research designs and types

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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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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Types of Research Designs Compared | Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

Read more about creating a research design

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The Four Types of Research Design — Everything You Need to Know

Updated: December 11, 2023

Published: January 18, 2023

When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.

In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.

Table of Contents

What is research design?

The four types of research design, research design examples.

Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.

Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.

Marketers use different types of research design when conducting research .

There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.

Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.

Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.

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Experimental

An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).

In other words, the researcher wants to test a causal relationship between two or more variables.

In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.

Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.

The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.

One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.

Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).

Correlational

A correlational design examines the relationship between two or more variables without intervening in the process.

Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.

For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.

Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.

Best for: Understanding the extent to which two or more variables are associated with each other in the real world.

Descriptive

Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.

Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.

SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.

For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

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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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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

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5 Research design

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

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

Key attributes of a research design

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

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

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

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

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

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

Improving internal and external validity

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

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

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

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

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

Popular research designs

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

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

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

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

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

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

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

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

Selecting research designs

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

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

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

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• Indian J Anaesth
• 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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Quasi-Experimental Research Design – Types, Methods

Table of Contents

Quasi-Experimental Design

Quasi-experimental design is a research method that seeks to evaluate the causal relationships between variables, but without the full control over the independent variable(s) that is available in a true experimental design.

In a quasi-experimental design, the researcher uses an existing group of participants that is not randomly assigned to the experimental and control groups. Instead, the groups are selected based on pre-existing characteristics or conditions, such as age, gender, or the presence of a certain medical condition.

Types of Quasi-Experimental Design

There are several types of quasi-experimental designs that researchers use to study causal relationships between variables. Here are some of the most common types:

Non-Equivalent Control Group Design

This design involves selecting two groups of participants that are similar in every way except for the independent variable(s) that the researcher is testing. One group receives the treatment or intervention being studied, while the other group does not. The two groups are then compared to see if there are any significant differences in the outcomes.

Interrupted Time-Series Design

This design involves collecting data on the dependent variable(s) over a period of time, both before and after an intervention or event. The researcher can then determine whether there was a significant change in the dependent variable(s) following the intervention or event.

Pretest-Posttest Design

This design involves measuring the dependent variable(s) before and after an intervention or event, but without a control group. This design can be useful for determining whether the intervention or event had an effect, but it does not allow for control over other factors that may have influenced the outcomes.

Regression Discontinuity Design

This design involves selecting participants based on a specific cutoff point on a continuous variable, such as a test score. Participants on either side of the cutoff point are then compared to determine whether the intervention or event had an effect.

Natural Experiments

This design involves studying the effects of an intervention or event that occurs naturally, without the researcher’s intervention. For example, a researcher might study the effects of a new law or policy that affects certain groups of people. This design is useful when true experiments are not feasible or ethical.

Data Analysis Methods

Here are some data analysis methods that are commonly used in quasi-experimental designs:

Descriptive Statistics

This method involves summarizing the data collected during a study using measures such as mean, median, mode, range, and standard deviation. Descriptive statistics can help researchers identify trends or patterns in the data, and can also be useful for identifying outliers or anomalies.

Inferential Statistics

This method involves using statistical tests to determine whether the results of a study are statistically significant. Inferential statistics can help researchers make generalizations about a population based on the sample data collected during the study. Common statistical tests used in quasi-experimental designs include t-tests, ANOVA, and regression analysis.

Propensity Score Matching

This method is used to reduce bias in quasi-experimental designs by matching participants in the intervention group with participants in the control group who have similar characteristics. This can help to reduce the impact of confounding variables that may affect the study’s results.

Difference-in-differences Analysis

This method is used to compare the difference in outcomes between two groups over time. Researchers can use this method to determine whether a particular intervention has had an impact on the target population over time.

Interrupted Time Series Analysis

This method is used to examine the impact of an intervention or treatment over time by comparing data collected before and after the intervention or treatment. This method can help researchers determine whether an intervention had a significant impact on the target population.

Regression Discontinuity Analysis

This method is used to compare the outcomes of participants who fall on either side of a predetermined cutoff point. This method can help researchers determine whether an intervention had a significant impact on the target population.

Steps in Quasi-Experimental Design

Here are the general steps involved in conducting a quasi-experimental design:

• Identify the research question: Determine the research question and the variables that will be investigated.
• Choose the design: Choose the appropriate quasi-experimental design to address the research question. Examples include the pretest-posttest design, non-equivalent control group design, regression discontinuity design, and interrupted time series design.
• Select the participants: Select the participants who will be included in the study. Participants should be selected based on specific criteria relevant to the research question.
• Measure the variables: Measure the variables that are relevant to the research question. This may involve using surveys, questionnaires, tests, or other measures.
• Implement the intervention or treatment: Implement the intervention or treatment to the participants in the intervention group. This may involve training, education, counseling, or other interventions.
• Collect data: Collect data on the dependent variable(s) before and after the intervention. Data collection may also include collecting data on other variables that may impact the dependent variable(s).
• Analyze the data: Analyze the data collected to determine whether the intervention had a significant impact on the dependent variable(s).
• Draw conclusions: Draw conclusions about the relationship between the independent and dependent variables. If the results suggest a causal relationship, then appropriate recommendations may be made based on the findings.

Quasi-Experimental Design Examples

Here are some examples of real-time quasi-experimental designs:

• Evaluating the impact of a new teaching method: In this study, a group of students are taught using a new teaching method, while another group is taught using the traditional method. The test scores of both groups are compared before and after the intervention to determine whether the new teaching method had a significant impact on student performance.
• Assessing the effectiveness of a public health campaign: In this study, a public health campaign is launched to promote healthy eating habits among a targeted population. The behavior of the population is compared before and after the campaign to determine whether the intervention had a significant impact on the target behavior.
• Examining the impact of a new medication: In this study, a group of patients is given a new medication, while another group is given a placebo. The outcomes of both groups are compared to determine whether the new medication had a significant impact on the targeted health condition.
• Evaluating the effectiveness of a job training program : In this study, a group of unemployed individuals is enrolled in a job training program, while another group is not enrolled in any program. The employment rates of both groups are compared before and after the intervention to determine whether the training program had a significant impact on the employment rates of the participants.
• Assessing the impact of a new policy : In this study, a new policy is implemented in a particular area, while another area does not have the new policy. The outcomes of both areas are compared before and after the intervention to determine whether the new policy had a significant impact on the targeted behavior or outcome.

Applications of Quasi-Experimental Design

Here are some applications of quasi-experimental design:

• Educational research: Quasi-experimental designs are used to evaluate the effectiveness of educational interventions, such as new teaching methods, technology-based learning, or educational policies.
• Health research: Quasi-experimental designs are used to evaluate the effectiveness of health interventions, such as new medications, public health campaigns, or health policies.
• Social science research: Quasi-experimental designs are used to investigate the impact of social interventions, such as job training programs, welfare policies, or criminal justice programs.
• Business research: Quasi-experimental designs are used to evaluate the impact of business interventions, such as marketing campaigns, new products, or pricing strategies.
• Environmental research: Quasi-experimental designs are used to evaluate the impact of environmental interventions, such as conservation programs, pollution control policies, or renewable energy initiatives.

When to use Quasi-Experimental Design

Here are some situations where quasi-experimental designs may be appropriate:

• When the research question involves investigating the effectiveness of an intervention, policy, or program : In situations where it is not feasible or ethical to randomly assign participants to intervention and control groups, quasi-experimental designs can be used to evaluate the impact of the intervention on the targeted outcome.
• When the sample size is small: In situations where the sample size is small, it may be difficult to randomly assign participants to intervention and control groups. Quasi-experimental designs can be used to investigate the impact of an intervention without requiring a large sample size.
• When the research question involves investigating a naturally occurring event : In some situations, researchers may be interested in investigating the impact of a naturally occurring event, such as a natural disaster or a major policy change. Quasi-experimental designs can be used to evaluate the impact of the event on the targeted outcome.
• When the research question involves investigating a long-term intervention: In situations where the intervention or program is long-term, it may be difficult to randomly assign participants to intervention and control groups for the entire duration of the intervention. Quasi-experimental designs can be used to evaluate the impact of the intervention over time.
• When the research question involves investigating the impact of a variable that cannot be manipulated : In some situations, it may not be possible or ethical to manipulate a variable of interest. Quasi-experimental designs can be used to investigate the relationship between the variable and the targeted outcome.

Purpose of Quasi-Experimental Design

The purpose of quasi-experimental design is to investigate the causal relationship between two or more variables when it is not feasible or ethical to conduct a randomized controlled trial (RCT). Quasi-experimental designs attempt to emulate the randomized control trial by mimicking the control group and the intervention group as much as possible.

The key purpose of quasi-experimental design is to evaluate the impact of an intervention, policy, or program on a targeted outcome while controlling for potential confounding factors that may affect the outcome. Quasi-experimental designs aim to answer questions such as: Did the intervention cause the change in the outcome? Would the outcome have changed without the intervention? And was the intervention effective in achieving its intended goals?

Quasi-experimental designs are useful in situations where randomized controlled trials are not feasible or ethical. They provide researchers with an alternative method to evaluate the effectiveness of interventions, policies, and programs in real-life settings. Quasi-experimental designs can also help inform policy and practice by providing valuable insights into the causal relationships between variables.

Overall, the purpose of quasi-experimental design is to provide a rigorous method for evaluating the impact of interventions, policies, and programs while controlling for potential confounding factors that may affect the outcome.

Advantages of Quasi-Experimental Design

Quasi-experimental designs have several advantages over other research designs, such as:

• Greater external validity : Quasi-experimental designs are more likely to have greater external validity than laboratory experiments because they are conducted in naturalistic settings. This means that the results are more likely to generalize to real-world situations.
• Ethical considerations: Quasi-experimental designs often involve naturally occurring events, such as natural disasters or policy changes. This means that researchers do not need to manipulate variables, which can raise ethical concerns.
• More practical: Quasi-experimental designs are often more practical than experimental designs because they are less expensive and easier to conduct. They can also be used to evaluate programs or policies that have already been implemented, which can save time and resources.
• No random assignment: Quasi-experimental designs do not require random assignment, which can be difficult or impossible in some cases, such as when studying the effects of a natural disaster. This means that researchers can still make causal inferences, although they must use statistical techniques to control for potential confounding variables.
• Greater generalizability : Quasi-experimental designs are often more generalizable than experimental designs because they include a wider range of participants and conditions. This can make the results more applicable to different populations and settings.

Limitations of Quasi-Experimental Design

There are several limitations associated with quasi-experimental designs, which include:

• Lack of Randomization: Quasi-experimental designs do not involve randomization of participants into groups, which means that the groups being studied may differ in important ways that could affect the outcome of the study. This can lead to problems with internal validity and limit the ability to make causal inferences.
• Selection Bias: Quasi-experimental designs may suffer from selection bias because participants are not randomly assigned to groups. Participants may self-select into groups or be assigned based on pre-existing characteristics, which may introduce bias into the study.
• History and Maturation: Quasi-experimental designs are susceptible to history and maturation effects, where the passage of time or other events may influence the outcome of the study.
• Lack of Control: Quasi-experimental designs may lack control over extraneous variables that could influence the outcome of the study. This can limit the ability to draw causal inferences from the study.
• Limited Generalizability: Quasi-experimental designs may have limited generalizability because the results may only apply to the specific population and context being studied.

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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.

Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

• Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
• Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

• Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
• Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
• Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

• Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
• Regression Analysis : This method examines the relationship between dependent and independent variables.
• Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

• Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
• Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

• Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

• Experiments : Controlled experiments are designed to test the causality.
• Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

• Statistical Analysis : Includes various statistical tests and measures.
• Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
• Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

• Content Analysis : Systematic coding and categorizing of textual information.
• Thematic Analysis : Identifying themes and patterns within qualitative data.
• Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

• Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

• Clustering : Grouping data points with similar characteristics.
• Association Rule Learning : Identifying interesting relations between variables in large databases.
• Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

• Hadoop and Spark : Frameworks for processing and analyzing large datasets.
• NoSQL Databases : Designed to handle unstructured data.
• Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

• Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
• Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
• Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
• investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
• Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
• Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
• Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
• Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

• Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
• Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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18 Different Types of Survey Methods + Pros & Cons

There are many reasons why surveys are important. Surveys help researchers find solutions, create discussions, and make decisions. They can also get to the bottom of the really important stuff, like, coffee or tea? Dogs or cats? Elvis or The Beatles? When it comes to finding the answers to these questions, there are 18 different types of survey methods to use.

Create your first survey, form, or poll now!

18 Different Types of Survey Methods

Different surveys serve different purposes, which is why there are a number of them to choose from. “What are the types of surveys I should use,” you ask? Here’s a look at the 18 types of survey methods researchers use today.

1. Interviews

Also known as in-person surveys or household surveys, this used to be one of the most popular types of survey to conduct. Researchers like them because they involve getting face-to-face with individuals. Of course, this method of surveying may seem antiquated when today we have online surveying at our fingertips. However, interviews still serve a purpose. 

Researchers conduct interviews when they want to discuss something personal with people. For example, they may have questions that may require extensive probing to uncover the truth. Sure, some interviewees may be more comfortable answering questions confidentially behind a keyboard. However, a skilled interviewer is able to put them at ease and get genuine responses. They can often go deeper than you may be able to using other surveying methods. 

Often, in-person interviews are recorded on camera. This way, an expert can review them afterward. They do this to determine if the answers given may be false based on an interviewee’s change in tone. A change in facial expressions and body movements may also be a signal they pick up on. 

2. Intercept Surveys

While interviews tend to choose respondents and have controls in place, intercept surveys (or “man on the spot”) surveys are conducted at certain locations or events. This involves having an interviewer, or multiple interviewers, scoping out an area and asking people, generally at random, for their thoughts or viewpoints on a particular topic. 

3. Focus Groups

These types of surveys are conducted in person as well. However, focus groups involve a number of people rather than just one individual. The group is generally small but demographically diverse and led by a moderator. The focus group may be sampling new products, or to have a discussion around a particular topic, often a hot-button one. 

The purpose of a focus group survey is often to gauge people’s reaction to a product in a group setting or to get people talking, interacting—and yes, arguing—with the moderator taking notes on the group’s behavior and attitudes. This is often the most expensive survey method as a trained moderator must be paid. In addition, locations must be secured, often in various cities, and participants must be heavily incentivized to show up. Gift cards in the $75-100 range for each survey participant are the norm.   

4. Panel Sampling

Recruiting survey-takers from a panel maintained by a research company is a surefire way to get respondents. Why? Because people have specifically signed up to take them. The benefit of these types of surveys for research, of course, is there you can be assured responses. In addition, you can filter respondents by a variety of criteria to be sure you’re speaking with your target audience.

The downside is data quality. These individuals get survey offers frequently. So, they may rush through them to get their inventive and move on to the next one. In addition, if you’re constantly tapping into the same people from the same panel, are you truly getting a representative sample?

5. Telephone Surveys

Most telephone survey research types are conducted through random digit dialing (RDD). RDD can reach both listed  and  unlisted numbers, improving sampling accuracy. Surveys are conducted by interviewers through computer-assisted telephone interviewing (CATI) software. CATI displays the questionnaire to the interviewer with a rotation of questions.  

Telephone surveys started in the 1940s. In fact, in a  recent blog , we recount how the predictions for the 1948 presidential election were completely wrong because of sampling bias in telephone surveys. Rising in popularity in the late 50s and early 60s when the telephone became common in most American households, telephone surveys are no longer a very popular method of conducting a survey. Why? Because many people refuse to take telephone surveys or simply are not answering calls from a number they don’t recognize.

6. Post-Call Surveys

If a telephone survey is going to be conducted, today it is usually a post-call survey. This is often accomplished through IVR, or interactive voice response. IVR means there is no interviewer involved. Instead, customers record answers to pre-recorded questions using numbers on their touch-tone keypads. If a question is open-ended, the interviewee can respond by speaking and the system records the answer. IVR surveys are often deployed to measure how a customer feels about a service they just received. For example, after calling your bank, you may be asked to stay on the line to answer a series of questions about your experience.

Most post-call surveys are either  NPS surveys  or customer satisfaction (CSAT) surveys. The former asks the customer “How likely are you to recommend our organization to a f riend or family based on your most recent interaction?” while the CSAT survey asks customers “How satisfied are you with the results of your most recent interaction?”.   NPS survey results reflect how the customer feels about the brand, while CSAT surveys a re all about individual agent and contact center performance.   

7. SMS Text Surveys

Many people rarely using their phone to talk anymore, and ignore calls from unknown numbers. This has given rise to the SMS (Short Messaging Service) text survey. SMS surveys are delivered via text to people who have opted in to receive notifications from the sender. This means that there is usually some level of engagement, improving response rates. The one downside is that questions typically need to be short, and answers are generally 1-2 words or simply numbers (this is why many NPS surveys, gauging customer satisfaction, are often conducted via SMS text). Be careful not to send too many text surveys, as a person can opt-out just as easily, usually by texting STOP.

8. Mail-in Surveys / Postal Surveys

These are delivered right to respondents’ doorsteps! Mail surveys were frequently used before the advent of the internet when respondents were spread out geographically and budgets were modest. After all, mail-in surveys didn’t require much cost other than the postage. 

So are mail-in surveys going the way of the dinosaur? Not necessarily. They are still occasionally more valuable compared to different methods of surveying. Because they are going to a specific name and home address, they often feel more personalized. This personalization can prompt the recipient to complete the survey. 

They’re also good for surveys of significant length. Most people have short attention spans, and won’t spend more than a few minutes on the phone or filling out an online survey. At least, not without an incentive! However, with a mail-in survey, the person can complete it at their leisure. They can fill out some of it, set it aside, and then come back to it later. This gives mail-in surveys a relatively high response rate.

9. Kiosk Surveys

These surveys happen on a computer screen at a physical location. You’ve probably seen them popping up in stores, hotel lobbies, hospitals, and office spaces. These days, they’re just about anywhere a researcher or marketer wants to collect data from customers or passers-by.  Kiosk surveys  provide immediate feedback following a purchase or an interaction. They collect responses while the experience is still fresh in the respondent’s mind. This makes their judgment more trustworthy. Below is an example of a SurveyLegend kiosk survey at McDonald’s. The kiosk survey collects information, thanks the respondent for their feedback, and then resets for the next customer. Read how to  create your own kiosk survey here .

10. Email Surveys

Email surveys are one of the most effective surveying methods as they are delivered directly to your audience via their online account. They can be used by anyone for just about anything, and are easily customized for a particular audience. Another good thing about email surveys is you can easily see who did or did not open the survey and make improvements to it for a future send to increase response rates. You can also A/B test subject lines, imagery, and so on to see which is more effective. SurveyLegend offers dozens of different types of online survey questions, which we explore in our blog  12 Different Types of Survey Questions and When to Use Them (with Examples) .

11. Pop-up Surveys

A pop-up survey is a feedback form that pops up on a website or app. Although the main window a person is reading on their screen remains visible, it is temporarily disabled until a user interacts with the pop-up, either agreeing to leave feedback or closing out of it. The survey itself is typically about the company whose site or app the user is currently visiting (as opposed to an intercept survey, which is an invitation to take a survey hosted on a different site).

A pop-up survey attempts to grab website visitors’ attention in a variety of ways, popping up in the middle of the screen, moving in from the side, or covering the entire screen. While they can be intrusive, they also have many benefits. Read about the  benefits of pop-up surveys here .

12. Embedded Surveys

The opposite of pop-up surveys, these surveys live directly on your website or another website of your choice. Because the survey cannot be X’ed out of like a pop-up, it takes up valuable real estate on your site, or could be expensive to implement on someone else’s site. In addition, although the  embedded survey  is there at all times, it may not get the amount of attention a pop-up does since it’s not “in the respondent’s face.”

13. Social Media Surveys

There are more than  3.5 billion people  are using social media worldwide, a number projected to increase to almost 4.5 billion in 2025. This makes social media extremely important to marketers and researchers. Using platforms such as Facebook, Twitter, Instagram, and the new Threads, many companies and organizations send out social media surveys regularly. Because people check their social media accounts quite regularly, it’s a good way to collect responses and monitor changes in satisfaction levels or popular opinion. Check out our blog on  social media surveys  for more benefits and valuable tips.

14. Mobile Surveys

Mobile traffic has now overtaken desktop computers as the most used device for accessing the internet, with more than 54% of the share. But don’t fret – you don’t have to create an entirely new survey to reach people on their phones or tablets. Online poll makers like SurveyLegend are responsive, so when you create a desktop version of a survey, it automatically becomes mobile-friendly. The survey renders, or displays, on any device or screen regardless of size, with elements on the page automatically rearranging themselves, shrinking, or expanding as necessary. Learn more about our  responsive surveys .

15. Mobile App Surveys

Today, most companies have a mobile app. These can be an ideal way to conduct surveys as people have to willingly download your app; this means, they already have a level of engagement with your company or brand making them more likely to respond to your surveys.

16. QR Code Surveys

QR Code or QRC is an abbreviation of “Quick Response Code.” These two-dimensional encoded images, when scanned, deliver hidden information that’s stored on it. They’re different from barcodes because they can house a lot more information, including website URLs, phone numbers, or up to 4,000 characters of text. The recent QR code comeback provides a good opportunity for researchers to collect data. Place the QR code anywhere – on flyers, posters, billboards, commercials – and all someone had to do is scan it with the mobile device to have immediate access to a survey. Read more about the  benefits of QR code surveys .

17. Delphi Surveys

A Delphi survey is a structured research method used to gather the collective opinions and insights of a panel of experts on a particular topic. The process involves several rounds of questionnaires or surveys. Each round is designed to narrow things down until a consensus or hypothyses can be formed. One of the key features of the Delphi survey research is that participants are unknown to each other, thereby eliminating influence.

18. AI Surveys

Artificial intelligence is the latest types of survey method. Using AI, researchers allow the technology to ask survey questions. These “Chatbots” can even ask follow-up questions on the spot based on a respondent’s answer. There can be drawbacks, however. If a person suspects survey questions are coming from AI, they may be less likely to respond (or may respond incorrectly to mess with the AI). Additionally, AI is not good with emotions, so asking sensitive questions in an emotionless manner could be off putting to people.  Read more about AI Surveys .

Online Surveys: Ideal for Collecting Data and Feedback

That’s not all. People can take online surveys just about anywhere thanks to mobile devices. The use of these devices across age groups is balancing out as well. Check out smartphone use by age group below.

With more and more people accessing the internet through their mobile devices, now you can reach teens while they’re between classes and adults during their subway commute to work. Can’t say that for those other types of surveys !

Online surveys are also extremely cost-efficient. You don’t have to spend money on paper, printing, postage, or an interviewer. This significantly reduces set-up and administration costs. This also allows researchers and companies to send out a survey very expeditiously. Additionally, many online survey tools provide in-depth analysis of survey data. This saves you from having to spend money on further research once the survey is complete. 

Researchers have their pick of options when it’s time to survey people. Which method you choose may depend upon cost, reach, and the types of questions.

Now, you may be wondering, “ Where can I make free surveys ?” You can get started with free online surveys using SurveyLegend! He re are a few things that make SurveyLegend the ideal choice for different types of surveys for research ( or for fun) .

• When it comes to surveys, brief is best to keep respondents attention. So, SurveyLegend automatically collects some data, such as the participant’s location, reducing the number of questions you have to ask.
• People like eye candy and many surveys are just plain dull. SurveyLegend offers beautifully rendered pre-designed surveys that will get your participant’s attention – and keep it through to completion!
• Today, most people take surveys on mobile devices. Often surveys desktop surveys don’t translate well, resulting in a high drop-off rate. SurveyLegend’s designs are responsive, automatically adjusting to any screen size.

What’s your favorite method of surveying people? (Hey… that’s a good topic for a survey!) Sound off in the comments!

Frequently Asked Questions (FAQs)

The 10 most common survey methods are online surveys, in-person interviews, focus groups, panel sampling, telephone surveys, post-call surveys, mail-in surveys, pop-up surveys, mobile surveys, and kiosk surveys.

Benefits of online surveys include their ability to reach a broad audience and that they are relatively inexpensive.

Kiosk surveys are surveys on a computer screen at the point of sale.

A focus group is an in-person interview or survey involving a group of people rather than just one individual. The group is generally small but demographically diverse, and led by a moderator. 

Jasko Mahmutovic

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• Published: 21 November 2023

Connecting with fans in the digital age: an exploratory and comparative analysis of social media management in top football clubs

• Edgar Romero-Jara 1 ,
• Francesc Solanellas 2 ,
• Joshua Muñoz   ORCID: orcid.org/0000-0001-6220-6328 2 &
• Samuel López-Carril   ORCID: orcid.org/0000-0001-5278-057X 3  

Humanities and Social Sciences Communications volume  10 , Article number:  858 ( 2023 ) Cite this article

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• Business and management
• Cultural and media studies

In a globalised society, characterised by increasingly demanding markets and the accelerated growth of the digital approach, sports organisations face the challenge of connecting with fans, generating and maintaining audiences and communicating with stakeholders creatively and efficiently. Social media has become a fundamental tool, with engagement as a critical measurement element. However, despite its popularity and use, many questions about its application, measurement and real potential in the sports sector still need to be answered. Therefore, the main objective of this study is to carry out a descriptive and comparative analysis of the engagement generated through social media posts by elite football clubs in Europe, South America and North America. To this purpose, 19,745 Facebook, Twitter and Instagram posts were analysed, through the design, validation and application of an observation instrument, using content analysis techniques. The findings show evidence of a priority focus on “Marketing” and “Sports” type messages in terms of frequency, with high engagement rates. They were also showing a growing stream of “ESG” type messages, with a low posting frequency but engagement rates similar to “Marketing” and “Sport”. “Institutional” messages remain constant in all football clubs. “Commercial” messages still have growth potential in both regards, frequency and engaging fans, representing an opportunity for digital assets. Also, specific format combinations that generate greater engagement were identified: “text/image” and “text/videos” are the format combinations more used by football clubs on Facebook, Twitter and Instagram; however, resulting in different engagement rates. This study showed evidence of different social media management strategies adopted according to region, obtaining similar engagement rates. This research concludes with theoretical and practical applications that will be of interest to both academics and practitioners to maximise the potential of social media for fan engagement, social initiatives and as a marketing tool.

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Introduction.

In a context of booming technology and high organisational competitiveness (Ratten, 2020 ), digital tools have evolved from an essential add-on to crucial strategic and operational elements in sports organisations (Stegmann et al., 2021 ). Fans increasingly demand a connection with their favourite athletes and teams (Su et al., 2020 ) through digital channels such as social media, podcasts (Rohden et al., 2023 ), Esports (Cuesta-Valiño et al., 2022 ), among others. Today’s digitised world presents therefore, an opportunity for brands, sponsors, sports properties, and other stakeholders to interact in a complex and emotionally charged sector (Su et al., 2022 ) for fans from different age generations (Sheldon et al., 2021 ). Understanding and getting to know fans are at the forefront of every sports organisation’s objective.

Social media plays a fundamental role due to their ability to reach multiple audiences faster and generate a sense of connection with fans through a key measurement element: engagement (Doyle et al., 2022 ). Sports organisations, specifically football clubs, invest time, people and resources in managing social media to achieve their brand positioning and commercial and communication objectives (Anagnostopoulos et al., 2018 ; Maderer et al., 2018 ), with Facebook, Twitter and more recently, Instagram, being the most widely used (Abeza et al., 2019 ; Machado et al., 2020 ). However, the real potential of social media and its optimal use still poses many questions to be answered.

Although there are previous studies that have explored some aspects of social media in a sports context (e.g., Anagnostopoulos et al., 2018 ; Mastromartino and Naraine, 2022 ; Su et al., 2020 ), the potential impact and efficiency of content posted by football clubs on their social media channels remains unclear. For example, several studies point to various factors that contribute to fan engagement on social media depending on elements such as the type of content, the format used (e.g. photo, text or a combination of both) or the social media platform (see Einsle et al., 2023 ; Maderer et al., 2018 ; Su et al., 2020 ). This gap in the literature prompts a call to action from across the domains of sports marketing and sports management. Identifying the elements generated by football clubs on their official social media profiles can help them improve their marketing strategies and better support their fans. Based on this need and opportunity for management improvement, this study addresses the following research question:

RQ . What are the main characteristics of Facebook, Twitter, and Instagram posts from elite football clubs to understand the content type, format and social media platform that generate the highest engagement among social media consumers?

Grounded on the theoretical framework of relationship marketing, the main objective of this study is to carry out a descriptive and comparative analysis of the engagement generated through social media posts on Facebook, Twitter and Instagram by elite football clubs in Europe, South America and North America, using a categorisation approach developed from an existing model in the literature (see Solanellas et al., 2022 ), as well as the identification of key elements of high-impact social media posts. For this purpose, a new instrument was designed, validated and applied to analyse the use of social media as a marketing tool in sports management. By conducting this exploration, this paper contributes to the literature on sports marketing by identifying which social media and which types of content provoke the most interaction among fans. As a result, football team managers can gain a better understanding of how to target and personalise potential commercial and branding actions, thereby reinforcing the loyalty and commitment of fans to football clubs, and opening or consolidating new lines of action aligned with the strategic objectives of sport entities. Furthermore, the findings and conclusions presented in this study can assist sports managers in the decision-making process, as well as in planning, organising, directing, and effectively controlling social media platforms, thus enhancing engagement with fans in a digital environment.

The article is structured as follows. Firstly, the literature review presents the main theoretical and conceptual elements, focusing on social media and their relationship with marketing theory in sports and football. Secondly, the methodological aspects guiding the study’s process are detailed, including sample, instrument, research procedure, and data analysis. Thirdly, the study’s main results are presented. Fourth, the discussion section critically examines the findings in the context of existing literature, offering practical and theoretical implications for both academics and practitioners. Finally, the study concludes with the main conclusions and limitations.

Literature review

Social media and sports, a combination of great potential.

Social media is a collective term for media tools, platforms, and applications allowing consumers to connect, communicate, and collaborate (Williams and Chinn, 2010 ). They encourage interaction between users and the organisation and provide information from customers and the organisation faster than through conventional media (Kümpel et al., 2015 ; Shilbury et al., 2014 ). Furthermore, social media is considered a mass phenomenon due to its ability to transmit information in an agile and interactive way (Vivar, 2009 ), as well as a unique form of communication that transcends geographical and social boundaries through the instantaneous communication of information (Filo et al., 2015 ). Social Media is used in different sectors for marketing activities (Chen, 2023 ), brand equity and loyalty (Malarvizhi et al., 2022 ) to understand consumer´s behaviour, brand positioning, business revenue opportunities and social communication (Ramos et al., 2019 ). However, although the first studies about this phenomenon have been explored in the sports industry field, there is still a need for more evidence about its real potential, essential elements, and efficiency measurement in the sector.

Due to the high graphic, interactive and visual content of social media, their use in the sports industry, a sector of strong emotional influence, has become more relevant and pervasive in the last decade (Hull and Abeza, 2021 ), where the interest of the viewer has become crucial and increasingly demanding (Nisar et al. 2018 ). The differences that make the sports industry unique and particular are, among others: immediate results and changes (Davis and Hilbert, 2013 ) in addition to the fact that every decision is “in the spotlight” of the public (alluding to the complexity of fans, athletes, coaches, media and other stakeholders). Thus, athletes, teams and sports organisations have been using social media as part of their public relations and communication efforts (Filo et al., 2015 ; Pegoraro, 2010 ; Yan et al., 2019 ) to engage with their partners and fans (Zakerian et al., 2022 ), promoting interactions and increasing engagement with the sport product, as well as with the team in general (Abeza et al., 2019 ; Parganas and Anagnostopoulos, 2015 ).

The linking of social media within the integrated marketing communication process has changed communication strategies and consumer outreach, where marketing managers must include these tools when developing and executing their customer-focused promotional strategies (Lee and Kahle, 2016 ; Rehman et al., 2022 ). On the other hand, social media, directly and indirectly, impacts revenue generation and favours negotiation with sponsors due to their notoriety, visibility, and reach (Mastromartino and Naraine, 2022 ; Parganas and Anagnostopoulos, 2015 ). They are therefore considered a key tool for building and enhancing a brand’s reputation (Maderer et al., 2018 ) and an ideal platform to advertise and increase the visibility of a brand or company, as well as to interact with and analyse the actions of their fans and followers (Abeza et al., 2017 ; García-Fernández et al., 2015 ; Herrera-Torres et al., 2017 ).

Social media has also been used in sports education in recent years (Sanz-Labrador et al., 2021 ). Moreover, their application is increasingly common in construction and dissemination related to social responsibility (López-Carril and Anagnostopoulos, 2020 ; Sharpe et al., 2020 ). In this way, they have also become a key tool for interacting with fans, addressing a strengthened social approach, and gaining engagement from athletes, sponsors, and authorities (Einsle et al., 2023 ; Oviedo et al., 2014 ; Su et al., 2020 ). Beyond the digital environment, Cuesta-Valiño et al. ( 2021 ) pointed out the relevance of considering the emerging sustainable management approach to measure sports organisations’ goals. One of the most relevant challenges for this industry is to issue social media posts efficiently, using the proper formatting resources and at the right time, to generate the most significant possible impact and engagement.

Relationship marketing theory applied to social media in sports

The sports industry is a fast-growing and increasingly diverse market worldwide (Kim and Andrew, 2016 ). Football (soccer in North America) is one of the most popular sports worldwide as well as a cultural manifestation, characterised by its high emotional level and economic, political and social relevance (Bucher and Eckl, 2022 ; Petersen-Wagner and Ludvigsen, 2022 ). Only in Spain, the sports sector generates 3.3% of the Gross Domestic Product (GDP), of which 1.37% is produced through football (PWC, 2020 ).

Globalisation has demanded an adaptation at all levels due to the endless search for immediacy and access to information, where the business of sports is becoming more and more relationship-based and the importance of generating engagement (Einsle et al., 2023 ; Fried and Mumcu, 2017 ; García-Fernández et al., 2017 ) is one of the most relevant variables in generating loyalty in sports organisations (Loranca-Valle et al., 2021 ; Núñez-Barriopedro et al., 2021 ). Sports consumers are seen as “channels” through which sports products can be promoted (O’Shea and Alonso, 2011 ), and sports fans have become both the consumer and the advocates of the product. This is where relationship marketing theory helps us to better understand this phenomenon. As Abeza and Sanderson ( 2022 , p. 287) point out, relationship marketing theory “is based on the idea that a relationship between two parties creates additional value for those involved”. This theory is one of the most widely used to understand the phenomenon of social media in sports (Abeza and Sanderson, 2022 ) as highlighted by numerous authors who have used it in their studies (e.g., Abeza et al., 2017 , 2019 , 2020 ; Su et al., 2020 ; Williams and Chinn, 2010 ).

Merging the roots of relationship marketing theory (Möller and Halinen, 2000 ) and the particular characteristics of the sports sector, and taking into account the perspective of short-term transactions and immediate economic benefits (Abeza et al., 2017 ), social media represents opportunities for better knowledge about fans, more advanced consumer–organisation interaction, efficient fan engagement, efficient use of resources and agile evaluation of the relationship between fans and organisation (Abeza et al., 2019 , 2020 ). In view of this, and in line with Abeza and Sanderson ( 2022 ), social media thus becomes a channel through which to establish, maintain and cultivate long-term relationships beneficial to both parties (in our study, football clubs and fans).

Previous studies have addressed the use of specific social media in the context of sports, such as Facebook (Achen, 2019 ; Meng et al., 2015 ; Pegoraro et al., 2017 ; Waters et al., 2009 ), Twitter (Blaszka et al., 2012 ; Hambrick et al., 2010 ; Lovejoy and Saxton, 2012 ; Winand et al., 2019 ; Witkemper et al., 2012 ) and Instagram (Anagnostopoulos et al., 2018 ; Machado et al., 2020 ; Zakerian et al., 2022 ), because of the relevance in the use of these platforms in the sports sector. From another broader perspective, Solanellas et al. ( 2022 ) propose a practical analysis of multiple social media in sports organisations from a content categorisation point of view.

The results and contributions of the studies mentioned above, reveal the importance of further exploring the social media fan engagement phenomenon as a strategic perspective (Tafesse and Wien, 2018 ) and the added value that social media can generate in sports. In this sense, it is relevant for sports managers to know which techniques, methodologies and perspectives to use. Furthermore, as stated by Abeza and Sanderson ( 2022 ), it is necessary to go deeper into the theories behind its use. Taking these aspects into account, this work presents a new instrument of observation and measurement of social media posts by football organisations, as a basis for understanding and deepening the knowledge about the digital audience and its impact on the different objectives of the organisation. Thus, the study draws on relationship marketing theory to better understand how sports managers can make the most of the possibilities offered by social media to generate added value from the interaction between fans and football clubs. Particularly, the developed instrument focuses on the analysis of the type of content published by football clubs, categorising it into dimensions, as well as the engagement of the different publications according to the type of dimension to which they belong.

With a view to the implementation of the instrument, and to contribute to the literature related to the use of social media as a marketing tool in sports, this study analyses Facebook, Twitter and Instagram posts issued by elite football clubs from Europe, South America and North America, using a practical approach to content categorisation and taking the engagement factor as a key element for comparison.

Methodology

This study adopts an exploratory, descriptive, and comparative research design (Andrew et al., 2011 ) using the observational method and content analysis techniques. Content analysis involves the recounting and comparison of content, followed by the interpretation of the underlying context. It has been widely used in social media communication research, specifically in sports settings (e.g., Anagnostopoulos et al., 2018 ; Wang and Zhou, 2015 ; Winand et al., 2019 ), to interpret textual data through systematic classification, coding, and identifying themes or patterns (Hsieh and Shannon, 2005 ). First, exploratory studies are particularly useful when the phenomenon under investigation is in constant evolution (such as social media as a marketing tool), as well as when there are several factors and variables at play (Andrew et al., 2011 ). In this study, these are linked to the engagement that can be caused by the type of content or format used by elite football clubs on their social media accounts. Second, the descriptive aspect of the research design aims to describe and quantify the engagement levels in social media for the selected football clubs. By Collecting and analysing quantitative data on the interaction metrics, including likes, comments, shares, and follower counts, the study provided a comprehensive overview of the current state of engagement, and other variables, among the clubs, helping to build a foundation for further analysis and comparison. Lastly, the comparative aspect of the research design (Andrew et al., 2011 ) is valuable in this study because it enables a cross-regional analysis of three of the most traditional social media platforms. The study compared the engagement practices, elements, and strategies across three key regions of the football industry worldwide. Understanding potential differences can be useful for sports managers to design more optimised social media marketing strategies.

Considering the study design and observational method applied in this research (Anguera-Argilaga et al., 2011 ), a nonprobable sample design (see Battaglia, 2008 ) was established following several steps to make the following three decisions: (1) selection of football clubs, (2) social media platforms, and (3) period of time studied.

First, a geographical criterion was used to determine the origin of the football clubs under study. This criterion was based on a comprehensive and global perspective, considering factors such as historical significance, popularity, sporting achievements, and the modernisation of football worldwide. Based on these considerations, three regions were selected for analysis: Europe and South America, renowned for their broad global relevance and football tradition (e.g., the winning national teams of the 22 editions of the FIFA World Cup so far are from Europe and South America [Venkat, 2023 ]). Next, North America was chosen for its ascending market growth potential and global efforts to promote football. This is exemplified by upcoming milestones, such as the organisation of the FIFA World Cup 2026 in the United States, Mexico, and Canada, as well as the recent arrival of Lionel Messi into Major League Soccer (see Mizrahi, 2023 ). These three regions are governed by the three most influential regional football bodies of FIFA: Europe (UEFA), South America (CONMEBOL), and North America (CONCACAF). Second, to select the most relevant football clubs in these three regions, we followed some of the selection criteria set in similar studies (e.g., Anagnostopoulos et al., 2018 ; Maderer et al., 2018 ). Therefore, the rankings of four of the most influential football organisations or websites were considered: (1) the International Federation of Football History and Statistics (IFFHS) club ranking, (2) the Football World Rankings website, (3) the FIFA club and league ranking, and (4) the Transfermarkt player ranking website (of great relevance in the player transfer market). As a result of this process, 24 teams were pre-selected (9 from Europe, 9 from South America and 6 from North America) according to the objectives and the study design and the author’s agreement (Andrew et al., 2011 ; Anguera-Argilaga et al., 2011 ; Battaglia, 2008 ; Hernández-Sampieri et al., 2014 ). Finally, a random draw was made resulting in a selection of six teams from Europe, six from South America and four from North America (with a limit of two teams per league). This process resulted in the 16 teams whose use of social media is analysed in this study (see Table 1 ).

Following, social media to be analysed in the study were selected. It was noted in the literature that Facebook had been one of the first social media to be used by football clubs and other sports organisations, either to connect with fans or purely for informational purposes (Achen, 2019 ; Waters et al., 2009 ). Twitter and Instagram are also platforms that have become relevant, not only for marketers in sports but also in other sectors (Anagnostopoulos et al., 2018 ; Wang and Zhou, 2015 ). Although the use of Facebook, Twitter and Instagram as marketing tools for football clubs has been studied (e.g., Machado et al. 2020 ; Maderer et al. 2018 ; Nisar et al., 2018 ), there is a lack of literature comparing their potential engagement across a sample of teams from different geographic regions. Thus, it was deemed appropriate to select these three social media sources for our study.

Finally, the periods over which the publications were to be extracted were determined. Among other authors, Ashley and Tuten ( 2015 ) point out that, in a social media environment, two to four weeks are sufficient for a wide variety of posts to be made in a regular and cyclical context, excluding exceptional milestones or events that could have an extraordinary impact on engagement and that could bias regular reading. Therefore, 45 days for each club and each social media is set as an appropriate observation period.

Once the sample selection criteria had been defined, the links of all publications from the clubs selected in the study on the three social media were extracted through the Fanpage Karma software that allows data to be collected and interpreted (Lozano-Blasco et al., 2021 ). After prior data analysis, the final sample consisted of 19,745 publications, a very similar figure to that used in other related studies (e.g., Maderer et al., 2018 ; Yan et al., 2019 ).

Instrument and research procedure

Based on the review of the techniques and methodologies used to analyse the use of social media as a marketing tool for football clubs in previous studies, we proceeded to design and develop an observation and data collection instrument in a Microsoft Excel Spreadsheet (.xlsx format), taking as a starting point the model of content analysis proposed by Solanellas et al. ( 2022 ). Due to the nature of the study, the .xlsx data collection format was chosen for its flexibility, allowing for manual data collection and the application of the categorisation tool post-by-post. This format has been successfully used as a data collection tool in previous social media content analysis studies in football (e.g., López-Carril and Anagnostopoulos, 2020 ).

To ensure its rigour, the codebook was subsequently submitted for review to nine field experts. The selection of these experts was undertaken via judgmental nonprobability sampling, a method commonly employed in the literature due to the specialised and ever-evolving nature of the subject (Andrew et al., 2011 ). These individuals were chosen based on specific criteria, encompassing their professional roles in specialised, coordinating, managerial, or directorial positions tied to the digital domain. Moreover, their academic background, particularly in marketing, methodology, or digital tools, was considered. To ensure an extensive grasp of the subject matter, the chosen experts were required to have a minimum of five years of experience in the area and to be actively participating in their respective roles. This approach aimed to incorporate diverse viewpoints, offering insights from a spectrum of angles relevant to this research. As a result, the panel of experts was comprised of the following professionals: the Head of Digital from a prominent European professional football league (1), a Marketing Manager and an International Communications Manager from leading professional football clubs (2), Directors of digital marketing and branding agencies (2), professors specialising in marketing and sports management at Spanish universities (2), and the Vice-President of Sales along with the Head of Digital from sports business intelligence consultancies (2).

Semi-structured interviews were undertaken with these chosen experts to delve into pertinent aspects linked to the study. An interview guide was developed, following the methodological aspects indicated in specialised works in this field (see Andrew et al., 2011 ; Anguera-Argilaga et al., 2011 ). Furthermore, the interview guide encompassed critical aspects of social media management and relevant facets of football club management (e.g., post formats, observation timeframes, platforms for capturing and analysing social media posts), drawing upon the elements and variables derived from studies conducted by Parganas and Anagnostopoulos ( 2015 ) as well as Solanellas et al. ( 2022 ). Additionally, these interviews comprised discussions about the conception and execution of the observation tool, which was employed as a supplementary instrument for data collection. Further variables relevant to the research objectives were explored within these interviews.

The qualitative insights garnered from the experts’ conclusive remarks offered valuable suggestions that contributed to refining the study’s development and enhancing the observation tool. This iterative approach ensured the harmonisation of the tool with the research objectives and its effective alignment with the study’s research questions. After incorporating the modifications suggested in the experts’ evaluations, the study’s codebook adhered to the variables and categories illustrated in Table 2 .

The .xlsx instrument sheet was then pilot-tested. Seventy-five publications (25 from Facebook, 25 from Instagram and 25 from Twitter) from three different football clubs were randomly selected, conforming to a total sample of 225 publications. The data were collected in an observation sheet in .xlxs format for analysis purposes. During the analysis process, including the discussion of possible discrepancies in interpreting each publication as belonging to one or another of the dimensions of the study’s codebook, the authors decided that each publication would be classified only in one dimension, depending on the type of content that predominates in each post.

To measure the level of reliability and accuracy of the instrument (Andrew et al., 2011 ), the intra-observer reliability method was applied, incorporating 10–12 minute breaks every 40–45 min of observation. After 15 days, the same publications were re-coded using the same established protocol. The results of the coding provided a Kappa coefficient of 0.949, demonstrating a very high level of agreement and reliability, following the scale of Landis and Koch ( 1977 ).

To measure the reliability and accuracy of the instrument (Andrew et al. 2011 ), the intra-observer reliability method was applied. In the first stage, the data was collected and coded post-by-post by applying the xlsx. sheet, incorporating 10–12 minute breaks every 40–45 min of observation to ensure the quality of the data observed and collected. The same posts were re-coded using the same established protocol in the second stage. To ensure a more accurate application of the codebook and to avoid potential bias, a 15-day impasse was established between the two data collections. The coding results between the two stages provided a Kappa coefficient of 0.949, demonstrating a very high level of agreement and reliability, following the scale of Landis and Koch ( 1977 ).

Finally, based on the interaction data collected with the data collection instrument, the variable of engagement with the publications was calculated by adapting the formulas used by the Fanpage Karma ( 2022 ) and Rival IQ (Feehan, 2023 ) platforms (Fig. 1 ).

Adapted from Fanpage Karma ( 2022 ) and Rival IQ (Feehan, 2023 ) platforms.

Therefore, after the protocol and the .xlsx observation instrument sheet were tested and validated, the final procedure was established as follows: (a) social media posts from Facebook, Twitter and Instagram of the selected football clubs were extracted automatically using the FanPage Karma license and added to the .xlsx observation instrument sheet; (b) according to the Study Codebook (see Table 2 ) the data was collected and registered manually into the .xlsx observation instrument sheet by clicking the posts one by one; c) we proceeded to set up a database coding the variables from the data collected to perform the statistical analyses.

Data analysis

A descriptive analysis of the engagement generated by publications on social media and their content (dimensions and formats) on Facebook, Instagram and Twitter was carried out. To analyse the differences in engagement generated by the posts on each social media according to their content, we used the t-test for independent samples and the one-factor ANOVA. The significance value established is <0.05. A chi-square test and correspondence analysis were applied to identify and visualise points of association between the key variables. Data analysis was performed using the SPSS statistical package, version 27.0.

As shown in Table 3 , of the 19,745 posts observed and analysed, Twitter accounted for 64%, followed by Facebook at 22% and Instagram at 14%. However, from the point of view of engagement, Instagram reflects an average of 1.873, well above the other social media. Facebook follows it with 0.112 and Twitter with 0.045, showing an inverse behaviour to the number of posts made.

Frequency and engagement

In Fig. 2 , we can observe the strategy used by each club in terms of the frequency of posts on Facebook, Twitter and Instagram, as well as the levels of engagement obtained. On Facebook, the football clubs analysed posts at different frequencies. In Europe, we observe that the clubs with the highest frequency of posts are Liverpool FC and Manchester United FC, with n  = 445 and n  = 486, respectively. In contrast, the Spanish clubs (Real Madrid FC and FC Barcelona) have the lowest frequency of posts ( n  = 195 and n  = 118, respectively). On the other hand, beyond this difference in frequency, they have very similar engagement ratios.

Frequency of posts and level of engagement generated on Facebook, Twitter and Instagram by the football clubs selected for this study (organised by regions).

The club with the highest frequency of publications is CR Flamengo from Brazil ( n  = 644); however, SE Palmeiras, the other Brazilian club studied, despite registering fewer publications in the same period ( n  = 289), shows much higher levels of engagement. SE Palmeiras (Brazil), Club Olimpia and Club Cerro Porteño (Paraguay), CF America (Mexico) and Atlanta United FC (USA) show the highest levels of engagement, with similar posting frequencies (between n  = 142 and n  = 241). On Twitter, the highest frequencies of posts were published compared to Facebook and Instagram, with CR Flamengo and Atlanta United FC being the clubs that posted the most ( n  = 1606 and n  = 2096, respectively). However, the levels of engagement identified show similar and homogeneous levels in the period analysed, regardless of the frequency of publications. On the other hand, the highest engagement levels were observed on Instagram, with a lower frequency of publications in all cases. Football clubs SE Palmeiras, CA River Plate, CF America and Atlanta United FC have the highest engagement values (2.5 and 3), with posting frequencies ranging from n  = 91 to n  = 154. European football clubs have very similar engagement ratios (around 1.00), while North American football clubs have different engagement values despite having similar posting frequencies ( n  = 91 and n  = 154).

Content dimensions of publications

As shown in Fig. 3 , we observe the dimensions proposed in this study, comparing the social media analysed and the engagement generated by each category. From this point of view, in terms of frequency, the “Marketing” and “Sport” dimensions are observed as the most used publication approaches by football clubs, followed by the “Institutional” dimension, “Commercial” and, finally, “ESG”. This order of frequency applies to Facebook, Twitter and Instagram.

Categorisation in the posts’ dimensions and their relationship with the engagement generated by Facebook, Twitter and Instagram of the football clubs analysed.

In terms of engagement, the social media Instagram is the one that registers considerably higher values than the rest of the social media analysed, with the “Marketing” dimension generating the highest engagement (2.03). It is followed by the “Institutional” dimension (1.78) and the “Sports” dimension (1.74), closing with the “Commercial” and “ESG” dimensions, with values of 1.54 and 1.41, respectively. Facebook is the following social media that generates the highest engagement.

In the case of Facebook (see Supplementary Table S1 ), the findings show a significance of the engagement means between the “Commercial” and the “Sports” ( p  = 0.000 < 0.05), “Institutional” ( p  = 0.001 < 0.05) and “Marketing” type of the posts in Facebook.

On the other hand, Twitter (see Supplementary Table S2 ) is the one that generates the minor engagement, with very similar values between the different dimensions, despite being the one with the highest frequency of publications (Fig. 3 ). Unlike the previous dimensions, the “Institutional”, “ESG”, and “Commercial” dimensions are those with the highest engagement values (0.07), followed by the “Marketing” and “Sports” dimensions (both with 0.04). However, in this social media platform, the “Institutional” type of content is statistically significant with “Sports” ( p  = 0.000 < 0.05), “Commercial” ( p  = 0.000 < 0.05) and “Marketing” ( p  = 0.000 < 0.05). Also, we can find significant engagement results between the “ESG” and the “Commercial” ( p  = 0.033 < 0.05) dimensions.

On Instagram (see Supplementary Table S3 ), the “Marketing” dimension has the highest engagement value, as does the “Institutional” dimension (both with 0.12). It is followed by the “Sports” dimension (0.11), “ESG” (0.10) and finally, “Commercial” (0.07) (Fig. 3 ). Nevertheless, as difference of Facebook and Twitter, the findings show a strong relevance of “Marketing” dimensions posts (Supplementary Table S3 ), linked significantly with “Sports” ( p  = 0.000 < 0.05), “Commercial” ( p  = 0.000 < 0.05) and “Institutional” ( p  = 0.002 < 0.05).

Types of formats in publications

Nine combinations of the most relevant formats have been identified in the publications analysed (Table 4 ), both in the frequency of use and engagement they generate.

On Facebook, the most frequent formats are “Text/Image” and “Text/Video” ( n  = 2031 and n  = 1265, respectively). However, the format with the highest engagement is “Image” (0.23), followed by “Text/Image” (0.13), “Text/Video” (0.12) and “Text/Link” (0.07). On Twitter, on the other hand, the “Text/Image” format is the most used ( n  = 4412), “Text” ( n  = 2499), “Text/Video” ( n  = 2239) and “Image” ( n  = 1534), with the “Text/Video” and “Text/Image” format combinations (0.07) registering the highest engagement. On Instagram, due to the nature of social media, the most frequent format is “Text/Image” ( n  = 1986). In terms of engagement, the formats “Image” (2.20), “Text/Image” (1.95), “Text/Image/Polls” (1.93) and “Video” (1.84) have the highest values.

The correspondence analysis (Fig. 4 ) shows the degree of association between the variables and the categorisation dimensions proposed in this study in a relative position map. The chi-squared test yielded a result of 1027.65. The “Marketing” dimension shows a closer relationship with the “video” and “image” format resources. The “ESG” and “Institutional” content type shows an association with the “Image” and “Text” formats. The “Commercial” dimension, based on the characteristics of the categorisation, shows a relationship with the “Link” format as ideal points of association, considering the frequency and engagement analysed.

Correspondence analysis (dimensions and formats).

Nowadays, sports organisations and athletes use social media for communication purposes, brand positioning, visibility (Maderer et al., 2018 ; Winand et al., 2019 ; Zakerian et al., 2022 ) and even for potential business (Parganas and Anagnostopoulos, 2015 ), dedicating effort and resources. Previous studies reinforce the need to categorise the message delivered to understand this phenomenon according to the objective (Filo et al., 2015 ) and content analysis for effect (Meng et al., 2015 ). However, its optimal use still leaves many questions. The complexity of the market is evolving towards the need to understand the fan as a premise in a sector characterised by its high emotional charge. In the past, strategies focused on attracting and retaining fans. However, the current trend shows increased relevance in generating engagement (Oviedo et al., 2014 ) to generate links with fans. The sports industry, especially in the digital environment, is in an era where the goal is not just getting new followers and post social media content but interact and engage “to know the users better”.

First, this study provides evidence of relevant frequency-engagement relationships according to the dimensions of the study, depending on the type of social media used (Facebook, Twitter and Instagram). Regarding the dimensions of the content published, the posts related to “Marketing” and “Sport” are the most frequent due to the natural and traditional use of these tools as communicative, brand positioning and informative elements (Lee and Kahle, 2016 ; Rehman et al., 2022 ; Winand et al., 2019 ). This is attributable to the need for clubs to generate emotional content (such as videos or images of past iconic matches or campaigns involving athletes), on the one hand, and to broadcast messages alluding to sporting performance and results. Nevertheless, the findings show different engagement impacts not directly linked to the frequency of the posts but influenced by other elements, such as the social media platform, the dimension of the content and the format. The evidence shows there are specific content dimensions that statistically generate more engagement in each platform.

On Facebook, the most traditional platform football clubs use provides a more balanced frequency-engagement ratio, with a strong engagement with “commercial” content. This platform was one of the social media platforms that started monetising in other industries, characterised for its high brand impact, where the know-how and the platform interphase are more friendly to focus on this type of posts (and in some cases, to launch joint posts with brands). Even with the positive engagement impact of this platform, it is observed that efforts of this nature in the digital sphere are scarce in comparison to the rest, making this a relevant aspect in the spectrum of growth and an opportunity to explore, especially with the new assets that are appearing in the market and the growth of e-commerce.

On Twitter, on the other hand, the dimension that works best for engaging in “Institutional” is linked to “Sports”, “Marketing” and “Commercial” content, but not with “ESG”. However, the “ESG” linked with “Commercial” dimensions statically gets significantly more impact on this platform. The “ESG” dimension is emerging as this platform is used for promoting socio-political activities and promoting more altruistic purposes as previous authors as López-Carril and Anagnostopoulos ( 2020 ), and Sharpe et al. ( 2020 ) noted. This strategy shows a possible intention to use social media not only for marketing (communication) or sporting purposes but also as an element with socio-political aspects. The nature of Twitter as a microblogging site with the highest number of posts with the lower means of engagement, is more attractive for the audience looking for quick and summarised information because of its ability to increase the visibility and awareness of fans (Abeza et al., 2017 ). Sports managers can focus on this type of message for a potential higher engagement on Twitter.

In contrast, on Instagram, the focus is on “Marketing” content. This platform shows the lowest number of post frequency, with a high engagement means, attributable to the platform’s audio–visual formats and more interactive content, ratifying its growing popularity among users. As a fast-growing platform, there is a major link with “Sports”, “Institutional” and “Commercial” dimensions, which makes it an ideal platform for emotional content, easy to connect with brands, athletes, and sports properties, counting with a larger and more varied audience looking mainly, as the evidence suggests, for entertainment and club’s closeness perception. Therefore, like Anagnostopoulos et al. ( 2018 ), we recommend sports managers use Instagram for marketing purposes, considering the context as a relevant factor.

Finally, this study reveals the post format’s relevance as another key element. In this sense, on Facebook, the highest engagement values are generated by “Image” and “Text/Image” formats, as on Instagram and Twitter; however, in each social media platform, the frequencies generated by these records are different. In any case, the power of the image as valuable content in marketing stands out, as it has also been highlighted in previous studies (e.g., Anagnostopoulos et al., 2018 ; Doyle et al., 2022 ; Machado et al., 2020 ). Nevertheless, the results obtained regarding the engagement triggered by video format posts on Facebook, Twitter and Instagram are not as conclusive, as other studies have pointed out (e.g., Su et al., 2020 ). Probably because these social media are not focused on that format as other social media such as TikTok or YouTube may be. Regardless, based on the results obtained, it is necessary for sports managers and academics to continue to explore and make the appropriate combinations of the dimensions of content type categorised in this study, the publication format, as well as the social media used to channel them.

Theoretical implications

Built upon the framework of relationship marketing, this study brings theoretical value to the realms of sports marketing, sports management, and fan engagement, spanning across four distinct lines of action.

Firstly, the research introduces a novel theoretical approach to social media strategies by employing a 5-dimensional content categorisation system aligned with the strategic pillars of football organisations. Previous studies have predominantly approached the role of social media in sports reactively, primarily focusing on communication and branding aspects. In contrast, this study contributes to the literature by adopting a strategic perspective towards social media, establishing a linkage between the study dimensions and football club strategies. This foundation paves the way for future research to delve deeper into each proposed dimension, potentially identifying sub-groups and exploring them in greater detail. The proposed dimensions serve to systematically organise the primary facets of football organisations for digital context analysis, a realm of increasing importance within the sports industry. As such, this work marks a pioneering step towards a novel approach in this area of study.

Secondly, this study establishes a fresh frequency-engagement approach for social network management, dispelling the notion that post frequency directly correlates with generated engagement. In doing so, this work highlights additional pivotal factors beyond post frequency that influence engagement among users of football-related social media. This perspective is aligned with the ethos of Web 2.0, underscoring the significance of engaging and connecting with fans.

Thirdly, from a theoretical perspective, this study introduces an innovative analytical proposition focusing on prominent international football clubs. This innovation is realised through the calculation and translation of engagement ratios, facilitating cross-entity comparisons independent of geographical location and follower count. The instrument developed and applied in this study acts as a tool to identify valuable digital practices within the industry.

Finally, this study stands out by conducting simultaneous analyses of posts across three prominent social media platforms (Facebook, Twitter, and Instagram), adopting a distinctive multi-platform approach that is seldom observed in comparable studies which often focus on a single social media platform. Gaining insights into the effects of cross-platform and cross-format postings can empower sports managers to make strategic decisions with a comprehensive perspective.

Practical implications

This study introduces a novel practical tool designed for the computation of fan engagement across the Facebook, Twitter, and Instagram accounts of football clubs globally. Consequently, sports managers can employ this instrument to gain a more realistic comprehension of the performance of social media accounts belonging to clubs. Furthermore, the developed tool facilitates the assessment of fan engagement in relation to the content type being published. This capability can aid sports managers in fortifying the bond between clubs and their followers by generating heightened value through strategic social media initiatives.

It is important to note that sports managers should consider both internal factors (club tradition, organisational culture) and external factors (competition, fan behaviour, sports results) within the context of clubs. This consideration is essential for developing and planning optimal digital strategies and for generating the best possible engagement with the audience. This research furnishes empirical evidence for understanding, in a practical and actionable manner, the pivotal components of a social media post. This understanding permits the visualisation of optimal combinations of these elements, thereby increasing the likelihood of sports managers guiding the club toward success and fostering substantial user engagement. Therefore, football team managers can apply the findings of this study to plan, monitor, and evaluate the club’s social media content for increased engagement and “closeness” with digital fans. They can combine various formats based on individual post requirements to achieve the desired results. Additionally, football team managers can analyse club identity and overall strategies more practically and coherently, facilitating the planning and execution of more effective commercial, brand positioning, institutional, and other relevant digital goals, with engagement serving as a key metric.

Conclusions

Social media plays a key role in today’s sports management, especially in football clubs, due to its global reach and ability to interact and connect with fans in an industry of great popularity, emotional charge, and economic, political and social impact. This exploratory research grounded in relationship marketing theory provided a comparison of the engagement generated by elite football clubs under a unique categorisation proposal, derived and adapted from existing literature, which addresses dimensions linked to strategic areas of football organisations and takes into consideration key elements such as frequency and format combinations used to analyse the efficiency of posts on Facebook, Twitter and Instagram.

Based on the results obtained, three lines of action stand out. First, concerning the type of content of the post, the “Marketing” and “Sports” dimensions are the preferred categories for football clubs in terms of post frequency. Regarding the engagement rates, on Facebook, the “Commercial” dimension shows an opportunity for growth and development due to the good engagement impact and due to the technological boom and the emergence of new digital assets. On Twitter, the emerging “ESG” linked to “Commercial” perspective and the “Institutional” dimension gets a significant impact on Twitter. On Instagram, the “Marketing” dimension linked to “Sports”, “Institutional” and “Commercial”, makes this platform ideal for emotional and marketing purposes. Second, concerning social media sources, this study provides evidence that Instagram is the social media that generates the most engagement using the lowest frequency of posts, followed by Facebook and Twitter. There is no direct evidence that links the post’s frequency with the engagement generated. Finally, concerning the type of format of the post, the combination of formats that generates the most engagement in all cases is “Image”, “Text/Image”, and “Text/Video”.

In short, this research stimulates a practical reflection for professionals and academics on the exploration, analysis, and evaluation of the management of social media in football clubs, using the observation method and content analysis techniques, applying elements of reliability and scientific rigour. The results obtained in this study offer practical and managerial implications in sports management, fan engagement, digital marketing, and social media, among others, through a proposal for categorisation and unique variables, taking engagement and its influence within the context of analysis as the axis.

The above conclusions should be taken into consideration viewing a series of limitations of the study. Firstly, the sample is limited to one sport (football) and not a large number of football clubs from different regions of the world. Secondly, despite the high number of posts analysed, these are located over a short period of time, and it may be relevant to analyse the engagement of posts at different times of the season, as these can influence the type of content and the engagement of fans with the posts. Thirdly, the study is limited to analysing engagement on Facebook, Twitter and Instagram, leaving aside the analysis of the possibilities that other booming social media, such as TikTok or Twitch, are having in the field of marketing. Nevertheless, these limitations can be a starting point for future research lines including, among others: (a) to assess the application and feasibility of the technique for measuring social media engagement included in this work in other football organisations (e.g. leagues) or social media platforms (e.g., TikTok, Twitch); (b) to incorporate new variables of study (e.g., size of the social mass of sports clubs, financial budget, trophies won); (c) to conduct the study considering different phases of the sports season (e.g.; preseason, season, playoffs; postseason); (d) to analyse fan engagement relation of geographical regions to understand the digital user’s behaviours; (e) to conduct the study adding engagement prediction models in social media; and (f) to incorporate this model on an AI language to suggest and predict digital user engagement in a simulated context.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge the experts who contributed their excellent technical knowledge and valuable inputs to the development of this work and the Fanpage Karma platform for providing the software licence to support this research. Edgar Romero-Jara would like to acknowledge the funding support of the pre-doctoral scholarship “National Academic Excellence Scholarship Programme Carlos Antonio López (BECAL)”, granted by the Government of Paraguay. Samuel López-Carril would like to acknowledge the funding support of the postdoctoral contract “Juan de la Cierva-formación 2021” (FJC2021-0477779-I), granted by the Spanish Ministry of Science and Innovation and by the European Union through the NextGenerationEU Funds (Plan de Recuperación, Transformación y Resilencia).

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ER-J (corresponding author) and FS: conception and design of the work. ER-J and JM: analysis and methodology. ER-J and SL-C: literature review, interpretation of data, drafting of the work. FS: supervised this work. All authors made substantial contributions, discussed the results, revised critically for important intellectual content, and approved the final version of the work.

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Romero-Jara, E., Solanellas, F., Muñoz, J. et al. Connecting with fans in the digital age: an exploratory and comparative analysis of social media management in top football clubs. Humanit Soc Sci Commun 10 , 858 (2023). https://doi.org/10.1057/s41599-023-02357-8

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