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Research Design 101

Everything You Need To Get Started (With Examples)

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

Research design for qualitative and quantitative studies

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

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Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

research design study cases

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

research design study cases

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 .

research design study cases

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.

Solly Khan

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


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


This was really helpful. thanks


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

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

Sam Msongole

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

Robyn Pritchard

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


how to cite this page


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

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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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research design study cases

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.


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


  • 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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What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.


Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

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Dovetail streamlines case study research to help you uncover and share actionable insights

  • What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:


An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”


An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."


Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”


Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

  • When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

  • What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

  • What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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Writing a Case Study

Hands holding a world globe

What is a case study?

A Map of the world with hands holding a pen.

A Case study is: 

  • An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
  • Used to examine an identifiable problem confirmed through research.
  • Used to investigate an individual, group of people, organization, or event.
  • Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

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Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

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What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

Triangulation image with examples

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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research design study cases

Case Study Research Design

The case study research design have evolved over the past few years as a useful tool for investigating trends and specific situations in many scientific disciplines.

This article is a part of the guide:

  • Research Designs
  • Quantitative and Qualitative Research
  • Literature Review
  • Quantitative Research Design
  • Descriptive Research

Browse Full Outline

  • 1 Research Designs
  • 2.1 Pilot Study
  • 2.2 Quantitative Research Design
  • 2.3 Qualitative Research Design
  • 2.4 Quantitative and Qualitative Research
  • 3.1 Case Study
  • 3.2 Naturalistic Observation
  • 3.3 Survey Research Design
  • 3.4 Observational Study
  • 4.1 Case-Control Study
  • 4.2 Cohort Study
  • 4.3 Longitudinal Study
  • 4.4 Cross Sectional Study
  • 4.5 Correlational Study
  • 5.1 Field Experiments
  • 5.2 Quasi-Experimental Design
  • 5.3 Identical Twins Study
  • 6.1 Experimental Design
  • 6.2 True Experimental Design
  • 6.3 Double Blind Experiment
  • 6.4 Factorial Design
  • 7.1 Literature Review
  • 7.2 Systematic Reviews
  • 7.3 Meta Analysis

The case study has been especially used in social science, psychology, anthropology and ecology.

This method of study is especially useful for trying to test theoretical models by using them in real world situations. For example, if an anthropologist were to live amongst a remote tribe, whilst their observations might produce no quantitative data, they are still useful to science.

research design study cases

What is a Case Study?

Basically, a case study is an in depth study of a particular situation rather than a sweeping statistical survey . It is a method used to narrow down a very broad field of research into one easily researchable topic.

Whilst it will not answer a question completely, it will give some indications and allow further elaboration and hypothesis creation on a subject.

The case study research design is also useful for testing whether scientific theories and models actually work in the real world. You may come out with a great computer model for describing how the ecosystem of a rock pool works but it is only by trying it out on a real life pool that you can see if it is a realistic simulation.

For psychologists, anthropologists and social scientists they have been regarded as a valid method of research for many years. Scientists are sometimes guilty of becoming bogged down in the general picture and it is sometimes important to understand specific cases and ensure a more holistic approach to research .

H.M.: An example of a study using the case study research design.

Case Study

The Argument for and Against the Case Study Research Design

Some argue that because a case study is such a narrow field that its results cannot be extrapolated to fit an entire question and that they show only one narrow example. On the other hand, it is argued that a case study provides more realistic responses than a purely statistical survey.

The truth probably lies between the two and it is probably best to try and synergize the two approaches. It is valid to conduct case studies but they should be tied in with more general statistical processes.

For example, a statistical survey might show how much time people spend talking on mobile phones, but it is case studies of a narrow group that will determine why this is so.

The other main thing to remember during case studies is their flexibility. Whilst a pure scientist is trying to prove or disprove a hypothesis , a case study might introduce new and unexpected results during its course, and lead to research taking new directions.

The argument between case study and statistical method also appears to be one of scale. Whilst many 'physical' scientists avoid case studies, for psychology, anthropology and ecology they are an essential tool. It is important to ensure that you realize that a case study cannot be generalized to fit a whole population or ecosystem.

Finally, one peripheral point is that, when informing others of your results, case studies make more interesting topics than purely statistical surveys, something that has been realized by teachers and magazine editors for many years. The general public has little interest in pages of statistical calculations but some well placed case studies can have a strong impact.

How to Design and Conduct a Case Study

The advantage of the case study research design is that you can focus on specific and interesting cases. This may be an attempt to test a theory with a typical case or it can be a specific topic that is of interest. Research should be thorough and note taking should be meticulous and systematic.

The first foundation of the case study is the subject and relevance. In a case study, you are deliberately trying to isolate a small study group, one individual case or one particular population.

For example, statistical analysis may have shown that birthrates in African countries are increasing. A case study on one or two specific countries becomes a powerful and focused tool for determining the social and economic pressures driving this.

In the design of a case study, it is important to plan and design how you are going to address the study and make sure that all collected data is relevant. Unlike a scientific report, there is no strict set of rules so the most important part is making sure that the study is focused and concise; otherwise you will end up having to wade through a lot of irrelevant information.

It is best if you make yourself a short list of 4 or 5 bullet points that you are going to try and address during the study. If you make sure that all research refers back to these then you will not be far wrong.

With a case study, even more than a questionnaire or survey , it is important to be passive in your research. You are much more of an observer than an experimenter and you must remember that, even in a multi-subject case, each case must be treated individually and then cross case conclusions can be drawn .

How to Analyze the Results

Analyzing results for a case study tends to be more opinion based than statistical methods. The usual idea is to try and collate your data into a manageable form and construct a narrative around it.

Use examples in your narrative whilst keeping things concise and interesting. It is useful to show some numerical data but remember that you are only trying to judge trends and not analyze every last piece of data. Constantly refer back to your bullet points so that you do not lose focus.

It is always a good idea to assume that a person reading your research may not possess a lot of knowledge of the subject so try to write accordingly.

In addition, unlike a scientific study which deals with facts, a case study is based on opinion and is very much designed to provoke reasoned debate. There really is no right or wrong answer in a case study.

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a research strategy whose characteristics include

  • a focus on the interrelationships that constitute the context of a specific entity (such as an organization, event, phenomenon, or person),
  • analysis of the relationship between the contextual factors and the entity being studied, and
  • the explicit purpose of using those insights (of the interactions between contextual relationships and the entity in question) to generate theory and/or contribute to extant theory. 

SAGE Research Methods Videos

What is the value of working with case studies.

Professor Todd Landman explains how case studies can be used in research. He discusses the importance of choosing a case study correctly and warns about limitations of case study research.

This is just one segment in a series about case studies. You can find the rest of the series in our SAGE database, Research Methods:


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  • v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.


Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.


Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

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

Source of support: Nil

Conflict of interest: None

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Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Research Design

Research Design


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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This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 26 (2024)

This is a member publication of University College London (Jisc)

Twitter Analysis of Health Care Workers’ Sentiment and Discourse Regarding Post–COVID-19 Condition in Children and Young People: Mixed Methods Study

Authors of this article:

Author Orcid Image

Original Paper

  • Macarena Chepo 1 * , RN, BSN, MPH, PhD   ; 
  • Sam Martin 2, 3 * , MSc, PhD   ; 
  • Noémie Déom 2 , MSc   ; 
  • Ahmad Firas Khalid 4 , MD, PhD   ; 
  • Cecilia Vindrola-Padros 2 , BA, MA, PhD  

1 School of Nursing, Universidad Andrés Bello, Santiago, Chile

2 Department of Targeted Intervention, University College London, London, United Kingdom

3 Oxford Vaccine Group, Churchill Hospital, University of Oxford, Oxford, United Kingdom

4 Canadian Institutes of Health Research Health System Impact Fellowship, Centre for Implementation Research, Ottawa Hospital Research Institute, Otawa, ON, Canada

*these authors contributed equally

Corresponding Author:

Sam Martin, MSc, PhD

Department of Targeted Intervention

University College London

Charles Bell House 43-45

Foley Street

London, W1W 7TY

United Kingdom

Phone: 44 (0)20 3108 3232

Email: [email protected]

Background: The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post–COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post–COVID-19 condition on this demographic.

Objective: With a social media analysis of the discourse surrounding the prevalence of post–COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post–COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post–COVID-19 condition and identify critical areas and future directions for researchers and policy makers.

Methods: From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post–COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs.

Results: We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post–COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences.

Conclusions: The perceptions described on Twitter by HCWs concerning the presence of the post–COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post–COVID-19 condition.


More than 3 years after the outbreak of the COVID-19 pandemic [ 1 ], the social, political, and economic impact of this phenomenon has been more than significant, considering >700 million worldwide cases and nearly 7 million people’s deaths [ 2 ]. Given the scale of the phenomenon, it is imperative for all countries to thoroughly examine the lessons gleaned from the pandemic, particularly regarding a matter that has raised significant concern among the populace: the long-term effects experienced by individuals who have had COVID-19, spanning weeks, months, or even years after their initial infection [ 3 ]. This phenomenon, referred to as post–COVID-19 condition (or more commonly “long COVID”), warrants careful consideration and analysis [ 4 ].

There is increasing information regarding the clinical manifestation of this condition, particularly in the adult population. The worldwide prevalence has been estimated at approximately 50% to 70% in individuals hospitalized during acute COVID-19 infection and 10% to 12% in vaccinated cases [ 5 ]. While children and young people have a low likelihood of severe COVID-19 infection [ 6 ], the information available to date indicates that the presence of post–COVID-19 condition in this group may be as disabling as in adults, reaching a prevalence rate of 23.4% (range 3.7%-66.5%) [ 7 ].

An agreed definition by the World Health Organization indicates that post–COVID-19 condition in children and young people is a condition that occurs “in individuals with a history of confirmed or probable SARS-CoV-2 infection when experiencing symptoms lasting at least two months which initially occurred within three months of acute COVID-19” [ 8 ]. Post–COVID-19 condition strongly impacts daily functioning and can develop or continue after COVID-19 infection and may fluctuate or relapse over time [ 4 , 8 , 9 ].

Among the symptoms most frequently attributable to post–COVID-19 condition in children and young people are fatigue, altered smell or anosmia, and anxiety [ 8 ]. However, other symptoms have also been reported, such as sleep disturbances, difficulty in concentrating, abdominal pain, myalgia or arthralgia, earache or ringing in ears, mood swings, persistent chest pain, stomach pain, light sensitivity, diarrhea, heart palpitations, and skin lesions [ 8 , 10 ]. One of England’s most significant studies is the Children and Young People With Long COVID study by Stephenson et al [ 11 ]. This national research matched longitudinal and cohort studies in adolescent individuals aged 11 to 17 years and found the presence of symptoms in 35.4% of the adolescent individuals who tested positive at baseline and 8.3% who of the adolescent individuals who tested negative at baseline. A total of 3 months after testing, 66.5% of those who tested positive and 53.3% of those who tested negative had any symptoms [ 11 ]. However, Stephenson et al [ 12 ] recently indicated that in a 6-month follow-up, the prevalence of specific symptoms reported at the time of the polymerase chain reaction testing decreased over time, where, for example, the prevalence of chills, fever, myalgia, cough, and sore throat among those who tested positive decreased from 10% to 25% to <3%.

As research on the symptoms, prevalence, and treatment of post–COVID-19 condition in children and young people continues, it is essential to add to the literature by developing studies that determine the condition’s impact on this group, considering that they are experiencing a range of unwanted symptoms that disrupt their quality of life and that of their families.

Considering that listening to the voices of families and health workers could be helpful to broaden the knowledge achieved in post–COVID-19 condition in children and young people, a powerful tool could be social media, such as Twitter (subsequently rebranded as X). With >3729 million daily active users, Twitter has become one of the most important social platforms in the world [ 13 ]. People used Twitter during the COVID-19 pandemic for different purposes, such as world leaders communicating with citizens [ 14 , 15 ], organizations monitoring movement [ 16 ], scientists studying public discourse around the pandemic [ 17 , 18 ], and researchers performing sentiment analysis [ 19 - 21 ]. In the case of physicians and health care workers (HCWs), Twitter has been used to share and evaluate scientific evidence, guidelines, and technical advice [ 22 - 24 ] and track the course and burden of disease [ 25 ].

Using the social media monitoring platform Pulsar [ 26 ], we aimed to explore HCWs’ perceptions concerning post–COVID condition in children and young people in the United Kingdom between January 2021 and January 2022. We aimed to contribute to the emerging knowledge on post–COVID-19 condition in children and young people and identify critical areas and future directions for researchers and policy makers.

We considered a mixed methods approach to be a pragmatic research paradigm. We analyzed data by conducting a Collaborative and Digital Analysis of Big Qualitative Data in Time Sensitive Contexts (LISTEN) [ 27 ]. This mixed methods analysis consisted of iterative cycles intercalating team discussion and using digital text and discourse analytics tools to analyze related social media data [ 27 ]. We used the LISTEN method to perform quantitative and qualitative analyses of Twitter posts, extracted through the Pulsar platform [ 26 ], related to the experience of post–COVID-19 condition in children and young people in the United Kingdom (eg, phrases, words, hashtags, videos, and images), published between January 1, 2021, and January 31, 2022. We created an advanced Boolean search for keywords mentioning “long COVID” and corelated words, hashtags, and symptoms; furthermore, we filtered for user accounts who identified as HCWs in their Twitter biography description ( Multimedia Appendix 1 ).

Quantitative analysis of all tweets included the following: (1) engagement analysis, where we specifically measured reactions to posts, for example, a retweet, a share, or a comment or quote made toward a tweet; (2) sentiment and emotion analysis, where we measured the positive or negative sentiment in the words and tone of each post within the context of post–COVID-19 condition and HCW’s roles ( Multimedia Appendix 2 ); (3) emotion analysis, where we measured the emotions expressed in the tweets, classified as sadness, anger, disgust, fear, and joy; (4) frequency analysis, where we observed the frequency of keywords and themes in the data set; (5) segmentation analysis, where we measured the key connections or relationships between keywords and their frequent use in the same context; (6) demographic analysis, where we measured the occupation, gender (man or woman or nonbinary or unknown), and city of origin related to the users posting tweets; and (7) analyses, where we evaluated the most influential accounts and the most mentioned websites.

Big qualitative analysis was carried out through thematic discourse analysis of the data sample, using InfraNodus [ 28 ], specifically analyzing the key themes and topics of concern expressed throughout the data set. A codebook was constructed based on the mapping of themes agreed upon by 3 researchers (ND, SM, and MC; Multimedia Appendix 3 ).

The principal investigators (ND, AFK, SM, and MC) interpreted and analyzed the data collected, following the recommendations for rigorous research provided by Creswell and Poth [ 29 ]. Using the LISTEN method [ 27 ], we aimed to show that the integration of qualitative insights through thematic analysis with the quantitative backing of topic modeling can offer a comprehensive view of the discourse. This mixed methods approach allows us to capture the richness of qualitative data while leveraging the objectivity of quantitative measures. Our initial data harvest of the larger corpus data from the Pulsar platform captured 300,000 tweets; this data harvest helped to underpin the software’s sentiment analysis modeling of this specific data set, providing a robust quantitative foundation. The addition of further qualitative data analyses from a smaller qualitative sample allowed for an in-depth understanding of nuanced conversations, particularly when exploring new or complex phenomena such as post–COVID-19 condition in children and young people, with the provision of insights into the context, subtext, and sentiment behind the tweets offering valuable snapshots of public perception and discourse. We used an iterative mixed methods approach, iterating between team discussions and using digital analytics tools to discern relevant themes from the Twitter data corpus. Specifically, we used InfraNodus for thematic analysis, which incorporates a topic modeling script for analyzing and identifying key topics of concern with a data set and provides a structured and objective interpretation of the data. The coding process involved 3 independent researchers (MC, SM, and ND), each with expertise in health care, social network analysis, and digital global health. When initial coding disagreements arose, we meticulously tagged any queries and discussed the posts in question. These instances led to 3 structured meetings wherein the research team deliberated collaboratively to resolve conflicting interpretations. This approach resulted in an 81.99% (2122/2588) initial intercoder agreement rate for the tweets analyzed. For the remaining instances where consensus was not initially reached, the majority rule was applied to finalize theme codings. To quantify the reliability of our coding procedure, with 81.99% (2122/2588) of the tweets coded identically, we used the Cohen κ score, which provides a measure of interrater agreement adjusted for chance. Including the calculation of all variations, this score was calculated to be approximately κ=0.70, indicating good agreement among the coders.

Ethical Considerations

The study only collected data from publicly accessible social networks that have been anonymized by various means, particularly by replacing all usernames and links with anonymous text and summaries of tweets that have been edited, retaining the original message, avoiding direct quotations being identifiable, and ensuring that no information is provided on the identity of the individuals who posted the content studied on the platform.

Internet research requires researchers to carefully consider guidelines to determine whether ethics approval and informed consent are needed [ 30 ]. On the basis of the terms set out by the Research Ethics Committee at the University College London [ 31 ], the study was considered exempt from formal ethics approval for the following reasons: (1) study involving information freely available in the public domain, such as published biographies, newspaper accounts of an individual’s activities, and published minutes of a meeting, that although is considered personal under the Data Protection Act, would not require ethics review; and (2) study involving anonymized records and data sets in the public domain, such as data sets available through the Office for National Statistics or the UK Data Archive where appropriate permissions have already been obtained and it is not possible to identify individuals from the information provided.

Therefore, we anonymized all records and data sets collected during the study to make identification impossible. We removed social media usernames from the data samples. No direct or easily traceable quotes have been included. These measures align with best practices [ 32 - 35 ]. While this study was beyond the scope of the human ethics committee, we adhered to the principles of ethics: beneficence, nonmaleficence, autonomy, and justice [ 36 ]. We collected and analyzed data through secure encrypted servers via the Meltwater and InfraNodus platforms.

Audience Analysis

During the period from January 2021 to January 2022, we obtained 300,000 tweets from 936 accounts. After filtering for relevant posts (refer to inclusion and exclusion criteria in Multimedia Appendix 1 ), we analyzed a sample of 2588 tweets using mixed methods analysis. In terms of gender (man, woman, nonbinary, or unknown), 32.88% (851/2588) were female individuals, 23.49% (608/2588) were male individuals, and 43.59% (1128/2588) were unknown. According to the description given in the user’s biography, the most frequently self-reported terms were “NHS” (582/2588, 22.49%), “health” (230/2588, 8.89%), “medical” (168/2588, 6.49%), “nurse” (166/2588, 6.41%), “clinical” (160/2588, 6.18%), “mum” (158/2588, 6.11%), “doctor” (145/2588, 5.6%), and “GP” (145/2588, 5.6%). In terms of city, tweets came mainly from London (958/2588, 37.02%), Newcastle upon Tyne (326/2588, 12.6%), Redcar (160/2588, 6.18%), Manchester (140/2588, 5.41%), and Bradford (111/2588, 4.29%).

Regarding profession described in the user’s biography, the most frequently mentioned roles were nurses (176/2588, 6.8%); medical roles, for example, paramedic and nursing assistant (173/2588, 6.68%); clinical roles, for example, surgeon, physiotherapist, and anesthesiologist (160/2588, 6.18%); general practitioners (GPs), for example, hospital GP or local surgery GP (142/2588, 5.49%); and physician (140/2588, 5.41%). The most frequent organization affiliated with was the National Health Service (587/2588, 22.68%).

Most Influential Accounts

One of the accounts that generated the highest number of mentions and, therefore, some of the most influence, as they were the ones that talked the most about post–COVID-19 condition in children and young people, was the account for @longcovidkids (593/2588, 22.91% tweets), related to the most shared website [ 37 ] , an international UK-based charity for families and children living with post–COVID-19 condition. Although the account was created in October 2020, it was first mentioned in our data collection timeline on January 1, 2021. It offers web support services, funding, and research participation and represents children and young people living with post–COVID-19 condition in expert forums, research panels, health organizations, and parliamentary groups. The other most shared web pages were (the United Kingdom) [ 38 ], (the United Kingdom) [ 39 ], (the United States) [ 40 ], and (the United States) [ 41 ]. This shows that in the United Kingdom, there was a mixed influence of UK and US link resources linked to HCW Twitter users in the United Kingdom.

Keyword Analysis

The volume of social media engagement in the discussion about the post–COVID-19 condition experience in children and young people in the United Kingdom reached 1400 posts, 1550 engagements, and 1.9 million impressions. Overall, comments were very responsive to government decisions regarding the vaccination program and school closures ( Multimedia Appendix 4 ). During the first peak of comments in January 2021, the amount of discourse expanded leading up to March 2021, when there were different announcements of school closures, and the guidelines were delivered regarding the priority groups of the vaccination program (frontline HCW and people aged >80 years first). The highest engagement was between June and July 2021, which coincides with the government announcement regarding the availability of vaccines for people aged >18 years. The third peak of comments occurred in September 2021, the same month the authorities announced the extension of the vaccination program to children aged 12 to 15 years.

Top Keywords Analysis

The top words in posts associated with children and young people’s experience of post–COVID-19 condition in the United Kingdom were “Children” (352/2588, 13.6%), “kids” (160/2588, 6.18%), “people” (158/2588, 6.11%), “Young” (148/2588, 5.72%), and “schools” (83/2588, 3.21%). The top hashtags were #longcovid (1387/2588, 53.59%), #longcovidkids (448/2588, 17.31%), #covid19 (370/2588, 14.3%), and #covid (176/2588, 6.8%).

Sentiment and Emotions Analysis

According to sentiment analysis, 99.38% (2572/2588) of the posts reflected negative sentiments and 0.62% (16/2588) reflected positive sentiments. Negative sentiments were mainly associated with comments on hospitalization figures related to the COVID-19 pandemic, criticism of pandemic mitigation policies, and vaccination of children and young people. Furthermore, positive sentiments mainly concerned acknowledgments around decreasing numbers of community support groups.

The primary emotions identified were as follows:

  • Sadness (1752/2588, 67.7%), such as in the following tweet:
@[Username] Really upset, after my tough on-call last night. Hospitalisations are still going up, and Gov announcement minismises the effect of long-COVID in adults and children. It’s so hard to keep spirits up today. But we’ll try and continue doing our best in the NHS.
  • Joy (367/2588, 14.18%), such as in the following tweet:
@[Username] It’s been an amazing day! [...] I’ve been able to share the experience I’ve gained treating children and adolescents with Long COVID over the last year.
  • Fear (233/2588, 9%), as seen in the following tweet:
@[Username] It’s really urgent that young people get the message that they need to get vaccinated. Long COVID is ruining many people’s lives! It’s not a lie or hypochondria, there are real, physiological changes, please understand!

Segmentation Analysis

This analysis revealed the critical clusters of conversation around the main topics of concern within the discourse network around post–COVID-19 condition. Comments were distributed in 4 key conversation segments as follows:

  • People, schools, and prevention (1734/2588, 67%): Most of the comments related to measures taken in terms of COVID-19 prevention in schools, concern about the risk of exposure, and sharing experiences of infection in schools.
  • Health, adults, and impact (401/2588, 15.49%): Comments mainly reflected concerns and uncertainty about the long-term effect of post–COVID-19 condition on both children and young people and adults.
  • Cases, virus, and risk (326/2588, 12.6%): Comments reflected worries about the associated risks and long-term consequences attributable to post–COVID-19 condition (in both adults and children and young people) and the constant mutation of the virus, which will create a permanent risk in the population.
  • Months, distress, and symptoms (106/2588, 4.1%): Some HCWs used Twitter to share how children and young people experience post–COVID-19 condition and the extent of these symptoms. Some HCWs exemplified certain typical manifestations, such as fatigue.

Discourse Analysis by Theme

To better understand the topics discussed from the segmentation analysis, we performed a discourse analysis of the key co-occurring themes and topics of concern shared within discussions regarding post–COVID-19 condition in children and young people. The following themes emerged ( Textbox 1 ): concern or uncertainty for the future, school attendance, mask protection from COVID-19, vaccine uptake, infection rates, policy (support or skepticism), understanding and visualizing symptoms, child mental health, access to care, community support, and research ( Figures 1 and 2 ).

  • Concern for the future or uncertainty (615/2588, 23.76% tweets): Most comments showed a concern for the future, focusing on shared statistics regarding the rate and spread of infection in children and young people and how this would affect future health outcomes. Furthermore, this group expressed concern regarding political decisions; the presence of illness in loved ones; the eventual overload and response capacity of the health system in the face of an increase in post–COVID-19 condition cases; and the need for training of health care workers (HCWs) to deal with comorbid, potentially long-term symptoms ( Figure 1 A).
  • Schools (460/2588, 17.77% tweets): Comments aimed to promote vaccination policies for schoolchildren and flexible measures regarding teachers’ work and attendance, considering cases of people with prolonged symptoms. In addition, several tweets expressed dissatisfaction with school risk mitigation measures, such as the use of face masks and air filters ( Figure 1 B).
  • Vaccine (386/2588, 14.9% tweets): Most tweets from this group showed their disapproval of the constant changes in the government’s decisions regarding schools and priority groups for vaccination. Between March and June 2021, the first set of tweets criticized the lack of priority in the vaccination program for schoolchildren and other at-risk groups (such as teachers). Once the authorities announced a vaccination program for schoolchildren aged 12 to 15 years ( Multimedia Appendix 4 ), most comments promoted vaccination for this group. A few comments (78/2588, 3.01%) shared concerns about the vaccine’s efficacy for children, based on the experiences of COVID-19 reinfection in adults despite having received the recommended initial doses. However, to a lesser extent (26/2588, 1%), there was a refusal to vaccinate children, citing fear of possible adverse effects. Nonetheless, it is worth noting that the community frequently refuted such comments ( Figure 1 C).
  • Share statistics (334/2588, 12.91% tweets): Frequently, HCWs shared statistical data, such as the number of affected children and young people, the number of post–COVID-19 condition cases, and hospital admissions and deaths. Some of these data were used to validate the existence of the post–COVID-19 phenomenon or to express concern about it ( Figure 1 D).
  • Policy (316/2588, 12.21% tweets): The comments were responsive to the policies emanating from the authorities over time ( Multimedia Appendix 4 ). There were 5 main criticisms, including changes in school closure or opening policies; HCWs question why the authorities ignore the evidence of post–COVID-19 cases in children and young people, leading them to question whether decision makers have sufficient training to control the pandemic adequately; the failure to include teachers and school workers in the COVID-19 vaccination program as well as the younger population; the lack of mitigation measures in schools, such as improvements in ventilation systems and mandatory use of masks; and the herd immunity as a plan in the government’s hidden agenda , that is, to promote work and activate the economy ( Figure 1 E).
  • “Proof” (280/2588, 10.82% tweets): Most tweets in this group argued regarding the existence of children and young people with post–COVID-19 condition through pictures; statistics; scientific papers; and personal, family, and professional experiences ( Figure 1 F).
  • Signs and symptoms (189/2588, 7.3% tweets): Among the symptoms described, chronic fatigue and exhaustion were the most frequent symptoms, which prevent normal activities. Other symptoms were respiratory: dyspnea, chronic cough, and shortness of breath; gastrointestinal: acute or intense abdominal pain, nausea, bloating, gastroparesis, and change in smell or taste; muscular: severe joint pain, “painful foot” and difficulty with physical activity; mental health: anxiety and low mood; topical: rash, skin rashes, and redness and pain in the eyes; and nonspecific symptoms, such as chest pain, heart palpitations, constant high body temperature, precocious puberty, hormonal changes, and erectile dysfunction ( Figure 2 A).
  • Face masks (119/2588, 4.6% tweets): Face masks were widely promoted, especially in schools, because HCWs considered them as a practical and straightforward strategy to control the pandemic ( Figure 2 B).
  • Skepticism (101/2588, 3.9% tweets): Comments showed reticence toward post–COVID-19 condition in children and young people. Some of the arguments focused on a perceived lack of clarity in the clinical manifestations and stressed the need to better differentiate the post–COVID-19 condition from other related symptomatologies, such as mood disorders (eg, depression and anxiety due to confinement). In contrast, several arguments agreed on the need for more scientific evidence, arguing that post–COVID-19 condition in children and young people are isolated. Other users claimed not to know of such cases instead of calling post–COVID-19 condition in children and young people SMS text message an exaggeration. In addition, several arguments favored releasing restrictions for children and young people, particularly arguments related to the use of masks, because of possible associated risks, for example, hypoxia ( Figure 2 C).
  • Mental health (54/2588, 2.09% tweets): Symptoms attributable to mental health problems in children and young people were also a concern. For instance, HCWs mentioned sadness, fear of infecting their family, anxiety regarding sick parents, stress, night terrors, self-harm, and suicidal ideation. Furthermore, users discussed a perceived lack of specific support for children and young people and their families in situations such as hospitalization; prolonged COVID-19 condition; admission to intensive care; and death of a family member, schoolmate, or teacher, all situations that triggered permanent stress in these groups ( Figure 2 D).
  • Community support or asking for advice (93/2588, 3.59% tweets): Some HCWs used Twitter to ask for guidance on a specific issue or share experiences of having post–COVID-19 condition or caring for children and young people or family members. Furthermore, they shared informative infographics provided by experts regarding post–COVID-19 condition in children and young people ( Figure 2 E).
  • Access to health care or treatment (72/2588, 2.78% tweets): Some HCWs mentioned the lack of specialist (cardiology) support, concerns regarding prolonged National Health Service burnout, and criticisms regarding how follow-up was carried out concerning the relative symptomatology of children and young people with post–COVID-19 condition. At the same time, opening new centers for children and young people with post–COVID-19 condition generated different reactions. On the one hand, some HCWs recognized it as a substantial development, but on the other hand, some HCWs recognized it as proof of the existence of post–COVID-19 condition in children and young people, which raised concerns for the future ( Figure 2 F).
  • Research (52/2588, 2% tweets): Under this theme, tweets largely promoted study on post–COVID-19 condition in children and young people or highlighted the need for further study on the subject ( Figure 2 G).
  • Images (57/2588, 2.2% tweets): Images shared were primarily from scientific studies, including infographics (from organizations such as National Health Service or @LongCovidKids) and visualization of children and young people’s symptoms, such as rashes, COVID-19 toe, and joint pain. Most infographics shared by organizations (and not individuals), such as the organization LongCovidKids, were related to statistics, such as the number of children and young people with post–COVID-19 condition or the quantification of the type of symptoms experienced. Shared photographs tended to show the more “visually recognizable” symptoms of post–COVID-19 condition, such as skin lesions, rashes, or inflammation. The less visible symptoms, such as chronic fatigue and neurological issues, were represented with photographs of children and young people lying, sleeping under blankets, or duvets or on hospital beds ( Figure 2 H).

research design study cases

Principal Findings

Our primary objective was to explore HCWs’ perceptions concerning post–COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. Our findings indicated that comments made by HCWs on Twitter were responsive to announcements issued by authorities regarding the management of the COVID-19 pandemic in the United Kingdom and associated regulations on the operation of schools. The most frequent feelings and emotions were negative, mainly sadness. In turn, we identified relevant themes for HCWs, such as uncertainty or concern about the future; policies; and regulations for the prevention, management, and addressing both COVID-19 and post–COVID-19 condition in children and young people; vaccination; and the use of Twitter as a strategy to share scientific literature, management strategies, and clinical and personal experiences.

Concern from HCWs regarding the policies for addressing the COVID-19 pandemic in the children and young people in the United Kingdom (including vaccination and schools) was a recurring theme in our findings. Furthermore, concern regarding the side effects of the COVID-19 vaccine and how the vaccine might interact with preexisting physiological symptoms of post–COVID-19 condition in children and young people was a topic of discussion. Similarly, the constant change in policy making in the United Kingdom, as public health bodies and governments have tried to understand and adapt to the emergence of post–COVID-19 condition, have added to the strength of this ongoing debate [ 42 ]. The lack of up-to-date evidence on post–COVID-19 condition in children and young people prompted HCWs to rely on Twitter during the pandemic to communicate relevant information. Twitter has a broad audience reach; is used as a communication tool by politicians, health bodies, and other key influences; and facilitates real-time updates [ 43 ]. During the pandemic, HCWs, primarily those in frontline roles and local response coordination, have often been challenged to become credible spokespersons for pandemic information [ 44 ]. Such credibility directly influences public confidence and decision-making, ultimately determining the success or failure of a public health intervention [ 43 ].

Furthermore, failures in risk communication could explain the presence of uncertainty and negative feelings associated with school regulations. When people are upset, distressed, or fearful, they often do not trust the authority, decrease the perceived validity of the communication received, and find information processing difficult [ 45 ]. In this regard, Fotheringham et al [ 46 ] indicated that during 2020, school leaders in the United Kingdom faced pressures and challenges related to translating and enacting school policies, particularly with the perceived lack of agency shared by the government concerning being able to translate centrally issued guidelines. In turn, Tomson et al [ 47 ] reported that the pandemic has negatively impacted the well-being of leaders in all types of schools and across all demographic groups, affecting their ability to think clearly and solve work-related problems. Given that the protection and care of children and young people health during the COVID-19 pandemic ultimately rests with school leaders, the search for support strategies that focus on the needs of these groups becomes an urgent necessity.

Findings in Relation to Other Studies

Using Twitter’s information, this is one of the first studies to capture health professionals’ perceptions of prolonged COVID-19 in the children and young people in the United Kingdom. However, other studies have addressed post–COVID-19 condition on this social network. Callard and Peregov [ 48 ] reviewed how, through social platforms such as Twitter, patients made the persistence and heterogeneity of COVID-19 symptoms visible, thus catapulting the inclusion of post–COVID-19 condition as a relevant phenomenon in clinical and policy debates. In contrast, other authors in the last 2 years have explored on various platforms (including Twitter) the persistence of symptoms and emotional impact after months of suspected and confirmed diagnosis of COVID-19 [ 49 - 55 ], including the period of vaccination. Furthermore, others have explored web discussions regarding this phenomenon [ 56 ]. Several of these authors agree on a perceived lack of support and specific resources from governmental bodies, a lack of information or clarity in the instructions given, and the absence of formal mechanisms to allow the voices of patients and the community to be heard. The above point is critical as it highlights the gap between the needs of the population and the response provided by policy makers, which not only translates into a gap in access to health services but also limits citizen participation in decision-making on the issues that affect their own health and increases distrust toward regulations and instructions issued by the government.

Implications for Policy and Practice

Several policy recommendations and implications are targeted at various stakeholders to consider while implementing future policy guidelines to address post–COVID-19 health care delivery. First, policy makers should consider investing appropriate resources to collect data regarding post–COVID-19 condition in children and young people, specifically on the impact of COVID-19 on the mental health of children and young people. This implies working closely with researchers to streamline data collection and reporting on post–COVID-19 condition. Second, policy makers should consider providing a basic level of psychosocial support with access to quality mental health and psychosocial support services for HCWs, school staff, parents, and children and young people experiencing post–COVID-19 condition. This implies strengthening health systems, community-based programming, and mobilization. Policies must include documenting the impact of mental health and psychosocial support interventions and innovative approaches to be more widely disseminated and scaled up across different contexts and target population groups. Third, to address the criticism around frequent changes in school closure and opening policies, decision makers should develop clear, easy-to-understand school mitigation plans informed by the best available evidence. The plans should incorporate teachers, school workers, and parents to ensure all voices are included in the policy plan. Fourth, policy makers should adopt a shared decision-making approach incorporating HCWs in the decision-making process for managing the COVID-19 pandemic. Finally, government decision makers should set post–COVID-19 pandemic recovery policies informed from a health equity perspective and how this affects children and young people living with post–COVID-19 condition, factoring in childhood, family income, housing, domestic violence, access to health care, and racism.

In terms of the needed clearer road map for recommendations to support training strategies for HCWs and school staff regarding post–COVID-19 condition in children and young people, we have outlined the following 10 steps.

Step 1: Data Collection and Analysis

Our study underlines the critical need for comprehensive data on post–COVID-19 condition’s impact on the mental health of children and young people. As a first step, it is recommended that policy makers should allocate resources for the systematic collection and analysis of data on post–COVID-19 condition in children and young people, particularly focusing on mental health outcomes. These data should be used to identify the most prevalent symptoms and the most effective treatment strategies. In this context, it is recommended that experts emphasize the importance of early detection and medical consultation for mental health issues in children and young people diagnosed with post–COVID-19 condition, including mood changes, irritability, social withdrawal, memory problems, difficulty in concentrating, anxiety, depression, posttraumatic stress, school absenteeism, and suicidal ideation [ 57 , 58 ]. This entails working closely with researchers to streamline data collection and reporting on post–COVID-19 condition.

Step 2: Psychosocial Support Framework

It has been noted that globally, programs for managing post–COVID-19 condition in children and young people are heterogeneous, ranging from the use of physiotherapy, pediatric occupational therapy, and psychological support to interventions aimed at lifestyle modifications [ 59 ]. This diversity could impact differential outcomes in the treatment, recovery, and timely and effective rehabilitation of children and young people with post–COVID-19 condition. Upon analyzing the wider literature and the social media data in this study, it is recommended that a basic level of psychosocial support should be established. This would involve ensuring access to quality mental health services for HCWs, school staff, parents, and children and young people with post–COVID-19 condition. This framework should be integrated into the health system and community-based programming, emphasizing the mobilization of resources and strengthening of support networks. It is suggested that the psychosocial support framework should facilitate access to quality mental health services and support networks that are robust and responsive. Community engagement gleaned from further Twitter discourse analysis should be a helpful guide in the development of these services to ensure they meet the real and expressed needs of children and young people with post–COVID-19 condition. Practical examples of basic psychosocial support include using web support services; individual or group therapy sessions; school-based emotional support programs; and counseling sessions aimed at parents, family members, or school staff.

Step 3: Educational Mitigation Plans

The frequent policy changes around school closures highlight the necessity for stable and clear educational mitigation plans. It is recommended that these plans should be directly informed by the evidence collected and further analysis of sentiments and emotions surrounding post–COVID-19 condition in schools. Incorporating the viewpoints of teachers, parents, and school staff, as identified in our thematic analysis, will ensure that the mitigation strategies are comprehensive, feasible, and sensitive to the psychosocial impact on children and young people. School staff and policy makers should collaborate to develop clear, evidence-informed educational mitigation plans. These plans should be straightforward and involve teachers, school workers, and parents in their creation, ensuring a unified approach that considers the voices of all stakeholders.

Step 4: Shared Decision-Making in Health Care

In health care settings, the adoption of a shared decision-making model is crucial, enabling HCWs to actively contribute to the formulation of COVID-19 and post–COVID-19 policies. This inclusive approach ensures that frontline workers can provide valuable insights toward policy development. To facilitate this, the establishment of advisory committees composed of representatives from HCWs is recommended. This committee can convene regularly to deliberate on key decisions pertaining to the COVID-19 pandemic management, including prevention measures, resource distribution, and vaccination strategies. Such collaborative groups have demonstrated effectiveness in identifying priority needs within the context of a pandemic [ 60 ].

Step 5: Health Equity in Policy Setting

Post–COVID-19 recovery policies should be set with a health equity lens. This means considering factors such as family income, housing, domestic violence, access to health care, and racism and how these factors affect children and young people living with post–COVID-19 condition. Our findings emphasize the importance of framing post–COVID-19 recovery policies through a lens of health equity. The concerns raised by HCWs regarding the socioeconomic impacts, such as family income and access to health care, underline the need for policies that address not just the medical aspects of post–COVID-19 condition but also the social determinants of health. An equitable approach will ensure that children and young people from diverse backgrounds receive appropriate support.

Step 6: Documenting and Disseminating Interventions

It is vital to document the impact of mental health and psychosocial support interventions. In this context, it is crucial to implement innovative strategies to disseminate unbiased information about post–COVID-19 condition among health care professionals and educators working with children and young people, ensuring it reaches different contexts and populations. These strategies may include creating interactive multimedia resources, such as videos and mobile apps; organizing webinars; actively using social media; and forming web support groups. These groups will provide a space where patients, health care professionals, and educators can share their experiences and knowledge regarding post–COVID-19 condition. These actions will not only help reduce isolation and social stigma but also strengthen support for these groups considered vulnerable [ 61 ].

Step 7: Developing a Clear Communication Strategy

Policy makers must develop a clear communication strategy to address frequent policy changes and mitigate confusion. This strategy should be informed by the data collected and analysis conducted in Step 1. The data reveal a palpable sense of uncertainty and frustration due to frequent policy shifts, underscoring the need for a clear and consistent communication strategy. This strategy should be grounded in the evidence gathered from the health care community’s discourse and aim to minimize confusion by providing timely, transparent, and reliable information regarding post–COVID-19 policies and support services.

Step 8: Training and Support Strategies

On the basis of the findings of the comprehensive data analysis, specific training and support strategies should be developed for HCWs and school staff. These strategies should be focused on the practical aspects of identifying and managing post–COVID-19 condition in children and young people. For instance, training sessions could include practical workshops on recognizing post–COVID-19 symptoms in children and adolescents, conducting diagnostic assessments, and implementing appropriate treatment and support interventions.

Step 9: Continuous Feedback and Policy Adaptation

The continuous evolution of the post–COVID-19 phenomenon demands an iterative approach to policy making. On the basis of our study, we recommend establishing feedback mechanisms with HCWs and school staff to monitor the reception and effectiveness of implemented policies. This feedback, coupled with ongoing research, should inform policy adaptations to ensure they remain aligned with the evolving landscape of post–COVID-19 condition and its impact on children and young people.

Step 10: Evaluation and Research

Finally, there should be a commitment to ongoing evaluation and research. This will involve not only monitoring the implementation of the abovementioned steps but also supporting new research to fill any remaining gaps in understanding the long-term effects of COVID-19 on children and young people.

This sequence of steps is designed to be iterative and responsive, ensuring that the recommendations from the study are translated into concrete actions that adapt to emerging data and research findings.

Strengths and Limitations

A key strength of this study is that our social media analysis of post–COVID-19 condition contributes toward an emerging understanding of reported experiential, emotional, and practical dimensions of post–COVID-19 condition in children and young people specifically and questions of vaccine hesitancy in children and young people with post–COVID-19 condition. This is one of the few studies to collect HCWs’ perceptions regarding post–COVID-19 condition in children and young people in the United Kingdom using information from Twitter. We identify key areas that need considering attention and focus, such as the provision of psychosocial support with access to quality mental health resources to alleviate the impact of post–COVID-19 condition in children and young people and the development of clear post–COVID-19 pandemic recovery guidelines that are informed by health equity perspective, and how this affects children and young people living with post–COVID-19 condition.

One of the limitations this study acknowledges is the definition of post–COVID-19 condition in children and young people. When data were collected, the lack of consensus on the definition of post–COVID-19 condition in children and young people forced us to formulate a definition of post–COVID-19 condition in children and young people based on the available literature. Furthermore, this study is limited to the perceptions of people who used descriptors in their web biography attributable to HCWs; therefore, our results only represent some HCWs in the United Kingdom and those in other countries. In turn, this research collected data from Twitter only; therefore, further inquiry into HCWs’ perceptions of post–COVID-19 condition in children and young people required expanding to other data sources or social networks and including languages other than English. We acknowledge that demographic factors, geographic location, and individual daily activities of social media users can significantly influence language use and word choice, introducing potential biases in tweet-based data. Such biases are inherent in any analysis of social media content and can affect the generalizability of findings. For instance, our study relies on Twitter data, which do not encompass the full spectrum of global or the UK public opinion on post–COVID-19 condition in children and young people. While Twitter serves as a valuable platform for capturing real-time sentiments and experiences, it is not fully representative of all demographics and geographic regions. Our results may reflect the perspectives of more vocal or active social media users, which may not correspond to the silent majority or those without access to social media. In addition, the absence of geotagged information for many users limits our ability to conduct a more nuanced spatial analysis of the sentiments expressed.

Furthermore, our study is built upon the recognition that social media data may overrepresent certain demographic groups while underrepresenting others, such as the older population or those without reliable internet access. This skew can influence the apparent prevalence of certain views or experiences of post–COVID-19 condition. Moreover, individuals’ patterns of daily life, reflected in their social media use and content, contribute additional layers of complexity and potential bias to the discourse analyzed.

Consistent with scholarly precedents on the subject [ 62 , 63 ], our study acknowledges these biases as intrinsic limitations of social media–based research. Although our analysis did not control for these factors, we recognize their potential impact on our results. Future studies would benefit from incorporating a broader array of data sources, including interviews or focus groups, to provide a more representative and comprehensive understanding of post–COVID-19 condition in children and young people. This approach would complement our Twitter-based findings and help mitigate the biases inherent in social media data.


More than a year after the start of the COVID-19 pandemic, the perceptions described on Twitter by HCWs concerning the presence of post–COVID-19 condition in children and young people appear to be a relevant and timely issue as well as very responsive to the declarations and guidelines issued by the health authorities over time. The most prominent group within the discourse studied was the activist or lobbying organization @LongCovidKids, which shared the most tweets and images over the period studied. We recommend that future research focus on how web health activism is organized and carried out for children and young people with post–COVID-19 condition. Such a strategy would allow for a better understanding of the scope and impact of this phenomenon and how it can influence decision-making. Furthermore, we suggest different mitigation strategies, support, and training of HCWs and school staff regarding manifestations and treatment of post–COVID-19 condition in children and young people across all demographic areas.


The authors would like to thank the Rapid Research Evaluation and Assessment Lab, Department of Targeted Intervention, University College London, London, United Kingdom, whose support has been essential for developing this project.

Conflicts of Interest

None declared.

Filters used for the search strategy on Twitter.

Sentiment analysis framework: attitudes toward post–COVID-19 condition in children and young people.

Theme codebook: examples of tweets that fit into main themes tagged for mention of children and young people with post–COVID-19 condition.

Timeline of national governmental policies and guidelines regarding children and young people.

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Edited by A Mavragani; submitted 20.06.23; peer-reviewed by R Gore, A Wahbeh; comments to author 02.11.23; revised version received 14.02.24; accepted 08.03.24; published 17.04.24.

©Macarena Chepo, Sam Martin, Noémie Déom, Ahmad Firas Khalid, Cecilia Vindrola-Padros. Originally published in the Journal of Medical Internet Research (, 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.

The Daily

New study shows effect of socio-economic factors—housing, food, neighborhood—to predict diabetic patients’ risk of heart failure

A recent study by  Case Western Reserve University  used national data from U.S. military veterans with diabetes to validate and modify a widely accepted model used to predict the risk of heart failure in diabetic patients.

The model, called the WATCH-DM score, is used to predict the likelihood of heart failure in diabetes patients within five years.

But because it overlooks the influence of social determinants of health‚ such as housing, food and a patient’s neighborhood, the researchers used a social deprivation index (SDI), a multi-component summary score, to adjust the WATCH-DM score. 

The SDI, introduced by the  Robert Graham Center,  a group of clinical researchers, can quantify the level of disadvantage in particular areas using food, housing, transportation and community conditions. Prior research demonstrated this score is directly proportional to the level of health disparities observed in communities.

The study identified about 1 million U.S. veterans with type 2 diabetes without heart failure treated as outpatients at Veterans Affairs medical sites nationally in 2010.

Researchers used patient zip codes to obtain their SDI, which was then entered into the risk calculator to determine how likely they would be hospitalized for heart failure within five years.   

While the hospitalization rate for heart failure for the whole cohort of more than 1 million patients was 5.39%, this incidence varied from 3% (in the least socially deprived) to 11% (in the most deprived). 

Researchers found that, depending on the patients’ other clinical information, adding the SDI into the risk-prediction model could even double the probability of that patient developing heart failure in the next five years.

Photo of Salil Deo

The team of investigators then optimized the WATCH-DM score for each SDI group using a statistical correction factor and improved its predictive accuracy across the whole range of the social determinants of health. 

“We found that adding the SDI enhanced the WATCH-DM score’s ability to forecast risk,” said Salil Deo, an associate professor in the  Department of Surgery at Case Western Reserve School of Medicine, who led the study. “These results highlight the necessity of including social determinants of health in any future clinical risk prediction algorithms. This will increase their accuracy, which will benefit patients by improving their health outcomes.”

View the calculator. 

“We hope our study encourages healthcare providers to adopt a wholistic approach when treating patients in the future,” Deo said. “Understanding and quantifying social inequity is likely the first step we can take toward trying to ensure that it does not affect the health of our patients.” 

For more information, contact Patty Zamora at [email protected] .


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