research design concept and types

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

Need a helping hand?

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

You Might Also Like:

10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

• << Previous: Purpose of Guide
• Next: Design Flaws to Avoid >>
• Last Updated: May 21, 2024 11:14 AM
• URL: https://libguides.usc.edu/writingguide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Types of Research Designs Compared | Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorise different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyse
• The sampling methods , timescale, and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location.

The first thing to consider is what kind of knowledge your research aims to contribute.

Prevent plagiarism, run a free check.

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: How will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Choosing among all these different research types is part of the process of creating your research design , which determines exactly how the research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Types of Research Designs Compared | Examples. Scribbr. Retrieved 21 May 2024, from https://www.scribbr.co.uk/research-methods/types-of-research-designs/

Is this article helpful?

Shona McCombes

Other students also liked, sampling methods | types, techniques, & examples, qualitative vs quantitative research | examples & methods, between-subjects design | examples, pros & cons.

Research Design

• First Online: 13 April 2022

Cite this chapter

• Yanmei Li 3 &
• Sumei Zhang 4  

909 Accesses

This chapter introduces methods to design the research. Research design is the blueprint of how to conduct research from conception to completion. It requires careful crafts to ensure success. The initial step of research design is to theorize key concepts of the research questions, operationalize the variables used to measure the key concepts, and carefully identify the levels of measurements for all the key variables. After theorization of the key concepts, a thorough literature search and synthetization is imperative to explore extant studies related to the research questions. The purpose of literature review is to retrieve ideas, replicate studies, or fill the gap for issues and theories that extant research has (or has not) investigated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education, 98 (1), 53–66.

Article   Google Scholar  

Creswell, J. W., Plano Clark, V. L., & Garrett, A. L. (2008). Methodological issues in conducting mixed methods research design. In M. M. Bergman (Ed.), Advances in mixed methods research: Theories and application (pp. 66–83). Sage.

Google Scholar  

Li, Y., & Walter, R. (2013). Single-family housing market segmentation, post-foreclosure resale duration, and neighborhood attributes. Housing Policy Debate, 23 (4), 643–665. https://doi.org/10.1080/10511482.2013.835331

Opoku, A., Ahmed, V., & Akotia, J. (2016). Choosing an appropriate research methodology and method. In V. Ahmed, A. Opoku, & Z. Aziz (Eds.), Research methodology in the built environment: A selection of case studies . Routledge.

Pickering, C., Johnson, M., & Byrne, J. (2021). Using systematic quantitative literature reviews for urban analysis. In S. Baum (Ed.). Methods in Urban Analysis (Cities Research Series) (pp. 29–49) . Singapore: Springer.

Download references

Author information

Authors and affiliations.

Florida Atlantic University, Boca Raton, FL, USA

University of Louisville, Louisville, KY, USA

Sumei Zhang

You can also search for this author in PubMed   Google Scholar

Electronic Supplementary Material

(docx 13 kb), rights and permissions.

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Li, Y., Zhang, S. (2022). Research Design. In: Applied Research Methods in Urban and Regional Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-93574-0_3

Download citation

DOI : https://doi.org/10.1007/978-3-030-93574-0_3

Published : 13 April 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-93573-3

Online ISBN : 978-3-030-93574-0

eBook Packages : Mathematics and Statistics Mathematics and Statistics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

I Am Disconnected – Tuesday CX Thoughts

May 21, 2024

20 Best Customer Success Tools of 2024

May 20, 2024

AI-Based Services Buying Guide for Market Research (based on ESOMAR’s 20 Questions) 

Data Information vs Insight: Essential differences

May 14, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Research Design & Method

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

• << Previous: Introduction
• Next: Survey Research >>
• Last Updated: Aug 21, 2023 10:42 AM

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Crit Care Med
• 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.

TERMS USED IN RESEARCH DESIGNS

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.

CLASSIFICATION OF STUDY DESIGNS

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

• Privacy Policy

Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Understanding External Validity and its Role in Research Design

This essay discusses external validity in scientific research and its significance in ensuring that study findings are applicable beyond specific experimental conditions. It examines how external validity helps researchers determine whether results can be generalized to different populations, settings, and contexts. The essay highlights challenges in achieving high external validity due to the specificity of research environments, participant characteristics, and timing. It also explains strategies like replication studies and field experiments that help increase the generalizability of results. The essay emphasizes the importance of balancing internal and external validity, acknowledging that while highly controlled studies can ensure internal accuracy, they might not reflect real-world conditions. Ultimately, external validity is crucial for producing research that informs effective policies, clinical guidelines, and societal changes.

How it works

Within the domain of scientific inquiry, the concept of external validity assumes paramount importance, serving as a linchpin for ensuring the relevance and broad applicability of research findings beyond the confines of a singular study. Put succinctly, external validity scrutinizes the extent to which the outcomes of a given investigation can be extrapolated to diverse populations, environments, temporal epochs, or contextual milieus. This conceptual framework assumes significance by bridging the chasm between meticulously controlled research settings and the kaleidoscopic intricacies of real-world scenarios, thereby furnishing researchers with the assurance that their conclusions possess trans-situational validity.

External validity assumes a pivotal role in buttressing the overall veracity of scientific inquiry, furnishing scientists with the means to discern whether their revelations hold substantive import and utility for wider swathes of humanity. Nonetheless, attaining elevated levels of external validity often presents an arduous undertaking owing to the idiosyncratic nature of experimental methodologies. Numerous investigations are conducted within controlled environs or with highly circumscribed samples to mitigate the influence of confounding variables, thereby complicating the process of extrapolating findings to disparate cohorts. For instance, a psychological study confined to the precincts of university campuses might furnish insights germane solely to that demographic stratum, precluding facile generalization to alternative age cohorts or cultural constellations.

Pivotal determinants impinging upon external validity encompass the demographic attributes of study participants, the ambient ambiance of experimental locales, and the temporal dynamics of data accrual. Researchers routinely resort to random sampling methodologies to obviate selection biases and assemble a heterogeneous cohort reflective of the broader populace. The ambient setting assumes salience as outcomes gleaned within sterile laboratory settings might not seamlessly transmute into real-world vicinities. Furthermore, external validity can be influenced by the temporal dimension of investigations, particularly when grappling with phenomena susceptible to temporal vicissitudes induced by cultural, economic, or technological flux.

Researchers endeavor to fortify external validity through recourse to replication studies, which seek to reproduce experimental paradigms under varying conditions to corroborate the robustness of findings. Engaging in multi-site investigations spanning disparate geographical realms or demographic constituencies also serves to validate the generalizability of findings across multifarious locales and participant profiles. Augmenting the verisimilitude of study designs by approximating real-world conditions more faithfully or deploying field experiments conducted within natural habitats constitutes an additional stratagem employed to enhance external validity.

Nonetheless, a judicious equilibrium between internal and external validity is indispensable. While internal validity safeguards against spurious attributions of causality by delineating the genuine effects attributable to independent variables as opposed to extraneous factors, an undue emphasis on this facet may inadvertently compromise external validity. For instance, a rigorously controlled laboratory investigation might succeed in eradicating most confounding variables but might concurrently engender an artificial milieu divorced from real-world veracity.

On occasion, external validity is consciously sacrificed to scrutinize a phenomenon within a specific subgroup or contextual milieu. Clinical trials, for instance, might pivot towards patients exhibiting highly circumscribed medical afflictions or demographic profiles. While such findings might lack broad generalizability, they furnish invaluable insights germane to the target demographic.

In summation, the salience of external validity cannot be overstated. Research endeavors endeavoring to inform policy formulation, clinical dictums, or societal transformations necessitate a degree of generalizability to obviate the formulation of recommendations that are ineffectual or even deleterious. Researchers ought to meticulously orchestrate their investigations, cognizant of the demographic cohorts and environmental terrains that their findings aspire to impact. By acknowledging and redressing the vicissitudes of external validity, scientists can engender more resilient and universally applicable research findings that withstand the rigors of real-world complexity.

Cite this page

Understanding External Validity and Its Role in Research Design. (2024, May 21). Retrieved from https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/

"Understanding External Validity and Its Role in Research Design." PapersOwl.com , 21 May 2024, https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/

PapersOwl.com. (2024). Understanding External Validity and Its Role in Research Design . [Online]. Available at: https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/ [Accessed: 22 May. 2024]

"Understanding External Validity and Its Role in Research Design." PapersOwl.com, May 21, 2024. Accessed May 22, 2024. https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/

"Understanding External Validity and Its Role in Research Design," PapersOwl.com , 21-May-2024. [Online]. Available: https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/. [Accessed: 22-May-2024]

PapersOwl.com. (2024). Understanding External Validity and Its Role in Research Design . [Online]. Available at: https://papersowl.com/examples/understanding-external-validity-and-its-role-in-research-design/ [Accessed: 22-May-2024]

Don't let plagiarism ruin your grade

Hire a writer to get a unique paper crafted to your needs.

Our writers will help you fix any mistakes and get an A+!

Please check your inbox.

You can order an original essay written according to your instructions.

Trusted by over 1 million students worldwide

1. Tell Us Your Requirements

2. Pick your perfect writer

3. Get Your Paper and Pay

Hi! I'm Amy, your personal assistant!

Don't know where to start? Give me your paper requirements and I connect you to an academic expert.

short deadlines

100% Plagiarism-Free

Certified writers

What are you looking for?

Most popular topics.

• Sustainable Aviation Fuel (SAF)

• Hydrogen-powered
• Technology & Tests

Airbus’ ambition is to bring to market the world’s first hydrogen-powered commercial aircraft by 2035. To get there, our ZEROe project is exploring a variety of configurations and technologies, as well as preparing the ecosystem that will produce and supply the hydrogen. 

Hydrogen propulsion to power future aircraft

All four ZEROe concepts are powered by hydrogen. 

In the case of hydrogen combustion , gas turbines with modified fuel injectors  and fuel systems are powered with hydrogen in a similar manner to how aircraft are powered today. 

A second method, hydrogen fuel cells, creates electrical energy which in turn powers electric motors that turn a propeller or fan. This is a fully electric propulsion system, quite different to the propulsion system on aircraft currently in service. 

These technologies are complementary, and the benefits are additive.

ZEROe Concept Aircraft

Range: 2,000+ nm | Passengers: <200

Two hybrid-hydrogen turbofan engines provide thrust. The liquid hydrogen storage and distribution system is located behind the rear pressure bulkhead.

Range: 1,000+ nm | Passengers: <100

Two hybrid-hydrogen turboprop engines, which drive eight-bladed propellers, provide thrust. The liquid hydrogen storage and distribution system is located behind the rear pressure bulkhead.

Blended-Wing Body (BWB)

The Blended-Wing Body’s exceptionally wide interior opens up multiple options for hydrogen storage and distribution. Here, the liquid hydrogen storage tanks are located underneath the wings. Two hybrid-hydrogen turbofan engines provide thrust.

Fully electrical concept

Range: 1,000 nm | Passengers: <100

The fully electrical concept  was revealed in December 2020. It is based on a fully electrical propulsion system powered by fuel cells.

Airbus at VivaTech

Join us from 22-25 May 2024 | #VivaTech

Discover ZEROe concepts

Technology and testing.

Airbus is meeting a number of technology and testing milestones as it moves towards its ambition of bringing to market a hydrogen-powered commercial aircraft by 2035. 

Many of these milestones revolve around establishing the means of propulsion, either via hybrid hydrogen-electric fuel cells or direct hydrogen combustion . Airbus has even established a joint venture with automotive supplier ElringKlinger AG, Airbus Aerostack , to develop hydrogen fuel cell stacks at the heart of an electric propulsion system.

Airbus is exploring both hydrogen-combustion and fuel-cell propulsion technologies, for which demonstrators have been launched. It has also set-up dedicated Development Centres in France, the UK, Germany and Spain to work on tanks and cryogenic fuel systems.

Airbus test aircraft A380 MSN1 is taking the lead in testing these technologies that will be vital to bringing a hydrogen-powered commercial aircraft to market.

In our mission to bring a hydrogen powered aircraft

Meet some of the faces behind ZEROe, and read about the passions, dreams and ambitions that drive them. Want to join them? We’re hiring! Airbus is looking for the brightest and best to design the future of aviation. Find out which specialist skills and profiles are in demand.

Meet Natalia

Fuel Cell System Engineer | Hamburg, Germany

As a Fuel Cell System Engineer in Hamburg, Natalia Medina Cabello brings her interest in renewable energy to life through her work on the ZEROe hydrogen-powered concept aircraft. Here she gives us an inside look at her responsibilities on the project and how she came to join the team.

Meet Fabien

Value Assessment & A/C Economics | Blagnac, France

First inspired to join Airbus as a teen when he saw the A380 take flight for the first time, Sustainable Aviation Strategist Fabien Romero now assesses business models for another groundbreaking Airbus project: the ZEROe hydrogen-powered concept aircraft.

The race for hydrogen-powered commercial aviation starts on the ground. Hydrogen has to be produced, transported and stored in the right quantity, at the right time, place and cost. Its production and use must be regulated and certified.

Airbus believes the deployment of hydrogen infrastructure at airports is a prerequisite to support the widespread scale-up and adoption of hydrogen aircraft. We’re partnering with hydrogen producers and distributors worldwide, airports and airlines to build the right ecosystem to operate a hydrogen-powered aircraft by 2035. We’re already bringing together all the key players to the table.

This concept involves collaborating with airports to develop a stepped approach to decarbonise airport facilities, ground operations and transport using hydrogen.

Discover our Airbus' ZEROe Series

The countdown to zeroe: episode 1: tanks.

How does an aircraft fly using hydrogen? Whether hydrogen is burned directly or converted into electricity in fuel cells, it first needs to be safely stored at -253°! Find out how our teams in Toulouse, Nantes and Bremen are collaborating to design and manufacture innovative cryogenic storage that will enable hydrogen-powered flight.

The countdown to ZEROe: Episode 2: Fuel Cell Systems

The countdown to ZEROe: Episode 3: Fuel Cell Systems Testing

Latest news.

Montréal–Trudeau International Airport, Toronto Pearson International Airport and Vancouver International Airport sign with Airbus and ZeroAvia for hydrogen hubs at Canadian airports

Airbus, Houston Airports, Center for Houston’s Future join forces to study feasibility of hydrogen hub at George Bush Intercontinental Airport

Airbus, Delta, Plug Power, Hartsfield-Jackson Atlanta International Airport study feasibility of hydrogen hub at world’s busiest airport

Airbus Aerostack

Fuel cell stacks: The heart of pioneering hydrogen-powered aviation

The NOVA Newsletter

Airbus innovation newsletter

The NOVA Newsletter is your go-to source for the latest stories on the disruptive technologies and ground-breaking aerospace projects that will transform the way we fly today and tomorrow. 

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

Published on 21.5.2024 in Vol 8 (2024)

This is a member publication of Open University

A Web-Based Intervention to Support the Mental Well-Being of Sexual and Gender Minority Young People: Mixed Methods Co-Design of Oneself

Authors of this article:

Original Paper

• Katherine Brown 1 , PhD   ; 
• Mathijs F G Lucassen 2 , PhD   ; 
• Alicia Núñez-García 3 , PhD   ; 
• Katharine A Rimes 4 , DPhil, DClinPsy   ; 
• Louise M Wallace 3 , PhD   ; 
• Rajvinder Samra 3 , PhD  

1 Centre for Research in Psychology and Sports Science, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom

2 School of Health & Psychological Sciences, City, University of London, London, United Kingdom

3 School of Health, Wellbeing & Social Care, The Open University, Milton Keynes, United Kingdom

4 Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom

Corresponding Author:

Katherine Brown, PhD

Centre for Research in Psychology and Sports Science

School of Life and Medical Sciences

University of Hertfordshire

College Lane

Hatfield, AL109AB

United Kingdom

Phone: 44 1707 284 615

Email: [email protected]

Background: Sexual and gender minority youth are at greater risk of compromised mental health than their heterosexual and cisgender peers. This is considered to be due to an increased burden of stigma, discrimination, or bullying resulting in a heightened experience of daily stress. Given the increasing digital accessibility and a strong preference for web-based support among sexual and gender minority youth, digital interventions are a key means to provide support to maintain their well-being.

Objective: This paper aims to explicate the co-design processes and underpinning logic of Oneself , a bespoke web-based intervention for sexual and gender minority youth.

Methods: This study followed a 6-stage process set out by Hagen et al (identify, define, position, concept, create, and use), incorporating a systematic scoping review of existing evidence, focus groups with 4 stakeholder groups (ie, sexual and gender minority youth, professionals who directly support them, parents, and UK public health service commissioners), a series of co-design workshops and web-based consultations with sexual and gender minority youth, the appointment of a digital development company, and young adult sexual and gender minority contributors to create content grounded in authentic experiences.

Results: Oneself features a welcome and home page, including a free accessible to all animation explaining the importance of using appropriate pronouns and the opportunity to create a user account and log-in to access further free content. Creating an account provides an opportunity (for the user and the research team) to record engagement, assess users’ well-being, and track progress through the available content. There are three sections of content in Oneself focused on the priority topics identified through co-design: (1) coming out and doing so safely; (2) managing school, including homophobic, biphobic, or transphobic bullying or similar; and (3) dealing with parents and families, especially unsupportive family members, including parents or caregivers. Oneself’s content focuses on identifying these as topic areas and providing potential resources to assist sexual and gender minority youth in coping with these areas. For instance, Oneself drew on therapeutic concepts such as cognitive reframing, stress reduction, and problem-solving techniques. There is also a section containing relaxation exercises, a section with links to other recommended support and resources, and a downloads section with more detailed techniques and strategies for improving well-being.

Conclusions: This study contributes to research by opening up the black box of intervention development. It shows how Oneself is underpinned by a logic that can support future development and evaluation and includes diverse co-designers. More interactive techniques to support well-being would be beneficial for further development. Additional content specific to a wider range of intersecting identities (such as care-experienced Asian sexual and gender minority youth from a minority faith background) would also be beneficial in future Oneself developments.

International Registered Report Identifier (IRRID): RR2-10.2196/31036

Introduction

Worldwide, it is estimated that up to 10% of the adolescent population identifies as being either a sexual or gender minority youth; that is, they identify as lesbian, gay, bisexual, transgender, queer, or another sexual or gender minority (lesbian, gay, bisexual, transgender, and queer [LGBTQ+]) [ 1 - 3 ]. Sexual and gender minority youth are known to be at greater risk of poor mental health than their heterosexual and cisgender peers [ 1 , 4 ]. This elevated risk is suggested to be largely related to an increased burden of stigma, discrimination, or bullying resulting in a heightened experience of stress in their day-to-day lives [ 5 , 6 ]. Clearly, work needs to continue to improve social environments for sexual and gender minority youth to reduce the additional stress they experience, but this will take time. In parallel, research is needed to identify what can be done to support sexual and gender minority youth to protect their mental health and well-being and help them build the skills and resilience they will need to thrive. This is increasingly important for the youngest sexual and gender minority youth as there is evidence suggesting that they are coming out at an earlier age than previous generations [ 6 , 7 ]. Their younger age may mean that they have had less time and opportunity to develop strong support networks and coping skills compared with those who come out at an older age [ 6 , 8 ].

Current and recent generations of young people have grown up in the digital age. Often referred to as digital natives [ 9 ], they have only experienced a world with access to the internet [ 9 ]. The latest data suggest that almost all homes in the United Kingdom have access to the internet [ 10 ] and 97% of individuals aged 12 to 15 years have their own mobile phone, with the vast majority using it to access the internet [ 11 ]. Young people are also known to spend much of their time in web-based spaces, which can assist their early attempts to seek information or obtain support on the issues they face. Similarly, a UK Department of Health and Social Care–commissioned report highlighted a strong preference among sexual and gender minority youth to access help on the internet, whereby 82.3% (n=572) of sexual and gender minority youth participants reported being “likely” or “very likely” to choose support in this format [ 12 ]. For this reason, providing web-based resources to support sexual and gender minority youth and the adults who assist them could be a widely accessible and relatively low-cost public health approach to improving their health and well-being.

In this paper, we present the detailed systematic steps we took to develop Oneself, a bespoke digital web-based resource to support sexual and gender minority youth regarding some of the most pressing challenges associated with growing up and being a sexual and gender minority young person. Drawing on the “identify, define, position, concept, create, and use” stages set out by Hagen et al [ 13 ] for participatory design with young people in mental health promotion, the process initially involved a scoping review of the strategies used in existing interventions [ 14 ]; in-depth interviews with adult experts who support sexual and gender minority youth, including parents; and focus groups with sexual and gender minority youth. We then engaged in a co-design process involving workshops with sexual and gender minority youth to determine priorities for the focus of the content and the look and feel of the resource and develop aspects of the content itself.

In addition to drawing on evidence from the scoping review, we drew on the firsthand expertise of sexual and gender minority youth as participatory research and co-design with intended end users of interventions are essential for their optimization in pragmatic terms. For example, knowledge about the needs of unique subpopulations may be limited, and co-design processes can help enhance an intervention’s acceptability [ 15 - 18 ]. In instances in which a group is frequently marginalized, such as sexual and gender minority youth, co-design is especially important because it represents a way to empower and democratize research and its outputs [ 19 ]. Co-design with underserved populations, including sexual and gender minority youth, allows pertinent diversity considerations to be addressed, for instance, factors regarding language, symbols, and character use in digital mental health technologies [ 20 ]. Hence, co-design processes are an attempt to help inform the creation of acceptable resources and assist in not only avoiding further alienating populations such as sexual and gender minority youth but also offering them a voice and greater inclusion. The approach applied by Hagen et al [ 13 ] was specifically chosen because it has been applied successfully in the past to support sexual and gender minority youth in terms of their mental health. Making intervention development processes replicable and transparent in how they are intended to bring about change for end users is also recognized as important for developing the science of health and well-being [ 21 ]. With this in mind, we outlined what we planned to do at the start of our project in our published study protocol [ 6 ]. This protocol was submitted in June 2021, before the project officially commenced.

This paper sets out the systematic stages involved in developing Oneself for sexual and gender minority youth and describes how the findings or outcomes from each stage fed into content development and refinements. It also aims to clearly explicate how each feature and its content are intended to support sexual and gender minority youth and promote change so that any future research involving Oneself can incorporate evaluation against the logic that underpins it.

In accordance with our published protocol [ 6 ], we set out to follow the stages in intervention co-design as outlined by Hagen et al [ 13 ]. Intervention development and co-design are rarely a straightforward, linear process. In practice, some tasks need to happen in parallel, and researchers and coproducers may need to cycle back and repeat elements of the process as additional challenges emerge and new insights arise. The 6 stages of co-design involving adult experts and sexual and gender minority youth are set out below with a brief description of project activities involved in developing the resource aligned to that stage and links to the relevant methods section where more detail is provided ( Textbox 1 [ 13 ]).

• Focus groups with sexual and gender minority youth (see the Interviews and Focus Groups With Sexual and Gender Minority Youth, Adult Experts, and Parents: Identify and Define Stages section)
• Systematic scoping review (see the Systematic Scoping Review: Identify and Define Stages section)
• Interviews with adult experts and parents (see the Interviews and Focus Groups With Sexual and Gender Minority Youth, Adult Experts, and Parents: Identify and Define Stages section)
• Team co-development to finalize decisions and solutions (see the Findings of the Research Team’s Co-Design Meetings in June 2022: Design, Position, and Concept Stages section)
• Initial co-design workshops with sexual and gender minority youth and email and web-based consultation (see the Initial Co-Design Workshops With Sexual and Gender Minority Youth and Email or Web-Based Consultations: Position and Concept Stages section)
• Appointment of digital developer (see the Appointment of Digital Developer [Preparation for Delivering the Concept, Create, and Use Stages] section)
• Team co-development to finalize decisions and solutions (see the Research Team Co-Development to Finalize Decisions on the Focus and Topic Areas [Define, Position, and Concept Stages] section)
• Initial co-design workshops with sexual and gender minority youth and email or web-based consultation (see the Initial Co-Design Workshops With Sexual and Gender Minority Youth and Email or Web-Based Consultations: Position and Concept Stages section)
• Questionnaire to assess look and feel design options (see the Initial Co-Design Workshops With Sexual and Gender Minority Youth and Email or Web-Based Consultations: Position and Concept Stages section)
• Appointment of sexual and gender minority community members through specialist media and modeling agencies (see the Appointment of Sexual and Gender Minority Contributors Through Specialist Media and Modeling Agencies: Concept Stage section)
• Further co-design workshops with sexual and gender minority youth (see the Further Co-Design Workshops With Sexual and Gender Minority Youth: Create Stage section)
• Filming with sexual and gender minority contributors (see the Introducing the Sexual and Gender Minority Contributors: Create Stage section)
• Development work by appointed digital provider (see The Oneself Resource section)
• Feedback from think aloud user interviews (MFG Lucassen, unpublished data, 2024)
• Feedback from adult expert interviews (MFG Lucassen, unpublished data, 2024)

Ethical Considerations

Ethics approval for the aspects of the study involving human participation was granted by the Human Research Ethics Committee at The Open University (OU) before data collection began (ethics approval HREC/4059/Lucassen). All participants, both adults (eg, professionals who directly support sexual and gender minority youth) and adolescents, gave full informed consent to participate and signed a consent form to indicate this. Young people aged <16 years also required written parental consent to participate. Study data were anonymized before analysis, and all consent records were stored separately. Following the anonymization of interview and focus group transcripts, recordings and transcripts with person-identifiable information were deleted. Where applicable, participants were reimbursed for any transport costs associated with taking part and given a £20 (US $25.56) gift voucher per interview or focus group as a token of gratitude for their involvement.

Systematic Scoping Review: Identify and Define Stages

The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines [ 22 ] were followed, and studies were included if they contained primary data on psychosocial coping strategies for sexual and gender minority youth, were conducted with adolescents (aged 10-19 years), and were published in English. The MEDLINE, Embase, and PsycINFO databases were searched. Search terms included a range of terms to capture a sexual and gender minority focus (eg, “gender minorit*” or “LGB*”) and a range of terms for psychosocial coping strategies (eg, “Coping*,” “adaptive,” and “resilience”). No date restrictions were applied, and the searches ran up to January 19, 2022. A descriptive approach to synthesizing the evidence, as recommended by Arksey and O’Malley [ 23 ], was used. The methods and findings of the scoping review have been published elsewhere [ 14 ]. The systematic scoping review ran in parallel to the focus groups with sexual and gender minority youth and interviews with adult experts and parents, which are reported in the following section.

Interviews and Focus Groups With Sexual and Gender Minority Youth, Adult Experts, and Parents: Identify and Define Stages

A total of 6 focus groups, each with between 3 and 10 sexual and gender minority youth participants, were conducted between November 2021 and February 2022. To reach and recruit participants in the applicable age range from the target communities, we worked with 3 organizations supporting LGBTQ+ youth to advertise the opportunity. Focus groups were run in conjunction with these organizations, with their staff also attending to help young people feel comfortable and supported. Staff also assisted in the process of obtaining informed consent from sexual and gender minority youth and, for those aged <16 years, from their parents or guardians. Due to COVID-19 restrictions, all focus groups were hosted via videoconference. The sessions were audio recorded and transcribed. Once accurate transcripts were approved (by MFGL or ANG) and fully anonymized, the focus group electronic audio recordings were deleted. Participants were all secondary school-aged, primarily between the ages of 12 and 20 years, with those aged ≤15 years in a separate focus group. In total, 4 participants aged ≤25 years took part in the focus groups with the older participants because they had special educational needs (eg, learning disabilities) and, as such, were still engaged in secondary-level education or training. Table 1 provides demographic information about sexual and gender minority youth focus group participants; 81% (29/36) of the sexual and gender minority youth were gender minority youth (ie, their gender identity was not the same as their sex as recorded at birth). Many participants (14/36, 39%) were bisexual or pansexual. Approximately 1 in 5 sexual and gender minority youth (7/36, 19%) were of dual heritage (eg, European and West African) or from a migrant background (eg, the other White participants who were not White British).

In parallel, 16 one-to-one interviews were conducted with adult experts based in England, with 6% (1/16) of the participants in Wales, including parents of sexual and gender minority youth, between October 2021 and January 2022. A total of 25% (4/16) of the adults held posts as commissioners of public health services relevant to sexual health and well-being, roles that included consideration of the needs of sexual and gender minority youth. In total, 25% (4/16) of the experts worked in frontline practitioner roles supporting the health and well-being of young people, including sexual and gender minority youth (eg, clinicians working in child and adolescent mental health services). In total, 25% (4/16) of the experts were community-based professionals, such as sexual and gender minority youth workers and policing staff focused on reducing the mistreatment of sexual and gender minority individuals. A total of 25% (4/16) of the adults were parents of a sexual and gender minority adolescent interested in better supporting sexual and gender minority youth. As with the sexual and gender minority youth focus groups, interviews were conducted using videoconference software and audio recorded, and transcripts of the interviews were produced. Once anonymized and approved as accurate (by MFGL or ANG), the electronic audio recordings were deleted.

a 6 focus groups in total with between 5 and 11 participants each; 44 participants in total (including 8 youth workers).

b This item was an open-ended question; as such, 3 gender minority youths wrote Male or Female (ie, Male and Female here does not necessarily equate to being cisgender and male or female).

c N/A: not applicable.

Appointment of Digital Developer (Preparation for Delivering the Concept, Create, and Use Stages)

In January 2022, a tender specification for a digital developer was created based on the outcomes at that time from the identify, define, and position work outlined previously. A range of commercial developers were notified of the tender, and after a competitive process involving an assessment of providers’ submissions and web-based interviews, Bluestep Solutions Limited (Bluestep for brevity) were appointed. They supported the research team in the task of translating the findings that emerged from the preceding evidence-gathering stages (ie, the scoping review, interviews, and focus groups) to content for the digital resource. Bluestep’s expertise resided in developing engaging and user-friendly content aligned with the research team’s evidence-informed approach. Co-design workshops with sexual and gender minority youth participants were conducted to refine the pilot content and improve its look and feel (described in the Findings of the Co-Design Workshops With Sexual and Gender Minority Youth: Position and Concept Stages section). Bluestep provided a map of the potential structure and parameters of the digital resource that could be developed within the available budget. The original budget was £41,000 (approximately US $50,000). Some savings were made in the project’s overall budget, and additional funds were also sourced through the OU, resulting in a final budget of nearly £50,000 (approximately US $61,000). To remain within budget, Bluestep indicated that the research team should focus on 3 core sections of content and have only 1 full day of filming.

Initial Co-Design Workshops With Sexual and Gender Minority Youth and Email or Web-Based Consultations: Position and Concept Stages

Two initial co-design workshops were held with (1) older sexual and gender minority youth aged ≥16 years (May 2022) and (2) younger sexual and gender minority youth aged 12 to 15 years (June 2022). The main aim of these workshops was to identify the priority issues and challenges faced by sexual and gender minority youth on which to focus and the preferred solutions and strategies that should be highlighted. To achieve this, 10 possible topics or issues and 11 possible solutions or strategies were presented to them based on data from the scoping review and the earlier interviews with adults and focus groups with sexual and gender minority youth. A modified nominal group technique [ 24 ] was used to facilitate this process. This involved structured voting before group discussions on the possible topics for inclusion, where all attendees were given an opportunity to express their views and preferences.

In June 2022, Bluestep created a selection of visual concepts ( Multimedia Appendix 1 ) with different color palettes and visual tones of voice represented by imagery. For example, the inclusive visualized toolkit included a bright rainbow color palette, and the message toning was intended to represent inclusivity and messaging that “we’re all in it together.” The overall concepts were also set out alongside some suggested names (created from a marketing perspective) from Bluestep for the digital resource. The suggested names, which drew on commercial marketing expertise from Bluestep, included the following:

• MEE: Mindful Education & Enlightenment for LGBTQ+
• Oneself: Defined by you, allied by us
• Free to be: Mindful tools for your journey

These visual concepts and suggested names were shared with our sexual and gender minority youth workshop participants, and they gave their feedback with support from youth workers via email and in a web-based consultation session via videoconference. The ultimate decisions about concepts, color schemes, and names were strongly informed by the sexual and gender minority youth’s views and we were led by their preferences. A set of questions to prompt discussions regarding preferences was provided to the youth workers supporting the consultation process.

Research Team Co-Development to Finalize Decisions on the Focus and Topic Areas (Define, Position, and Concept Stages)

Following the second sexual and gender minority youth co-design workshop, the research team met to reflect on the voting decisions of sexual and gender minority youth and discuss their own ideas for the priority content and sections in the resource and its features (eg, video clips and animations). In addition to professional expertise, members of the team also have lived experience from their personal lives on which to draw (eg, MFGL is a White migrant, queer male individual and gender role nonconformer; RS is from an ethnic minority group and has lived experience of mental illness [ 25 ]; and KB is White British, grew up with a sibling who identifies as a gay cisgender male, and has lived experience of mental illness). The team held 2 meetings 1 week apart in June 2022.

Questionnaire to Assess the Look and Feel of the Design Options: Concept Stage

Parallel to the co-design and development work outlined previously, Bluestep produced a number of design concepts for consideration by our sexual and gender minority youth workshop attendees and the research team ( Multimedia Appendix 1 ). A questionnaire was developed that asked sexual and gender minority youth workshop attendees to consider the designs and some other key features related to the look and feel of the resource, such as whether the characters featured should be real people or fully animated or whether the characters should be acting out scenarios versus sharing their own personal experiences as sexual and gender minority individuals ( Multimedia Appendix 2 ).

Appointment of Sexual and Gender Minority Contributors Through Specialist Media and Modeling Agencies: Concept Stage

On the basis of our understanding of the need for credible sources to deliver messages in our intervention, and because the dramatizations we had initially envisaged for Oneself in our original study protocol were deemed too contrived and artificial by sexual and gender minority youth, we made a notable decision. In particular, it was identified that real sexual and gender minority young adults, who can talk authentically about their own experiences growing up as sexual and gender minority individuals, would be an important feature of Oneself . In July 2022, the process of recruiting 3 sexual and gender minority young adult contributors or community members was initiated. We applied to modeling and talent agencies given that we wanted contributors who were comfortable in front of cameras. We were provided with a dozen portfolios of different potential sexual and gender minority contributors and short introductory video clips on why they were interested in being involved in the development of Oneself . The research team and sexual and gender minority youth considered the clips separately, and the sexual and gender minority youth voted on their preferred contributors or community members. Feedback on the initial possible contributors highlighted that there was a lack of diversity, particularly regarding ethnicity and body size (ie, they looked “too much like models”). In our attempts to ensure a broader representation, we went back a second time to the agencies to obtain further potential contributor options.

Further Co-Design Workshops With Sexual and Gender Minority Youth: Create Stage

In total, 2 additional co-design workshops were held in September 2022 and January 2023. Co-design workshops were hosted in person with MFGL, Bluestep, or ANG present. Audio recordings were transcribed, and once accurate transcripts were approved (by MFGL or ANG) and fully anonymized, the co-design workshop audio recordings were deleted. Participants were all secondary school-aged, primarily between the ages of 12 and 20 years, with those aged ≤15 years in a separate workshop. Demographic information about workshop participants is presented in Table 2 . A total of 93% (14/15) of the participants were gender minority youth (ie, their gender identity was not the same as their sex as recorded at birth), and 60% (9/15) were bisexual or pansexual. Approximately one-quarter of sexual and gender minority youth (4/15, 27%) were of a dual heritage (eg, Asian and Black) or from a migrant background (eg, White participants who were not White British). An in-person consultation also bridged co-design workshops 3 and 4. This was not recorded.

a 4 co-design workshops in total (with between 5 and 8 youth participants each); 19 participants in total (including 4 youth workers).

Results of the Systematic Scoping Review: Identify and Define Stages

The findings of the scoping review have been published previously [ 14 ]; however, a summary is presented in this section of what we learned that fed into our thinking about the content for Oneself . A total of 68 articles were identified as meeting the review criteria. The oldest paper dated from 2008, and more than half (25/68, 51%) were published from 2017 onward. Most studies (40/68, 59%) were small scale (ie, with <50 participants), and more than two-thirds (47/68, 69%) were conducted in the United States. In total, 26 studies included sexual minority youth only, a further 28 included sexual and gender minority young people, and 14 studies included only gender minority young people.

A total of 24 of the included articles focused on 17 unique interventions to support sexual and gender minority youth. More than half of the intervention papers (13/24, 54% studies) focused on both sexual and gender minority youth. In total, 9 studies included only sexual minority young people, and 2 studies focused on gender minority youth only. Of the 17 interventions, the most frequently cited therapeutic modality was cognitive behavioral therapy (11/24, 46% studies and 6/17, 35% interventions). Common features described in these interventions, including those with CBT-based modalities, are summarized in Table 3 [ 14 ].

Most of the interventions involved in-person delivery (14/24, 58% studies). In total, 5 (56%) out of 9 interventions were delivered in a digital format. In addition to the strategies and techniques outlined in Table 3 , it was also noted that interventions often sought to affirm sexual and gender minority youth identities and give a message of hope to intervention users (eg, “I won’t always feel this way” in the Rainbow SPARX intervention [ 26 ]).

A total of 44 of the included studies did not focus on interventions per se. Instead, they were mainly qualitative studies (with some mixed methods studies combining survey and qualitative data) that explored the experiences of sexual and gender minority youth and the strategies they used to cope with the challenges they face. Table 4 [ 14 ] summarizes the commonly identified strategies and tools for sexual and gender minority youth drawn from these studies and applied to Oneself .

Taken together, the strategies listed in Tables 3 and 4 gave us a comprehensive list of potential contenders to make up the core content and features of Oneself . We drew on this information and the findings we present in the following section from our focus groups and interviews to develop the content of the 3 topic areas identified as most important.

a CBT: cognitive behavioral therapy.

Results of Interviews and Focus Groups With Sexual and Gender Minority Youth, Adult Experts, and Parents: Identify and Define Stages

To expedite drawing out the relevant data from the focus group and interview transcripts and inform Oneself’s development, the data were divided between the research team and examined carefully. Detailed notes were made regarding the sorts of issues that the various stakeholders identified as important to address. Details on strategies and tools that were deemed useful in participants’ experiences were also extracted. The issues and strategies identified were revised during 2 team meetings in June 2022. A more detailed framework analysis [ 27 ] of the data is underway and will be published in due course.

The rapid data extraction process provided us with a series of initial issues and potential areas or populations of focus. MFGL and ANG then met to construct a long list of the main issues (n=10) and the potential solutions or strategies (n=11) that emerged from the findings of the scoping review and the interviews and focus groups with stakeholders. These are summarized in Textboxes 2 and 3 .

• How to deal with unsupportive parents or other family members
• How to deal with bullying at school (eg, name calling)
• How to deal with the challenges associated with coming out
• How to deal with negativity directed at lesbian, gay, bisexual, transgender, and queer people (eg, from a religion)
• How to deal with misgendering
• How to deal with feeling isolated or alone
• How to deal with stigma (eg, homo-, bi-, or transphobia)
• How to deal with web-based abuse (eg, trolls saying nasty things)
• How to explore and make sense of your sexuality or gender
• How to deal with people not believing you about your sexuality or gender
• Educate teachers and others on how to better support lesbian, gay, bisexual, transgender, and queer (LGBTQ+) youth so that school environments can be improved for LGBTQ+ youth
• Educate parents (and other people in the community) on how to better support LGBTQ+ youth so that communities can be improved for LGBTQ+ youth
• Help young people with practical issues—in particular, finding a toilet that they can safely use
• Allow the young person to connect directly with other LGBTQ+ young people so that they can talk to someone else who understands
• Coming out and how to do this safely—highlight that it is OK not to come out (and it is also OK to change one’s mind)
• Up-to-date and accurate information on sexuality and gender to help them make sense of their identity
• How to find supportive people via web-based environments so that they have a better support network
• Help young people figure out what they can and cannot change themselves so that they know what to focus their energy on
• Use affirmations (positive messages) about the young person (eg, “I deserve kindness” and “my gender is not an inconvenience”) to help people feel even better about themselves
• Help young people engage in creative activities (eg, art and music) to make them feel better
• Provide the contact phone numbers and details for supports available to LGBTQ+ youth so that they know where to go for extra help

First Proposed Structure and Designs After Appointing the Digital Developer: In Preparation for the Concept, Create, and Use Stages

Bluestep provided a map of the potential structure and parameters of the digital resource that it would be possible to develop within the available budget, specifically a wireframe. A copy of the structure is provided in Multimedia Appendix 3 . This illustrates the inclusion of 3 core features or sections of content and a “free” (all content is free to access, but the main content requires the user to create an account with a username or email address and password) taster section of content proposed as important to engage potential users and educate the wider public (eg, teachers).

Findings of the Co-Design Workshops With Sexual and Gender Minority Youth: Position and Concept Stages

Table 5 shows the average rank order preferences from the adapted nominal group technique voting in relation to priority issues or topics to cover within the Oneself resource. Participants ranked their highest-priority topic as rank 1 and their lowest-priority topic as rank 10. The lowest average rank order identifies the highest preference among the group. Dealing with unsupportive parents or other family members and dealing with bullying were the highest-ranked topics to cover. Table 6 presents the average rank order preferences for possible solutions or strategies to include in Oneself . The highest-ranking content included educating parents and teachers to help improve the quality of the environments they live in.

a LGBTQ+: lesbian, gay, bisexual, transgender, and queer.

Findings of the Research Team’s Co-Design Meetings in June 2022: Design, Position, and Concept Stages

The first co-design meeting with the research team began by reflecting on the rank order preferences of the sexual and gender minority youth (presented previously). It was acknowledged that, although clear priorities emerged from the data, there was also considerable variability in the rank order preferences. With the budget and practical limits to the amount of content that we could include, we could not create an ideal resource to suit all sexual and gender minority youth needs. However, given the identification by Bluestep that we could have three main sections with featured content, the selection of the top three topic areas was straightforward: (1) coming out and doing so safely; (2) managing school, including homophobic, biphobic, or transphobic bullying or similar; and (3) dealing with parents and families, especially unsupportive family members, including parents or caregivers. We found the favored focus area or population being about educating parents, teachers, and other community members to be outside the scope given our budget to date and as the resource was always intended to be primarily for sexual and gender minority youth themselves rather than adults who support them. The resource is designed to center the experiences of sexual and gender minority youth, but we expect that Oneself will ultimately support parents, teachers, and other community members by increasing awareness and visibility of sexual and gender minority youth experiences. We do acknowledge that there are important challenges in balancing (individual-focused) support for sexual and gender minority youth with promoting social justice through education of adult stakeholders. As was done in this study, it is important to consider these elements in parallel because they are interactive. While we decided not to explicitly target adults at this stage, we acknowledged this request as being part of sexual and gender minority youth’s desire for the environments they live in to be better and more supportive of them, hence the decision to prioritize the educational animation about pronouns, intended for a wider audience (including parents and teachers). We also reflected on the fact that, while the main purpose of the content should be to help young people cope with situations independently, it could also be useful for educating parents, teachers, and other members of the community. Specifically, the resource could help them understand the unique challenges of growing up as a sexual and gender minority youth and how they can act and respond supportively to promote positive social change. At this stage, we thought that the formats we might use to present content could be videos or animations depicting narratives of sexual and gender minority youth everyday experiences, possibly with some interactive content or features for the user.

Sexual and gender minority youth understandably had a range of perspectives and ideas about what should be covered in Oneself . We identified 9 such specific suggestions. For instance, we were cautioned against educating Oneself users on the various sexuality and gender “labels” used by a young person given that the terminology is continually evolving (and frequently contested). Another sexual and gender minority youth felt strongly that we should acknowledge the difficulties associated with challenging environments; for example, “you cannot change everyone,” and therefore, a sexual and gender minority youth must know how (and when) to “walk away.” They also wanted us to ensure that our sexual and gender minority contributors would represent as much diversity as possible. By the end of the research team discussions, there was a growing sense that we could cover, to some degree, many of the preferred solutions or strategies that had been discussed and voted on by sexual and gender minority youth in their co-design workshops, with a focus on the top 3 topics or issues.

It was beyond the scope and resources of Oneself to provide a web-based community space where sexual and gender minority youth could connect with each other safely in real time as this would likely require constant monitoring and ongoing administration. However, advice on where or how to do this elsewhere could be included, along with links to other supportive resources. It was decided that the resource would focus on supporting sexual and gender minority youth directly. We aimed to center the young person in this resource, with Oneself often talking directly to them and trying to focus on them and their needs, for instance, by using language or terms and concepts that map to the concerns they have raised with us as the research team. This act of centering is in direct contrast to the marginalization that they may face daily. It was also intended to have a dual purpose of potentially serving to educate the wider community, including parents and teachers. It was felt that, because the 3 main topics focused on dealing with challenges that can have a detrimental effect on well-being, the resource needed to include evidence-based tools and resources known to support and enhance mental well-being, such as relaxation techniques and other relevant means of coping. It also needed to include content that felt empowering of developing and evolving identities to support and develop users’ self-esteem.

Findings of the Questionnaire to Assess the “Look and Feel” Design Options: Concept Stage

The wireframe structure of Oneself (which was designed to include some introductory content) was confirmed first. This included a log-in feature to access the 3 main content sections and recommended additional resources and sources of help and support. The log-in feature, with the associated gathering of demographic data, was deemed necessary to capture future user information related to Oneself. Next, Bluestep worked with the research team to develop a questionnaire posing different design concepts and options for the look and feel of the resource. The full questionnaire and the options posed are presented in Multimedia Appendix 2 . The preferences that this process helped identify are briefly summarized in the following paragraph.

Although the idea for having full animations with voice actors was rated favorably by many sexual and gender minority youth participants, a clear overall preference emerged for using real people talking about their firsthand experiences growing up as sexual and gender minority youth, as well as the inclusion of sexual and gender minority youth “influencers” or public figures. There were also clear indications that the resource would most likely be accessed on a smartphone by sexual and gender minority youth and that video clips should include audio subtitles (to enable viewing without sound on; however, this is also valuable for accessibility reasons), and most indicated that they would use headphones to listen to content, too. On the basis of sexual and gender minority youth feedback, video-based content should ideally not exceed 60 seconds; some were willing to watch longer clips when the content was engaging. Downloadable information sheets, for access again offline, were identified as useful, and sexual and gender minority youth participants favored a color palette that was pastel and informed by the “progress rainbow flag.”

Team Consultation Based on the Questionnaire Feedback Led to Plans for Inclusion of Sexual and Gender Minority Contributors: Concept and Create Stages

The feedback we obtained about the inclusion of sexual and gender minority contributors (ie, not actors playing a role) led to further consultation about the format of the resource and a decision to focus the main content on testimonial or account footage from sexual and gender minority young adults who could reflect on their experiences with the topics selected when they were growing up. We set out to identify individuals from modeling and talent agencies who would be willing to provide this kind of content, as described in the Appointment of Sexual and Gender Minority Contributors Through Specialist Media and Modeling Agencies: Concept Stage section.

The process of assessing potential sexual and gender minority content contributors resulted in the appointment of 3 people who identified as sexual and gender minority individuals who were willing to be involved for a set fee. Between them they represented diversity in terms of gender and sexual identity, body shape and size, ability, and ethnicity. More details about those selected are provided in the following section.

Design Concept Selection via Email and Web-Based Consultation With Sexual and Gender Minority Youth and Outcomes From Co-Design Workshops 3 and 4: Position, Concept, and Create Stages

Concept 1 ( Figure 1 ) was a clear favorite in terms of the color scheme, and it was described as more “friendly” and inclusive than concept 2 ( Figure 2 ). There was a question regarding the icons in both concepts (ie, symbols transposed over certain images); sexual and gender minority youth did not feel that the icons represented the topics adequately, and therefore, wording or text would be needed, which would defeat the purpose of using icons. In concept 1, a “share” function was seen as more understandable as it was interpreted as a speech bubble, though this could be made even clearer.

From concept 2, sexual and gender minority youth liked the “squiggly lines” in the designs if they could be incorporated into concept 1’s color scheme. It was preferred that design elements from both concepts could be used in the final resource, although sexual and gender minority youth were clear not at the same time as it would be too much on one image.

The sexual and gender minority youth participants were asked if they thought that including the OU (lead university for the project) logo on the resource was a good idea. Most participants felt that it would give people confidence in the quality of the resource as OU is a well-known brand in the United Kingdom. The preferred name for the resource, of the 3 suggestions, was Oneself , but they considered the inclusion of the originally proposed taglines to be too long. Consequently, we did not use a subsequent lengthy tagline in combination with the name Oneself across the whole resource.

Table 7 provides a summary of the workshops and consultations by date, including what was covered and how it aligns with the co-design stages by Hagen et al [ 13 ].

a OU: The Open University.

Introducing the Sexual and Gender Minority Contributors: Create Stage

Bluestep shortlisted 10 candidate sexual and gender minority young adult contributors for the research team, who in turn shortlisted 5 to present to the young people in co-design workshop 3. There were some unforeseen recruitment difficulties. For example, the selected racial and ethnic minority gay man and a transgender woman (who was one of the sexual and gender minority youth’s top choices) were unfortunately not able to participate as initially agreed. For instance, one of them became concerned about how publicly accessible Oneself would be once released (ie, they could be “outed” to a whole range of people known to them). Thus, 2 female contributors were selected from the initial shortlist, and both were rated very favorably by the sexual and gender minority youth. As it was important for the project to reflect diversity across gender identity, sexuality, race, and disability, a further search for a third contributor was carried out in October 2022. Finally, 3 contributors were selected and approved by the young people: Chloe, Lilly, and Georgie.

Georgie, also known as Triple Minor, uses they, she, or he pronouns and is transgender nonbinary. Georgie wanted to contribute to Oneself because they were keen to be the much needed representation that is often lacking within LGBTQ+ communities.

Lilly uses she or her pronouns and is pansexual. Lilly wanted to contribute to Oneself because, when she was younger, she would have loved to have heard more about queer perspectives. This is why she wanted to talk about her own experiences.

Chloe uses she or her pronouns and is a lesbian. Chloe wanted to contribute to Oneself because she believes it is important for the younger LGBTQ+ community to feel supported and comfortable in their sexuality and be able to hear the voices and perspectives of queer people.

Bluestep developed and circulated a creative brief for the 3 sexual and gender minority contributors explaining the requirements for filming ( Figures 3 and 4 ).

Filming took place on November 29, 2022, in a London-based studio. On the day, all 3 sexual and gender minority contributors were asked the same questions on the topics of school, coming out, and friends and family ( Multimedia Appendix 4 ). Filming was done against a green screen so that animations could be added later. Rough-cut footage included approximately 35 minutes of Chloe, Lilly, and Georgie each and 10 minutes of a group recording. ANG transcribed these rough cuts, which comprised 28 pages in total, and summarized their content into key points and quotes that could be shared with the young people. These were given to the sexual and gender minority youth in co-design workshop 4, who rated the points and quotes, adding their own reflections. For instance, the sexual and gender minority youth found Lilly’s advice to cope if someone reacts negatively to coming out helpful—she said the following: “Remember you are not alone. It may take time, but you’ll find your community and people that get you and understand you.” However, the sexual and gender minority youth found Georgie’s advice for teachers and students to manage bullying at school (ie, “zero tolerance” for this) too vague as most schools should have zero tolerance policies but there is still a need for proactiveness to enforce them. A summary of these points, organized by topic (coming out, school, and family and friends) and divided into challenges and solutions and strategies with key quotes to include, was then given to Bluestep to create 2- to 3-minute–long rough cuts of each video, which combined live footage and animation. This resulted in a total of 6 videos— Parents and Families: Some Common Challenges , Parents and Families: Some Strategies , School: Some Common Challenges , School: Some Strategies , Coming Out: Some Common Challenges , and Coming Out: Some Strategies . These were reviewed several times for content, design, storyline, accessibility, and subtitles and finally refined before approval by MFGL and ANG.

The Oneself Resource

Oneself was divided into 7 web pages: a home page ( Figure 5 ); the 3 topics of parents and families, school, and coming out; downloads; chilling out; and resources. To access the entire toolkit, the user needs to log in and complete a brief baseline measure of well-being (ie, the 5-item World Health Organization Well-Being Index). The home page is free to access for anyone, although images of the contributors are reserved for the logged-in user.

The home page includes a description of Oneself ; quotes; and extracts of what the user could find in the resource, for instance, the 3 topics. These were designed to prompt the user to log in to access the content. The home page also features an animation of the meaning and use of pronouns created in collaboration with sexual and gender minority youth from Rainbow Power (a sexual and gender minority youth group) run by the Free2B Alliance in England.

Each topic area began with two parts: (1) the problems and challenges that sexual and gender minority youth face in relation to that topic and (2) potential strategies and solutions to these issues. Each topic area included videos and social polls, which were then followed by activities, downloadable exercises, and external resources (see Multimedia Appendix 5 for an example).

Each topic area had 2 live footage videos: the first with sexual and gender minority contributors talking about common challenges on the topic based on their own experiences and the second with sexual and gender minority contributors talking about solutions, strategies, and advice on the topic based on their own experiences. Live footage was mixed with an animated background, highlighting what contributors were speaking about with color, illustrations, or additional text.

Each topic area also had 2 social polls, the first of which asked users to reflect on their own experiences on the topic. For example, for Coming Out , the question was as follows: “have you come out to others about your sexuality or gender yet?” A second social poll question asked users to reflect on which contributor’s experience was most similar to their own. After answering, the percentage of responses to the question became visible to the user. This was designed so that the user could understand others’ experiences and feel part of the Oneself community.

In total, 2 exercises or activities per topic area were designed to help the user reflect on the topic in greater depth and learn more about how to implement strategies and advice in managing challenges. For instance, an exercise in “Parents and Families” is framed as follows: “Some LGBTQ+ young people have repeatedly described online environments as ‘lifesaving’ at times. Reflect on your experiences of creating an online support network for yourself.” This is followed by an “Explore More” button that takes the user to another page where they can read through several strategies and choose the ones that fit them best ( Figure 6 ).

Downloads or downloadable exercises for each topic area were drafted by MFGL and ANG and then checked and refined by other research team members. Downloads were designed to tackle the problems and challenges described in each of the topics—2 downloadable guides addressing issues relevant to each topic provide detailed written information on strategies and solutions regarding them. In the case of Parents and Families , these look at standing up for yourself (communication) and problem-solving. In the case of School , they focus on finding allies and rejecting negativity (ie, the ABCD method). In the case of Coming Out , they support the coming out journey and finding hope. Downloads can also be found grouped together under the Downloads tab. An overview of the logic underpinning the development and content of Oneself is depicted in Figure 7 .

Finally, each topic area provided 2 external resources leading to organizations and web pages that can offer sexual and gender minority youth further support, such as advice, community resources, or helplines. These were chosen in agreement with the research team. External resources can also be found grouped together under the Resources tab.

All content was interspersed with quotes from our sexual and gender minority youth participants and the 3 sexual and gender minority contributors as well as short comments and advice linked to the social polls.

Finally, a Chilling Out section was included promoting relaxation exercises as well as 2 additional external resources. These exercises consisted of 3 recordings, each led by a sexual and gender minority contributor following a script provided by the research team ( Figure 8 ). A stress levels scale of 1 to 10 was available to complete before and after listening to each recording to help the user reflect on whether it had been a useful and calming exercise for them. Oneself was designed so that users can rate content with between 1 and 5 stars as they work through it, providing the research team with feedback.

Principal Findings

This study aimed to set out the systematic and iterative approach undertaken to develop a web-based resource to support the mental well-being of sexual and gender minority youth so that they can deal effectively with the specific challenges of growing up LGBTQ+. Providing this kind of support was identified as important because sexual and gender minority youth are at greater risk of poor mental health outcomes than their cisgender and heterosexual peers [ 1 , 4 , 6 , 14 ]. This study demonstrated how project activities are mapped against the 6 stages of co-design set out by Hagen et al [ 13 ]. In particular, it showed how extant research evidence and engagement with a range of stakeholders and representatives of end users were drawn on to make decisions about the content and design of the final resource, named Oneself . The logic underpinning the content of the resource is also set out to support the design of future process and outcome evaluations of Oneself . Initial usability and end-user feedback has been gathered through a set of “think aloud” interviews and post use reflection interviews with sexual and gender minority youth and adult stakeholders. The findings of the latter study will be reported in detail elsewhere (MFG Lucassen, unpublished data, 2024). The feedback to date has been largely positive, with all sexual and gender minority youth testers saying they would recommend the resource to others. There have also been some points of constructive and critical feedback, in particular from adult stakeholders, which will need to be considered in future development work (eg, that a greater range of experiences should be included, such as those of cisgender gay male individuals and individuals from minority faith backgrounds).

Strengths and Limitations of Oneself

There are several strengths and limitations to Oneself in its current format. Strengths include the fact that Oneself represents one of the first digital mental well-being–related resources reported on that is designed specifically to meet the needs of sexual and gender minority youth. The design drew on the evidence base for techniques to support their well-being [ 14 ] and was developed in collaboration with 4 different stakeholder groups: sexual and gender minority youth, adults who work with sexual and gender minority youth, parents of sexual and gender minority youth, and commissioners of public health services focused on their needs. In doing so, the process of development included a wide variety of relevant perspectives and looked to build on what is already known about supporting the well-being of this population. It is also a strength of the resource that its development included adolescents aged <16 years and was inclusive of gender and sexual minority groups rather than focusing solely on gender or sexual minority groups. This is a departure from previous interventions that have typically focused on those aged >16 years only and selected to focus on either gender or sexual minority groups [ 14 ]. Although there are important differences between sexual and gender minority experiences, there is also considerable overlap, including compromised mental well-being for many. Some young people will ultimately identify as being both a sexual and gender minority, which makes the resource’s recognition of both groups important.

Limitations of the resource include the fact that, given the budget constraints, much of the available funding had to be channeled into creating basic initial functionality that would be likely to engage and sustain interest from the target end users. This meant that much of the evidence-informed content that we might expect to have the greatest effect on mental health and well-being had to be included within the more text-heavy “downloads” section. Although, in early consultation work, sexual and gender minority youth suggested that these “downloads” were a good way to provide additional resources for use offline, it was later acknowledged that young people do not want to have to read a lot of text when engaging with the content (MFG Lucassen, unpublished data, 2024). Common evidence-based features for supporting mental health and well-being include relaxation exercises [ 28 ], behavioral activation [ 29 ], problem-solving [ 28 - 30 ], helping people recognize problematic cognitions [ 26 ], and cognitive restructuring [ 26 ]. Future iterations will need to focus on bringing more of this content into the interactive elements of Oneself. However, in doing so, it will also be important to consider whether such features are best delivered via pure self-help or whether optimal delivery requires engagement with an adult who can help structure what are often quite complex therapeutic activities (eg, sexual and gender minority youth can feasibly be supported by “e-coaches” to complete resources such as Oneself ).

The sexual and gender minority youth involved in co-designing Oneself included almost one-quarter of individuals who were of dual heritage (eg, European and West African) or from a migrant background (eg, several of the White participants). Furthermore, gender minority youth, who have been traditionally underrepresented in LGBTQ+ research [ 31 ], were very well represented, as were bisexual and pansexual participants. Nonetheless, content could have been improved in relation to intersectionality, such as the fact that there is a need to represent sexual and gender minority youth more complexly in terms of sexual and gender minority youth’s social positions (eg, across ethnicity, religion, and social class). Future iterations need to look at making the resource more relatable to additional underrepresented groups, as was suggested during co-design processes (eg, for care-experienced Asian sexual and gender minority youth from a minority faith background), who may face different and complex challenges growing up as sexual and gender minority youth.

Self-help digital resources and interventions have the potential to be very cost-effective [ 32 ]. They can be relatively low cost to produce, with the potential for very high reach given evidence of increasing digital access and capability, particularly among young people [ 10 , 11 ]. Despite this, it is likely that those with the greatest vulnerabilities and at the most risk of poor mental well-being may be the least likely to access suitable web-based spaces with ease (eg, those with limited funds to purchase data for a mobile phone). Therefore, reaching these individuals needs to be carefully considered by those who are responsible for identifying and tackling such needs, including youth support organizations, schools, and commissioners of services. In addition, digital resources such as Oneself need to keep up with the rapid pace of progress and evolution in the web-based world. Young people have high expectations and are savvy consumers of web-based media, and they anticipate polished and engaging products. Keeping a resource such as Oneself comprehensive, up-to-date, and relevant in terms of content and look and feel requires ongoing funding. Relatedly, sexual and gender minority youth highlighted the importance of educating others, in particular teachers and parents, as this would bolster their overall mental well-being. In future funded work, we would like to develop resources specifically for adults, potentially within the overall Oneself intervention. Finally, something we identified that we would not be able to achieve with Oneself , at least for now, was direct access to support and interaction from a sexual and gender minority youth peer group. Although this was desired, providing it would involve considerable resources to monitor and approve content and messaging and avoid harm that could be caused by web-based bullying and harassment. Investigating how to provide this sense of community more fully in a web-based space warrants further attention. Ideally, such spaces should be structured in such a way that the experiences of sexual and gender minority youth can be shared without any pressure to divulge information that could identify a young person or lead to instances of “oversharing” (which sexual and gender minority youth may regret at a later stage). Case studies, as presented in Oneself with the contributors, could offer a safe means to discuss personal issues without the need for self-disclosure. We think that establishing and maintaining web-based community spaces in the context of digital mental health technologies requires further study to ensure that such spaces are both acceptable and viable. However, a noteworthy shortcoming of direct access to ongoing human support and interaction given the associated costs and practical considerations (eg, whether an intervention can realistically be provided 24 hours a day, 7 days a week) are limitations in terms of an intervention’s likely reach.

Strengths and Limitations of the Research

The research we have conducted in developing Oneself, and this paper specifically, makes an important contribution to needed literature that opens the “black box” of intervention development [ 33 ]. Attempting to record the process of development, including the co-design stages, as accurately and comprehensively as possible and placing it within the public domain via open access publishing contributes to the Open Science Agenda by making it accessible, inclusive, and transparent [ 34 ]. Being explicit about the logic that underpins the intervention content in terms of how it is intended to have an effect on factors associated with maintenance (or not) of mental well-being is also important to support the design of future evaluation studies [ 35 ].

Co-design work is complex and challenging to do well. We believe that aspects of our co-design efforts were of merit, in particular our inclusion of younger sexual and gender minority youth (which included participants as young as 12 years of age) and our engagement of sexual and gender minority youth from the “Identify” all the way through to the “Use” stages of the process [ 13 ]. We drew heavily on sexual and gender minority youth’s views to decide on the topic areas to focus on and in deciding on the “look and feel” of Oneself . We also made key changes to the resource in response to sexual and gender minority youth feedback, such as not using dramatizations, as was initially envisaged. Challenges to the co-design processes included the COVID-19 pandemic at the start of the project, which meant that work with sexual and gender minority youth was conducted using videoconference software at a time when adolescents were frequently fatigued by web-based forms of communication. Connected to this was our awareness that assisting in the creation of Oneself was one of the many demands placed on the sexual and gender minority youth involved in co-design, and as such, we sought to use sexual and gender minority youth’s time efficiently. Consequently, we limited the number of workshops conducted and carried out some consultations via email, which was less robust. In the future, we could enhance our co-design efforts and move closer to partnership (as opposed to consultation as defined by Arnstein [ 36 ] in her ladder of participation) by helping a number of older sexual and gender minority youth learn more about evidence-based techniques for supporting mental well-being and subsequently getting them to design features of the content. These older adolescents could be employed as coresearchers, and they could draft and further develop content with our ongoing support.

Most sexual and gender minority youth involved in the co-design of Oneself were gender minority young people, which is a strength given that these youth are underserved by mental health services [ 4 ]. However, a limitation of our research was that we struggled to recruit cisgender adolescents to join the co-design workshops and, as such, may have underrepresented the views or specific needs of certain youth (eg, cisgender lesbian and gay youth). Relatedly, it is likely that some groups or individuals who may need intervention support the most are among those least likely to get involved in co-design or research activities (eg, sexual and gender minority youth who do not feel safe to “come out”) leading to intervention development and associated research more generally, which misses the perspective of those who are “not out.” Acknowledging this potential issue is important, and striving to reach the underheard and underserved must remain a priority of future research.

Summary, Conclusions, and Next Steps

This study aimed to set out the process involved in co-designing and developing Oneself , a digital resource to support sexual and gender minority youth in building and maintaining their resilience to cope with the everyday challenges of growing up LGBTQ+ and support their mental health and well-being more generally. It is hoped that, in the future, this resource will be extended so that it is also of use for educating adults who wish to support sexual and gender minority youth. We have explained the included content and the logic that underpins its use and acknowledged a range of strengths and limitations of what has been achieved so far. Priorities for future efforts will be to specifically address critique and feedback provided by adults and sexual and gender minority youth during their “think aloud” interviews (MFG Lucassen, unpublished data, 2024); build in additional characteristics translating evidence-based content into interactive features; and continue to incorporate diverse voices in co-design, including consideration of how intersectionality may need to be more integrated. The next steps include applying for further research funding to continue our evaluation and development activities.

Acknowledgments

The authors would like to thank all the study participants, academic advisors, and organizational partners for this project, such as the lesbian, gay, bisexual, transgender, and queer organizations, specifically Free2B Alliance and METRO Charity. The authors also thank their other partners, which include 2 county councils, a clinical commissioning group, and the Centre for Policing Research and Learning (at The Open University). The authors would also like to thank Lauren Walker for her feedback on earlier drafts of this paper. Funding for this project was provided by the UK Medical Research Council (grant MR/V031449/1).

Data Availability

The data sets generated during and analyzed during this study are not publicly available due to their sensitive nature but are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors are the codevelopers of Oneself but do not stand to gain financially from its future use and have no further conflicts of interest to declare.

Concept design options for Oneself.

Questionnaire for sexual and gender minority youth as part of co-design of Oneself.

Wireframe website map for Oneself.

Instructions given to sexual and gender minority contributors for filming video content.

Screenshots from Oneself.

• Lucassen MF, Stasiak K, Samra R, Frampton CM, Merry SN. Sexual minority youth and depressive symptoms or depressive disorder: a systematic review and meta-analysis of population-based studies. Aust N Z J Psychiatry. Aug 2017;51(8):774-787. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Reisner SL, Poteat T, Keatley J, Cabral M, Mothopeng T, Dunham E, et al. Global health burden and needs of transgender populations: a review. Lancet. Jul 23, 2016;388(10042):412-436. [ FREE Full text ] [ CrossRef ] [ Medline ]
• LGBT+ Pride 2021 global survey points to a generation gap around gender identity and sexual attraction. Ipsos. 2021. URL: https:/​/www.​ipsos.com/​en/​lgbt-pride-2021-global-survey-points-generation-gap-around-gender-identity-and-sexual-attraction [accessed 2024-03-13]
• Clark TC, Lucassen MF, Bullen P, Denny SJ, Fleming TM, Robinson EM, et al. The health and well-being of transgender high school students: results from the New Zealand adolescent health survey (Youth'12). J Adolesc Health. Jul 2014;55(1):93-99. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. Sep 2003;129(5):674-697. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lucassen MF, Samra R, Rimes KA, Brown KE, Wallace LM. Promoting resilience and well-being through co-design (the PRIDE project): protocol for the development and preliminary evaluation of a prototype resilience-based intervention for sexual and gender minority youth. JMIR Res Protoc. Feb 01, 2022;11(2):e31036. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lucassen MF, Clark TC, Denny SJ, Fleming TM, Rossen FV, Sheridan J, et al. What has changed from 2001 to 2012 for sexual minority youth in New Zealand? J Paediatr Child Health. Apr 10, 2015;51(4):410-418. [ CrossRef ] [ Medline ]
• Gnan GH, Rahman Q, Ussher G, Baker D, West E, Rimes KA. General and LGBTQ-specific factors associated with mental health and suicide risk among LGBTQ students. J Youth Stud. Feb 17, 2019;22(10):1393-1408. [ CrossRef ]
• Evans C, Robertson W. The four phases of the digital natives debate. Human Behav and Emerg Tech. Jun 17, 2020;2(3):269-277. [ FREE Full text ] [ CrossRef ]
• Petrosyan A. Internet usage in the United Kingdom - statistics and facts. Statista. URL: https://www.statista.com/topics/3246/internet-usage-in-the-uk/#topicOverview [accessed 2024-03-11]
• Children and parents: media use and attitudes report 2022. Ofcom. 2022. URL: https:/​/www.​ofcom.org.uk/​__data/​assets/​pdf_file/​0024/​234609/​childrens-media-use-and-attitudes-report-2022.​pdf [accessed 2024-03-11]
• McDermott E, Hughes E, Rawlings VE. Queer futures: understanding lesbian, gay, bisexual and trans (LGBT) adolescents’ suicide, self-harm and help-seeking behaviour. Department of Health Policy Research Programme Project. 2018. URL: https://www.queerfutures.co.uk/wp-content/uploads/2016/06/Queer-Futures-Final-Report.pdf [accessed 2024-03-23]
• Hagen P, Collin P, Metcalf A, Nicholas M, Rahilly K, Swainston N. Participatory design of evidence-based online youth mental health promotion, prevention, early intervention and treatment. Young and Well Cooperative Research Centre. 2012. URL: https:/​/www.​westernsydney.edu.au/​__data/​assets/​pdf_file/​0005/​476330/​Young_and_Well_CRC_IM_PD_Guide.​pdf [accessed 2024-03-11]
• Lucassen MF, Núñez-García A, Rimes KA, Wallace LM, Brown KE, Samra R. Coping strategies to enhance the mental wellbeing of sexual and gender minority youths: a scoping review. Int J Environ Res Public Health. Jul 18, 2022;19(14):8738. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jagosh J, Macaulay AC, Pluye P, Salsberg J, Bush PL, Henderson J, et al. Uncovering the benefits of participatory research: implications of a realist review for health research and practice. Milbank Q. Jun 18, 2012;90(2):311-346. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Donetto S, Pierri P, Tsianakas V, Robert G. Experience-based co-design and healthcare improvement: realizing participatory design in the public sector. Des J. May 07, 2015;18(2):227-248. [ CrossRef ]
• Smith B, Williams O, Bone L, The Moving Social Work Co-production Collective. Co-production: a resource to guide co-producing research in the sport, exercise, and health sciences. Qual Res Sport Exerc Health. Mar 19, 2022;15(2):159-187. [ CrossRef ]
• Walker L, Dawson S, Brady S, Hillison E, Horspool M, Jones G, et al. Co-producing a physical activity intervention with and for people with severe mental ill health – the spaces story. Qual Res Sport Exerc Health. Jan 08, 2023;15(2):235-247. [ CrossRef ]
• Beresford P. From ‘other’ to involved: user involvement in research: an emerging paradigm. Nord Soc Work Res. Nov 2013;3(2):139-148. [ CrossRef ]
• Bevan Jones R, Stallard P, Agha SS, Rice S, Werner-Seidler A, Stasiak K, et al. Practitioner review: co-design of digital mental health technologies with children and young people. J Child Psychol Psychiatry. Aug 22, 2020;61(8):928-940. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Michie S, Abraham C, Eccles MP, Francis JJ, Hardeman W, Johnston M. Strengthening evaluation and implementation by specifying components of behaviour change interventions: a study protocol. Implement Sci. Feb 07, 2011;6:10. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. Feb 2005;8(1):19-32. [ CrossRef ]
• Gallagher M, Hares T, Spencer J, Bradshaw C, Webb I. The nominal group technique: a research tool for general practice? Fam Pract. Mar 1993;10(1):76-81. [ CrossRef ] [ Medline ]
• Samra R. Beyond epistemic injustice: when perceived realities conflict. Harv Rev Psychiatry. 2023;31(5):223-225. [ CrossRef ] [ Medline ]
• Lucassen MF, Merry SN, Hatcher S, Frampton CM. Rainbow SPARX: a novel approach to addressing depression in sexual minority youth. Cogn Behav Pract. May 2015;22(2):203-216. [ CrossRef ]
• Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. Sep 18, 2013;13:117. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Heck NC. The potential to promote resilience: piloting a minority stress-informed, GSA-based, mental health promotion program for LGBTQ youth. Psychol Sex Orientat Gend Divers. Sep 2015;2(3):225-231. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Duarté-Vélez Y, Bernal G, Bonilla K. Culturally adapted cognitive-behavior therapy: integrating sexual, spiritual, and family identities in an evidence-based treatment of a depressed Latino adolescent. J Clin Psychol. Aug 21, 2010;66(8):895-906. [ CrossRef ] [ Medline ]
• Diamond GM, Diamond GS, Levy S, Closs C, Ladipo T, Siqueland L. Attachment-based family therapy for suicidal lesbian, gay, and bisexual adolescents: a treatment development study and open trial with preliminary findings. Psychotherapy (Chic). Mar 2012;49(1):62-71. [ CrossRef ] [ Medline ]
• Lucassen MF, Burford J. Educating for diversity: an evaluation of a sexuality diversity workshop to address secondary school bullying. Australas Psychiatry. Oct 30, 2015;23(5):544-549. [ CrossRef ] [ Medline ]
• Gentili A, Failla G, Melnyk A, Puleo V, Tanna GL, Ricciardi W, et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. Front Public Health. 2022;10:787135. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Murray J, Baxter R, Lawton R, Hardicre N, Shannon R, Langley J, et al. Unpacking the Cinderella black box of complex intervention development through the Partners at Care Transitions (PACT) programme of research. Health Expect. Aug 2023;26(4):1478-1490. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Open science. United Nations Educational, Scientific and Cultural Organization. URL: https://www.unesco.org/en/open-science?hub=686 [accessed 2024-03-11]
• The Magenta Book: HM treasury guidance on what to consider when designing an evaluation. HMTreasury, Government of UK. 2020. URL: https://www.gov.uk/government/publications/the-magenta-book [accessed 2024-03-11]
• Arnstein SR. A ladder of citizen participation. J Am Inst Plann. Jul 1969;35(4):216-224. [ CrossRef ]

Abbreviations

Edited by A Mavragani; submitted 20.11.23; peer-reviewed by E Layland; comments to author 07.02.24; revised version received 23.02.24; accepted 26.02.24; published 21.05.24.

©Katherine Brown, Mathijs F G Lucassen, Alicia Núñez-García, Katharine A Rimes, Louise M Wallace, Rajvinder Samra. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

Microsoft Research Blog

Microsoft at chi 2024: innovations in human-centered design.

Published May 15, 2024

Share this page

• Share on Facebook
• Share on Twitter
• Share on LinkedIn
• Share on Reddit
• Subscribe to our RSS feed

The ways people engage with technology, through its design and functionality, determine its utility and acceptance in everyday use, setting the stage for widespread adoption. When computing tools and services respect the diversity of people’s experiences and abilities, technology is not only functional but also universally accessible. Human-computer interaction (HCI) plays a crucial role in this process, examining how technology integrates into our daily lives and exploring ways digital tools can be shaped to meet individual needs and enhance our interactions with the world.

The ACM CHI Conference on Human Factors in Computing Systems is a premier forum that brings together researchers and experts in the field, and Microsoft is honored to support CHI 2024 as a returning sponsor. We’re pleased to announce that 33 papers by Microsoft researchers and their collaborators have been accepted this year, with four winning the Best Paper Award and seven receiving honorable mentions.

This research aims to redefine how people work, collaborate, and play using technology, with a focus on design innovation to create more personalized, engaging, and effective interactions. Several projects emphasize customizing the user experience to better meet individual needs, such as exploring the potential of large language models (LLMs) to help reduce procrastination. Others investigate ways to boost realism in virtual and mixed reality environments, using touch to create a more immersive experience. There are also studies that address the challenges of understanding how people interact with technology. These include applying psychology and cognitive science to examine the use of generative AI and social media, with the goal of using the insights to guide future research and design directions. This post highlights these projects.

MICROSOFT RESEARCH PODCAST

AI Frontiers: The future of scale with Ahmed Awadallah and Ashley Llorens

This episode features Senior Principal Research Manager  Ahmed H. Awadallah , whose work improving the efficiency of large-scale AI models and efforts to help move advancements in the space from research to practice   have put him at the forefront of this new era of AI.

Best Paper Award recipients

DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing   Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala , Chenglong Wang   GUIs used for editing visualizations can overwhelm users or limit their interactions. To address this, the authors introduce DynaVis, which combines natural language interfaces with dynamically synthesized UI widgets, enabling people to initiate and refine edits using natural language.  

Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking   Nikhil Sharma, Q. Vera Liao , Ziang Xiao   Conversational search systems powered by LLMs potentially improve on traditional search methods, yet their influence on increasing selective exposure and fostering echo chambers remains underexplored. This research suggests that LLM-driven conversational search may enhance biased information querying, particularly when the LLM’s outputs reinforce user views, emphasizing significant implications for the development and regulation of these technologies.  

Piet: Facilitating Color Authoring for Motion Graphics Video   Xinyu Shi, Yinghou Wang, Yun Wang , Jian Zhao   Motion graphic (MG) videos use animated visuals and color to effectively communicate complex ideas, yet existing color authoring tools are lacking. This work introduces Piet, a tool prototype that offers an interactive palette and support for quick theme changes and controlled focus, significantly streamlining the color design process.

The Metacognitive Demands and Opportunities of Generative AI   Lev Tankelevitch , Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar , Abigail Sellen , Sean Rintel   Generative AI systems offer unprecedented opportunities for transforming professional and personal work, yet they present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. This paper shows that metacognition—the psychological ability to monitor and control one’s thoughts and behavior—offers a valuable lens through which to understand and design for these usability challenges.  

Honorable Mentions

B ig or Small, It’s All in Your Head: Visuo-Haptic Illusion of Size-Change Using Finger-Repositioning Myung Jin Kim, Eyal Ofek, Michel Pahud , Mike J. Sinclair, Andrea Bianchi   This research introduces a fixed-sized VR controller that uses finger repositioning to create a visuo-haptic illusion of dynamic size changes in handheld virtual objects, allowing users to perceive virtual objects as significantly smaller or larger than the actual device. 

LLMR: Real-time Prompting of Interactive Worlds Using Large Language Models   Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores , Jaron Lanier   Large Language Model for Mixed Reality (LLMR) is a framework for the real-time creation and modification of interactive mixed reality experiences using LLMs. It uses novel strategies to tackle difficult cases where ideal training data is scarce or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. 

Observer Effect in Social Media Use   Koustuv Saha, Pranshu Gupta, Gloria Mark, Emre Kiciman , Munmun De Choudhury   This work investigates the observer effect in behavioral assessments on social media use. The observer effect is a phenomenon in which individuals alter their behavior due to awareness of being monitored. Conducted over an average of 82 months (about 7 years) retrospectively and five months prospectively using Facebook data, the study found that deviations in expected behavior and language post-enrollment in the study reflected individual psychological traits. The authors recommend ways to mitigate the observer effect in these scenarios.

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming   Hussein Mozannar, Gagan Bansal , Adam Fourney , Eric Horvitz   By investigating how developers use GitHub Copilot, the authors created CUPS, a taxonomy of programmer activities during system interaction. This approach not only elucidates interaction patterns and inefficiencies but can also drive more effective metrics and UI design for code-recommendation systems with the goal of improving programmer productivity. 

SharedNeRF: Leveraging Photorealistic and View-dependent Rendering for Real-time and Remote Collaboration   Mose Sakashita, Bala Kumaravel, Nicolai Marquardt , Andrew D. Wilson   SharedNeRF, a system for synchronous remote collaboration, utilizes neural radiance field (NeRF) technology to provide photorealistic, viewpoint-specific renderings that are seamlessly integrated with point clouds to capture dynamic movements and changes in a shared space. A preliminary study demonstrated its effectiveness, as participants used this high-fidelity, multi-perspective visualization to successfully complete a flower arrangement task. 

Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination   Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu, Dana Kulzhabayeva, Minyi Ma, Rachel Kornfield, Syed Ishtiaque Ahmed, Alex Mariakakis, Mary P. Czerwinski , Anastasia Kuzminykh, Michael Liut, Joseph Jay Williams   In this study, the authors explore the potential of LLMs for customizing academic procrastination interventions, employing a technology probe to generate personalized advice. Their findings emphasize the need for LLMs to offer structured, deadline-oriented advice and adaptive questioning techniques, providing key design insights for LLM-based tools while highlighting cautions against their use for therapeutic guidance.

Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration   Haotian Li, Yun Wang , Huamin Qu This paper evaluates data storytelling tools using a dual framework to analyze the stages of the storytelling workflow—analysis, planning, implementation, communication—and the roles of humans and AI in each stage, such as creators, assistants, optimizers, and reviewers. The study identifies common collaboration patterns in existing tools, summarizes lessons from these patterns, and highlights future research opportunities for human-AI collaboration in data storytelling.

Learn more about our work and contributions to CHI 2024, including our full list of publications , on our conference webpage .

Related publications

Generative echo chamber effects of llm-powered search systems on diverse information seeking, understanding the role of large language models in personalizing and scaffolding strategies to combat academic procrastination, sharednerf: leveraging photorealistic and view-dependent rendering for real-time and remote collaboration, big or small, it’s all in your head: visuo-haptic illusion of size-change using finger-repositioning, llmr: real-time prompting of interactive worlds using large language models, reading between the lines: modeling user behavior and costs in ai-assisted programming, observer effect in social media use, where are we so far understanding data storytelling tools from the perspective of human-ai collaboration, the metacognitive demands and opportunities of generative ai, piet: facilitating color authoring for motion graphics video, dynavis: dynamically synthesized ui widgets for visualization editing, continue reading.

Research Focus: Week of May 13, 2024

Research Focus: Week of April 15, 2024

Research Focus: Week of March 18, 2024

Advancing human-centered AI: Updates on responsible AI research

Research areas.

Related events

• Microsoft at CHI 2024

Related labs

• Microsoft Research Lab - Asia
• Microsoft Research Lab - Cambridge
• Microsoft Research Lab - Redmond
• Microsoft Research Lab – Montréal
• AI Frontiers
• Follow on Twitter
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram

Share this page: