is essential to ensure that those in vulnerable communities are not hindered from involvement in interventions to improve nutrition. Encouraging uptake by such communities could be provided if salient local shared values, norms and culture were taken into account [ 21 ].

In a recent paper, Memon et al., (2021) highlight the usefulness of first creating a cultural protocol that can precede and guide subsequent stages of community engagement or intervention design to ensure that salient local values are known to external researchers coming into the community [ 16 ]. We adopt the use of the concept of a cultural protocol, referring to locally-generated guidance about key values, norms, behaviours and customs relevant to working with the local community. However, we prefer the term, ‘culturally-informed protocol’ since this relates to only cultural elements deemed salient by the researchers, and locally, rather than any comprehensive notion of culture, nor extending beyond the research context.

Memon et al. (2021), point out links between the creation of such a protocol and existing codes of practice that have already been created for some cultures such as the Te Ara Tika, a Guideline for Māori Research Ethics [ 22 ]. Currently, research and interventions in Global Health can be informed by a stage of formative research involving one-to-one interviews, focus groups or direct observations, which can sometimes be ethnographic in nature such as within Focussed Ethnographic Studies or Rapid Assessment Procedures [ 23 , 24 , 25 ]. Although these methods can be effective to inform intervention designs, they have disadvantages like: can take long periods to complete [ 26 ], can be resource intensive [ 26 ] and can lack cultural acceptability [ 27 ]. These limitations may account for the frequent neglect of their use generally, highlighted by Aubel and Chibanda (2022) [ 14 ]. Additionally, none of these methods work towards making explicit local values, or towards the creation of a culturally-informed protocol. In brief, the literature suggests a need to develop alternative methods of Formative Research for understanding locally relevant cultural elements, that are less time-consuming and can generate data that is more easily translatable to intervention design. In addition, these approaches must be applicable in different cultures. Additionally, the protocols produced must be actionable and practical not only for guiding interactions between research teams but also for guiding the initial stages of intervention design.

The work presented here aims to address several of these needs. It includes an exploration of the usefulness of the WeValue InSitu ( WVIS ) approach because that has previously been shown, in environmental management domains, to offer a way to gather in-depth values-based perspectives from a target population [ 28 , 29 ] It was first created through action research, and co-designed to enable civil society organisations to better understand and measure the values-based aspects of their work [ 30 ]. The core WeValue InSitu process (detailed in Table 1 ) involves the crystallization of shared values, with a facilitator guiding a group of participants with shared experiences, through cycles of tacit meaning-making (using a stage of photo-elicitation and triggering) [ 31 ], until they can articulate more explicitly their shared values, in concise and precise statements. These statements are then linked together in a framework by the participants. In an example case in Nigeria, the results of the WVIS approach hinted at the creation of a culturally-informed protocol through an analysis of the shared values frameworks to find cultural themes for the creation of an indicator tool that was used to evaluate several development scenarios based on their social acceptability [ 29 ].

Furthermore, it has been found that if a group of WVIS participants take part in a specialised focus group discussion (FGD), named Perspectives EXploration (PEX:FGD) immediately afterward the main workshop, then they easily and articulately express their perspectives on the topics raised for discussion - and with allusions to the shared values they had crystallised just prior. In an example from Shanghai, the PEX:FGDs focussed on eliciting perspectives on climate change, which were shown to be closely linked with the cultural themes existing within the shared values frameworks produced immediately prior [ 32 ]. In that case, the PEX:FGDs allowed the cultural themes generated during the main WVIS workshop to be linked more closely to the research question. Those results suggested that the WVIS plus PEX:FGD approach could be used to create a specialised culturally-informed protocol for improved intervention design.

In the study presented here, the WVIS approach was explored for the purpose of creating culturally-informed protocols to inform the planning of interventions within two localities of the UKRI GCRF Action Against Stunting Hub [ 33 ]. The work was carried out in two parts. Firstly, the WVIS main workshop was used to elicit cultural themes within the target communities, indicating key elements to consider to ensure ethical engagement. Secondly, the PEX focus group discussions focussed on life practices related to stunting which we explored for the purpose of tailoring the culturally-informed protocols to the specific purpose of improving the design of an example intervention. The Action Against Stunting Hub works across three sites where stunting is highly prevalent but via different determinants: East Lombok in Indonesia (estimated 36% of under-fives stunted), Kaffrine in Senegal (estimated 16% of under-fives stunted) and Hyderabad in India (estimated 48% of under-fives stunted) [ 34 ]. We propose that, the information about local shared values in a given site could be used to inform the design of several interventions, but for our specific exploration the focus here is a proposed ‘egg intervention’, in which pregnant women would be provided with an egg three times per week as supplement to their diet. This study proposes that identifying shared values within a community, alongside information about local life practices, provides critical cultural information on the potential acceptability and uptake of this intervention which can be used to generate culturally-informed protocols consisting of recommendations for improved intervention design.

In this paper we aim to explore the use of the WVIS approach to create culturally-informed protocols to guide engagement and inform the design of localised egg interventions to alleviate stunting in East Lombok, Indonesia and Kaffrine, Senegal. We do this by analysing data about local shared values that are crystallized using the WeValue InSitu ( WVIS ) process to provide clear articulation of local values, followed by an analysis of life practices discussed during PEX:FGD to tailor the culturally-informed protocols for the specific intervention design.

Study setting

This research was exploratory rather than explanatory in nature. The emphasis was on demonstrating the usefulness of the WeValue InSitu ( WVIS ) approach to develop culturally-informed protocols of practical use in intervention design, in different cultural sites. This study was set within a broader shared-values workstream within the UKRI GCRF Action Against Stunting Hub project [ 33 ]. The Hub project, which was co-designed and co-researched by researchers from UK, Indonesia, Senegal and India, involves cohorts of 500 women and their babies in each site through pregnancy to 24 months old, using cross-disciplinary studies across gut health, nutrition, food systems, micro-nutrition, home environment, WASH, epigenetics and child development to develop a typology of stunting. Alongside these health studies are studies of the shared values of the communities, obtained via the WVIS approach described here, to understand the cultural contexts of that diverse health data. In this study the data from East Lombok, Indonesia and Kaffrine, Senegal were used: India’s data were not yet ready, and these two countries were deemed sufficient for this exploratory investigation.

The WVIS approach

The WVIS approach is a grounded scaffolding process which facilitates groups of people to make explicit their shared values in their own vocabulary and within their own frames (details in Fig. 1 and activities in Table 1 ). The first stage of the WVIS is Contextualisation, whereby the group identifies themselves and set the context of their shared experiences, for example, as ‘mothers in East Lombok, Indonesia’. Subsequently, there is a stage of Photo Elicitation, in which the group are first asked to consider what is important, meaningful or worthwhile to them about their context (e.g., ‘being mothers in East Lombok, Indonesia’) and then asked to choose photos from a localised set that they can use as props to help describe their answer to the group [ 29 ]. After this, a localised Trigger List is used. This Trigger List consists of 109 values statements that act as prompts for the group. Examples of these values statements are included below but all the statements begin with “it is important to me/us that…”. The group are asked to choose which statements within the trigger list resonate with them, and those are taken forward for group intersubjective discussion. After a topic of their shared values has been explored, the group begin to articulate and write down their own unique statements of them. These also all begin with “It is important to me/us that…”. After discussing all pressing topics, the group links the written statements on the table into a unique Framework, and one member provides a narrative to communicate it to ‘outsiders’. The WVIS provides a lens of each group’s local shared values, and it is through this lens that they view the topics in the focus group discussions which immediately follow, termed Perspectives EXplorations (PEX:FGDs).

Schematic of the macro-level activities carried out during the WeValue InSitu ( WVIS ) main workshop session

This results in very grounded perspectives being offered, of a different nature to those obtained in questionnaires or using external frameworks [ 31 ]. The specific PEX:FGD topics are chosen as pertinent to stunting contextual issues, including eating habits, food systems and environments, early educational environments, and perceptions of stunting. The local researchers ensured that all topics were handled sensitively, with none that could cause distress to the participants. The data for this study were collected over 2 weeks within December 2019–January 2020 in workshops in East Lombok, Indonesia, and 2 weeks within December 2020 in Kaffrine, Senegal.

The PEX:FGDs were kept open-ended so that participants could dictate the direction of the discussion, which allowed for topics that may not have been pre-considered by the facilitators to arise. Sessions were facilitated by local indigenous researchers, guided in process by researchers more experienced in the approach, and were carried out in the local languages, Bahasa in East Lombok, Indonesia and French or Wolof in Kaffrine, Senegal.

Development of localised WVIS materials

Important to the WVIS approach is the development of localised materials (Table 1 ). The main trigger list has been found applicable in globalised places where English is the first language, but otherwise the trigger lists are locally generated in the local language, incorporating local vocabulary and ways of thinking. To generate these, 5–8 specific interviews are taken with local community members, by indigenous university researchers, eliciting local phrases and ways of thinking. This is a necessary step because shared tacit values cannot be easily accessed without using local language. Examples of localised Trigger Statements produced this way are given below: (they all start with: “It is important to me/us that…”):

…there is solidarity and mutual aid between the people

…I can still be in communication with my children, even if far away

…husbands are responsible for the care of their wives and family

…the town council fulfils its responsibility to meet our needs

…people are not afraid of hard, and even manual work

Study participants

The group participants targeted for recruitment, were selected by local country Hub co-researchers to meet two sets of requirements. For suitability for the WVIS approach they should be between 3 and 12 in number; belong to naturally existing groups that have some history of shared experiences; are over 18 years old; do not include members holding significantly more power than others; and speak the same native language. For suitability in the PEX:FGD to offer life practices with relevance to the research topic of stunting, the groups were chosen to represent stakeholders with connections to the food or learning environment of children (which the Action Against Stunting Hub refer to as the Whole Child approach) [ 33 ]. The university researchers specialising in shared values from the UK, and Senegal and Indonesia respectively, discussed together which stakeholder groups might be appropriate to recruit. The local researchers made the final decisions. Each group was taken through both a WVIS workshop and the immediately-subsequent PEX:FGD.

Data collection and analysis

Standard data output from the WeValue session includes i) the jointly-negotiated bespoke Statements of shared values, linked together in their unique Framework, and ii) an oral recording of a descriptive Narrative of it, given by the group. These were digitized to produce a single presentation for each group as in Fig. 2 . It represents the synthesised culmination of the crystallisation process: a portrait of what was ‘important’ to each stakeholder group. Separately, statements from the group about the authenticity/ownership of the statements are collected.

An illustrative example of one digitized Shared Values Framework and accompanying Narrative from a teacher’s group in East Lombok, Indonesia. The “…” refers to each statement being preceded by “It is important to us that…”

When these Frameworks of ‘Statements of Shared Values’ are viewed across all the groups from one locality (Locality Shared Values Statements), they provide portraits of ‘what is important’ to people living there, often in intimate detail and language. They can be used to communicate to ‘outsiders’ what the general cultural shared values are. In this work the researchers thematically coded them using Charmaz constructionist grounded theory coding [ 35 ] to find broad Major Cultural Themes within each separate locality.

The second area of data collection was in the post- WVIS event: the PEX:FGD for each group. A translator/interpreter provided a running commentary during these discussions, which was audio recorded and then transcribed. The specific topics raised for each group to discuss varied depending on their local expertise. This required completely separate workstreams of coding of the dataset with respect to each topic. This was carried out independently by two researchers: one from UK (using NVivo software (Release 1.3.1)) and one from the local country, who resolved any small differences. All the transcripts were then collated and inductively, interpretively analysed to draw out insights that should be relayed back to the Action Against Stunting Hub teams as contextual material.

The extracts of discussion which were identified as relevant within a particular Hub theme (e.g. hygiene) were then meta-ethnographically synthesised [ 36 ] into ‘Hub Theme Statements’ on each topic, which became the core data for later communication and interrogation by other researchers within the Action Against Stunting Hub. These statements are interpretations of participants’ intended meanings, and links from each of them to data quotes were maintained, enabling future interpretations to refer to them for consistency checks between received and intended meaning.

In this investigation, those Hub Theme Statements (derived from PEX:FGD transcripts) were then deductively coded with respect to any topics with potential implications of the egg intervention. Literature regarding barriers and facilitators to nutrition interventions indicated the following topics could be relevant: attitudes to accepting help; community interactions; cooking and eating habits; traditional beliefs about malnutrition; sharing; social hierarchies [ 12 , 37 , 38 ] to which we added anything related to pregnancy or eggs. This analysis produced our Egg Intervention Themes from the data.

The Major Cultural Themes and Egg Intervention Themes were then used to create a set of culture-based recommendations and intervention specific recommendations respectively for each locality. These recommendations were then combined to form specialized culturally-informed protocols for the egg intervention in each locality: East Lombok, Indonesia and Kaffrine, Senegal. The process is displayed schematically in Fig.  3 .

Schematic representation of the method of production of the culturally-informed protocol for each locality

The preparation of the localised WVIS materials at each site took 6 hours of interview field work, and 40 person hours for analysis. The 10 workshops and data summaries were concluded within 10 workdays by two people (80 person hours). The analysis of the PEX:FGD data took a further 80 person hours. Thus, the total research time was approximately 200 person hours.

The stakeholder group types are summarised in Table 2 . The data is presented in three parts. Firstly, the Major Cultural Themes found in East Lombok, Indonesia and in Kaffrine, Senegal are described – the ones most heavily emphasised by participants. Then, the Egg Intervention Themes and finally, the combined set of Recommendations to comprise a culturally-informed protocol for intervention design for each location. Quotations are labelled INDO or SEN for East Lombok, Indonesia and Kaffrine, Senegal, respectively.

Major cultural themes from frameworks and narratives

These were derived from the Locality Shared Values Statements produced in the WVIS .

East Lombok, Indonesia

Religious values.

Islamic values were crucially important for participants from East Lombok, Indonesia and to their way of life. Through living by the Quran, participating in Islamic community practices, and teaching Islamic values to their children, participants felt they develop their spirituality and guarantee a better afterlife for themselves and their children. Participants stated the Quran tells them to breastfeed their children for 2 years, so they do. Despite no explicit religious official curriculum in Kindergarten, the teachers stated that it was important to incorporate religious teaching.

“East Lombok people always uphold the religious values of all aspects of social life.”

“It is important for me to still teach religious values even though they are not clearly stated in the curriculum.” – Workshop 1 INDO (teachers).

“In Quran for instance, we are told to breastfeed our kids for 2 years. We can even learn about that ” – Workshop 3 INDO (mothers).

Related to this was the importance of teaching manners to children and preventing them from saying harsh words. Teachers stated that it was important to create a happy environment for the children and to ensure that they are polite and well-behaved. Similarly, mothers emphasised the need to teach their children good religious values to ensure they will be polite and helpful to their elders.

“Children don’t speak harsh words.”

“My children can help me like what I did to my parents”.

– Workshop 8 INDO (mothers).

Togetherness within families and the community

The Locality Shared Values Frameworks stressed the importance of togetherness, both within family and community. Comments mentioned it being important that people rely heavily on their family and come together in times of need to support each other and provide motivation. This was also important more broadly, in that people in society should support each other, and that children grow up to contribute to society. This was also reflected in comments around roles within the family. Despite women being primary care givers, and men working to finance the family, participants stated that they follow a process of consultation to make decisions, and when facing hardships.

“that we have the sense of kinship throughout our society”.

“We have togetherness as mothers”.

“For the family side, whatever happens we need to be able to be united as a whole family. We need to have the [sense of] forgiveness for the sake of the children” – Workshop 2 INDO (mothers).

Attitudes about extra-marital pregnancy

In East Lombok, Indonesia, it was essential to both mothers and fathers that pregnancy happened within a marriage, this was to ensure that the honour of the family was upheld and that the lineage of the child was clear. The potential danger to health that early pregnancies can cause was also acknowledged.

“If they don’t listen to parents’ advice, there will be the possibility of pre-marital pregnancy happening, which will affect the family [so much].

The affect is going to be ruining the good name, honour and family dignity. When the children [are] born outside [of] marriage, she or he will have many difficulties like getting a birth certificate [and] having a hard time when registering to school or family” - Workshop 4 INDO (mothers).

“ To make sure that our children avoid getting married at a very young age and moreover [avoid] having free sex so that they will not get pregnant before the marriage” - Workshop 9 INDO (fathers).

Kaffrine, Senegal

The Major Cultural Themes which emerged from the Kaffrine data are described below. As these are grounded themes, they are different than those seen in East Lombok, Indonesia.

Access to healthcare

A recurring theme amongst the groups in Kaffrine were aspirations of affordable and easy-to-access healthcare. Community health workers stated the importance of encouraging women to give birth in hospitals and spoke of the importance of preventing early pregnancy which result from early marriages. Giving birth in hospitals was also a concern for Public Office Administrators who highlighted that this leads to subsequent issues with registering children for school. Mothers and fathers stated the importance of being able to afford health insurance and access healthcare so that they could take care of themselves.

“That the women give birth in the hospital” – Workshop 11 SEN (CHWS).

“To have affordable health insurance ” – Workshop 10 SEN (mothers).

“To have access to health care ” – Workshop 3 SEN (fathers).

“It is important that women give birth in the hospital in order to be able to have a certificate that allows us to establish the civil status” – Workshop 9 SEN (administrators).

Additionally, Community health workers spoke of their aspiration to have enough supplements to provide to their community so as to avoid frustration at the lack of supply, and mothers spoke of their desire to be provided with supplements.

“To have dietary supplements in large quantities to give them to all those who need them, so as not to create frustration” – Workshop 11 SEN (CHWS).

Another aspect of access to healthcare, was mistrust between fathers and community health workers. Community health workers explained that sometimes men can blame them when things go wrong in a pregnancy or consider their ideas to be too progressive. Thus, to these community health workers the quality of endurance was very important.

“Endurance (Sometimes men can accuse us of influencing their wives when they have difficulties in conceiving)” – Workshop 5 SEN (CHWs).

Another recurring theme was the importance of having secure employment and a means to support themselves; that there were also jobs available for young people, and that women had opportunities to make money to help support the family. This included preventing early marriages so girls could stay in school. Having jobs was stated as essential for survival and important to enable being useful to the community and society.

“To have more means of survival (subsistence) to be able to feed our families”.

“To have a regular and permanent job”.

“We assure a good training and education for our children so that they will become useful to us and the community”.

“ Our women should have access to activities that will support us and lessen our burden” – Workshop 3 SEN (fathers).

It was considered very important to have a religious education and respect for religious elders. Moreover, living by, and teaching, religious values such as being hard working, humble and offering mutual aid to others, was significant for people in Kaffrine.

“Have an education in the Islamic Culture (Education that aligns with the culture of Islam)”.

“Respect toward religious leaders” – Workshop 3 SEN (fathers).

“ To organize religious discussions to develop our knowledge about Islam ” - Workshop 10 SEN (mothers).

“ Have belief and be prayerful and give good counselling to people ” - Workshop 4 SEN (grandmothers).

Egg intervention themes from each country from perspectives EXplorations focus group discussion data

Below are results of analyses of comments made during the PEX:FGDs in East Lombok, Indonesia and Kaffrine, Senegal. The following codes were used deductively: attitudes to accepting outside help, traditional gender roles, food sharing, traditional beliefs, social hierarchies and understanding of stunting and Other. These topics were spoken about during open discussion and were not the subject of direct questions. For example, topics relating to traditional gender roles came up in East Lombok, during conversations around the daily routine. Thus, in order to more accurately reflect the intended meaning of the participants, these were labelled food practices, under the “Other” theme. If any of the themes were not present in the discussion, they are not shown below.

Attitudes to accepting outside help

Few mentions were made that focussed on participants attitudes to accepting outside help, but participants were sure that they would not make changes to their menus based on the advice of outside experts. Additionally, teachers mentioned that they are used to accepting help from local organisations that could to help them to identify under-developed children.

“ We don’t believe that [the outsiders are] going to change our eating habits or our various menus ” – Workshop 3 INDO (Mothers).

Traditional gender roles

In East Lombok, mothers spoke about how their husbands go to work and then provide them with daily money to buy the food for the day. However, this was discussed in relation to why food is bought daily and is thus discussed below in the topics Other – Food practices.

Food sharing

In East Lombok, Indonesia, in times when they have extra food, they share it with neighbours, in the hope that when they face times of hardship, their neighbours will share with them. Within the household, they mentioned sharing food from their plate with infants and encouraging children to share. Some mothers mentioned the importance of weekly meetings with other mothers to share food and sharing food during celebrations.

“ Sometimes we share our food with our family. So, when we cook extra food, we will probably send over the food to our neighbour, to our families. So, sometimes, with the hope that when we don’t have anything to eat, our neighbour will pay for it and will [share with] us.” – Workshop 3 INDO (Mothers).

“Even they serve food for the kids who come along to the house. So, they teach the kids to share with their friends. They provide some food. So, whenever they play [at their] house, they will [eat] the same.” – Workshop 2 INDO (Mothers).

Understanding of stunting

The teachers in East Lombok were aware of child stunting through Children’s Development Cards provided by local healthcare organizations. They stated that they recognise children with nutrition problems as having no patience period, no expression, no energy for activities and less desire to socialise and play with other children. The teachers said that stunted children do not develop the same as other children and are not as independent as children who are the proper height and weight for their development. They also stated that they recognise stunted children by their posture, pale faces and bloated stomachs. They explained how they usually use the same teaching methods for stunting children, but will sometimes allow them to do some activities, like singing, later, once the other children are leaving.

“ They have no patience period, don’t have any energy to do any of the activities. No expression, only sitting down and not mingling around with the kids. They are different way to learn. They are much slower than the other kids .” – Workshop 1 INDO (teachers).

“ When they are passive in singing, they will do it later when everyone else is leaving, they just do it [by] themselves ” – Workshop 1 INDO (teachers).

Specific views on eggs

In East Lombok, Indonesia, there were no superstitions or traditional beliefs around the consumption of eggs. When asked specifically on their views of eggs, and if they would like to be provided with eggs, women in East Lombok said that they would be happy to accept eggs. They also mentioned that eggs were a food they commonly eat, feed to children and use for convenience. Eggs were considered healthy and were common in their house.

“ We choose eggs instead. If we don’t have time, we just probably do some omelettes or sunny side up. So, it happens, actually when we get up late, we don’t have much time to be able to escort our kids to the school, then we fry the eggs or cook the instant noodles. And it happens to all mothers. So, if my kids are being cranky, that’s what happens, I’m not going to cook proper meals so, probably just eggs and instant noodles.” – Workshop 3 INDO (Mothers).

Other important topics – food practices

Some detailed themes about food practices were heard in East Lombok, Indonesia. The women were responsible for buying and preparing the food, which they purchased daily mainly due to the cost (their husbands were paid daily and so provided them with a daily allowance) and lack of storage facilities. They also bought from mobile vendors who came to the street, because they could buy very small amounts and get occasional credit. The mother decided the menu for the family and cooked once per day in the morning: the family then took from this dish throughout the day. Mothers always washed their fruits and vegetables and tried to include protein in their meals when funds allowed: either meat, eggs, tofu or tempeh.

“ One meal a day. They [the mothers] cook one time and they [the children] can eat it all day long. Yes, they can take it all day long. They find that they like [to take the food], because they tend to feel hungry.” – Workshop 6 INDO (Mothers).

“ They shop every day because they don’t have any storage in their house and the other factor is because the husband has a daily wage. They don’t have monthly wage. In the morning, the husband gives the ladies the money and the ladies go to the shop for the food. ” – Workshop 4 INDO (Mothers).

In Kaffrine, the following themes emerged relating to an egg intervention: they were different in content and emphasis to Lombok and contained uniquely local cultural emphases.

Mothers were welcoming of eggs as a supplement to improve their health during pregnancy and acknowledged the importance of good nutrition during pregnancy. However, they also mentioned that their husbands can sometimes be resistant to accepting outside help and provided an example of a vaccination programme in which fathers were hesitant to participate. However, participants stated that the Government should be the source of assistance to them (but currently was not perceived to be so).

“But if these eggs are brought by external bodies, we will hesitate to take it. For example, concerning vaccination some fathers hesitate to vaccinate their children even if they are locals who are doing it. So, educating the fathers to accept this is really a challenge” – Workshop 11 SEN (CHWs).

Some traditional gender roles were found to be strong. The participants emphasised that men are considered the head of the household, as expected in Islam, with the mother as primary caregiver for children. This is reflected in the comments from participants regarding the importance of Islam and living their religious values. The men thus made the family decisions and would need to be informed and agree to any family participation in any intervention – regardless of the education level of the mother. The paternal grandmother also played a very important role in the family and may also make decisions for the family in the place of the father. Community Health Workers emphasised that educating paternal grandmothers was essential to improve access to healthcare for women.

“There are people who are not flexible with their wives and need to be informed. Sometimes the mother-in-law can decide the place of the husband. But still, the husband’s [permission] is still necessary.” – Workshop 1 SEN (CHWs).

“[We recommend] communication with mothers-in-law and the community. Raise awareness through information, emphasizing the well-being of women and children.” – Workshop 1 SEN (CHWs).

“The [grand]mothers take care of the children so that the daughters in-law will take care of them in return So it’s very bad for a daughter in law not to take care of her mother in-law. Society does not like people who distance themselves from children.” – Workshop 4 SEN (grandmothers).

Social hierarchies

In addition to hierarchies relating to gender/position in the family such as grandmothers have decision making power, there was some mention of social hierarchies in Kaffrine, Senegal. For example, during times of food stress it was said that political groups distribute food and elected officials who choose the neighbourhoods in which the food will be distributed. Neighbourhood leaders then decide to whom the food is distributed, meaning there is a feeling that some people are being left out.

“ It’s political groups that come to distribute food or for political purposes…organizations that often come to distribute food aid, but in general it is always subject to a selection on the part of elected officials, in particular the neighbourhood leaders, who select the people they like and who leave the others ” – Workshop 11 SEN (CHWs).

Participants explained that during mealtimes, the family will share food from one large plate from which the father will eat first as a sign of respect and courtesy. Sometimes, children would also eat in their neighbour’s house to encourage them to eat.

“ Yes, it happens that we use that strategy so that children can eat. Note that children like to imitate so that’s why we [send them to the neighbour’s house]” – Workshop 11 SEN (CHWs)”.

Traditional beliefs about malnutrition

In Kaffrine, Senegal, some participants spoke of traditional beliefs relating to malnutrition, which are believed by fewer people these days. For example, uncovered food might attract bad spirits, and any person who eats it will become ill. There were a number of food taboos spoken of which were thought to have negative consequences for the baby, for example watermelon and grilled meat which were though to lead to birth complications and bleeding. Furthermore, cold water was thought to negatively impact the baby. Groups spoke of a tradition known as “bathie” in which traditional healers wash stunted children with smoke.

“ There are traditional practices called (Bathie) which are practiced by traditional healers. Parents are flexible about the practice of Bathie ” – Workshop 1 SEN (CHWs).

Causes of malnutrition and stunting were thought to be a lack of a balanced diet, lack of vitamin A, disease, intestinal worms, poor hygiene, socio-cultural issues such as non-compliance with food taboos, non-compliance with exclusive breastfeeding and close pregnancies. Malnutrition was also thought by some to be hereditary. Numerous signs of malnutrition were well known amongst the groups in Kaffrine. For example, signs of malnutrition were thought to be a big bloated belly, diarrhoea, oedema of the feet, anaemia, small limbs and hair loss as well as other symptoms such as red hair and a pale complexion. Despite this, malnutrition was thought to be hard to identify in Kaffrine as not all children will visit health centres, but mothers do try to take their babies heights and weights monthly. The groups were aware of the effect of poverty on the likelihood of stunting as impoverished parents cannot afford food. Furthermore, the groups mentioned that there is some stigma towards stunted children, and they can face mockery from other children although most local people feel pity and compassion towards them. Malnourished children are referred to as Khiibon or Lonpogne in the local language of Wolof.

“ It is poverty that is at the root of malnutrition, because parents do not have enough money [and] will have difficulty feeding their families well, so it is the situation of poverty that is the first explanatory factor of malnutrition here in Kaffrine” – Workshop 9 SEN (administrators).

“It can happen that some children are the victim of jokes for example of mockery from children of their same age, but not from adults and older ” – Workshop 9 SEN (administrators).

Pregnancy beliefs

In Kaffrine, Senegal, there were concerns around close pregnancies, and pregnancies in women who were too young, and for home births. Within the communities there was a stigma around close pregnancies, which prevented them from attending antenatal appointments. Similarly, there were superstitions around revealing early pregnancies, which again delayed attendance at health centres.

Groups acknowledged the role of good nutrition, and mentioned some forbidden foods such as salty foods, watermelon and grilled meat (which sometimes related back to a traditional belief that negative impacts would be felt in the pregnancy such as birth complications and bleeding). Similarly, drinking cold water was thought to negatively affect the baby. Beneficial foods mentioned included vegetables and meat, during pregnancy.

“ Often when a woman has close pregnancies, she can be ashamed, and this particularly delays the time of consultation” – Workshop 5 SEN (CHWs).

“Yes, there are things that are prohibited for pregnant women like salty foods” – Workshop 11 SEN (CHWs).

In Kaffrine, Senegal, some participants spoke of a traditional belief that if a pregnant woman consumes eggs then her baby might be overweight, or have problems learning how to talk. Despite this, mothers in Kaffrine said that they would be happy to accept eggs as a supplement, although if supplements are provided that require preparation (such as powdered supplements), they would be less likely to accept them.

“These restrictions are traditional, and more women no longer believe that eggs will cause a problem to the child. But if these eggs are brought by external bodies, we will hesitate to take it.” – Workshop 11 SEN (CHWs).

“They don’t eat eggs before the child starts speaking (the child only eats eggs when he starts talking). This is because it’s very heavy and can cause bloating and may also lead to intestinal problems.” – Workshop 4 SEN (grandmothers).

Other important topics – access to health services

For the participants in Kaffrine, Senegal, accessing health services was problematic, particularly for pre- and post-natal appointments, which faced frequent delays. Some women had access due to poor roads and chose to give birth at home. Access issues were further compounded by poverty and social factors, as procedures in hospitals can be costly, and women with close pregnancies (soon after an earlier one) can feel shame from society and hide their pregnancy.

“Women really have problems of lack of finances. There are social services in the hospital; but those services rarely attend to women without finances. Even when a child dies at birth they will require money to do the necessary procedure ” – Workshop 11 SEN (CHWs).

Creation of the culturally-informed protocols

Recommendations that comprise a culturally-informed protocol for intervention design in each locality are given in Table 3 .

The Major Cultural Themes, and specific Egg Intervention Themes drawn out from only 9–11 carefully planned group sessions in each country provided a rich set of recommendations towards a culturally-informed protocol for the localised design of a proposed Egg Intervention for both East Lombok, Indonesia and Kaffrine, Senegal. A culturally-informed protocol designed in this way comprises cultural insights which are worthy of consideration in local intervention design and should guide future stages of engagement and provide a platform from which good rapport and trust can be built between researchers and the community [ 16 ]. For example, in Kaffrine, Senegal, the early involvement of husbands and grandmothers is crucial, which reflects values around shared decision making within families that are noted to be more prevalent in LMICs, in contrast to individualistic values in HICs [ 16 , 39 ]. Similarly, due to strong religious values in both East Lombok, Indonesia and Kaffrine, Senegal, partnerships with Islamic leaders is likely to improve engagement. Past studies show the crucial role that religious leaders can play in determining social acceptability of interventions, particularly around taboo topics such as birth spacing [ 40 ].

The WVIS plus PEX:FGD method demonstrated here produced both broad cultural themes from shared values, which were in a concise and easy-to-understand format which could be readily communicated with the wider Action Against Stunting Hub, as well as life practices relevant to stunting in Kaffrine, Senegal and in East Lombok, Indonesia. Discussions of shared values during the WVIS main workshop provided useful cultural background within each community. PEX:FGD discussion uncovered numerous cultural factors within local life practices that could influence on the Egg Intervention engagement and acceptability. Combining themes from the WVIS workshop and PEX:FGDs allowed for specific recommendations to be made towards a culturally-informed protocol for the design of an Egg Intervention that included both broad cultural themes and specific Intervention insights (Table 3 ). For example, in Kaffrine, Senegal, to know that the husband’s authoritative family decision-making for health care (specific) is rooted in Islamic foundations (wider cultural) points to an Intervention Recommendation within the protocol, involving consultations with Islamic Leaders to lead community awareness targeting fathers. Similarly, in East Lombok, Indonesia the (specific) behaviour of breastfeeding for 2 years was underpinned by (wider cultural) shared values of living in Islam. This understanding of local values could prevent the imposition of culturally misaligned values, which Bernal and Adames (2017) caution against [ 17 ].

There are a number of interesting overlaps between values seen in the WVIS Frameworks and Narratives and the categories of Schwartz (1992) and The World Values Survey (2023) [ 41 , 42 ]. For example, in both Kaffrine, Senegal and East Lombok, Indonesia, strong religious values were found, and the groups spoke of the importance of practicing their religion with daily habits. This would align with traditional and conservation values [ 41 , 43 ]. Furthermore, in Kaffrine, Senegal participants often mentioned the importance of mutual aid within the community, and similar values of togetherness and respect in the community were found in East Lombok, Indonesia. These would seem to align with traditional, survival and conservation values [ 41 , 43 ]. However, the values mentioned by the groups in the WVIS workshops are far more specific, and it is possible that through asking what is most worthwhile, valuable and meaningful about their context, the participants are able to prioritise which aspects of their values are most salient to their daily lives. Grounded shared values such as these are generally neglected in Global Health Research, and values predominant in the Global North are often assumed to be universal [ 14 ]. Thus, by excluding the use of a predefined external framework, we minimized the risk of imposing our own ideas of values in the community, and increased the relevance, significance and local validity of the elicited information [ 28 ].

Participatory methods of engagement are an essential step in conducting Global Health Research but there is currently a paucity of specific guidance for implementing participatory methods in vulnerable communities [ 16 , 44 ]. In addition, there is acknowledgement in the literature that it is necessary to come into communities in LMICs without assumptions about their held values, and to use bottom-up participatory approaches to better understand local values [ 14 , 16 ]. The WVIS plus PEX:FGD methodology highlighted here exemplifies a method that is replicable in multiple country contexts [ 28 , 32 ] and can be used to crystallize local In Situ Shared Values which can be easily communicated to external researchers. Coupled with the specialised FGD (PEX:FGD), values-based perceptions of specific topics (in this case stunting) can be elicited leading to the creation of specific Culture-based recommendations. This therefore takes steps to answer the call by Memon and colleagues (2021) for the creation of cultural protocols ahead of conducting research in order to foster ethical research relationships [ 16 ]. We believe that the potential usefulness of the WVIS approach to guide engagement and inform intervention design is effectively demonstrated in this study and WVIS offers a method of making explicit local values in a novel and valuable way.

However, we acknowledge that our approach has several limitations. It has relied heavily on the local university researchers to debate and decide which participant stakeholder groups should be chosen, and although they did this in the context of the Whole Child approach, it would have been advantageous to have involved cultural researchers with a deeper understanding of cultural structures, to ensure sufficient opportunities for key cultural elements to emerge. This would have in particular strengthened the intervention design derived from the PEX:FGD data. For example, we retrospectively realised that our study could have been improved if grandmothers had been engaged in East Lombok. Understanding this limitation leads to suggestion for further work: to specifically investigate the overlap of this approach with disciplinary studies of culture, where social interactions and structures are taken into account via formal frameworks.

There are more minor limitations to note. For example, the WVIS approach can only be led by a trained and experienced facilitator: not all researchers can do this. A training programme is currently under development that could be made more widely available through online videos and a Handbook. Secondly, although the groups recruited do not need to be representative of the local population, the number recruited should be increased until theoretical saturation is achieved of the themes which emerge, which was not carried out in this study as we focussed on demonstrating the feasibility of the tool. Thirdly, there is a limit to the number of topics that can be explored in the PEX:FGDs within the timeframe of one focus group (depending on the stamina of the participants), and so if a wider range of topics need formative research, then more workshops are needed. Lastly, this work took place in a large, highly collaborative project involving expert researchers from local countries as well as international experts in WVIS : other teams may not have these resources. However, local researchers who train in WVIS could lead on their own (and in this Hub project such training was available).

The need for better understanding, acknowledgement and integration of local culture and shared values is increasing as the field of Global Health Research develops. This study demonstrates that the WVIS plus PEX:FGD shared values approach provides an efficient approach to contextualise and localise interventions, through eliciting and making communicable shared values and local life practices which can be used towards the formation of a culturally-informed protocols. Were this method to be used for intervention design in future, it is possible that more focus should be given to existing social structures and support systems and a greater variety of stakeholders should be engaged. This study thus contributes to the literature on methods to culturally adapt interventions. This could have significant implications for improving the uptake of nutrition interventions to reduce malnutrition through improved social acceptability, which could help progression towards the goal of Zero Hunger set within the SDGs. The transferability and generalisability of the WVIS plus PEX:FGD approach should now be investigated further in more diverse cultures and for providing formative research information for a wider range of research themes. Future studies could also focus on establishing its scaling and pragmatic usefulness as a route to conceptualising mechanisms of social acceptability, for example a mechanism may be that in communities with strong traditional religious values, social hierarchies involving religious leaders and fathers exist and their buy-in to the intervention is crucial to its social acceptability. Studies could also focus on the comparison or combination of WVIS plus PEX:FGD with other qualitative methods used for intervention design and implementation.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request [email protected], Orcid number 0000–0002–1811-4597. These include deidentified Frameworks of Shared Values and Accompanying Narrative from each Group; deidentified Hub Insight Statements of relevant themes.

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Acknowledgements

We thank the Hub PI, Claire Heffernan, for feedback on a late draft of the manuscript.

The Action Against Stunting Hub is funded by the Medical Research Council through the UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF), Grant No.: MR/S01313X/1.

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Values & Sustainability Research Group, School of Architecture, Technology and Engineering, University of Brighton, Brighton, UK

Annabel J. Chapman, Mahsa Firoozmand & Marie K. Harder

Department of Environmental Science and Engineering, Fudan University, Shanghai, People’s Republic of China

Chike C. Ebido, Rahel Neh Tening, Yanyan Huang & Marie K. Harder

Department of Zoology and Environmental Biology, University of Nigeria, Nsukka, Nigeria

Chike C. Ebido

Preventive Medicine and Public Health, Université Cheikh Anta Diop (UCAD), Dakar, Senegal

Ndèye Marème Sougou

Faculty of Psychology, Universitas Islam Negeri Syarif Hidayatullah, Jakarta, Indonesia

Risatianti Kolopaking

Southeast Asian Ministers of Education Organization Regional Centre for Food and Nutrition (SEAMEO RECFON) Universitas Indonesia, Jakarta, Indonesia

Risatianti Kolopaking & Rita Anggorowati

International Research Laboratory (IRL 3189) Environnement santé et sociétés/CNRS/UCAD, Dakar, Senegal

Amadou H. Diallo

Department of Medical Records and Health Information, Faculty of Health and Technology, Universitas Bandung, Bandung, Indonesia

Rita Anggorowati

Laboratory of Cultural Anthropology, IFAN, Université Cheikh Anta Diop (UCAD), Dakar, Senegal

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School of Education, Languages and Linguistics, Faculty of Humanities and Social Sciences, University of Portsmouth, Portsmouth, UK

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Contributions

MKH formulated the initial research question and study design. AJC developed the specific research question. Data collection in Senegal involved CCE, NMS, AHD, FBD, RNT, CEHAN and JM. Data collection in Indonesia involved RA, RK, YH and MKH. Cultural interpretation in Senegal Involved AHD, FBD, NMS, RNT and JM. Analysis involved AJC and MF. AJC and MKH wrote the paper.

Corresponding author

Correspondence to Marie K. Harder .

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Chapman, A.J., Ebido, C.C., Tening, R.N. et al. Creating culturally-informed protocols for a stunting intervention using a situated values-based approach ( WeValue InSitu ): a double case study in Indonesia and Senegal. BMC Public Health 24 , 987 (2024). https://doi.org/10.1186/s12889-024-18485-y

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Received : 27 September 2023

Accepted : 29 March 2024

Published : 09 April 2024

DOI : https://doi.org/10.1186/s12889-024-18485-y

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Global Lesson Study Policy, Practice, and Research for Advancing Teacher and Student Learning in STEM

Evolving Engineering Education: Online vs. In-Person Capstone Projects Compared (EEE-OIPC) Provisionally Accepted

• 1 Engineering and Physics Department, Texas A&M University Texarkana, United States

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This study compares online and face-to-face (F2F) instructional methods in Capstone Senior Design (CSD) projects across the disciplines of Electrical Engineering (EE) and Mechanical Engineering (ME). Through a comprehensive assessment involving project evaluations, advisor feedback, and self-peer reviews, it aims to gauge the efficacy of each approach in enhancing student success and learning outcomes. A key observation is the parity between online and F2F modalities in several metrics, yet F2F instruction distinctly advances teamwork and collaboration. Conversely, online environments show robust advisor evaluations, signifying effective mentoring despite hurdles in consistent team collaboration and project execution. Highlighting the imperative to blend online and traditional pedagogies, suggesting improved online strategies and a holistic curriculum to boost CSD students' learning experiences. These insights bear significance for ongoing and future STEM education research, stressing adaptable teaching techniques to better student experiences across varied settings. The outcomes yield important guidance for evolving STEM education research and practices, stressing the need for flexible teaching techniques to enrich learning in different educational environments. These findings are crucial for educators and institutions working to adapt their strategies to the changing landscape of online and F2F instruction in STEM areas.

Keywords: Capstone Senior Project, Online Learning, F2F, Teamwork, Engineering Education, project-based learning, group projects, Communication

Received: 19 Mar 2024; Accepted: 10 Apr 2024.

Copyright: © 2024 Znidi, Uddin and Morsy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Faycal Znidi, Texas A&M University Texarkana, Engineering and Physics Department, Texarkana, 75503, Texas, United States

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