paper study research design

Research Design 101

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

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 .

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).

Need a helping hand?

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.

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 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 .

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.

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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15 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

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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?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

Wow! This post has an awful explanation. Appreciated.

Thanks This has been helpful

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Shona McCombes

Organizing Academic Research Papers: Types of Research Designs

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

Introduction

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

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you can use, not the other way around!

General Structure and Writing Style

Action research design, case study design, causal design, cohort design, cross-sectional design, descriptive design, experimental design, exploratory design, historical design, longitudinal design, observational design, philosophical design, sequential design.

Kirshenblatt-Gimblett, Barbara. Part 1, What Is Research Design? The Context of Design. Performance Studies Methods Course syllabus . New York University, Spring 2006; Trochim, William M.K. Research Methods Knowledge Base . 2006.

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem as unambiguously as possible. In social sciences research, obtaining evidence relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe a phenomenon. However, researchers can often begin their investigations far too early, before they have thought critically about about what information is required to answer the study's research questions. Without attending to these design issues beforehand, the conclusions drawn risk being weak and unconvincing and, consequently, will fail to adequate address the overall research problem.

 Given this, the length and complexity of research designs can vary considerably, but any sound design will do the following things:

• Identify the research problem clearly and justify its selection,
• Review previously published literature associated with the problem area,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem selected,
• Effectively describe the data which will be necessary for an adequate test of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis which will be applied to the data in determining whether or not the hypotheses are true or false.

Kirshenblatt-Gimblett, Barbara. Part 1, What Is Research Design? The Context of Design. Performance Studies Methods Course syllabus . New Yortk University, Spring 2006.

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 the cyclic process repeats, continuing until a sufficient understanding of (or implement able 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?

• A collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research 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 also be regarded as a learning cycle.
• Action search studies often have direct and obvious relevance to practice.
• 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 studies because the researcher takes on responsibilities for encouraging change as well as for research.
• Action research is much harder to write up because you probably can’t use a standard format to report your findings effectively.
• 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.

Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Locoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605.; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey. 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 a 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 vaiety 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 extension of methods.
• 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.
• The intense exposure to study of the 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 intepretation of the findings can only apply to that particular case.

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; 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.

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 helps researchers understand why the world works the way it does through the process of proving a causal link between variables and 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 casual! 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.

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; 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.

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, r ather 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 on 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.
• Because of 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;  Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

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 diffrerences between or from among a variety of people, subjects, or phenomena rather than change. As such, researchers using this design can only employ a relative passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike the 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 contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• Provide only 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.

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 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.
• Descriptive research is often used as a pre-cursor to more quantitatively research designs, 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.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research can not 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;  McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

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 subject behaviors or responses.
• 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  experimental designed research 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; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to. The focus is on gaining insights and familiarity for later investigation or undertaken when problems are in a preliminary stage of investigation.

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 assumption, 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.
• Exploratory studies help establish research priorities.
• 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.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value in decision-making.
• 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; 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.

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute your hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, logs, 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 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 consistentally to ensure access.
• 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 rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

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.

A longitudinal study follows the same sample over time and makes repeated observations. With longitudinal surveys, for example, 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 and is sometimes referred to as a panel study.

• Longitudinal data allow the analysis of 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 to explain fluctuations in the data.
• 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; 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; 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.

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 a depth of 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 researchd esigns 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 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 possiblility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is studied is altered to some degree by the very presence of the researcher, therefore, skewing to some degree any data collected (the Heisenburg Uncertainty Principle).

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; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010.

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, on what 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.

Chapter 4, Research Methodology and Design . Unisa Institutional Repository (UnisaIR), University of South Africa;  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, D.C.: Falmer Press, 1994; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

• 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. 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 extensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed.
• 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 sample can be difficult.
• Because the sampling technique is not randomized, the design cannot be used to create conclusions and interpretations that pertain to an entire population. Generalizability from findings is limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Rebecca Betensky, Harvard University, Course Lecture Note slides ; 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; 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.  

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How to... Design a research study

The design of a piece of research refers to the practical way in which the research was conducted according to a systematic attempt to generate evidence to answer the research question. The term "research methodology" is often used to mean something similar, however different writers use both terms in slightly different ways: some writers, for example, use the term "methodology" to describe the tools used for data collection, which others (more properly) refer to as methods.

On this page

What is research design, sampling techniques, quantitative approaches to research design, qualitative approaches to research design, planning your research design.

The following are some definitions of research design by researchers:

Design is the deliberately planned 'arrangement of conditions for analysis and collection of data in a manner that aims to combine relevance to the research purpose with economy of procedure'.

Selltiz C.S., Wrightsman L.S. and Cook S.W. 1981  Research Methods in Social Relations, Holt, Rinehart & Winston, London, quoted in Jankowicz, A.D.,  Business Research Methods , Thomson Learning, p.190.)

The idea behind a design is that different kinds of issues logically demand different kinds of data-gathering arrangement so that the data will be:

• relevant to your thesis or the argument you wish to present;
• an adequate test of your thesis (i.e. unbiased and reliable);
• accurate in establishing causality, in situations where you wish to go beyond description to provide explanations for whatever is happening around you;
• capable of providing findings that can be generalised to situations other than those of your immediate organisation.

(Jankowicz, A.D.,  Business Research Methods  , Thomson Learning, p. 190)

The design of the research involves consideration of the best method of collecting data to provide a relevant and accurate test of your thesis, one that can establish causality if required (see  What type of study are you undertaking? ), and one that will enable you to generalise your findings.

Design of the research should take account of the following factors, which are briefly discussed below with links to subsequent pages or other parts of the site where there is fuller information.

What is your theoretical and epistemological perspective?

Although management research is much concerned with observation of humans and their behaviour, to a certain extent the epistemological framework derives from that of science. Positivism assumes the independent existence of measurable facts in the social world, and researchers who assume this perspective will want to have a fairly exact system of measurement. On the other hand, interpretivism assumes that humans interpret events and researchers employing this method will adopt a more subjective approach.

What type of study are you undertaking?

Are you conducting an exploratory study, obtaining an initial grasp of a phenomenon, a descriptive study, providing a profile of a topic or institution:

Karin Klenke provides an exploratory study of issues of gender in management decisions in  Gender influences in decision-making processes in top management teams  ( Management Decision , Volume 41 Number 10)

Damien McLoughlin provides a descriptive study of action learning as a case study in  There can be no learning without action and no action without learning  in ( European Journal of Marketing , Volume 38 Number 3/4)

Or it can be explanatory, examining the causal relationship between variables: this can include the testing of hypotheses or examination of causes:

Martin  et al.  examined ad zipping and repetition in  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  ( Marketing Intelligence & Planning , Volume 20 Number 1) with a number of hypotheses e.g. that people are more likely to remember an ad that they have seen repeatedly.

What is your research question?

The most important issue here is that the design you use should be appropriate to your initial question. Implicit within your question will be issues of size, breadth, relationship between variables, how easy is it to measure variables etc.

The two different questions below call for very different types of design:

The example  Dimensions of library anxiety and social interdependence: implications for library services  (Jiao and Onwuegbuzie,  Library Review , Volume 51 Number 2) looks at attitudes and the relationship between variables, and uses very precise measurement instruments in the form of two questionnaires, with 43 and 22 items respectively.

In the example  Equity in Corporate Co-branding  (Judy Motion  et al. ,  European Journal of Marketing , Volume 37 Number 7),  the RQs posit a need to describe rather than to link variables, and the methodology used is one of discourse theory, which involves looking at material within the context of its use by the company.

What sample size will you base your data on?

The sample is the source of your data, and it is important to decide how you are going to select it.

See  Sampling techniques .

What research methods will you use and why?

We referred above to the distinction between methods and methodology. There are two main approaches to methodology – qualitative and quantitative.

For more detail on each of the approaches,  Quantitative approaches to design  and  Qualitative approaches to design  later in this feature.

Note, you do not have to stick to one methodology (although some writers recommend that you do). Combining methodologies is a matter of seeing which part of the design of your research is better suited to which methodology.

How will you triangulate your research?

Triangulation refers to the process of ensuring that any defects in a particular methodology are compensated by use of another at appropriate points in the design. For example, if you carry out a quantitative survey and need more in depth information about particular aspects of the survey you may decide to use in-depth interviews, a qualitative method.

Here are a couple of useful articles to read which cover the issue of triangulation:

• Combining quantitative and qualitative methodologies in logistics research  by John Mangan, Chandra Lalwani and Bernard Gardner ( International Journal of Physical Distribution & Logistics Management , Volume 34 Number 7) looks at ways of combining methodologies in a particular area of research, but much of what they say is generally applicable.
• Quantitative and qualitative research in the built environment: application of "mixed" research approach  by Dilanthi Amaratunga, David Baldry, Marjan Sarshar and Rita Newton ( Work Study , Volume 51 Number 1) looks at the relative merits of the two research approaches, and despite reference to the built environment in the title acts as a very good introduction to quantitative and qualitative methodology and their relative research literatures. The section on triangulation comes under the heading 'The mixed (or balanced) approach'. 

What steps will you take to ensure that your research is ethical?

Ethics in research is a very important issue. You should design the research in such a way that you take account of such ethical issues as:

• informed consent (have the participants had the nature of the research explained to them)?
• checking whether you have permission to transcribe conversations with a tape recorder
• always treating people with respect, consideration and concern.

How will you ensure the reliability of your research?

Reliability

This is about the replicability of your research and the accuracy of the procedures and research techniques. Will the same results be repeated if the research is repeated? Are the measurements of the research methods accurate and consistent? Could they be used in other similar contexts with equivalent results? Would the same results be achieved by another researcher using the same instruments? Is the research free from error or bias on the part of the researcher, or the participants? (E.g. do the participants say what they believe the management, or the researcher, wants? For example, in a survey done on some course material, that on a mathematical module received glowing reports – which led the researcher to wonder whether this was anything to do with the author being the Head of Department!)

How successfully has the research actually achieved what it set out to achieve? Can the results of the study be transferred to other situations? Does x really cause y, in other words is the researcher correct in maintaining a causal link between these two variables? Is the research design sufficiently rigorous, have alternative explanations been considered? Have the findings really be accurately interpreted? Have other events intervened which might impact on the study, e.g. a large scale redundancy programme? (For example, in an evaluation of the use of CDs for self study with a world-wide group of students, it was established that some groups had not had sufficient explanation from the tutors as to how to use the CD. This could have affected their rather negative views.)

Generalisability

Are the findings applicable in other research settings? Can a theory be developed that can apply to other populations? For example, can a particular study about dissatisfaction amongst lecturers in a particular university be applied generally? This is particularly applicable to research which has a relatively wide sample, as in a questionnaire, or which adopts a scientific technique, as with the experiment.

Transferability

Can the research be applied to other situations? Particularly relevant when applied to case studies.

In addition, each of the sections in this feature on quantitative and qualitative approaches to research design contain notes on how to ensure that the research is reliable.

Some basic definitions

In order to answer a particular research question, the researcher needs to investigate a particular area or group, to which the conclusions from the research will apply. The former may comprise a geographical location such as a city, an industry (for example the clothing industry), an organisation/group of organisations such as a particular firm/type of firm, a particular group of people defined by occupation (e.g. student, manager etc.), consumption of a particular product or service (e.g. users of a shopping mall, new library system etc.), gender etc. This group is termed the  research population .

The  unit of analysis  is the level at which the data is aggregated: for example, it could be a study of individuals as in a study of women managers, of dyads, as in a study of mentor/mentee relationships, of groups (as in studies of departments in an organisation), of organisations, or of industries.

Unless the research population is very small, we need to study a subset of it, which needs to be general enough to be applicable to the whole. This is known as a  sample , and the selection of components of the sample that will give a representative view of the whole is known as  sampling technique  . It is from this sample that you will collect your data.

In order to draw up a sample, you need first to identify the total number of people in the research population. This information may be available in a telephone directory, a list of company members, or a list of companies in the area. It is known as a  sampling frame .

In  Networking for female managers' career development  (Margaret Linehan,  Journal of Management Development , Volume 20 Number 10), he sampling technique is described as follows:

"A total of 50 senior female managers were selected for inclusion in this study. Two sources were used for targeting interviewees, the first was a listing of Fortune 500 top companies in England, Belgium, France and Germany, and, second, The Marketing Guide to Ireland. The 50 managers who participated in the study were representative of a broad range of industries and service sectors including: mining, software engineering, pharmaceutical manufacturing, financial services, car manufacturing, tourism, oil refining, medical and state-owned enterprises."

Sampling may be done either a  probability  or a  non-probability  basis. This is an important research design decision, and one which will depend on such factors as whether the theory behind the research is positivist or idealist, whether qualitative or quantitative methods are used etc. Note that the two methods are not mutually exclusive, and may be used for different purposes at different points in the research, say purposive sampling to find out key attitudes, followed by a more general, random approach.

Note that there is a very good section from an online textbook on sampling: see William Trochim's  Research Methods Knowledge Base .

Probability sampling

In  probability  sampling, each member of a given research population has an equal chance of being selected. It involves, literally, the selection of respondents at random from the sampling frame, having decided on the sample size. This type of sampling is more likely if the theoretical orientation of the research is  positivist , and the methodology used is likely to be  quantitative .

Probability sampling can be:

• random  – the selection is completely arbitrary, and a given number of the total population is selected completely at random.
• systematic  – every  nth element  of the population is selected. This can cause a problem if the interval of selection means that the elements share a characteristic: for example, if every fourth seat of a coach is selected it is likely that all the seats will be beside a window.
• stratified   random  – the population is divided into segments, for example, in a University, you could divide the population into academic, administrators, and academic related (related professional staff). A random number of each group is then selected. It has the advantage of allowing you to categorise your population according to particular features. A.D. Jankowicz provides useful advice (Business Research Methods,Thomson Learning, 2000, p.197).

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) uses stratified sampling: the study concentrates on three sectors within the EU, chemicals, electronics and vehicles, with the sample being stratified within this sector.

• cluster  – a particular subgroup is chosen at random. The subgroup may be based on a particular geographical area, say you may decide to sample particular areas of the country.

Non probability sampling

Here, the population does not have an equal chance of being selected; instead, selection happens according to some factor such as:

• convenience/accidental  – being present at a particular time e.g. at lunch in the canteen. This is an easy way of getting a sample, but may not be strictly accurate, because the factor you have chosen is based on your convenience rather than on a true understanding of the characteristics of the sample.

In  "Saying is one thing; doing is another": the role of observation in marketing research  ( Qualitative Market Research: An International Journal , Volume 2 Number 1), Matthews and Boote use a two-stage sampling process, with convenience sampling followed by time sampling: see their methodology.

• "key informant technique" – i.e. people with specialist knowledge
• using people at selected points in the organisational hierarchy 
• snowball, with one person being approached and then suggesting others.

In "The benefits of the implementation of the ISO 9000 standard: empirical research in 288 Spanish companies", a sample was selected based on all certified companies in a particular area, because this was where the highest number of certified companies could be found.

• quota  – the assumption is made that there are subgroups in the population, and a quota of respondents is chosen to reflect this diversity. This subgroup should be reasonably representative of the whole, but care should be taken in drawing conclusions for the whole population. For example, a quota sample taken in New York State would not be representative of the whole of the United States.

Monitoring consumer confidence in food safety: an exploratory study , de Jonge  et al . use quota sampling using age, gender, household size and region as selection variables in a food safety survey. Read about the methodology under Materials and methods.

Non probability sampling methods are more likely to be used in qualitative research, with the greater degree of collaboration with the respondents affording the opportunity of greater detail of data gathering. The researcher is more likely to be involved in the process and be adopting an  interpretivist theoretical  stance.

Calculating the sample size

In purposive sampling, this will be determined by judgement; in other more random types of sample it is calculated as a  proportion  of the sampling frame, the key criterion being to ensure that it is representative of the whole. (E.g. 10 per cent is fine for a large population, say over 1000, but for a small population you would want a larger proportion.)

If you are using stratified sampling you may need to adjust your strata and collapse into smaller strata if you find that some of your sample sizes are too small.

The response rate

It is important to keep track of the response rate against your sample frame. If you are depending on postal questionnaires, you will need to plan into your design time to follow up the questionnaires. What is considered to be a good response rate varies according to the type of survey: if you are, say, surveying managers, then a good response would be 50 per cent; for consumer surveys, the response rate is likely to be lower, say 10 to 20 per cent.

The thing that characterises quantitative research is that it is objective. The assumption is that facts exist totally independently and the researcher is a totally  objective  observer of situations, and has no power to influence them. At such, it probably starts from a positivist or empiricist position.

The research design is based on one iteration in collection of the data: the categories are isolated prior to the study, and the design is planned out and generally not changed during the study (as it may be in qualitative research).

What is my research question? What variables am I interested in exploring?

It is usual to start your research by carrying out a  literature review , which should help you formulate a research question.

Part of the task of the above is to help you determine what  variables  you are considering. What are the key variables for your research and what is the relationship between them – are you looking to  explore  issues, to  compare  two variables or to look at  cause and effect ?

The Dutch heart health community intervention "Hartslag Limburg": evaluation design and baseline data  (Gaby Ronda  et al. ,  Health Education , Volume 103 Number 6) describes a trial of a cardiovascular prevention programme which indicated the importance of its further implementation. The key variables are the types of health related behaviours which affect a person's chance of heart disease.

The following studies compare variables:

Service failures away from home: benefits in intercultural service encounters  (Clyde A Warden  et al. ,  International Journal of Service Industry Management , Volume 14 Number 4) compares service encounters (the independent variable) inside and outside Taiwan (the dependent variable) in order to look at certain aspects of 'critical incidents' in intercultural service encounters.

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) looks at managerial flexibility in relation to different types of business, service and manufacturing.

They can also look at cause and effect:

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (Brett A.S. Martin  et al. ,  Marketing Intelligence & Planning , Volume 20 Number 1), the authors look at two variables associated with advertising, notably zipping and fast forwarding, and in their effect on a third variable, consumer behaviour - i.e. ability to remember ads. Furthermore, it looks at the interaction between the first two variables - i.e. whether they interact on one another to help increase recall.

What is the hypothesis?

It is usual with quantitative research to proceed from a particular hypothesis. The object of research would then be to test the hypothesis.

In the example quoted above,  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers , the researchers decided to explore a neglected area of the literature: the interaction between ad zipping and repetition, and came up with three hypotheses:

The influence of zipping H1 . Individuals viewing advertisements played at normal speed will exhibit higher ad recall and recognition than those who view zipped advertisements.

Ad repetition effects H2 . Individuals viewing a repeated advertisement will exhibit higher ad recall and recognition than those who see an advertisement once.

Zipping and ad repetition H3 . Individuals viewing zipped, repeated advertisements will exhibit higher ad recall and recognition than those who see a normal speed advertisement that is played once.

What are the appropriate measures to use

It is very important, when designing your research, to understand  what  you are measuring. This will call for a close examination of the issues involved: is your measure suitable to the hypothesis and research question under consideration? The type of scale you will use will dictate the statistical procedure which you can use to analyse your data, and it is important to have an understanding of the latter at the outset in order to obtain the correct level of analysis, and one that will throw the best light on your research question, and help test your hypothesis.

It is also important to understand what type of data you are trying to collect. Are you wanting to collect data that relates simply to different types of categories, for example, men and women (as in, say, differences in decision-making between men and women managers), or do you want to rank the data in some way? Choices as far as the nature of data are concerned again dictate the type of statistical analysis.

Data can be categorised as follows:

• Nominal – Representing particular categories, e.g. men or women.
• Ordinal – Ranked in some way such as order of passing a particular point in a shopping centre.
• Interval – Ranked according to the interval between the data, which remains the same. Most typical of this type of data is temperature.
• Ratio – Where it is possible to measure the difference between different types of data - for example applying a measurement.
• Scalar – This type of data has intervals between it, which are not quantifiable.

Note that some of the above categories, especially 'interval' and 'ratio' are drawn from a scientific model which assumes exact measurement of data (temperature, length etc.). In management research, you are unlikely to want to or be able to apply such a high degree of exactitude, and are more likely to be measuring less exact criteria which do not have an exact interval between them.

Here are some examples of use of data in management research. This one illustrates the use of different categories:

The concept of fit in services flexibility and research: an empirical approach  (see above) uses an approach which itemises the different aspects which the researchers wished to measure flexibility mix, performance and the form's general data. 

This one looks at categories and also at ranked data (ordinal):

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (also see above), the measure involved 2 (speed of ad presentation: normal, fast-forwarded) ×\ 2 (repetition: none, one repetition) between-subjects factorial design.

The following examples look at measures on a scale, which may relate to tangible factors such as frequency, or more intangible ones which relate to attitude or opinion:

How many holidays do you take in a year?

One __  Between 2 and 5 __  Between 5 and 10 __  More than 10 __

Tick the option which most agrees with your views.

Navigating my way around the CD was:

Very easy __  Easy __  Neither easy nor hard __  Hard __  Very hard __

The later type of data are very common in management research, and are known as scalar data. A very common measure for such data is known as the Likert scale:

Strongly agree __________ Agree __________ Neither agree nor disagree __________ Disagree __________ Strongly disagree __________

How will I analyse the data?

Quantitative data are invariably analysed by some sort of statistical means, such as a t-test, a chi test, cluster analysis etc. It is very important to decide at the planning stage what your method of analysis will be: this will in turn affect your choice of measure. Both your analysis and measure should be suitable to test your hypothesis.

You need also to consider what type of package will you need to analyse your data. It may be sufficient to enter it into an Excel spreadsheet, or you may wish to use a statistical package such as SPSS or Mintab.

What are the instruments used in quantitative research?

Or, put more simply, what methods will you use to collect your data?

In scientific research, it is possible to be reasonably precise by generating experiments in laboratory conditions. Whilst the  field experiment  has a place in management research, as does  observation , the most usual instrument for producing quantitative data is the  survey , most often carried out by means of a  questionnaire .

You will find numerous examples of questionnaires and surveys in research published by Emerald, as you will in any database of management research. Questionnaires will be discussed at a later stage but here are some key issues:

• It is important to know exactly what questions you want answers to. A common failing is to realise, once you have got the questionnaire back, that you really need answers to a question which you never asked. Thus the questionnaire should be rigorously researched and the questions phrased as precisely as possible.
• You are more likely to get a response if you give people a reason to respond - commercial companies sometimes offer a prize, which may not be possible or appropriate if you are a researcher in a university, but it is usual in that case to give the reason behind your research, which gives your respondent a context. Even more motivational is the ease with which the questionnaire can be filled in.
• How many responses will I need? This concerns the eventual size of your dataset and depends upon the degree of complexity of your planned analysis, how you are treating your variables (for example, if you are wanting to show the effect of a variable, you will need a larger response size, likewise if you are showing changes in variables).

Other instruments that are used in quantitative research to generate data are experiments, historical records and documents, and observation.

Note that some authors claim that for a design to be a  true experiment , items must be randomly assigned to groups; if there is some sort of control group or multiple measures, then it may be  quasi experimental . If your survey fits neither of these descriptions, it may according to these authors be sufficient for descriptive purposes, but not if you seek to establish a causal relationship.

For more information on types of design, see William Trochim's Research Methods Knowledge Base section on  types of design .

What are the advantages and drawbacks of quantitative research?

The main advantage of quantitative research is that it is easy to determine its rigour: because of the objectivity of quantitative studies, it is easy to replicate them in another situation. For example, a well-constructed questionnaire can be used to analyse job satisfaction in two different companies; likewise, an observation studying consumer behaviour in a shopping centre can take place in two different such centres.

Quantitative methods are also good at obtaining a good deal of reliable data from a large number of sources. Their drawback is that they are heavily dependent on the reliability of the instrument: that is, in the case of the questionnaire, it is vital to ask the right questions in the right way. This in turn is dependent upon having sufficient information about a situation, which is not always possible. In addition, quantitative studies may generate a large amount of data, but the data may lack depth and fail to explain complex human processes such as attitudes to organisational change, or how how learning takes place.

For example, a quantitative study on a piece of educational software may show that on the whole people felt that they had learnt something, but may not necessarily show how they learnt, which an observation could.

For this reason, quantitative methods are often used in conjunction with qualitative methods: for example, qualitative methods of interviewing may be used as a way of finding out more about a situation in order to draw up an informed quantitative instrument; or to explore certain issues which have appeared in the quantitative study in greater depth.

Qualitative research operates from a different epistemological perspective than quantitative, which is essentially objective. It is a perspective that acknowledges the essential difference between the social world and the scientific one, recognising that people do not always observe the laws of nature, but rather comprise a whole range of feelings, observations, attitudes which are essentially subjective in nature. The theoretical framework is thus likely to be interpretivist or realist. Indeed, the researcher and the research instrument are often combined, with the former being the interviewer, or observer – as opposed to quantitative studies where the research instrument may be a survey and the subjects may never see the researcher.

In an  interview for Emerald ,  Professor Slawomir Magala , Editor of the  Journal of Organizational Change Management , has this to say about qualitative methods:

"We follow the view that the social construction of reality is personal, experienced by individuals and between individuals – in fact, the interactions which connect us are the building blocks of reality, and there is much meaning in the space between individuals."

As opposed to the statistical reliance of quantitative research, data from qualitative research is based on observation and words, and analysis is based on interpretation and pattern recognition rather than statistical analysis.

Miles and Huberman list the following as typical criteria of qualitative research:

• Intense and prolonged contact in the field.
• Designed to achieve a holistic or systemic picture.
• Perception is gained from the inside based on actors' understanding.
• Little standardised instrumentation is used.
• Most analysis is done with words.
• There are multiple interpretations available in the data.

Miles, M. and Huberman, A.M. (1994) Qualitative Data Analysis: An Expanded Sourcebook , Sage, London

To what types of research questions is qualitative research relevant?

Qualitative research is best suited to the types of questions which require exploration of data  in depth  over a not particularly large sample. For example, it would be too time consuming to ask questions such as "Please describe in detail your reaction to colour x" to a large number of people, it would be more appropriate to simply ask "Do you like colour x" and give people a "yes/no" option. By asking the former question to a smaller number of people, you would get a more detailed result.

Qualitative research is also best suited to  exploratory  and  comparative  studies; to a more limited extent, it can also be used for  "cause-effect"  type questions, providing these are fairly limited in scope.

One of the strengths of qualitative research is that it allows the researcher to gain an in-depth perspective, and to grapple with complexity and ambiguity. This is what makes it suitable to analysis of  particular  groups or situations, or unusual events.

What is the relationship of qualitative research to hypotheses?

Qualitative research is usually inductive: that is, researchers gather data, and then formulate a hypothesis which can be applied to other situations.

In fact, one of the strengths of qualitative research is that it can proceed from a relatively small understanding of a particular situation, and generate new questions during the course of data collection as opposed to needing to have all the questions set out beforehand. Indeed, it is good practice in quantitative research to go into a situation as free from preconceptions as possible.

How will you analyse the data?

There is not the same need with qualitative research to determine the measure and the method of analysis at an early stage of the research process, mainly because there are no standard ways of analysing data as there are for quantitative research: it is usual to go with whatever is appropriate for the research question. However, because qualitative data usually involves a large amount of transcription (e.g. of taped interviews, videos of focus groups etc.) it is a good idea to have a plan of how this should be done, and to allow time for the transcription process.

There are a couple of attested methods of qualitative data analysis:  content analysis , which involves looking at emerging patterns, and  grounded analysis , which involves going through a number of guided stages and which is closely linked to  grounded theory .

What are the main instruments of qualitative research?

Or put another way, what are the main methods used to collect data? These can be organised according to their methodology (note, the following is not an exhaustive list, for which you should consult a good book on qualitative research):

Ethnographic methods

As the name suggests, this methodology derives from anthropology and involves observing people as a participant within their social and cultural system. Most common methods of data collection are:

• Interviewing, which means discussions with people either on the phone, by email or in person when the purpose is to collect data which is by its nature unquantifiable and more difficult to analyse by statistical means, but which provides in-depth information. The interview can be either:  Structured , which means that the interviewer has a set number of questions.  Semi-structured , which means that the interviewer has a number of questions or a purpose, but the interview can still go off in unanticipated directions.
• Focus groups, which is where a group of people are assembled at one time to give their reaction to a product, or to discuss an issue. There is usually some sort of facilitation which involves either guided discussion or some sort of product demonstration.
• Participant observation – the researcher observes behaviour of people in the organisation, their language, actions, behaviour etc.

For some examples of participant observation, see Methods of empirical research ,  and for examples of interview technique, see  Techniques of data collection and analysis .

Historical analysis

This is literally, the analysis of historical documents of a particular company, industry etc. It is important to understand exactly what your focus is, and also which historical school or theoretical perspective you are drawing on.

Grounded theory

This is an essentially inductive approach, and is applied when the understanding of a particular phenomenen is sought. A feature is that the design of the research has several iterations: there is initial exploration followed by a theory which is then tested.

In  Grounded theory methodology and practitioner reflexivity in TQM research  ( International Journal of Quality & Reliability Management  , Volume 18 Number 2), Leonard and McAdam use grounded theory to explore TQM, on the grounds that quantitative methods "fail to give deep insights and rich data into TQM in practice within organizations", and that it is much more appropriate to listen to the individual experiences of participants. 

Action research

This is a highly participative form of research where the research is carried out in collaboration with those involved in a particular process, which is often concerned with some sort of change.

Narrative methods

This is when the researcher listens to the stories of people in the organisation and triangulates them against official documents.

Discourse theory

This methodology draws on a theory which allows language to have a meaning that is not set but is negotiated through social context.

Helen Francis in  The power of "talk" in HRM-based change  ( Personnel Review , Volume 31 Number 4) describes her use of discourse theory as follows:

"The approach to discourse analysis drew upon Fairclough's seminal work in which discourse is treated as a form of social practice and meaning is something that is essentially fluid and negotiated rather than being authored individually (Fairclough, 1992, 1995).

"For Fairclough (1992, 1995) the analysis of discursive events is three dimensional and includes simultaneously a piece of text, an instance of discursive practice, and an instance of social practice. Text refers to written and spoken language in use, while "discursive practices" allude to the processes by which texts are produced and interpreted. The social practice dimension refers to the institutional and organisational factors surrounding the discursive event and how they might shape the nature of the discursive practice.

"For the purposes of this research, the method of analysis included a description of the language text and how it was produced or interpreted amongst managers and their subordinates. Particular emphasis was placed on investigating the import of metaphors that are characteristic of HRM, and the introduction of HRM-based techniques adopted by change leaders in their attempt to privilege certain themes and issues over others."

Fairclough, N., 1992,  Discourse and Social Change , Polity Press, Cambridge.

Fairclough, N., 1995,  Critical Discourse Analysis: Papers in the Critical Study of Language , Longman, London.

Discourse theory can be applied to the written as well as the spoken word and can be used to analyse marketing literature as in the following example:

Equity in corporate co-branding: the case of Adidas and the all-blacks  by Judy Motion  et al.  ( European Journal of Marketing , Volume 37 Number 7), where discourse theory is used to analyse branding messages.

How rigorous is qualitative research?

It is often considered harder to demonstrate the rigour of qualitative research, simply because it may be harder to replicate the conditions of the study, and apply the data in other similar circumstances. The rigour may partly lie in the ability to generate a theory which can be applied in other situations, and which takes our understanding of a particular area further.

Rigour in qualitative research is greatly aided by:

• confirmability - which does not necessarily mean that someone else would adopt the same conclusion, but rather there is a clear audit trail between your data and your interpretation; and that interpretations are based on a wide range of data (for example, from several interviews rather than just one). (This is related to  triangulation , see below.)
• authenticity - are you drawing on a sufficiently wide range of rich data, do the interpretations ring true, have you considered rival interpretations, do your informants agree with your interpretation?

In  Cultural assumptions in career management: practice implications from Germany;  (Hansen and Willcox,  Career Development International , Volume 2 Number 4), the main method used is ethnographic interviews, and findings are verified by comparing data from the two samples.

Reliability is also enhanced if you can triangulate your data from a number of different sources or methods of data collection, at different times and from different participants.

Dennis Cahill, in  When to use qualitative methods: a new approach  ( Marketing Intelligence & Planning , Volume 14 Number 6), has this to say about the reliability of qualitative research:

"While there are times when qualitative techniques are inappropriate to the research goal, or appropriate only in certain portions of a research project, quantitative techniques do not have universal applicability, either. Although these techniques may be used to measure "reality" rather precisely, they often suffer from a lack of good descriptive material of the type which brings the information to life. This lack is particularly felt in corporate applications where implementation of the results is sought. Therefore, whether one has any interest in the specific research described above, if one is involved in implementation of research results – something we all should be involved in – the use of qualitative research at midpoint is a technique with which we should become familiar.

"It is at this point that some qualitative follow up – interviews or focus groups for example – can serve to flesh out the results, making it possible for people at the firm to understand and internalize those results."

Can qualitative research be used in with quantitative research?

Whereas some researchers only use either qualitative or quantitative methodologies, the two are frequently combined, as when for example qualitative methods are used exploratatively in order to obtain further information prior to developing a quantitative research instrument. In other cases, qualitative methods are used to complement quantitative methods and obtain a greater degree of descriptive richness:

In  When to use qualitative methods: a new approach , Dennis Cahill describes how qualitative methods were used after an extensive questionnaire used to carry out research for a new publication dedicated to the needs of the real estate market. The analysis for the questionnaire produced a five-segment typology (winners, authentics, heartlanders, wannabes and maintainers), which was tested by means of an EYE-TRAC test, when a selected sample was videotaped looking at a magazine of houses for sale.

Once you have established the key features of your design, you need to create an outline project plan which will include a budget and a timetable. In order to do this you need to think first about the activities of your data collection: how much data are you collecting, where etc. (See the section on  Sampling techniques .) You also need to consider your time period for data collection.

Over what time period will you collect your data?

This refers to two types of issues:

Type of study

Should the research be a 'snapshot', examining a particular phenomenon at a particular time, or should it be  longitutinal , examining an issue over a time period? If the latter, the object will be to explore changes over the period.

A longitudinal study of corporate social reporting in Singapore  (Eric W K Tsang,  Accounting, Auditing & Accountability Journal , Volume 11 Number 5) examines social reporting in that country from 1986 to 1995.

Methodology

Sometimes, you may have 'one shot' at the collection of your data - in other words, you plan your sample, your method of data collection, and then analyse the result. This is more likely to be the case if your research approach is more quantitative.

However, other types of research approach involve stages in the collection of data. For example, in  grounded theory  research, data is collected and analysed and then the process is repeated as more is discovered about the subject. Likewise in  action research , there is a cyclical process of data collection, reflection and more collection and analysis.

If you adopt an approach where you  combine quantitative and qualitative methods , then this methodology will dictate that you do a series of studies, whether qualitative followed by quantitative, or vice versa, or qualitative/quantitative/qualitative.

Grounded theory methodology and practitioner reflexivity in TQM research  (Leonard and McAdam,  International Journal of Quality & Reliability Management , Volume 18 Number 2) adopts a three-stage approach to the collection of data.

Doing the plan

The following are some of the costs which need to be considered:

• Travel to interview people.
• Postal surveys, including follow-up.
• The design and printing of the questionnaire, especially if there is use of Optical Mark Reader (OMR) and Optical Character Recognition (OCR) technology.
• Programming to "read" the above.
• Programming the data into meaningful results.
• Transcription of any tape recorded interviews.
• Cost of design of any internet survey.
• Employment of a research assistant.

Timetabling

Make a list of the key stages of your research. Does it have several phases, for example, a questionnaire, then interviews?

How long will each phase take? Take account of factors such as:

• Sourcing your sampling frame
• Determining the sample
• Approaching interview subjects
• Preparations for interviews
• Writing questionnaires
• Response time for questionnaires (include a follow-up stage)
• Analysing the responses
• Writing the report

When doing a schedule, it's tempting to make it as short as possible in the belief that you actually can achieve more in the time than you think. However, it's very important to be as accurate as possible in your scheduling.

Planning is particularly important if you are working to a specific budget and timetable as for example if you are doing a PhD, or if you are working on a funded research project, which has a specific amount of money available and probably also specific deadlines.

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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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Research Methods Guide: Research Design & Method

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Research Methods (sociology-focused)

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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).

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How to choose your study design

Affiliation.

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

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

Keywords: experimental studies; observational studies; research method.

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

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Research Design: Definition, Types, Characteristics & Study Examples

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

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

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

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

What Is a Research Design: Definition

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

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

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

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

Research design example

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

What Makes a Good Study Design? 

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

A good research design should be:

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

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

Research Study Design Structure

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

A typical design includes 5 main components:

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

Research Design Essential Characteristics

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

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

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

Key Concepts in Research Design

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

Primary concepts

Types of Approaches to Research Design

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

Quantitative Research  

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

Qualitative Research 

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

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Types of Research Designs & Examples

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

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

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

1. Experimental

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

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

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

2. Correlational

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

Correlational studies encompass such features: 

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

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

3. Descriptive 

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

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

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

4. Diagnostic

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

Among essential hallmarks of explanatory studies are: 

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

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

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

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

1. Determine Your Aims 

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

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

2. Select a Type of Research Design

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

3. Define Your Population and Sampling Methods

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

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

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

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

4. Decide on Your Data Collection Methods

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

Data collection methods in various studies

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

5. Arrange Your Data Collection Process

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

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

Measures and scales 

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

Operationalization 

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

Operationalization in study design example

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

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

6. Choose Data Analysis Techniques

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

Qualitative data may require thematic analysis or content analysis.

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

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

Research Design Checklist

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

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

Bottom Line on Research Design & Study Types

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

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

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FAQ About Research Study Designs

1. what is a study design.

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

2. What is the purpose of a research design?

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

3. What is the importance of research designs?

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

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

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

4. What are the 4 types of study designs?

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

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

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

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• What Is Predictive Validity? | Examples & Definition
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Title: can llms generate novel research ideas a large-scale human study with 100+ nlp researchers.

Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

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Published Clinical Research Conducted at the Clinical Center

The NIH Clinical Center is the world's largest hospital entirely devoted to clinical research. It is a national resource that makes it possible to rapidly translate scientific observations and laboratory discoveries into new approaches for diagnosing, treating, and preventing disease.

Clinical research is at the heart of the Clinical Center's mission.

Over 1,600 clinical research studies are conducted at the NIH Clinical Center, including those focused on cancer, infectious diseases, blood disorders, heart disease, lung disease, alcoholism and drug abuse. Most of these studies are sponsored by the Institutes and Centers at NIH.

Here is a sample of abstracts from the clinical research conducted at the NIH Clinical Center and published in a peer-reviewed medical journal in 2018. Links to the full text and video formats are provided if available.

The National Institutes of Health Measure of Healing Experience of All Life Stressors (NIH-HEALS): Factor Analysis and Validation

Published in: PLOS ONE (December 2018)

The NIH-HEALS was developed and validated to better understand and measure psychosocial spiritual healing. Sometimes in the midst of significant life stressors including severe or life-threatening disease, psychosocial healing can be experienced.

Read the article .

MRI Robot for Prostate Focal Laser Ablation: An Ex Vito Study in Human Prostate

Published in: Journal of Imaging (November 2018)

Researchers have designed and integrated the hardware and software of a prototype robot that successfully demonstrated - in an ex vivo prostate tissue from a human cadaver - the potential to improve the clinical workflow, accuracy, and effectiveness of MRI-guided focal laser ablation of prostate cancer.

Performance of RGM Medium for the Isolation of Nontuberculous Mycobacteria from Respiratory Specimens from Non-Cystic Fibrosis Patients

Published in: Journal of Clinical Microbiology (November 2018)

RGM medium - an agar-based culture medium - provided a simple and rapid method to identify fast-growing nontuberculous mycobacteria in non-cystic fibrosis patients with chronic lung cancer.

On the Threshold of a New Analgesic: Shaping a Novel Treatment for Osteoarthritis Pain

Published in: ISAP Pain Research Forum (September 2018)

Alternatives to obtain a non-opioid analgesic for severe pain and chronic pain conditions are discussed. A promising development is resiniferatoxin, which can target peripheral nerve terminals through a local interventional approach versus systematic dosing to target the central nervous system.

Challenges in Pulmonary Hypertension: Controversies in Treating the Tip of the Iceberg. A Joint National Institutes of Health Clinical Center and Pulmonary Hypertension Association Symposium Report

Published in: American Journal of Respiratory and Critical Care Medicine (July 2018)

The unmet need for innovative therapeutic approaches to pulmonary hypertension is apparent across World Health Organization patient subtypes. Improvements in the clinical phenotyping of pulmonary hypertension are a necessary first step to better utilize existing therapies and develop new ones.

A Proposal for a Rational Transfusion Strategy in Patients of European and North African Descent with Weak D Type 4.0 and 4.1 Phenotype

Published in: Blood Transfusion (March 2018)

Patients with weak D blood types 4.0 and 4.1 are so rarely associated with alloanti-D production that treatment guidelines should be changed to recommend D positive blood transfusions and no anti-D immunoglobulin prophylaxis.

A Role for Hydrocortisone Therapy in Septic Shock?

Published in: The New England Journal of Medicine (March 2018)

Eagerly awaited to provide definitive guidelines for or against the use and effects of corticosteroids in treating septic shock were the results of two landmark studies: the Adjunctive Corticosteroid Treatment in Critical Ill Patients with Septic Shock trial and the Activated Protein C and Corticosteroid for Human Septic Shock trial.

Transcriptional Changes in Dorsal Spinal Cord Persist After Surgical Incision Despite Preemptive Analgesia with Peripheral Resiniferatoxin

Published in: Anesthesiology (March 2018)

A new approach is presented for post-operative pain control using a naturally occurring plant molecule called resiniferatoxin (RTX) to block post-operative incisional pain. RTX is not an opioid and does not act in the brain but rather on the nerve endings in the skin.

Ethics and Practice of Trials within Cohorts: An Emerging Pragmatic Trial Design

Published in: Clinical Trials (February 2018)

Trials within Cohorts is a promising new pragmatic randomized control trial design that is increasingly used in various countries. Although the asymmetric procedures for the experimental versus control arm subjects can initially raise ethical concerns, it is ethically superior to previous post-randomization consent designs.

Spironolactone-Induced Degradation of the TFIIH Core Complex XPB Subunit Suppresses NF-KB and AP-1 Signalling

Published in: Cardiovascular Research (January 2018)

Spironolactone-induced breakdown of the protein XPB reduces the expression of pro-inflammatory genes. This previously unrecognized anti-inflammatory mechanism may be beneficial in diseases with vascular inflammation, including pulmonary arterial hypertension, the focus of an ongoing clinical trial at the NIH Clinical Center.

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Read more articles about research in the NIH Clinical Center in 2018.

Deaths From Antibiotic-Resistant Infections Could Reach 39 Million by 2050, Study Suggests

A new paper analyzes three decades of fatalities around the world and predicts how “superbugs” will affect human health in the future

Daily Correspondent

More than 39 million people around the globe could die because of antibiotic-resistant infections between 2025 and 2050—a statistic that equates to about three deaths every minute, according to a new study.

The results, published Monday in the journal The Lancet , add to the growing body of evidence that drug-resistant “ superbugs ” are a major threat to public health.

“It’s a big problem, and it is here to stay,” says study co-author Christopher J. L. Murray , director of the University of Washington’s Institute for Health Metrics and Evaluation, to the Washington Post ’s Lizette Ortega.

Doctors, scientists and public health experts have long warned of the potential consequences of worsening antimicrobial resistance , or AMR. It occurs when bacteria, fungi and pathogens evolve to withstand existing medications, including antibiotics, making them harder to kill. Experts say the overuse of antibiotics—among both humans and livestock—has contributed to the problem , along with environmental factors that have allowed superbugs to thrive.

In the new study, an international team of scientists with the Global Research on Antimicrobial Resistance Project offer a detailed look at antimicrobial resistance around the world. They analyzed 520 million records from 204 countries and territories, including death certificates, hospital discharge documents and insurance claims. Then, they used statistical modeling to calculate deaths related to antimicrobial resistance from 1990 to 2021. They also made projections about how antimicrobial resistance would affect fatalities in the future.

In 1990, 1.06 million deaths were attributable to antimicrobial resistance, the team finds. That number rose to 1.27 million in 2019, then dipped to 1.14 million in 2021. (The researchers say the decrease was likely caused by health protocols put in place during the Covid-19 pandemic.)

Beyond these broad metrics, the researchers also zoomed in and looked at how antimicrobial resistance affected people of different ages. For kids ages 5 and younger, deaths attributable to antibiotic resistance declined by more than 50 percent between 1990 and 2021, “mostly due to vaccination, water and sanitation programs, some treatment programs, and the success of those,” Murray tells CNN ’s Jacqueline Howard. But for patients ages 70 and older, the number of deaths increased by more than 80 percent during the same period.

Over the last three decades, those opposite trends have largely balanced each other out. But as the world’s population ages, deaths among elderly people will likely outpace the decrease in deaths among younger people. The team estimates that deaths among children will be cut in half by 2050, but deaths among seniors will double.

Developing new antibiotics will help tackle the problem, potentially averting millions of deaths, per the paper. But improving access to those drugs is also necessary. Deaths from antimicrobial resistance will also affect regions of the world differently, with South Asia, Latin America, the Caribbean and sub-Saharan Africa likely to be hit the hardest, according to the study. Those low-resource regions also face a lack of access to quality health care, including antibiotics.

“Drug resistance is not their primary issue [in low-access regions]—their primary issue is bacterial infections itself,” says Ramanan Laxminarayan , an epidemiologist at One Health Trust who was not involved in the research, to Euronews Health ’s Gabriela Galvin.

The new paper is comprehensive and serves as another wake-up call about the need to combat superbugs. But “predicting antimicrobial resistance trends is very unreliable,” says Marlieke de Kraker , an epidemiologist at Geneva University Hospitals in Switzerland who was not involved with the research, to New Scientist ’s Michael Le Page. New superbugs can emerge or disappear at a moment’s notice, and scientists still don’t have a good understanding of what causes these unpredictable swings, she adds.

Still, the findings suggest “more must be done to protect people from this growing global health threat,” says study co-author Stein Emil Vollset , an epidemiologist at the Norwegian Institute of Public Health and the University of Washington’s Institute of Health Metrics, in a statement .

“We urgently need new strategies to decrease the risk of severe infections through vaccines, new drugs, improved health care, better access to existing antibiotics and guidance on how to use them most effectively,” he adds.

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Sarah Kuta is a writer and editor based in Longmont, Colorado. She covers history, science, travel, food and beverage, sustainability, economics and other topics.

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• v.60(9); 2016 Sep

Types of studies and research design

Mukul chandra kapoor.

Department of Anesthesiology, Max Smart Super Specialty Hospital, New Delhi, India

Medical research has evolved, from individual expert described opinions and techniques, to scientifically designed methodology-based studies. Evidence-based medicine (EBM) was established to re-evaluate medical facts and remove various myths in clinical practice. Research methodology is now protocol based with predefined steps. Studies were classified based on the method of collection and evaluation of data. Clinical study methodology now needs to comply to strict ethical, moral, truth, and transparency standards, ensuring that no conflict of interest is involved. A medical research pyramid has been designed to grade the quality of evidence and help physicians determine the value of the research. Randomised controlled trials (RCTs) have become gold standards for quality research. EBM now scales systemic reviews and meta-analyses at a level higher than RCTs to overcome deficiencies in the randomised trials due to errors in methodology and analyses.

INTRODUCTION

Expert opinion, experience, and authoritarian judgement were the norm in clinical medical practice. At scientific meetings, one often heard senior professionals emphatically expressing ‘In my experience,…… what I have said is correct!’ In 1981, articles published by Sackett et al . introduced ‘critical appraisal’ as they felt a need to teach methods of understanding scientific literature and its application at the bedside.[ 1 ] To improve clinical outcomes, clinical expertise must be complemented by the best external evidence.[ 2 ] Conversely, without clinical expertise, good external evidence may be used inappropriately [ Figure 1 ]. Practice gets outdated, if not updated with current evidence, depriving the clientele of the best available therapy.

Triad of evidence-based medicine

EVIDENCE-BASED MEDICINE

In 1971, in his book ‘Effectiveness and Efficiency’, Archibald Cochrane highlighted the lack of reliable evidence behind many accepted health-care interventions.[ 3 ] This triggered re-evaluation of many established ‘supposed’ scientific facts and awakened physicians to the need for evidence in medicine. Evidence-based medicine (EBM) thus evolved, which was defined as ‘the conscientious, explicit and judicious use of the current best evidence in making decisions about the care of individual patients.’[ 2 ]

The goal of EBM was scientific endowment to achieve consistency, efficiency, effectiveness, quality, safety, reduction in dilemma and limitation of idiosyncrasies in clinical practice.[ 4 ] EBM required the physician to diligently assess the therapy, make clinical adjustments using the best available external evidence, ensure awareness of current research and discover clinical pathways to ensure best patient outcomes.[ 5 ]

With widespread internet use, phenomenally large number of publications, training and media resources are available but determining the quality of this literature is difficult for a busy physician. Abstracts are available freely on the internet, but full-text articles require a subscription. To complicate issues, contradictory studies are published making decision-making difficult.[ 6 ] Publication bias, especially against negative studies, makes matters worse.

In 1993, the Cochrane Collaboration was founded by Ian Chalmers and others to create and disseminate up-to-date review of randomised controlled trials (RCTs) to help health-care professionals make informed decisions.[ 7 ] In 1995, the American College of Physicians and the British Medical Journal Publishing Group collaborated to publish the journal ‘Evidence-based medicine’, leading to the evolution of EBM in all spheres of medicine.

MEDICAL RESEARCH

Medical research needs to be conducted to increase knowledge about the human species, its social/natural environment and to combat disease/infirmity in humans. Research should be conducted in a manner conducive to and consistent with dignity and well-being of the participant; in a professional and transparent manner; and ensuring minimal risk.[ 8 ] Research thus must be subjected to careful evaluation at all stages, i.e., research design/experimentation; results and their implications; the objective of the research sought; anticipated benefits/dangers; potential uses/abuses of the experiment and its results; and on ensuring the safety of human life. Table 1 lists the principles any research should follow.[ 8 ]

General principles of medical research

Types of study design

Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research and epidemiological research [ Figure 2 ]. Basic research includes fundamental research in fields shown in Figure 2 . In almost all studies, at least one independent variable is varied, whereas the effects on the dependent variables are investigated. Clinical studies include observational studies and interventional studies and are subclassified as in Figure 2 .

Classification of types of medical research

Interventional clinical study is performed with the purpose of studying or demonstrating clinical or pharmacological properties of drugs/devices, their side effects and to establish their efficacy or safety. They also include studies in which surgical, physical or psychotherapeutic procedures are examined.[ 9 ] Studies on drugs/devices are subject to legal and ethical requirements including the Drug Controller General India (DCGI) directives. They require the approval of DCGI recognized Ethics Committee and must be performed in accordance with the rules of ‘Good Clinical Practice’.[ 10 ] Further details are available under ‘Methodology for research II’ section in this issue of IJA. In 2004, the World Health Organization advised registration of all clinical trials in a public registry. In India, the Clinical Trials Registry of India was launched in 2007 ( www.ctri.nic.in ). The International Committee of Medical Journal Editors (ICMJE) mandates its member journals to publish only registered trials.[ 11 ]

Observational clinical study is a study in which knowledge from treatment of persons with drugs is analysed using epidemiological methods. In these studies, the diagnosis, treatment and monitoring are performed exclusively according to medical practice and not according to a specified study protocol.[ 9 ] They are subclassified as per Figure 2 .

Epidemiological studies have two basic approaches, the interventional and observational. Clinicians are more familiar with interventional research, whereas epidemiologists usually perform observational research.

Interventional studies are experimental in character and are subdivided into field and group studies, for example, iodine supplementation of cooking salt to prevent hypothyroidism. Many interventions are unsuitable for RCTs, as the exposure may be harmful to the subjects.

Observational studies can be subdivided into cohort, case–control, cross-sectional and ecological studies.

• Cohort studies are suited to detect connections between exposure and development of disease. They are normally prospective studies of two healthy groups of subjects observed over time, in which one group is exposed to a specific substance, whereas the other is not. The occurrence of the disease can be determined in the two groups. Cohort studies can also be retrospective
• Case–control studies are retrospective analyses performed to establish the prevalence of a disease in two groups exposed to a factor or disease. The incidence rate cannot be calculated, and there is also a risk of selection bias and faulty recall.

Secondary research

Narrative review.

An expert senior author writes about a particular field, condition or treatment, including an overview, and this information is fortified by his experience. The article is in a narrative format. Its limitation is that one cannot tell whether recommendations are based on author's clinical experience, available literature and why some studies were given more emphasis. It can be biased, with selective citation of reports that reinforce the authors' views of a topic.[ 12 ]

Systematic review

Systematic reviews methodically and comprehensively identify studies focused on a specified topic, appraise their methodology, summate the results, identify key findings and reasons for differences across studies, and cite limitations of current knowledge.[ 13 ] They adhere to reproducible methods and recommended guidelines.[ 14 ] The methods used to compile data are explicit and transparent, allowing the reader to gauge the quality of the review and the potential for bias.[ 15 ]

A systematic review can be presented in text or graphic form. In graphic form, data of different trials can be plotted with the point estimate and 95% confidence interval for each study, presented on an individual line. A properly conducted systematic review presents the best available research evidence for a focused clinical question. The review team may obtain information, not available in the original reports, from the primary authors. This ensures that findings are consistent and generalisable across populations, environment, therapies and groups.[ 12 ] A systematic review attempts to reduce bias identification and studies selection for review, using a comprehensive search strategy and specifying inclusion criteria. The strength of a systematic review lies in the transparency of each phase and highlighting the merits of each decision made, while compiling information.

Meta-analysis

A review team compiles aggregate-level data in each primary study, and in some cases, data are solicited from each of the primary studies.[ 16 , 17 ] Although difficult to perform, individual patient meta-analyses offer advantages over aggregate-level analyses.[ 18 ] These mathematically pooled results are referred to as meta-analysis. Combining data from well-conducted primary studies provide a precise estimate of the “true effect.”[ 19 ] Pooling the samples of individual studies increases overall sample size, enhances statistical analysis power, reduces confidence interval and thereby improves statistical value.

The structured process of Cochrane Collaboration systematic reviews has contributed to the improvement of their quality. For the meta-analysis to be definitive, the primary RCTs should have been conducted methodically. When the existing studies have important scientific and methodological limitations, such as smaller sized samples, the systematic review may identify where gaps exist in the available literature.[ 20 ] RCTs and systematic review of several randomised trials are less likely to mislead us, and thereby help judge whether an intervention is better.[ 2 ] Practice guidelines supported by large RCTs and meta-analyses are considered as ‘gold standard’ in EBM. This issue of IJA is accompanied by an editorial on Importance of EBM on research and practice (Guyat and Sriganesh 471_16).[ 21 ] The EBM pyramid grading the value of different types of research studies is shown in Figure 3 .

The evidence-based medicine pyramid

In the last decade, a number of studies and guidelines brought about path-breaking changes in anaesthesiology and critical care. Some guidelines such as the ‘Surviving Sepsis Guidelines-2004’[ 22 ] were later found to be flawed and biased. A number of large RCTs were rejected as their findings were erroneous. Another classic example is that of ENIGMA-I (Evaluation of Nitrous oxide In the Gas Mixture for Anaesthesia)[ 23 ] which implicated nitrous oxide for poor outcomes, but ENIGMA-II[ 24 , 25 ] conducted later, by the same investigators, declared it as safe. The rise and fall of the ‘tight glucose control’ regimen was similar.[ 26 ]

Although RCTs are considered ‘gold standard’ in research, their status is at crossroads today. RCTs have conflicting interests and thus must be evaluated with careful scrutiny. EBM can promote evidence reflected in RCTs and meta-analyses. However, it cannot promulgate evidence not reflected in RCTs. Flawed RCTs and meta-analyses may bring forth erroneous recommendations. EBM thus should not be restricted to RCTs and meta-analyses but must involve tracking down the best external evidence to answer our clinical questions.

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• Volume 14, Issue 9
• Physiotherapy rehabilitation experiences of people with shoulder dislocation in ARTISAN study: a qualitative study
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• http://orcid.org/0000-0001-5504-7189 Seyran Naghdi 1 ,
• http://orcid.org/0000-0002-2992-048X David R Ellard 1 , 2 ,
• Rebecca Kearney 3
• on behalf of the ARTISAN team
• 1 Warwick Clinical Trials Unit , University of Warwick , Coventry , UK
• 2 University Hospitals Coventry and Warwickshire , Coventry , UK
• 3 Bristol Trials Centre , University of Bristol , Bristol , UK
• Correspondence to Seyran Naghdi; Seyran.Naghdi{at}warwick.ac.uk

Background Acute Rehabilitation following Traumatic anterior shoulder dISlocAtioN (ARTISAN) was a large trial comparing the clinical and cost-effectiveness of two rehabilitation interventions in adults with a first-time traumatic shoulder dislocation. Participants were allocated to receive either a single session of advice (ARTISAN) or a single session of advice and a programme of physiotherapy (ARTISAN plus). Trial results illustrated that additional physiotherapy after an initial session was not superior in improving functional outcomes for participants.

Objectives In this study, we aim to explore the experiences of a purposive sample of participants from both the ARTISAN and ARTISAN plus groups regarding their rehabilitation journey.

Design This is a semistructured interview-based study.

Setting The study was conducted in the United Kingdom.

Participants Thirty-one participants of ARTISAN trial: 16 participants from ARTISAN group and 15 from ARTISAN plus group.

Outcome measures and analysis The study follows the consolidated criteria for reporting qualitative research. The framework analysis was used to synthesise the participants’ experiences. The interviews were coded through NVivo 12.6.1.

Results Three dominant and interrelated topics emerged from the interview data: (1) feelings about their shoulder rehabilitation outcome, (2) judgement of ARTISAN rehabilitation materials, (3) assessment of shoulder rehabilitation service provision.

Conclusion Both forms of intervention have some merit for some individuals. Thus, it may be appropriate to look at the patients’ preference for offering treatment to them. Recognising and facilitating this will be of benefit to both the patients and healthcare as a whole.

• qualitative research
• physical therapy modalities

Data availability statement

Data are available upon reasonable request. All codes and quotations can be found in the supplementary file.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2024-083975

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STRENGTHS AND LIMITATIONS OF THIS STUDY

The study used semistructured interviews, allowing in-depth exploration of participants’ experiences.

Interviewers used prompts and rephrased questions to mitigate recall issues among older participants.

Due to the COVID-19 pandemic, the methodology was adapted from face-to-face interviews to telephone interviews and the planned return of transcripts to participants for checking was not done (due to remote working).

The shift to telephone interviews could have affected the depth of understanding of participants’ feelings and reactions.

The sample size and specific context may limit the transferability of the findings.

Introduction

The UK, National Institute for Health and Care Research, Health Technology Assessment-funded Acute Rehabilitation following Traumatic anterior shoulder dISlocAtioN (ARTISAN) trial took place between November 2018 and March 2022. The trial aimed to compare the clinical and cost-effectiveness of two rehabilitation interventions in adults with a first-time traumatic shoulder dislocation. 1 Participants, presenting with first-time traumatic shoulder dislocation, meeting the inclusion criteria, were randomly allocated to receive either a single session of advice or a single session of advice and a programme of physiotherapy, delivered by trained physiotherapists; 482 participants were randomised and screened from 41 NHS Trusts. 2

The trial reports that there was no evidence of a difference in the primary outcome (Oxford Shoulder Instability Score) at the primary endpoint (6 months) between the two groups. 2 Additionally, there were no statistically significant differences observed in the QuickDASH (a self-completed shortened version of the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire) scores, nor were there consistent differences in the EQ-5D-5L secondary outcomes. 2 Noting that the offer of additional physiotherapy after an initial session was not superior in improving functional outcomes for participants. 2

Embedded within the ARTISAN trial were a cost-effectiveness study and a qualitative interview study. 1 Understanding patient perspectives is essential for achieving successful treatment of anterior shoulder instability. 3 Patient adherence to rehabilitation protocols, their personal experiences and perceived barriers and facilitators play a significant role in influencing treatment outcomes. 4 Here, in this paper, we present the results of the ARTISAN interviews exploring their findings alongside the findings from the clinical effectiveness trial. 2 The results from the interview study, which were analysed before the effectiveness results were revealed, are presented here and provide insight into possible reasons for the outcome.

This is a qualitative study, exploring the experiences and reality of participants, via individual semistructured interviews. This study is in accordance with the consolidated criteria for reporting qualitative research 5 (see online supplemental appendix 1, table 1 ).

Supplemental material

Participants.

All participants, on consenting to participate in the trial, were informed about the interview substudy, and asked if they would be willing to be potentially contacted. A purposive sample was used, to ensure a diverse range of characteristics including location, treatment allocation, gender, age, of those who expressed an interest in participating. 6 7 Up to 50 interviews were planned to capture a comprehensive range of experiences related to shoulder dislocation rehabilitation. The decision to set the sample at around 50 was based on researchers’ experience and represented over 10% of the randomised population. Potential participants were contacted by the researcher (ZL), who confirmed their interest, provided information and arranged interviews at the 12-month postrandomisation timepoint. This was for two reasons, first it allowed participants sufficient time to undergo their rehabilitation journey and reflect on their experiences comprehensively, but second, and more importantly for the trial, we set it at this point in time, so that interviews were carried out outside of trial data collection and thus did not bias the main results. The interviewer was not known to the participants prior to the study and was centrally based.

Data collection

Semistructured interviews of participants from both arms were undertaken by a researcher (ZL) experienced in qualitative research methods. The interview schedule is presented in online supplemental appendix 2 . The interview topics were generated through a combination of literature review, expert input and patient feedback with pilot testing conducted to ensure reliability and validity. 8 Interviews were planned to be face-to-face, but the COVID-19 pandemic required the team to conduct most of these interviews by telephone. This shift to remote working away from the office meant that we had to adapt and while our protocol stated we would return transcripts to participants for checking this was not done. Three interviews were conducted in the participants’ homes, one at the participant’s workplace and the rest through telephone. All interviews were done between 11 February 2020 and 1 February 2021. The approximate time for each interview was 45 min. Field notes were written up as soon as possible after the interviews to record the interviewers’ immediate impressions. Interviews were digitally recorded, subjected to permission of each participant, and they transcribed verbatim by an independent university-approved transcription company and anonymised. All transcription interviews from audios were checked by another researcher (SN).

Data analysis

Data were analysed thematically using the Framework method, as follows: data familiarisation, identifying a thematic framework, indexing, charting, mapping and interpretation. 6 7 The analysis was conducted by researchers with extensive expertise in qualitative research methods. SN, who joined the trial team after the interviews were recorded, listened to the audio recordings to check the accuracy. She became familiar with the data. The transcripts were imported to the NVivo release V.12.6.1 9 to facilitate coding data and mapping them. SN generated initial relevant codes/topic; these were generally based around the specific questions asked of participants. The next analysis stage involved DRE collating the codes to note meaningful patterns in the data that were relevant to the research question. DRE and SN created themes and subthemes that captured the essence of the participants’ voices. Themes and subthemes are presented using quotes to illustrate the participant view.

Rigour was enhanced during the process by coding the first five transcriptions by two independent, experienced researchers (ZHL and SN). Discrepancies were addressed by DRE, who has extensive experience in process evaluation and played a key role as a coapplicant for the ARTISAN trial, contributing to its design. SN independently coded all transcripts and discussed them in detail with DRE. The results were presented before releasing a statistical and health economics result. Reflexivity was maintained throughout the analysis process, with researchers reflecting on their own biases and preconceptions, thereby enhancing the credibility, transferability and dependability of the findings. 10

The qualitative analyses and results were prepared and presented to the Chief Investigator, RK, who is a leading expert in the physiotherapy with extensive experience in randomised controlled trials, before the main trial results were known.

Patient and public involvement

Patient representatives provided input on study design, intervention development and dissemination plans, ensuring a comprehensive approach. Detailed patient and public involvement activities are outlined in the ARTISAN trial report. 11

A total of 102 participants, who had expressed an interest in involvement in the qualitative study at the time of consenting to be in the ARTISAN trial, were contacted. Seventeen declined to participate, and 54 did not respond. A total of 31 participants consented and were interviewed from both arms of the trial, ARTISAN and ARTISAN plus (see table 1 ). The participants were from 17 different trial sites in the UK. Of them, 16 participants were allocated to the ARTISAN’s arm, and 15 participants were allocated to the ARTISAN plus arm.

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Demographic characteristics of ARTISAN interview study participants

Baseline characteristics

The majority of the participants in the ARTISAN plus arm were men (11 out of 15), while there was a balance between participants (eight men and eight women) in the ARTISAN’s arm. The participants’ means (SD) of age were 49 (21) and 59 (17) for the ARTISAN and ARTISAN plus arms, respectively. Five participants in the ARTISAN’s arm and four in the ARTISAN plus arm stated they were involved in sporting activities that needed high levels of physical activity. Most of them described no difficulties with their shoulder before the injury. Only two and three participants in the ARTISAN and ARTISAN plus had a previous problem that goes back many years ago. There was a wide variety of the injury caused; however, seven participants (four in the ARTISAN plus arm and three in the ARTISAN’s arm) got an injury while doing sports activities such as football, rugby, weightlifting and riding a bike. The injury in the three participants in the ARTISAN group resulted in a fractured shoulder in addition to dislocation. The practitioner team decided to perform shoulder surgery for one participant in the ARTISAN plus arm due to pre-existing arthritis. Most participants in both groups believed they were provided the Ambulance and Emergency (A&E) services straightforwardly and quickly, while one participant in the ARTISAN’s arm faced a delay because he needed to be transferred from a small unit to another hospital with an A&E department. A shoulder dislocation for all participants was diagnosed by X-rays. After putting the shoulder back, they received painkillers to relieve the pain and were recommended to wear a sling for at least a week.

Theme identification and common themes

Our findings (detailed below) illustrate participants’ experiences of receiving and interacting with rehabilitation services.

Three dominant and interrelated topics emerged from the interview data questions: (1) feelings about their shoulder rehabilitation outcome, (2) judgement of ARTISAN rehabilitation materials, (3) assessment of shoulder rehabilitation services provision

Themes and their subthemes, within each of these topics, are reported below comparing and contrasting responses from both arms of the trial ((ARTISAN) advise only and those from (ARTISAN plus) advise plus a programme of physiotherapy). Quotations are used as exemplars of themes with each quote linked to a particular participant denoted by the arm of the trial, they were involved in AP=ARTISAN plus arm or A=ARTISAN followed by their id number (see table 1 ), their gender (m or f) and age (eg, A11, m, 31). See Appendix 3 for additional quotations.

Feelings about their shoulder rehabilitation outcome

This topic breaks down into several themes/subthemes, providing an insight into the assessment of their feelings about their recovery including movement and use of their shoulder and being able to get back to their previous activities or not. Noting that within the ARTISAN arm, fewer participants expressed satisfaction with their shoulder movement levels.

Shoulder status (How they feel their shoulder is now postrehabilitation)

Participants who received ARTISAN plus report improvement in returning to their normal life and working activities. Almost all of them expressed the status of their shoulder as ‘better’.

I feel good now, and then I can…I’m back to doing all the jobs I could do before (AP13, m 59), I think I'm back at 100 now (AP25, m,72), Probably getting up to 100% I guess. (AP27, m,72).

A few did still feel that they still a little way to go.

I would say yeah, probably 70% now. Yeah, around about that. Yes, yeah (AP3, m, 65), Like maybe more 60 than 50 in terms of movements (AP28, m, 36).

Compared with the ARTISAN arm where there were fewer participants who believed their shoulder was at a good level of movement.

…had almost complete range of movement (A21, f, 65). It’s absolutely fine… (A26, f, 68).

Satisfaction

Interestingly, most participants who had received ARTISAN plus apparently liked to recommend it to the others with the same problem because they were very satisfied with the result from having multiphysiotherapy sessions.

Yeah, I would recommend, I would definitely recommend anybody to go do that what I did, yeah, definitely. Definitely, yeah (AP12, f, 62).

The other source of satisfaction from the result for most of them was receiving positive reinforcement from the physiotherapist about outcomes and being told that they were doing the exercise correctly.

Only the reassurance that everything was going fine (AP22, m, 67), So, I suppose I needed those sorts of reassurances before I got into heavier physiotherapy (AP12, f, 62).

On the other hand, most people in the ARTISAN arm have found the exercises as the most helpful part of rehabilitation to get back their abilities.

Yeah. I would…I would tell them, you need that physio exercise, yeah (A19, m, 48).

Setting short-term goals and identifying milestones were noted as helpful by some participants in both groups. Helping them have a realistic view of their shoulder progression.

the goals were…because I think it kind of helps you set a realistic view of you know, going on to next session that if you could do this” (A19, m, 48).“Yeah, the goals were…because I think it kind of helps you set a realistic view of, you know, going on to the next session that if you could do this, it was literally things like being able to reach something from one of the cupboards in the kitchen, kind of thing. So, I think it’s really good to just help set umm and be able to achieve, also to help set expectations (A19, m, 48). The main point was the face-to-face setting of goals between sessions (AP27, m, 72). I do find useful writing down, there was a sheet in one of the books where you wrote down what you achieve every day. And that really worked for me as well because you weren’t in this wilderness you were actually working step by step towards something… you know you can get a lot of resources but actually the biggest resource is your own determination (AP5, f, 71)

Participants’ judgement about ARTISAN rehabilitation materials

All of the trial participants had the opportunity to use the provided rehabilitation training materials in addition to the support given by the physiotherapist in the sessions they all had with them. These materials seem to be more important for the ARTISAN participants as they generally only had the one in-person session with the physiotherapist. However, it is also important to know to what extent the participants have used the materials and what their experience is in using them.

Material usefulness

Almost all participants in the ARTISAN arm report using the booklets while only half of the ARTISAN plus arm reported used them. In addition, none of the ARTISAN plus participants report having used the videos and website compared with around half of those in the ARTISAN arm. The ARTISAN plus participants who have not used the materials believed that they had gained enough from their in-person visits with the physiotherapist; therefore, they did not feel the need to refer to the training materials.

just after the first (physio) session, I had the chance to have a very quick look at the booklets, so, it was enough exercise and guidance from physiotherapist over the sessions, …, didn’t feel need them! (AP7, m, 37). I had time, was off from work, almost at home, and was keen on going through the booklets, but only booklets, no website, and videos, so I think it was quite helpful (AP15, m, 33). I got those stuff (training materials), …, but yea! I don’t remember using them! yea, didn’t look at them at all (A18, f, 72) (I) got the booklet and everything, I went on the website, saw the videos and, yeah (A32, F, 59).

Problems with study materials provided

The main reported source of difficulty in using the materials provided was in the ARTISAN arm where there were problems reported accessing the video and internet-based materials. A few of the participants from ARTISAN have reported some problems with the booklets provided. These problems are very low in the ARTISAN plus arm.

There was but I don't have the internet at my house… (A21, f, 65), I couldn't quite find…I couldn't follow the instructions on those. And I couldn't log into the website. So, I couldn't get any additional information”. I had a booklet. I didn't find them all easy to follow. There was a couple…I mean they did run through it with me, the fracture clinic, but they more demonstrated it I would say. But I didn't find everything in the booklet that easy to follow. I did with the hospital physiotherapist, I actually had to do it because my arm was in better shape (A32, f, 59). I think there was one [picture] that wasn't quite clear, but I checked it out with her the next time I went (AP22, m, 67). I suppose the booklet umm is in some ways misinterpreted because it is not in 3D you know so surely for a picture… a picture does not always give you the right angle or the right umm motion to use (AP5, f, 71).

Material comprehensiveness and consistency

The content of the materials was consistent with what had been provided in face-to-face rehabilitation sessions in view of almost participants. The materials were also reported to be comprehensive and to meet the needs of the participants in both arms.

From the ARTISAN participants:

It was very good because you get…further on, you’ve sort of got more exercises to do. So, you really need that leaflet to show you the different exercises you need to do (A26, f, 68). Yeah well, the exercises we did were the exercises in the pamphlet that I was given which I said to you, and hmmm the physio she did them all with me… 2 or three times (A2, m, 56).

From the ARTISAN plus participants

It was just giving me alternatives to do. It’s the same kind of stress on the shoulder, but it was a different exercise. But more or less, there were those like, you know (AP28, m, 36). She (the physiotherapist) did. She did go through the booklet with it and marked certain things, you know like she wrote in the book the few exercises that I should be doing (AP1, m, 27).

Participant’s assessment of rehabilitation service provision

This theme reflects participants feelings about the provision of physiotherapy/rehabilitation in terms of the accessibility to the providing rehabilitation centres, physiotherapist performance and then the form of sessions provided. This part of the participants’ experiences reflects insights about the barriers that can influence the participants getting to the centres, and to what extent participant believe the physiotherapists have been engaged in their rehabilitation/treatment process.

Accessibility to the rehabilitation services

Several of the participants in ARTISAN plus have experienced some physical difficulties in attending the rehabilitation sessions, such as problems with driving, finding a car park and a considerable distance to the physiotherapy clinic or hospital from their home.

Taking into account quite a long distance to travel and the roads aren’t that great, I have to be taken in by my wife obviously I was not driving (AP3, m, 62). Sort of 2 hours right around the hospital so if I wasn’t close it would be more difficult (AP1, m, 27).

Most of the people in this group stated that there are no difficulties to attend the physiotherapy sessions. They also mentioned that the health centres were flexible in providing suitable slots for appointments. In addition, there were positive comments about public transport and employers who were supportive.

Nothing whatsoever because I was able to get a time slot that suited me, which was earlyish morning. And there was no delay, you know. There was…it was all perfectly right for me (AP27, m, 72). No, no. Well, they were quite supportive at work (AP28, m, 36), No, you can get a bus to the hospital (AP15, m, 33).

Most of the participants in the ARTISAN group report not having experienced any obstacles or barriers to accessing rehabilitation services.

I didn't (have trouble to attend), but I walked attending…walked attending the physio (A23, f, 53), that’s fine (A26, f, 68).

However, some barriers remained for a small number of ARTISAN participants.

I couldn't drive. I got some free transport from the transport people. And then…and that stopped when I was told that…they had to tell me I must get on a bus because they have to keep it for people that are more seriously ill than me. And I was, therefore, trying to sort that out, and eventually, I got a friend to take me, across the road…. (After requiring additional physiotherapy sessions for frozen shoulder). (A21, f, 65) Parking. It’s terrible up there (A29, m, 26).

The physiotherapist’s performance

The physiotherapist was considered a valuable source of motivation in improving the injured shoulder by participants in both arms of the trial. All participants expressed that their physiotherapists went through the exercises and correct movement in detail as much as they could. Giving feedback and setting short and long-time goals by physiotherapists had a positive impact on the participants’ feelings, especially for ARTISAN plus.

It gave us confidence (AP10, f, 25). I don’t know who he (the physiotherapist) was, but it was a good job (AP13, m, 59). She (the physiotherapist) pretty much answered the questions anyway as we went along you know (AP20, m, 81).

The participants in the ARTISAN’s arm also found an excellent experience with their physiotherapists.

he (the physiotherapist) was a really, really, really good physio. Very straightforward, practical, on to work which I really like (A19, m, 48). He’s…he (the physiotherapist) was very, very positive attitude so that’s about all really. No, they're all very positive about what to do next (A21, f, 65). (Participant who was given additional physiotherapy session by the clinician).

Physiotherapy session formats

Almost all participants in the ARTISAN group found the physiotherapy session helpful and informative; a few expressed they may have preferred having multiple physiotherapy sessions. A number noted that they felt that they benefited from a single session.

Yes, was helpful. He (the physiotherapist) gave me a booklet, he gave me lots of things and did say that if I had problems, then I was to go back (A14, f, 74). It was informative. They gave me a few exercises to do, and they scanned through all different movements to see where I was sort of suffering with it (A29, m, 26).

One ARTISAN participant noted that she experienced group-based physiotherapy sessions (outside of that provided by the trial). She was keen on attending these sessions because she thought it was an excellent opportunity to get peer support, to understand the limitations and develop coping strategies for dealing with issues that may arise.

Although everybody is different it was nice to talk to other people and other people have different things, but it was still nice to speak to someone in the same situation as you (A2, f, 56).

Two participants in the ARTISAN arm have sought out additional treatments or programmes apart from the training materials to increase their chance of returning to their previous sports activities.

I’m lucky enough that the club that I play rugby for has a physio that they employ as well, he recommended that the Derby shoulder stability programme, so I went along, did some of those exercises on that, and that was good, got to all progressions that I was trying to get back to, and the exercise that was provided from like the ARTISAN from the hospital was kind of due to get me back to general life, it probably would’ve been perfectly fine, but yeah, I want to go back to rugby so I know I needed to get my shoulder back to like full strength (A9, m, 24). Almost to the year, it was almost to the year and my shoulder was absolutely killing. I couldn't drive. It almost felt like well, I didn't know what was wrong with it. It felt like a frozen shoulder, and I ended up in the…I ended up going to a chiropractor (A23, f, 53).

Most participants with ARTISAN plus multiphysiotherapy sessions pointed out that they had enough chances to visit their physiotherapist to assess their shoulder movement, achieve their goals and receive instructions on doing the exercises correctly.

If I was doing something wrong, they could correct me (AP15, m, 33).

The most emphasised point in this group was getting reassurance and feedback from the physiotherapist.

Well, I suppose it’s…the fact that you can do a certain exercise over a period of time, then you get some feedback with a consultant to tell you how you’re doing (AP13, m, 59).

Several of them expressed that communication with a professional member such as a physiotherapist psychologically impacts the rehabilitation journey.

It certainly helped mentally. It gave you support. You felt that somebody was interested in trying to help, which was as much benefit as the physical side of it (AP20, m, 81).

A participant from the ARTISAN arm who received additional physiotherapy sessions highlights again the reassurance of having the contact with a therapist.

The other benefit really was the reassurance because I think one of the things that you did…that you worry about is you know it’s going to dislocate again (A19, m, 48).

The interview substudy, seamlessly integrated within the broader framework of the ARTISAN trial, served as a crucial avenue to explore the nuanced experiences of participants undergoing the trial treatments. By adopting a qualitative approach, we aimed to unravel the intricacies of their rehabilitation journeys and illuminate the factors influencing their adherence to the prescribed interventions. Our endeavour was not merely to supplement the quantitative findings of the main trial but to enrich the understanding of the outcomes by capturing the subjective narratives of the participants. Through the lens of 31 in-depth interviews, we explored the multifaceted dimensions of their experiences, ranging from their feelings about shoulder rehabilitation outcomes to their assessments of the provided rehabilitation services.

While the outcome of the trial found no difference between the two groups, the participant experiences give an insight that is a little different. 2 Similar findings were observed in the REPOSE trial, where qualitative data provided valuable context and a deeper understanding of patient experiences that were not captured by the quantitative results. 12 More ARTISAN plus participants reported that their expectations about their rehabilitation were met than those in the ARTISAN group. There were those, however, in both groups, who felt that they did not return to the level of physical activities that they would have liked (eg, Sports). Indeed, even participants in the ARTISAN plus group who had a programme of physiotherapy felt they were not reaching their preinjury levels of activity. A recent paper highlights that fear of recurrent dislocation may be a contributing factor to participants feeling they are not returning to their normal levels of physical activity. 3

Participants who only received the advice session (the ARTISAN group) do seem to want more engagement and more actual physiotherapy sessions. Several of the younger participants in the ARTISAN group have sought out additional treatment options (eg, exercise programmes, chiropractic sessions) in addition to what was provided to increase their chance of returning preinjury levels of activities. While ARTISAN plus participants, it seems were less likely to go for additional treatments outside of that provided.

The interviews give us an insight into the participants’ experiences in their rehabilitation journey. Involving them in the improvement journey seems to have a positive influence on their sense of improvement. Regardless of the participant’s allocated group, sex, age and history of doing exercise, the physiotherapist’s advice and efforts to give reassurance appear to play a crucial role in the participants’ mindset and confidence. Following operative and non-operative treatment of shoulder instability, physiotherapists plays a key role in the recovery process, supporting the patient and building strength and trust. 3 This highlights the importance of clearly communicating with participants, providing them with clear information and reassurance about their rehabilitation journey. This approach ensures effective treatment and contributes to a better overall experience and outcome for participants. Cridland et al , in a recent article, looking at patient experiences and perceptions of rotator cuff related shoulder pain rehabilitation, highlight the importance of participants’ trust in the health professional providing the rehabilitation. They note that this facilitates adherence and increases the belief that the condition is being effectively treated. 13 Main et al also emphasise the critical role of the therapeutic relationship in rehabilitation success. 14

Those participants who have engaged with the materials provided have found them helpful regardless of their background factors (sex, age and history of doing sport) and allocated arms. Both groups of participants have used the booklets and expressed good experiences with it.

The participants have raised some minor points regarding the difficulty with the booklets and the online materials were sometimes difficult to access. However, the participants in the ARTISAN plus group seem to have had a better chance of resolving this kind of problem because of having more contact with their therapist. Participants in both groups report using the videos and website less than the booklets. This is more obvious among the senior groups and women. Lack of internet access was an issue for several older participants, who struggled with digital materials. This underscores the need for alternative formats and enhanced support for digital resources to ensure all participants, regardless of their digital literacy or access to technology, can effectively engage with the provided materials. Addressing these aspects is crucial for improving the inclusivity and effectiveness of the future interventions. However, for those who viewed the video, it has given them a better idea of how to do the exercises correctly. The latter is more apparent among those participants at younger ages and with less severe injuries.

Strengths and limitations

It was found that some participants, especially elderly participants, faced challenges in recalling their rehabilitation journey experiences, potentially affecting the accuracy of their recall. To mitigate this issue, the interviewer used prompts as necessary and adjusted questions to improve understanding. The study also observed variations in recall accuracy among different demographic groups, indicating that the passage of time since their rehabilitation may impact memory differently. These findings underscore the importance of demographic considerations in qualitative research, ensuring a comprehensive understanding of participants’ experiences. In addition, because of the COVID-19 pandemic and lockdown, only the first four interviews were held face-to-face, and the rest were by telephone. This potentially could have some effects on understanding the participants’ feelings and reactions, as non-verbal cues and personal rapport are more challenging to capture over the phone. Despite these limitations, efforts were made to ensure comprehensive data collection by employing consistent interview techniques and maintaining a flexible approach to question phrasing.

While our study comprehensively explored the patient experience in the ARTISAN trial, we did not evaluate the perspectives of the healthcare practitioners delivering the intervention. Understanding the experiences and insights of physiotherapists could provide valuable context to our findings. Future research should consider their views on treatment implementation. This approach aligns with the current emphasis on shared decision-making models in healthcare, where the collaboration between patients and practitioners is crucial for optimal treatment outcomes. 15

While previous qualitative studies have explored the experiences of people with shoulder instability, this study provides an in-depth reflection on participants’ voices specifically with shoulder dislocation during their physiotherapy journey within the UK-based randomised trial. However, the transferability of findings may be limited.

The overall clinical trial outcome was very conclusive, in which extraphysiotherapy was not superior and thus may not be the best form of rehabilitation for those who experience a first-time traumatic shoulder dislocation. This interview study supports this finding in that it shows us that both forms of intervention have merit for some individuals. Thus, it may be that tailoring the treatment offered to the needs of the patient is appropriate here. Not all patients want regular clinic visits or indeed support from a health professional. Recognising and facilitating this will be of benefit to both the patients and healthcare as a whole.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Ethical approval for the interview study was included in the ethical approval of the main trial (Wales REC 3, REC reference: 18/WA/0236, 2018). All participants gave written informed consent prior to taking part. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We would like to thank Ziheng Liew for conducting the interviews. We also would like to thank all the participants who attended the interview sessions. In addition, we acknowledge this paper is written on behalf of the ARTISAN Team, and we would like to acknowledge the expert support of the ARTISAN Team which comprised: Helen Parsons, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0000-0002-2765-3728 ); Aminul Haque, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0000-0003-3589-6751 ); James Mason, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0000-0001-9210-4082 ); Henry Nwankwo, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0000-0001-7401-1923 ), Helen Bradley, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0009-0003-1663-4462 ); Stephen Drew, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK, ( https://orcid.org/0000-0002-9523-682X ); Chetan Modi, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK, ( https://orcid.org/0009-0008-3337-4419 ); Howard Bush, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK, ( https://orcid.org/0000-0001-9360-0504 ); David Torgerson, York Clinical Trials Unit, University of York, York, UK, ( https://orcid.org/0000-0002-1667-4275 ); Martin Underwood, Warwick Clinical Trials Unit, University of Warwick, Warwick, UK, ( https://orcid.org/0000-0002-0309-1708 ).

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Contributors DRE and RK have designed the study. DRE and SN have analysed the data. All authors have contribution to drafting the manuscript and reviewed. SN is the corresponding author, and all authors are guarantors for the overall content of the manuscript.

Funding ARTISAN trial founded by NIHR, Health Technology Assessment (HTA) Programme, (16/167/56) 01/06/18. The University of Warwick and University Hospitals Coventry and Warwickshire NHS Trust are co-sponsorship for this trial.

Competing interests RK is co-chair of the NIHR Programme Grants for Applied Research (PGfAR) committee, a paid position in NIHR but unrelated to the trial. She is also a previous chair of the NIHR West Midlands Research for Patient Benefit (RfPB) committee and member of the NIHR Health Technology Assessment (HTA) Clinical Evaluation and Trials Committee and NIHR Integrated Clinical Academic (ICA) doctoral committee. RK, DRE, HP, AH, JM, HN, SD, CM, HB, DT, MU have all been awarded current and previous NIHR research grants. HP, MU and RK are co-investigators on grants funded by the Australian NHMRC and NIHR funded studies receiving additional support from Stryker Ltd.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer-reviewed.

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