types of research study designs

Types of Study Design

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Introduction

Study designs are frameworks used in medical research to gather data and explore a specific research question .

Choosing an appropriate study design is one of many essential considerations before conducting research to minimise bias and yield valid results .

This guide provides a summary of study designs commonly used in medical research, their characteristics, advantages and disadvantages.

Case-report and case-series

A case report is a detailed description of a patient’s medical history, diagnosis, treatment, and outcome. A case report typically documents unusual or rare cases or reports  new or unexpected clinical findings .

A case series is a similar study that involves a group of patients sharing a similar disease or condition. A case series involves a comprehensive review of medical records for each patient to identify common features or disease patterns. Case series help better understand a disease’s presentation, diagnosis, and treatment.

While a case report focuses on a single patient, a case series involves a group of patients to provide a broader perspective on a specific disease. Both case reports and case series are important tools for understanding rare or unusual diseases .

Advantages of case series and case reports include:

• Able to describe rare or poorly understood conditions or diseases
• Helpful in generating hypotheses and identifying patterns or trends in patient populations
• Can be conducted relatively quickly and at a lower cost compared to other research designs

Disadvantages

Disadvantages of case series and case reports include:

• Prone to selection bias , meaning that the patients included in the series may not be representative of the general population
• Lack a control group, which makes it difficult to conclude  the effectiveness of different treatments or interventions
• They are descriptive and cannot establish causality or control for confounding factors

Cross-sectional study

A cross-sectional study aims to measure the prevalence or frequency of a disease in a population at a specific point in time. In other words, it provides a “ snapshot ” of the population at a single moment in time.

Cross-sectional studies are unique from other study designs in that they collect data on the exposure and the outcome of interest from a sample of individuals in the population. This type of data is used to investigate the distribution of health-related conditions and behaviours in different populations, which is especially useful for guiding the development of public health interventions .

Example of a cross-sectional study

A cross-sectional study might investigate the prevalence of hypertension (the outcome) in a sample of adults in a particular region. The researchers would measure blood pressure levels in each participant and gather information on other factors that could influence blood pressure, such as age, sex, weight, and lifestyle habits (exposure).

Advantages of cross-sectional studies include:

• Relatively quick and inexpensive to conduct compared to other study designs, such as cohort or case-control studies
• They can provide a snapshot of the prevalence and distribution of a particular health condition in a population
• They can help to identify patterns and associations between exposure and outcome variables, which can be used to generate hypotheses for further research

Disadvantages of cross-sectional studies include:

• They cannot establish causality , as they do not follow participants over time and cannot determine the temporal sequence between exposure and outcome
• Prone to selection bias , as the sample may not represent the entire population being studied
• They cannot account for confounding variables , which may affect the relationship between the exposure and outcome of interest

Case-control study

A case-control study compares people who have developed a disease of interest ( cases ) with people who have not developed the disease ( controls ) to identify potential risk factors associated with the disease.

Once cases and controls have been identified, researchers then collect information about related risk factors , such as age, sex, lifestyle factors, or environmental exposures, from individuals. By comparing the prevalence of risk factors between the cases and the controls, researchers can determine the association between the risk factors and the disease.

Example of a case-control study

A case-control study design might involve comparing a group of individuals with lung cancer (cases) to a group of individuals without lung cancer (controls) to assess the association between smoking (risk factor) and the development of lung cancer.

Advantages of case-control studies include:

• Useful for studying rare diseases , as they allow researchers to selectively recruit cases with the disease of interest
• Useful for investigating potential risk factors for a disease, as the researchers can collect data on many different factors from both cases and controls
• Can be helpful in situations where it is not ethical or practical to manipulate exposure levels or randomise study participants

Disadvantages of case-control studies include:

• Prone to selection bias , as the controls may not be representative of the general population or may have different underlying risk factors than the cases
• Cannot establish causality , as they can only identify associations between factors and disease
• May be limited by the availability of suitable controls , as finding appropriate controls who have similar characteristics to the cases can be challenging

Cohort study

A cohort study follows a group of individuals (a cohort) over time to investigate the relationship between an exposure or risk factor and a particular outcome or health condition. Cohort studies can be further classified into prospective or retrospective cohort studies.

Prospective cohort study

A prospective cohort study is a study in which the researchers select a group of individuals who do not have a particular disease or outcome of interest at the start of the study.

They then follow this cohort over time to track the number of patients who develop the outcome . Before the start of the study, information on exposure(s) of interest may also be collected.

Example of a prospective cohort study

A prospective cohort study might follow a group of individuals who have never smoked and measure their exposure to tobacco smoke over time to investigate the relationship between smoking and lung cancer .

Retrospective cohort study

In contrast, a retrospective cohort study is a study in which the researchers select a group of individuals who have already been exposed to something (e.g. smoking) and look back in time (for example, through patient charts) to see if they developed the outcome (e.g. lung cancer ).

The key difference in retrospective cohort studies is that data on exposure and outcome are collected after the outcome has occurred.

Example of a retrospective cohort study

A retrospective cohort study might look at the medical records of smokers and see if they developed a particular adverse event such as lung cancer.

Advantages of cohort studies include:

• Generally considered to be the most appropriate study design for investigating the temporal relationship between exposure and outcome
• Can provide estimates of incidence and relative risk , which are useful for quantifying the strength of the association between exposure and outcome
• Can be used to investigate multiple outcomes or endpoints associated with a particular exposure, which can help to identify unexpected effects or outcomes

Disadvantages of cohort studies include:

• Can be expensive and time-consuming to conduct, particularly for long-term follow-up
• May suffer from selection bias , as the sample may not be representative of the entire population being studied
• May suffer from attrition bias , as participants may drop out or be lost to follow-up over time

Meta-analysis

A meta-analysis is a type of study that involves extracting outcome data from all relevant studies in the literature and combining the results of multiple studies to produce an overall estimate of the effect size of an intervention or exposure.

Meta-analysis is often conducted alongside a systematic review and can be considered a study of studies . By doing this, researchers provide a more comprehensive and reliable estimate of the overall effect size and their confidence interval (a measure of precision).

Meta-analyses can be conducted for a wide range of research questions , including evaluating the effectiveness of medical interventions, identifying risk factors for disease, or assessing the accuracy of diagnostic tests. They are particularly useful when the results of individual studies are inconsistent or when the sample sizes of individual studies are small, as a meta-analysis can provide a more precise estimate of the true effect size.

When conducting a meta-analysis, researchers must carefully assess the risk of bias in each study to enhance the validity of the meta-analysis. Many aspects of research studies are prone to bias , such as the methodology and the reporting of results. Where studies exhibit a high risk of bias, authors may opt to exclude the study from the analysis or perform a subgroup or sensitivity analysis.

Advantages of a meta-analysis include:

• Combine the results of multiple studies, resulting in a larger sample size and increased statistical power, to provide a more comprehensive and precise estimate of the effect size of an intervention or outcome
• Can help to identify sources of heterogeneity or variability in the results of individual studies by exploring the influence of different study characteristics or subgroups
• Can help to resolve conflicting results or controversies in the literature by providing a more robust estimate of the effect size

Disadvantages of a meta-analysis include:

• Susceptible to publication bias , where studies with statistically significant or positive results are more likely to be published than studies with nonsignificant or negative results. This bias can lead to an overestimation of the treatment effect in a meta-analysis
• May not be appropriate if the studies included are too heterogeneous , as this can make it difficult to draw meaningful conclusions from the pooled results
• Depend on the quality and completeness of the data available from the individual studies and may be limited by the lack of data on certain outcomes or subgroups

Ecological study

An ecological study assesses the relationship between outcome and exposure at a population level or among groups of people rather than studying individuals directly.

The main goal of an ecological study is to observe and analyse patterns or trends at the population level and to identify potential associations or correlations between environmental factors or exposures and health outcomes.

Ecological studies focus on collecting data on population health outcomes , such as disease or mortality rates, and environmental factors or exposures, such as air pollution, temperature, or socioeconomic status.

Example of an ecological study

An ecological study might be used when comparing smoking rates and lung cancer incidence across different countries.

Advantages of an ecological study include:

• Provide insights into how social, economic, and environmental factors may impact health outcomes in real-world settings , which can inform public health policies and interventions
• Cost-effective and efficient, often using existing data or readily available data, such as data from national or regional databases

Disadvantages of an ecological study include:

• Ecological fallacy occurs when conclusions about individual-level associations are drawn from population-level differences
• Ecological studies rely on population-level (i.e. aggregate) rather than individual-level data; they cannot establish causal relationships between exposures and outcomes, as the studies do not account for differences or confounders at the individual level

Randomised controlled trial

A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention . It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.

In an RCT, participants are randomly assigned to two or more groups. One group receives the intervention being tested, such as a new drug or a specific medical procedure. In contrast, the other group is a control group and receives either no intervention or a placebo .

Randomisation ensures that each participant has an equal chance of being assigned to either group, thereby minimising selection bias . To reduce bias, an RCT often uses a technique called blinding , in which study participants, researchers, or analysts are kept unaware of participant assignment during the study. The participants are then followed over time, and outcome measures are collected and compared to determine if there is any statistical difference between the intervention and control groups.

Example of a randomised controlled trial

An RCT might be employed to evaluate the effectiveness of a new smoking cessation program in helping individuals quit smoking compared to the existing standard of care.

Advantages of an RCT include:

• Considered the most reliable study design for establishing causal relationships between interventions and outcomes and determining the effectiveness of interventions
• Randomisation of participants to intervention and control groups ensures that the groups are similar at the outset, reducing the risk of selection bias and enhancing internal validity
• Using a control group allows researchers to compare with the group that received the intervention while controlling for confounding factors

Disadvantages of an RCT include:

• Can raise ethical concerns ; for example, it may be considered unethical to withhold an intervention from a control group, especially if the intervention is known to be effective
• Can be expensive and time-consuming to conduct, requiring resources for participant recruitment, randomisation, data collection, and analysis
• Often have strict inclusion and exclusion criteria , which may limit the generalisability of the findings to broader populations
• May not always be feasible or practical for certain research questions, especially in rare diseases or when studying long-term outcomes

Dr Chris Jefferies

• Yuliya L, Qazi MA (eds.). Toronto Notes 2022. Toronto: Toronto Notes for Medical Students Inc; 2022.
• Le T, Bhushan V, Qui C, Chalise A, Kaparaliotis P, Coleman C, Kallianos K. First Aid for the USMLE Step 1 2023. New York: McGraw-Hill Education; 2023.
• Rothman KJ, Greenland S, Lash T. Modern Epidemiology. 3 rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Research Design 101

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

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

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

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

Correlational Research Design

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

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

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

Need a helping hand?

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

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

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

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

Quasi-Experimental Research Design

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

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

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

Research Design: Qualitative Studies

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

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

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

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

Grounded Theory Research Design

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

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

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

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

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

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

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

Case Study Design

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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15 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

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how to cite this page

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

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

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

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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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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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Study designs

This short article gives a brief guide to the different study types and a comparison of the advantages and disadvantages.

See also  Levels of Evidence  

These study designs all have similar components (as we’d expect from the PICO):

• A defined population (P) from which groups of subjects are studied
• Outcomes (O) that are measured

And for experimental and analytic observational studies:

• Interventions (I) or exposures (E) that are applied to different groups of subjects

Overview of the design tree

Figure 1 shows the tree of possible designs, branching into subgroups of study designs by whether the studies are descriptive or analytic and by whether the analytic studies are experimental or observational. The list is not completely exhaustive but covers most basics designs.

Figure: Tree of different types of studies (Q1, 2, and 3 refer to the three questions below)

> Download a PDF by Jeremy Howick about study designs

Our first distinction is whether the study is analytic or non-analytic. A  non-analytic  or  descriptive  study does not try to quantify the relationship but tries to give us a picture of what is happening in a population, e.g., the prevalence, incidence, or experience of a group. Descriptive studies include case reports, case-series, qualitative studies and surveys (cross-sectional) studies, which measure the frequency of several factors, and hence the size of the problem. They may sometimes also include analytic work (comparing factors “” see below).

An  analytic  study attempts to quantify the relationship between two factors, that is, the effect of an intervention (I) or exposure (E) on an outcome (O). To quantify the effect we will need to know the rate of outcomes in a comparison (C) group as well as the intervention or exposed group. Whether the researcher actively changes a factor or imposes uses an intervention determines whether the study is considered to be observational (passive involvement of researcher), or experimental (active involvement of researcher).

In  experimental  studies, the researcher manipulates the exposure, that is he or she allocates subjects to the intervention or exposure group. Experimental studies, or randomised controlled trials (RCTs), are similar to experiments in other areas of science. That is, subjects are allocated to two or more groups to receive an intervention or exposure and then followed up under carefully controlled conditions. Such studies controlled trials, particularly if randomised and blinded, have the potential to control for most of the biases that can occur in scientific studies but whether this actually occurs depends on the quality of the study design and implementation.

In  analytic observational  studies, the researcher simply measures the exposure or treatments of the groups. Analytical observational studies include case””control studies, cohort studies and some population (cross-sectional) studies. These studies all include matched groups of subjects and assess of associations between exposures and outcomes.

Observational studies investigate and record exposures (such as interventions or risk factors) and observe outcomes (such as disease) as they occur. Such studies may be purely descriptive or more analytical.

We should finally note that studies can incorporate several design elements. For example, a the control arm of a randomised trial may also be used as a cohort study; and the baseline measures of a cohort study may be used as a cross-sectional study.

Spotting the study design

The type of study can generally be worked at by looking at three issues (as per the Tree of design in Figure 1):

Q1. What was the aim of the study?

• To simply describe a population (PO questions) descriptive
• To quantify the relationship between factors (PICO questions) analytic.

Q2. If analytic, was the intervention randomly allocated?

• No? Observational study

For observational study the main types will then depend on the timing of the measurement of outcome, so our third question is:

Q3. When were the outcomes determined?

• Some time after the exposure or intervention? cohort study (‘prospective study’)
• At the same time as the exposure or intervention? cross sectional study or survey
• Before the exposure was determined? case-control study (‘retrospective study’ based on recall of the exposure)

Advantages and Disadvantages of the Designs

Randomised Controlled Trial

An experimental comparison study in which participants are allocated to treatment/intervention or control/placebo groups using a random mechanism (see randomisation). Best for study the effect of an intervention.

Advantages:

• unbiased distribution of confounders;
• blinding more likely;
• randomisation facilitates statistical analysis.

Disadvantages:

• expensive: time and money;
• volunteer bias;
• ethically problematic at times.

Crossover Design

A controlled trial where each study participant has both therapies, e.g, is randomised to treatment A first, at the crossover point they then start treatment B. Only relevant if the outcome is reversible with time, e.g, symptoms.

• all subjects serve as own controls and error variance is reduced thus reducing sample size needed;
• all subjects receive treatment (at least some of the time);
• statistical tests assuming randomisation can be used;
• blinding can be maintained.
• all subjects receive placebo or alternative treatment at some point;
• washout period lengthy or unknown;
• cannot be used for treatments with permanent effects

Cohort Study

Data are obtained from groups who have been exposed, or not exposed, to the new technology or factor of interest (eg from databases). No allocation of exposure is made by the researcher. Best for study the effect of predictive risk factors on an outcome.

• ethically safe;
• subjects can be matched;
• can establish timing and directionality of events;
• eligibility criteria and outcome assessments can be standardised;
• administratively easier and cheaper than RCT.
• controls may be difficult to identify;
• exposure may be linked to a hidden confounder;
• blinding is difficult;
• randomisation not present;
• for rare disease, large sample sizes or long follow-up necessary.

Case-Control Studies

Patients with a certain outcome or disease and an appropriate group of controls without the outcome or disease are selected (usually with careful consideration of appropriate choice of controls, matching, etc) and then information is obtained on whether the subjects have been exposed to the factor under investigation.

• quick and cheap;
• only feasible method for very rare disorders or those with long lag between exposure and outcome;
• fewer subjects needed than cross-sectional studies.
• reliance on recall or records to determine exposure status;
• confounders;
• selection of control groups is difficult;
• potential bias: recall, selection.

Cross-Sectional Survey

A study that examines the relationship between diseases (or other health-related characteristics) and other variables of interest as they exist in a defined population at one particular time (ie exposure and outcomes are both measured at the same time). Best for quantifying the prevalence of a disease or risk factor, and for quantifying the accuracy of a diagnostic test.

• cheap and simple;
• ethically safe.
• establishes association at most, not causality;
• recall bias susceptibility;
• confounders may be unequally distributed;
• Neyman bias;
• group sizes may be unequal.

25 Types of Research Designs

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Research design refers to the strategies and methods researchers employ to carry out their research and reach valid and reliable results.

It can refer to the collection, interpretation, and analysis of the dataset.

While various sources claim there are between 4 and 5 types of research design (each list, it seems, differs in its arguments), under each type are sub-types, representing the diversity of ways of going about conducting research.

For example, Jalil (2015) identified five types: descriptive, correlational, experimental, and meta-analytic. But the farther we broaden our scope to include the wide array of fields of study in academic research, the more we can incorporate – for example, in cultural studies, thematic content analysis is a very common, albeit somewhat alternative, way of designing a study of empirical data.

So, below, I present 25 potential forms of research design that can be employed in an academic empirical study.

Types of Research Designs

1. experimental research design.

The experimental research design involves manipulating one variable to determine if changes in one variable lead to changes in another variable.

An experimental research design tends to split research participants into two groups, known as the control group and experimental group(Abbott & McKinney, 2013). The control group receives nothing, or, a placebo (e.g. sugar pill), while the experimental group is provided the dependant variable (e.g. a new medication).

Participants are typically assigned to groups at random in order to control for any extraneous variables that could influence the results. Furthermore, the study may occur in a controlled environment where extraneous variables can be controlled and minimized, allowing for the analysis of cause-and-effect.

Example of Experimental Research Design In a study exploring the effects of sleep deprivation on cognitive performance, the researcher might take two groups of people. One group is deprived of sleep for 24 hours (experimental group), while the other group is allowed a full night’s sleep (control group). The researcher then measures the cognitive performance of both groups. If the sleep-deprived group performs significantly worse, it could be inferred that sleep deprivation negatively affects cognitive performance.

See Also: Experimental vs Observational Research Design

2. Causal Research Design

Causal research design is used when the goal is to find a cause-and-effect relationship between two variables – an independent vs dependent variable.

This design is used to determine whether one variable influences another variable (Ortiz & Greene, 2007).

Causal research involves conducting experiments where one or more variables are manipulated and the effects are measured.

It seeks to isolate cause-and-effect relationships by holding all factors constant except for the one under investigation (the independent variable). Researchers then observe if changes to the manipulated variables cause changes to the variable they are measuring (the dependent variable).

There are three criteria that must be met to determine causality in a causal research design:

• Temporal Precedence: This means the cause (independent variable) must occur before the effect (dependent variable). For example, if you are studying the impact of studying on test scores, the studying must occur before the test.
• Covariation of the Cause and Effect: Observing that a change in the independent variable is accompanied by a change in the dependent variable. For example, decreased class sizes (cause) might lead to improved test scores (effect), which we could plot on a chart.
• No Plausible Alternative Explanations: The researcher must be able to rule out other factors or variables that might be causing the observed effect. This is often the most challenging criteria to meet and is typically addressed through the use of control groups and random assignment in experimental designs (Ortiz & Greene, 2007)..

Example of Causal Research Design Consider a study that aims to investigate the impact of classroom size on academic achievement. The researchers choose a causal research design, where they collect data on the size of each classroom (independent variable) and then compare that to the average academic performance of each class group (dependent variable). They would then be bale to determine whether students in smaller classes perform at any different rate, on average, compared to larger class groups. If there is a difference, they may be able to demonstrate a causal relationship between classroom size and academic performance.

3. Correlational Research Design

A correlational research design is used when researchers want to determine if there is a relationship between two variables, but it does not necessarily mean that one variable causes changes in the other (Marczyk, DeMatteo & Festinger, 2010).

The primary goal is to identify whether two variables are related and if they move together, i.e., change in one variable is associated with the change in another variable (Abbott & McKinney, 2013; Marczyk, DeMatteo & Festinger, 2010). This relationship can be positive (both variables increase or decrease together), negative (one variable increases while the other decreases), or nonexistent (no connection between the variables).

However, unlike causal research design that we looked at above, correlation does not imply causation. Just because two variables correlate doesn’t mean that changing one variable will change the other.

Example of Correlational Research Design For example, researchers could be interested in finding out if there is a relationship between the amount of time spent on homework (variable one) and academic performance (variable two). If students who spend more time on homework tend to have better academic performance, then there is a positive correlation between these two variables. However, they may not be able to determine that this correlation implies causation. Other factors could be at play. To make it causal design, they may need to employ control and experimental groups in the study.

Also See: 15 Examples of Random Assignment

4. Diagnostic Research Design

Diagnostic research is a type of research that is conducted to identify and understand the nature of a phenomenon or to develop a profile of characteristics related to a certain issue (Abbott & McKinney, 2013; Leavy, 2022).

It is more precise and focused than exploratory research and goes further to provide additional insights about the specifics of the problem.

In the context of medical or psychological research, diagnostic research often involves detailed examinations or tests to identify the nature of a disease or disorder, its causes, symptoms, and effects. The objective of this research is to gain a deep understanding of the problem in order to provide a diagnosis or create an intervention (Leavy, 2022).

In non-clinical research, diagnostic research still focuses on understanding a particular issue or phenomenon in depth. Researchers collect data and investigate to determine the source of particular problems, behaviors, attitudes, or market trends. This could involve conducting detailed interviews, observations, surveys, or reviewing existing records.

Example of Diagnostic Research Design Suppose a teacher is curious about why students in her class are struggling with reading comprehension. She may conduct a diagnostic study where she individually assesses each student’s reading skills , looking for patterns of common difficulties. She may find that many of the students struggle with vocabulary, identifying main ideas, or making inferences. This insight can then guide her teaching strategies to improve students’ reading comprehension.

5. Exploratory research design

Exploratory research is a type of research conducted to clarify ambiguous problems or discover ideas that can be potential research topics.

This type of research is usually conducted when a problem is not clearly defined. It is the preliminary stage of research and helps to define the problem statement, understand the underlying phenomena, or set the stage for further research (Abbott & McKinney, 2013).

Exploratory research design does not aim to provide conclusive results or decide a course of action. Instead, it focuses on gaining insights and familiarity with the subject.

It’s typically characterized by its flexibility, as it allows researchers to shift their focus as new data and insights are collected. The main methods of data collection for exploratory research are survey research, qualitative research , literature reviews , case studies, and focus groups.

Exploratory Research Example Design Consider a business that is noticing a decline in its customer retention rates. They are not sure of the cause, so they decide to conduct exploratory research. They may start with open-ended surveys or interviews with their customers to understand their needs and challenges. Based on the initial feedback, they might find several possible causes – poor customer service, outdated product features, or increased competition. These insights can help define further research to fully understand and address the identified issues.

6. Observational research design

Observational research, as the name suggests, involves observing subjects in their natural environment without any manipulation or control by the researcher.

This can be done in a number of ways including direct observation, participant observation , unobtrusive observation, and structured observation (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).

Observational research is particularly valuable when researchers want to study behavior as it naturally occurs, without interference or intervention. It can provide a high degree of ecological validity , which means the behavior is likely a reflection of real life because it’s observed in a natural setting. However, observational research may be influenced by observer bias and can be time-consuming and difficult to replicate.

Example of Observational Research Design  A child psychologist may want to study the impact of playground design on children’s social interactions. Using observational research, they could spend time watching children play in different playground environments, recording their interactions and behaviors. This could reveal patterns such as more cooperative play on playgrounds with particular features, which could inform future playground design.

7. Descriptive research design

Descriptive research is a form of research design aims to accurately and systematically describe a situation, problem, phenomenon, service, or program, or provides information about, say, the living conditions of a community, or describes attitudes towards an issue (Abbott & McKinney, 2013;).

It provides a snapshot of the variables included in the study at a particular point in time.

Descriptive research does not fit neatly into the definition of either quantitative or qualitative research methodologies , but instead, it can utilize elements of both, often within the same study.

The descriptive function of research relies on instrumentation for measurement and observations. The descriptive research results in our ability to carefully describe the phenomena, events, or case under study.

Example of Descriptive Research A market research company is hired to understand the types of customers frequenting a new shopping mall. They may conduct descriptive research using methods such as surveys, interviews, and observations. This could result in a detailed description of customer demographics, preferences, and behaviors. The information could then be used by the mall’s management to make strategic business decisions.

8. Case study

Case study research is a design that involves studying a specific phenomenon, person, or group of people in a specific context (Bennett, 2004).

This allows you to go into depth in the study, gaining strong insights into a specific instance.

Case studies tend to be qualitative, not quantitative. The knowledge that can be generated via a case study project can reveal high-quality insights, but is not generalizable because there is not sufficient breadth of subjects or contexts in order to get a good grasp of whether the case study is representative of a broader experience.

Example of a Case Study A researcher conducts a case study in one classroom, examining a new teaching method that the teachers have implemented. The study focuses on how the teacher and students adapt to the new method, conducting semi-structured interviews with the teachers and students. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other educational settings, as statistical significance has not been established to achieve generalization.

See Also: Case Study Advantages and Disadvantages

9. Action research design

Action research is a research design that involves using the scientific method to study professional practice in the workplace and improve upon it.

The defining purpose of action research is to improve workplace practice. In this sense, it’s extremely practical, designed to achieve tangible results for a specific practitioner in a specific setting.

Gillis and Jackson (2002) offer a very concise definition of action research:

“systematic collection and analysis of data for the purpose of taking action and making change” (p.264).

Action research is often participatory, meaning the practitioner is both a participant in the research and the person studying the phenomenon (Macdonald, 2012).

This design is often cyclical, meaning the practitioner implements a change, studies it, then uses the feedback to implement another change, and so forth, until substantive change is made.

Example of Action Research Design I supervised one research student, Mark, who completed an action research study in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience. You can read his study here (Ellison & Drew, 2019).

10. Cross-sectional research design

A cross-sectional research design involves collecting data on a sample of individuals at one specific point in time (Levin, 2006).

Unlike longitudinal studies, which examine variables across a time horizon, a cross-sectional design will only collect data at one point in time.

The researchers will generally collect various datapoints at the one time to study how they are interrelated, the predominance of some other others, and so on.

A cross-sectional research is descriptive only, painting a picture of a sub-population being analyzed, but cannot determine cause and effect .

Cross-Sectional Research Example Psychologists could collect data on people’s socioeconomic status (for example, their current income levels, education, and occupation). During the study, they may also gather data on self-reported mental health status using validated Likert scales. Based on this dataset, the researchers then explored the relationship between socioeconomic status and profession and mental health. While this provided excellent descriptive insights about which professions and SES groups tend to have higher mental health concerns, the researchers could not determine causal factors through the cross-sectional study alone.

11. Sequential research design

Sequential research design is a method that combines both quantitative and qualitative research approaches, in a sequence, to gain a broader understanding of a research problem (Abbott & McKinney, 2013; Leavy, 2022).

This approach allows the researcher to take the benefits of both methods, using one method to enhance or inform the other.

It may take the form of:

• QUAN→QUAL: This design involves conducting quantitative analysis first, then supplementing it with a qualitative study.
• QUAL→QUAN: This design goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

This type of research design allows for flexibility and is particularly effective when the researcher doesn’t have a clear idea of the problems that will arise during the research.

It also allows the researcher to adapt the study according to the emerging results, which can lead to a more nuanced and informed understanding of a research problem. However, this research design can be time-consuming and requires substantial resources, as it involves two phases of research.

Sequential Research Example  A researcher interested in understanding the effectiveness of a new teaching method could first conduct quantitative research, such as a survey, to measure the overall student performance. Then, in the second phase, the researcher could conduct qualitative research, such as focus group discussions or interviews, to understand the students’ experiences with the new teaching method.

12. Cohort research design

Cohort research is a form of longitudinal study design that observes a defined group, or cohort, over a period of time.

The cohort can be defined by a common characteristic or set of characteristics. Cohort studies are often used in life sciences, social sciences , and health research (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).

Cohort research allows for the analysis of sequences and patterns in life events. It can be retrospective (observing historical data) or prospective (collecting data forward in time).

The major advantage of cohort research is its ability to study causation, i.e., to make definitive statements about cause-and-effect relationships. However, it can be time-consuming and expensive to conduct.

Cohort Research Example A health researcher could study a cohort of smokers and non-smokers over a period of 20 years to understand the long-term effects of smoking on lung health. The researcher could gather data at regular intervals, tracking changes in the participants’ health over time.

13. Historical research design

Historical research design involves studying the past to draw conclusions that are relevant to the present or the future (Danto, 2008).

This research method involves a deep dive into historical data to gain a clear understanding of past events, contexts, or phenomena.

Historical research helps us understand how past events inform current circumstances. It can include the examination of records, documents, artifacts, and other archival material (Danto, 2008).

However, the reliability of historical research is often challenged due to the accuracy of past records, potential bias in recorded histories, and the interpretive nature of the analysis.

Historical Research Example  A historian might conduct research on the economic impact of the Great Depression on the United States. They would likely analyze data from that era, such as economic indicators, governmental policies, and personal accounts to form a comprehensive understanding of the economic climate of the time.

14. Field research design

Field research is a qualitative method of research concerned with understanding and interpreting the social interactions, behaviors, and perceptions within a specific social or environmental setting.

It involves collecting data ‘in the field’, i.e., in a natural or social setting, and often involves direct and prolonged contact with participants.

Field research can include observations, interviews, and document review. The goal is to gain insights into a group’s practices, behaviors, and culture by observing and interacting with them in their natural environment. This method can provide rich, contextual data but is also time-intensive and requires significant planning to ensure representative sampling and accurate recording of data.

Field Research Example An anthropologist studying the social practices of a remote indigenous tribe may live with the tribe for several months, participating in their daily activities, observing, and documenting their practices and rituals. Through this field research, they can understand the tribe’s social structure, beliefs, and customs in

15. Systematic review

A systematic review is a type of research design that involves a comprehensive and structured overview of existing literature on a specific topic (Jalil, 2015).

This research method aims to collate all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question.

The systematic review follows a transparent and replicable methodology to minimize bias and ensure reliability.

It involves identifying, evaluating, and interpreting all available research relevant to the research question.

However, it can be time-consuming and resource-intensive and relies heavily on the availability and quality of existing studies.

Systematic Review Example A health researcher interested in the impact of a plant-based diet on heart disease might conduct a systematic review of all published studies on the topic. They would gather, analyze, and synthesize data from these studies to draw a comprehensive understanding of the current evidence base on this issue.

A survey research design involves gathering information from a sample of individuals using a standardized questionnaire or interview format (Fowler, 2013).

Surveys can be used to describe, compare, or explain individual and societal phenomena. Surveys allow for data collection from a large population, in a cost-effective and efficient manner (Fowler, 2013).

They can be delivered in various formats, such as online, telephone, mail, or in-person.

However, the reliability of survey data can be affected by several factors, such as response bias and sample representativeness.

Survey Example A market research company might use a survey to understand consumer preferences for a new product. They could distribute the survey to a representative sample of their target market, asking questions about preferences, behaviors, and demographics to inform the product’s development and marketing strategy.

17. Meta-analysis research design

A meta-analysis is a type of research design that involves looking over the current literature on a topic and assessing its quality, trends, and collective insights (Borenstein et al., 2021).

Meta-analysis doesn’t involve collecting first-hand data, but rather using secondary data in the form of the results of other peoples’ studies.

It then analyzes the quality and findings of each study in-depth, comparing and contrasting each study, and synthesizing the data from the collective studies deemed of sufficient quality, to see what collective knowledge these studies can provide (Borenstein et al., 2021).

Meta-analyses are considered some of the most valuable and respected research designs because they can demonstrate that there is sufficient data from the scientific community for an authoritative scientific account of a phenomenon or topic.

Meta-Analysis Example In the early 2000s, a few small studies arguing that vaccines caused autism caused moral panic in the media. In response, several meta-analyses emerged that combined the collective data from the scientific community. These meta-analyses demonstrated that, across the scientifically rigorous studies, overwhelming consensus showed there was no correlation between vaccines and autism (see: Taylor, Swerdfeger & Eslick, 2014).

18. Mixed-method research design

Mixed-method research design is a method that combines both quantitative (numerical data) and qualitative (non-numerical data) research techniques, methods, approaches, concepts or language into a single study.

This approach to research allows for the capturing of a more complete, holistic picture of the phenomena being studied (Leavy, 2022; Marczyk, DeMatteo & Festinger, 2010).

Mixed-method research can provide a more in-depth understanding of a research problem or question. It allows the researcher to explore complex phenomena and validate the findings.

However, it requires a thorough understanding of both quantitative and qualitative research methods and can be time-consuming.

Mixed-Methods Example An education researcher interested in student motivation might use a mixed-method approach. They could distribute a survey (quantitative method) to measure levels of motivation, and then conduct interviews ( qualitative method ) to gain a deeper understanding of factors influencing student motivation.

19. Longitudinal research design

Longitudinal studies take place over a long period of time to explore changes to the research subjects or variables over time (Neale, 2020).

This sort of study is often valuable in detecting correlations between variables over the course of an intervention.

By examining multiple data points at different period, it’s possible to record continuous changes within things like consumer behavior or demographics of a society (Vogl, 2023).

This makes a detailed analysis of change possible.

For example, a national census, conducted every 5 years, can be considered longitudinal. It gathers comparative demographic data that can show how the demographics of an area have changed over time.

Longitudinal Study Example The famous Minnesota Twins study examined identical twins who were raised in separate environments to examine whether behavioral and personality traits were genetic or environmental. The study by Thomas J Bouchard, which took place between 1979 to 1990, argued that identical twins who grew up separate and in different environments did not display any greater chances of being different from each other than twins that were raised together in the same house. The study indicated that similarities in personality and behavior between twins are likely genetic rather than environmental in nature, giving sway to the argument that nature is more powerful than nurture (Bouchard et. al., 1990).

20. Philosophical research design

Philosophical research is a research design that uses philosophical methods to address broad questions about issues such as reality, morality, existence, truth, justice, and freedom (Novikov & Novikov, 2013).

This type of research often involves broad, abstract thinking and deep contemplation on the fundamental nature of human existence.

Philosophical research often relies on the critical analysis of texts , argumentation, and the formulation of theories. It requires abstract thinking and logical reasoning, but it doesn’t usually involve empirical studies.

However, it’s invaluable for underpinning other research methods and for informing our understanding of fundamental principles and theories.

Philosophical Research Example A researcher studying ethics might use a philosophical research design to explore the concept of ‘justice’ in various societies. They would likely examine a variety of texts, historical contexts , and moral frameworks, before formulating a comprehensive theory of justice.

21. Grounded Theory

Grounded theory is characterized by a research study where no hypothesis is being tested. Instead, a hypothesis or ‘theory’ emerges out of the study (Tracy, 2019).

This goes against most research designs, where a researcher starts with a hypothesis and then they create a study to test the hypothesis. Then, they would usually come to a result affirming or debunking the study.

But in grounded theory, we start with a phenomenon, and then we go about studying it to identify themes and insights that emerge from the data. At the end of the study, the researchers would come up with a theory or hypothesis.

This has the strength of remaining open-minded about the possible outcomes of the study, and not being restricted to only studying a specifically noted hypothesis from the beginning.

Grounded Theory Example Developing a Leadership Identity   by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

22. Ethnographic Research Design

Ethnographic research is a qualitative research design that aims to explore and understand the culture, social interactions, behaviors, and perceptions of a group of people (Stokes & Wall, 2017).

The methodology is derived from the field of anthropology where researchers immerse themselves in the culture they’re studying to gather in-depth insights.

An ethnographic study is usually conducted over an extended period of time and involves observing and interacting with the participants in their natural setting  (Stokes & Wall, 2017).

This method can provide rich, detailed, and nuanced data. However, it is time-consuming, and its success heavily relies on the skill and sensitivity of the researcher to understand and interpret the cultural nuances of the group.

Ethnographic Research Example A researcher interested in understanding the impact of digital technology on the daily life of a remote indigenous tribe might spend several months living with the tribe. The researcher would observe and participate in their daily activities, conduct informal interviews, and take detailed field notes to capture the changes and influences brought about by digital technology.

23. Quasi-Experimental Research Design

A quasi-experimental research design resembles an experimental design but lacks the element of random assignment to treatment or control (Abbott & McKinney, 2013; Leavy, 2022).

Instead, subjects are assigned to groups based on non-random criteria. Quasi-experiments are often used in social sciences where it’s difficult or ethically problematic to manipulate independent variables and randomly assign participants (Ortiz & Greene, 2007).

While quasi-experimental designs help establish causal relationships, they can be subject to confounding variables, which may impact the validity of the results. Also, the lack of random assignment can result in selection bias .

Quasi-Experimental Design Example A researcher studying the impact of an educational program on students’ performance might compare the test scores of students who chose to participate in the program (the treatment group) with those who did not (the control group). The researcher could control for factors such as gender, age, and previous performance, but without random assignment, there could be other differences between the groups that impact the results.

24. Comparative Research Design

Comparative research is a research design that involves comparing two or more groups, cultures, variables, or phenomena to identify similarities and differences (Abbott & McKinney, 2013).

The comparison can be cross-sectional (comparing at a single point in time) or longitudinal (comparing over time).

Comparative research can provide insight into the effects of different variables and contribute to understanding social, economic, political, or cultural issues across different contexts. However, ensuring comparability can be challenging as factors influencing the variables being studied can vary widely between contexts.

Comparative Research Design Example A social scientist studying gender inequality might compare the wage gap, educational attainment, and political representation in several countries. The researcher would collect data from each country and conduct a comparative analysis to identify patterns, trends, and differences, contributing to a broader understanding of gender inequality globally.

25. Thematic Content Analysis

Content analysis  has a range of sub-designs, such as semiotic analysis,  multimodal analysis , and  discourse analysis . But overall, this design focuses on the analysis of texts and language.

A content analysis will involve systematic and objective coding and interpreting of text or media to identify patterns, biases , themes, ideologies, and so on (Schweigert, 2021).

They may focus on newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

The design is often thematic, involving deductive or inductive coding , whereby researchers look through the data for ‘codes’ such as word choice, word repetition, and other meaning-making elements which, combined, can give insights into themes that emerge throughout the texts.

Content Analysis Example Poorebrahim and Zarei (2013) employ a popular type of content analysis called critical discourse analysis (common in  poststructuralist  and  critical theory research ) to study newspapers in their study titled How is Islam Portrayed in Western Media? . This study combs through a group of media texts to explore the language and symbolism that is used in relation to Islam and Muslims. The study demonstrates how media content has the capacity to stereotype Muslims, representing anti-Islam bias or failure to understand the Islamic world.

Abbott, M. L., & McKinney, J. (2013).  Understanding and applying research design . John Wiley & Sons.

Bennett, A. (2004). Case study methods: Design, use, and comparative advantages.  Models, numbers, and cases: Methods for studying international relations ,  2 (1), 19-55.

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021).  Introduction to meta-analysis . John Wiley & Sons.

Danto, E. A. (2008).  Historical research . Oxford University Press.

Fowler Jr, F. J. (2013).  Survey research methods . London: Sage publications.

Gillis, A., & Jackson, W. (2002).  Research Methods for Nurses: Methods and Interpretation . Philadelphia: F.A. Davis Company.

Jalil, M. M. (2013). Practical guidelines for conducting research-Summarising good research practice in line with the DCED standard.  Available at SSRN 2591803 .

Leavy, P. (2022).  Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches . Guilford Publications.

Levin, K. A. (2006). Study design III: Cross-sectional studies.  Evidence-based Dentistry ,  7 (1), 24-25.

Macdonald, C. (2012). Understanding participatory action research: A qualitative research methodology option.  Canadian Journal of Action Research, 13 , 34-50.  https://doi.org/10.33524/cjar.v13i2.37  Mertler, C. A. (2008).  Action Research: Teachers as Researchers in the Classroom . London: Sage.

Marczyk, G. R., DeMatteo, D., & Festinger, D. (2010).  Essentials of research design and methodology  (Vol. 2). John Wiley & Sons.

Neale, B. (2020).  Qualitative longitudinal research: Research methods . Bloomsbury Publishing.

Novikov, A. M., & Novikov, D. A. (2013).  Research methodology: From philosophy of science to research design  (Vol. 2). CRC Press.

Ortiz, D., & Greene, J. (2007). Research design: qualitative, quantitative, and mixed methods approaches.  Qualitative Research Journal ,  6 (2), 205-208.

Stokes, P., & Wall, T. (2017).  Research methods . New York: Bloomsbury Publishing.

Taylor, L. E., Swerdfeger, A. L., & Eslick, G. D. (2014). Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies.  Vaccine ,  32 (29), 3623-3629.

Tracy, S. J. (2019).  Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Vogl, S. (2023). Mixed methods longitudinal research. In  Forum Qualitative Sozialforschung/Forum: Qualitative Social Research  (Vol. 24, No. 1).

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Study designs: Part 1 - An overview and classification

Affiliations.

• 1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India.
• 2 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.
• PMID: 30319950
• PMCID: PMC6176693
• DOI: 10.4103/picr.PICR_124_18

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

Keywords: Epidemiologic methods; research design; research methodology.

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Classification of research study designs

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Organizing Academic Research Papers: Types of Research Designs

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

Introduction

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

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

General Structure and Writing Style

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

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

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

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

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

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

Definition and Purpose

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

What do these studies tell you?

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

What these studies don't tell you?

• It is harder to do than conducting conventional studies because the researcher takes on responsibilities for encouraging change as well as for research.
• Action research is much harder to write up because you probably can’t use a standard format to report your findings effectively.
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action (e.g. change) and research (e.g. understanding) is time-consuming and complex to conduct.

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

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

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

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

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

Conditions necessary for determining causality:

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

Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed.  Thousand Oaks, CA: Pine Forge Press, 2007; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

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

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

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36;  Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

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

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

Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods. Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

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

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject.
• Descriptive research is often used as a pre-cursor to more quantitatively research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research can not be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999;  McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

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

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

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

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

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

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

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

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

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

Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

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

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

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

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

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

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010.

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

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

Chapter 4, Research Methodology and Design . Unisa Institutional Repository (UnisaIR), University of South Africa;  Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, D.C.: Falmer Press, 1994; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

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

Rebecca Betensky, Harvard University, Course Lecture Note slides ; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis . Wikipedia.  

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Research Methods Guide: Research Design & Method

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

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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• 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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Descriptive Research and Case Studies

Learning objectives.

• Explain the importance and uses of descriptive research, especially case studies, in studying abnormal behavior

Types of Research Methods

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions; to extensive, in-depth interviews; to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While surveys allow results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While existing records can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later, there is a tremendous amount of control over variables of interest. While performing an experiment is a powerful approach, experiments are often conducted in very artificial settings, which calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Clinical or Case Studies

Psychologists can use a detailed description of one person or a small group based on careful observation.  Case studies  are intensive studies of individuals and have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Case studies add descriptive richness. Case studies are also useful for formulating concepts, which are an important aspect of theory construction. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large study.

Sigmund Freud   developed  many theories from case studies (Anna O., Little Hans, Wolf Man, Dora, etc.). F or example, he conducted a case study of a man, nicknamed “Rat Man,”  in which he claimed that this patient had been cured by psychoanalysis.  T he nickname derives from the fact that among the patient’s many compulsions, he had an obsession with nightmarish fantasies about rats. 

Today, more commonly, case studies reflect an up-close, in-depth, and detailed examination of an individual’s course of treatment. Case studies typically include a complete history of the subject’s background and response to treatment. From the particular client’s experience in therapy, the therapist’s goal is to provide information that may help other therapists who treat similar clients.

Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology.

A commonly described limit of case studies is that they do not lend themselves to generalizability . The other issue is that the case study is subject to the bias of the researcher in terms of how the case is written, and that cases are chosen because they are consistent with the researcher’s preconceived notions, resulting in biased research. Another common problem in case study research is that of reconciling conflicting interpretations of the same case history.

Despite these limitations, there are advantages to using case studies. One major advantage of the case study in psychology is the potential for the development of novel hypotheses of the  cause of abnormal behavior   for later testing. Second, the case study can provide detailed descriptions of specific and rare cases and help us study unusual conditions that occur too infrequently to study with large sample sizes. The major disadvantage is that case studies cannot be used to determine causation, as is the case in experimental research, where the factors or variables hypothesized to play a causal role are manipulated or controlled by the researcher. 

Link to Learning: Famous Case Studies

Some well-known case studies that related to abnormal psychology include the following:

• Harlow— Phineas Gage
• Breuer & Freud (1895)— Anna O.
• Cleckley’s case studies: on psychopathy ( The Mask of Sanity ) (1941) and multiple personality disorder ( The Three Faces of Eve ) (1957)
• Freud and  Little Hans
• Freud and the  Rat Man
• John Money and the  John/Joan case
• Genie (feral child)
• Piaget’s studies
• Rosenthal’s book on the  murder of Kitty Genovese
• Washoe (sign language)
• Patient H.M.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about handwashing, we have other options available to us.

Suppose we send a researcher to a school playground to observe how aggressive or socially anxious children interact with peers. Will our observer blend into the playground environment by wearing a white lab coat, sitting with a clipboard, and staring at the swings? We want our researcher to be inconspicuous and unobtrusively positioned—perhaps pretending to be a school monitor while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. For example, psychologists have spent weeks observing the behavior of homeless people on the streets, in train stations, and bus terminals. They try to ensure that their naturalistic observations are unobtrusive, so as to minimize interference with the behavior they observe. Nevertheless, the presence of the observer may distort the behavior that is observed, and this must be taken into consideration (Figure 1).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. Although something as simple as observation may seem like it would be a part of all research methods, participant observation is a distinct methodology that involves the researcher embedding themselves into a group in order to study its dynamics. For example, Festinger, Riecken, and Shacter (1956) were very interested in the psychology of a particular cult. However, this cult was very secretive and wouldn’t grant interviews to outside members. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult. Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. [1]

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness in surveys when compared to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people do not always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the U.S. Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think it Over

Research has shown that parental depressive symptoms are linked to a number of negative child outcomes. A classmate of yours is interested in  the associations between parental depressive symptoms and actual child behaviors in everyday life [2] because this associations remains largely unknown. After reading this section, what do you think is the best way to better understand such associations? Which method might result in the most valid data?

clinical or case study:  observational research study focusing on one or a few people

correlational research:  tests whether a relationship exists between two or more variables

descriptive research:  research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

experimental research:  tests a hypothesis to determine cause-and-effect relationships

generalizability:  inferring that the results for a sample apply to the larger population

inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event

naturalistic observation:  observation of behavior in its natural setting

observer bias:  when observations may be skewed to align with observer expectations

population:  overall group of individuals that the researchers are interested in

sample:  subset of individuals selected from the larger population

survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

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• Slatcher, R. B., & Trentacosta, C. J. (2011). A naturalistic observation study of the links between parental depressive symptoms and preschoolers' behaviors in everyday life. Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43), 25(3), 444–448. https://doi.org/10.1037/a0023728 ↵

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• Table of Contents
• Chapter 1: Introduction
• Chapter 2: Creating Trustworthy Guidelines
• Chapter 3: Overview of the Guideline Development Process
• Chapter 4: Formulating PICO Questions
• Chapter 5: Choosing and Ranking Outcomes
• Chapter 6: Systematic Review Overview
• Chapter 7: GRADE Criteria Determining Certainty of Evidence
• Chapter 8: Domains Decreasing Certainty in the Evidence
• Chapter 9: Domains Increasing One's Certainty in the Evidence
• Chapter 10: Overall Certainty of Evidence
• Chapter 11: Communicating findings from the GRADE certainty assessment
• Chapter 12: Integrating Randomized and Non-randomized Studies in Evidence Synthesis

Related Topics:

• Advisory Committee on Immunization Practices (ACIP)
• Vaccine-Specific Recommendations
• Evidence-Based Recommendations—GRADE
• This ACIP GRADE handbook provides guidance to the ACIP workgroups on how to use the GRADE approach for assessing the certainty of evidence.

As described in section 4, authors at the protocol stage may decide that both RCTs and NRS need to be considered, and both types of evidence are retrieved and evaluated. Once the search is complete, the evidence is organized by study design as either randomized or non-randomized. The GRADE certainty of the RCTs should be evaluated first. After assessing each outcome separately, if there is high certainty in the body of evidence coming from RCTs, there is no need to further evaluate or use the NRS to complement or replace the RCTs. If the certainty of evidence from NRS is higher than RCTs, they can be considered as replacement evidence, especially if the NRS have low concerns with indirectness and imprecision. Reviewers might consider using NRS to complement evidence if RCTs do not provide data on populations of interest, or if the NRS studies provide evidence for possible effect modification. Figure 9 provides a visual representation of when NRS may be needed to support evidence from RCTs.

Figure 9: Flow chart depicting when to integrate RCTs and NRS in the evidence synthesis

References in this figure: 1

When high certainty evidence for an outcome is not available in the RCT body of evidence, NRS can be used. There are two scenarios in which this may occur 1 :

• When evidence from RCTs has low or very low certainty, NRS could help increase the overall certainty in the results. The NRS should be evaluated and if the certainty in the evidence is equal to or better than the certainty level of the RCTs, both types of evidence can be used in the decision-making process.
• If an RCT was conducted in men and the target population in the research question was women, NRS may be used to make judgements about the certainty in these results. If the NRS shows the intervention has the same effect in both men and women, then the NRSs can be used to complement the RCT. Conversely, if the studies had shown that there was a notable difference in men and women, the overall certainty in the RCT evidence may need to be downgraded.
• When the RCT evidence does not provide enough information about baseline risk of the control event, NRS may be used. For example, if the PICO question specified children between the ages of 12 and 15 as the target population, however the RCT evidence only provided baseline risk for children under the age of 5, NRS could be used to provide the control event rate for the target age group. The NRS could provide evidence that shows the baseline risk varies between populations or supports the evidence from the RCT.

When either of the two scenarios that result in the use of NRS occur, there are three ways in which the evidence can interact with the RCTs (Figure 10) 2

• Complementary NRS: The NRS can provide information on whether the intervention works similarly in different populations or if there are differential baseline risks between populations. Therefore, when the RCT evidence is indirect, NRS can be used to complement and contextualize as seen in the examples above.
• Sequential NRS: When evidence from RCTs is not sufficient, NRS can help by providing additional information. For example, NRS could provide information on long-term outcomes for patients involved in short-term RCTs. Additionally, when RCTs use surrogate outcomes, the NRS could help determine if the surrogate is relevant to patient-important outcomes.
• Replacement NRS: When the NRS is assessed and the results have a higher level of certainty than the body of evidence from RCTs, the NRS may replace the RCTs. In spite of the lack of randomization, if the NRS is more direct and has better certainty, then decision-makers can consider the NRS as the best available evidence.

Figure 10. Steps that systematic review authors might follow when considering NRS evidence (adapted)

References in this figure: 2

Figure 10 provides an overview of the steps taken when deciding whether to use NRS in addition to evidence from RCTs. When presenting both NRS and RCTs for an outcome in a systematic review, the results can either be presented separately as a narrative synthesis, in separate meta-analysis as a quantitative synthesis or a combination of the two 1 .

• Cuello-Garcia CA, Santesso N, Morgan RL, et al. GRADE guidance 24 optimizing the integration of randomized and non-randomized studies of interventions in evidence syntheses and health guidelines. J Clin Epidemiol. 2022/02// 2022;142:200-208. doi:10.1016/j.jclinepi.2021.11.026
• Schünemann HJ, Tugwell P, Reeves BC, et al. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods. 2013 2013;4(1):49-62. doi:10.1002/jrsm.1078

ACIP GRADE Handbook

This handbook provides guidance to the ACIP workgroups on how to use the GRADE approach for assessing the certainty of evidence.

Computational thinking with game design: An action research study with middle school students

• Open access
• Published: 17 September 2024

Cite this article

You have full access to this open access article

• Lorien Cafarella 1 &
• Lucas Vasconcelos   ORCID: orcid.org/0000-0001-9074-203X 2  

Middle school students often enter Computer Science (CS) classes without previous CS or Computational Thinking (CT) instruction. This study evaluated how Code.org’s block-based programming curriculum affects middle school students’ CT skills and attitudes toward CT and CS. Sixteen students participated in the study. This was a mixed methods action research study that used pre- and post-tests, surveys, artifacts, and interviews as data sources. Descriptive statistics, paired samples t-tests, and inductive thematic analysis were administered. Findings showed a statistically significant increase in participants’ algorithmic thinking, debugging, and pattern recognition skills but not in abstraction skills. Attitudes toward CT and CS improved but the difference was not statistically significant. Qualitative themes revealed benefits of game-based learning to promote CT skills, collaboration to promote successful error debugging, and enjoyment of programming resulting from a balance between structured guidance and creative freedom. Findings emphasize the importance of low-threshold and engaging strategies to introduce novice learners to CT and CS.

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• Artificial Intelligence
• Digital Education and Educational Technology

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1 Introduction

Students have limited exposure to Computer Science (CS) education in K-12 schools (Brown et al., 2014 ; Google & Gallup, 2020 ), which hampers their ability to develop Computational Thinking (CT) skills. CT includes problem-solving skills that students need to acquire in order to flourish in a digital era in which computer software drives several aspects of our lives (Román-González et al., 2017 ; Wing, 2006 ). As such, CT is an essential part of learning for all ages and should be incorporated into K-12 curricula (Runciman, 2011 ) so that students can not only learn how to use computers and consume technology, but also create technologies and use them for innovative problem solving (Kafai, 2016 ; Runciman, 2011 ).

Students’ limited access to CT and CS education is partly due to the decline in the number of qualified CS teachers in the past two decades (Kafai, 2016 ; Runciman, 2011 ). Several recommendations to address the CS teacher shortage include the creation of pathways for teachers to become CS endorsed and using funds for CS professional development (Computer Science Teachers Association, 2019 ). Lack of teacher preparation leads to inadequate CS instruction, which may prevent students from developing positive attitudes toward CT and CS. Very often, K-12 students think CS is unattainable, difficult to learn, and not enjoyable. It is imperative to teach CS to students in ways that integrate positive learning opportunities that not only promote CT skills but also improve their attitudes toward CT and CS.

One way to foster positive student attitudes toward CS education is by adopting fun, engaging, and lower-threshold computational activities that are part of an already established curriculum. Specifically, we propose a combination of game design and block-based programming to foster CT skills among young learners. Using block-based programming to design games (Akcaoglu, 2014 ; Akcaoglu & Kale, 2016 ) is a promising approach that allows students to practice CT skills while constructing a personally relevant artifact (Kafai & Burke, 2015 ; Ketenci et al., 2019 ). This study focuses on Code.org’s block-based programming curriculum for game design. To our knowledge, empirical studies seeking to investigate the impact of that curriculum on students’ CT skills and attitudes toward CT and CS are lacking. The present study addresses this literature gap.

2 Related literature

2.1 computational thinking.

The term computational thinking (CT) has received various definitions over the years. Wing ( 2006 ) explained that CT “involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to CS” (p. 33). CT was further defined by Dagli and Tokmak ( 2022 ) as a problem-solving process aimed to understand how computers work. Brennan and Resnick ( 2012 ) proposed a CT framework that includes three dimensions: computational and programming concepts, computational practices, and computational perspectives. The computational and programming concepts focused on sequences, loops, events, conditionals, operators, and data; computational practices included incremental and iterative programming, testing and debugging, reusing and remixing, and abstracting and modularizing; and computational perspectives emerge regarding the world and oneself through programming (Brennan & Resnick, 2012 ). CT has also been described as the cognitive processes needed to frame problems so that the solutions manifest as computational and algorithmic steps (Aho, 2012 ). For this study, we adopt Shute et al.’s ( 2017 ) definition of CT “as the conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts” (p. 1). This definition was chosen because it emphasizes the concepts of problem solving and creation of solutions which are central to CS education.

The literature on CT includes several skills that are associated with CT. Wing ( 2006 ) described the skills as problem-solving, recursive thinking, abstraction and decomposition, preparing for error correction, and heuristic reasoning. Shute et al. ( 2017 ) believed that the skills included in CT were decomposition, abstraction, algorithms, debugging, iteration, and generalization. Dagli and Tokmak ( 2022 ) defined CT skills as “interpreting and understanding the digital data, algorithmic thinking, critical thinking, and decision making” (p. 513). There is significant overlap in definitions of CT skills. For this study, CT is divided into four skills: algorithmic thinking, abstraction, debugging, and pattern recognition. These four skills are the basis for understanding computational activities whether these involve programming or not.

2.1.1 Algorithmic thinking

An algorithm is a sequence of steps (Peel & Friedrichsen, 2018 ). Algorithmic thinking is a “logical, organized way of thinking used to break down a complicated goal into a series of (ordered) steps using available tools” (Lockwood et al., 2016 , p. 1591). Algorithmic thinking entails creating a sequential order of actions that are logically organized and can be used to guide a machine or a human through the process of solving a complex problem (Chuechote et al., 2020 ). The processes of creating a recipe, designing a flowchart to guide decision making, and writing code that triggers an alarm when a home door is broken into are real applications of algorithms and algorithmic thinking.

2.1.2 Abstraction

Abstraction is the process of simplifying complex content or conveying only the important information that is needed for a given context or audience (Peel & Friedrichsen, 2018 ; Taub et al., 2014 ). In other words, abstraction involves gathering relevant information, discarding unrelated data to develop patterns, and discovering commonalities across different scenarios (Shute et al., 2017 ). In CS, practicing abstraction entails handling complexity by hiding unnecessary details (Cetin & Dubinsky, 2017 ) to hide chunks of an algorithmic sequence that can be accessed later if needed.

2.1.3 Debugging

Debugging is the process of error identification and correction when a solution does not work as expected (Dagli & Tokmak, 2022 ; Kim et al., 2022 ; Shute et al., 2017 ). Programs and other algorithmic sequences rarely work on a first attempt (Brennan & Resnick, 2012 ; Vasconcelos & Kim, 2020 ). When an error or bug is identified, one needs to read the program lines, locate the error, test a hypothesized solution, and evaluate the outcome. Hence, it is crucial for one to develop systematic strategies for dealing with problems and persisting through iterative rounds of debugging until the problem is addressed (Peel & Friedrichsen, 2018 ).

2.1.4 Pattern recognition

Pattern recognition is the ability to “identify patterns/rules underlying the data/information structure” (Shute et al., 2017 , p. 153). These patterns include specific programming concepts that are linked to events and interactions in the algorithmic sequence. To use pattern recognition, one must recognize patterns or sequences in previously written algorithms and then effectively apply them to a situation (Chalmers, 2018 ) to solve a different problem. So, the ability to identify patterns in a program and reuse or remix them is what pattern recognition entails (Prextová et al., 2018 ).

3 Attitudes toward CS and programming

Students often hold stereotypical beliefs about CS. These stereotypes include that CS is just about coding, it is only for smart people, it is boring, or requires a tremendous amount of work (Carter, 2006 ; Lewis et al., 2010 ; Taub et al., 2012 ; Vasconcelos et al., 2022 , 2023 ). Students also hold misconceptions about careers in the computing industry. They assume that programming is the foundation of computing jobs when in reality the foundation of CS is problem solving (Grover et al., 2015 ; Taub et al., 2012 ) regardless of whether it involves programming or not. Inaccurate perceptions about CS may lead to negative attitudes and lowered self-efficacy, which is a predicting factor of academic performance and future involvement in CS (Gunbatar & Karalar, 2018 ). Inaccurate perceptions of CS and the level of challenge in computational tasks lead to negative attitudes toward CS education and careers.

Another factor that may lead to negative attitudes is the nature of CT activities themselves (Gunbatar & Karalar, 2018 ). A study that introduced middle schoolers to unplugged CT activities without programming tasks found no improvement on students’ attitudes and intentions toward studying CS (Taub et al., 2012 ). Alternatively, students who perceive the programming task as too challenging or far beyond their abilities are more likely to disengage and give up (Durak, 2020 ). This may happen with text-based programming environments, which are too complex for novice learners who would need to type up commands and follow syntax rules. Students often perceive text-based programming as time consuming and not fun (Carter, 2006 ; Weintrop & Wilensky, 2018 ). Negative attitudes due to a high level of difficulty in programming may cause disengagement in CT activities, which in turn hinders development of CT skills (Zhao & Shute, 2019 ).

4 Game design to teach CT

4.1 game-based learning and digital games.

Game-based learning is broadly defined as an approach that uses games to promote playful and fun learning (Barman & Kjällander, 2022 ; Homer et al., 2020 ) while also promoting interaction with target educational content (Banihashem et al., 2023 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ; Lamb et al., 2018 ; Noroozi et al., 2020 ). This approach has been extensively adopted to improve learners’ motivation and engagement (Barreto et al., 2018 ; Leonard et al., 2016 ; Partovi & Razavi, 2019 ) across a variety of contexts. Game-based learning often features digital games, which are interactive and complex systems that immerse one in a scenario or virtual world as one uses gameplay mechanics, follows pre-determined rules, overcomes challenges, and interacts with other characters or environmental elements to achieve certain goals (Akcaoglu & Green, 2019 ; Barreto et al., 2018 ; Chen et al., 2020 ). Digital games may be designed purely for leisure and entertainment, and these are known as commercial games, or for educational purposes by exposing players to content that is embedded within their structure, and these are called educational or serious games (Dahalan et al., 2024 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ). While digital games can be played with mobile devices, computers, and consoles, this study focuses on browser-based games that can be designed and played with block-based programming languages.

4.2 Game design

Game design is a specific type of game-based learning in which students lead the process of designing and developing digital games using a set of tools and/or online platforms (Akcaoglu, 2014 ; Cheng et al., 2023 ; Kafai & Burke, 2015 ). Game design is inherently a constructionist approach (Kafai & Burke, 2015 ; Papert, 1980 ) as it involves construction of knowledge through designing, developing, and playing a digital game. In CS education, game design offers an open-ended opportunity to apply CT skills through creation of unique, interactive, and functional user interfaces (Jiang et al., 2022 ; Kafai & Burke, 2015 ; Repenning et al., 2010 ; Wang et al., 2023 ) using a block-based programming language. Game design grounds abstract CT concepts into concrete game play practices (Lu et al., 2023 ; Wang et al., 2023 ). For instance, designing a game engages learners in various levels of CT that involve identifying a problem and scenario to serve as the game foundation, breaking down the game problem into smaller parts that can be represented on the screen, abstracting and generalizing algorithmic sequences to control similar behaviors across multiple game elements, creating an algorithmic sequence of steps that encapsulate target concepts/commands to be used as the problem solution (that is, to win the game), and practicing analytical reasoning that goes with the iterative process of testing and debugging algorithms that control a game. Research has shown that game design can promote higher academic achievement in programming courses (Topalli & Cagiltay, 2018 ), greater motivation to use and understand CT (Ouahbi et al., 2015 ), and improved CT skills to apply concepts such as variables, loops, and if–then statements (Hsu & Tsai, 2023 ; Kafai & Burke, 2015 ; Mladenović et al., 2018 ).

Game design offers fun and engaging opportunities to manipulate game elements (e.g., a storyline, rewards) (Filippou et al., 2018 ; Noroozi et al., 2020 ) and develop CT skills, which may positively influence students’ attitudes toward CS. Moreover, reducing the challenge in programming tasks by adopting a block-based programming language can boost confidence and promote positive attitudes (Grover et al., 2015 ; Gunbatar & Karalar, 2018 ). Rather than typing up text to create commands, which requires knowledge of programming syntax, students just need to stack blocks to sequentially combine a series of commands that lead to a desired output (Vasconcelos & Kim, 2020 , 2022 ). Further, students argue that block-based programming environments are fun and more interactive than text-based programming (Weintrop, 2019 ; Weintrop & Wilensky, 2017 ). One study showed encouraging effects of video games and block-based programming through boosted confidence toward programming and participants’ developed self-identification as a programmer (Zhao & Shute, 2019 ). In addition, studies show promising results from using block-based programming through improved understanding of CT concepts and enhanced CT skills (Hsu & Tsai, 2023 ; Mladenović et al., 2018 ; Zur-Bargury et al. ( 2013 ). The literature about attitudes toward CS and CT (e.g., Choi, 2022 ; Mason & Rich, 2019 , 2020 ) and about Code.org’s block-based programming curriculum (e.g., Choi, 2022 ; Kale & Yuan, 2021 ; Kale et al., 2023 ; Lahullier, 2019 ) is prolific, but no study has assessed the impact of Code.org’s block-based programming curriculum on middle schooler’s CT skills and attitudes toward CT and CS. This is the literature gap that the present study addresses.

5 Purpose statement

The purpose of this action research study was to assess how Code.org’s block-based programming curriculum in game design affects middle school students’ CT skills and their attitudes toward CT and CS. The following research questions guided the study:

How and to what degree does a block-based programming curriculum in game design affect middle school students’ CT skills?

How and to what degree does a block-based programming curriculum in game design affect middle school students’ attitudes toward CT and CS?

6 The code.org CS discoveries curriculum

Code.org’s CS Discoveries curriculum is engaging, challenging, and developmentally appropriate for middle school students. Code.org is a platform that contains lessons on how to design and create games with block-based programming and discover CT and CS concepts without the burden of memorizing syntax (Kalelioglu, 2015 ). The lessons target the four CT skills focused on this study: algorithmic thinking, abstraction, debugging, and pattern recognition. Lesson activities rely on guided discovery to promote “a level of freedom for learners so that they explore the problem, identify patterns, and discover the underlying principles on the problem” (Kale & Yuan, 2021 , p. 622). Guided discovery is an approach to teaching small ideas or program structures that students can gradually connect to prior knowledge, which allows CS teachers to scaffold content learning. Activities are organized with an increasing level of challenge. Teacher lesson plans provide detailed notes about the content to be taught, suggested scripting to start class discussions, and answer keys.

The Code.org platform allows teachers to monitor student progress through a dashboard, provide feedback, and grade student-created programs (Kalelioglu, 2015 ). The teacher can see errors students make, identify the concepts students struggle with, and provide targeted feedback for program improvement. Furthermore, the platform has a keep working tag that shows students outstanding tasks and errors to be addressed.

6.1 Implementation timeline

This was a 9-week module that used Code.org’s Animations & Games Unit to teach CT. This class met every day face-to-face, Monday through Friday, for 45 min for the entire school year. All assignments were completed individually on students’ assigned Chromebooks. Participants were allowed to collaborate with each other, provide insights into other participants’ programs, help find errors, and encourage others to create fun and interactive games. The curriculum has a game design unit that is divided into two chapters. Chapter one contained lessons 1 through 17 where students learn to use sprites, variables, the draw-loop, conditionals, and user input. For example, participants learned how to order the code to create different elements in a scene (Fig.  1 ). Chapter two contains lessons 18 through 27 where students learn velocity, sprite collisions, functions, and game design process. For instance, participants learned how to program user input to create an interactive character and generate a visual outcome (Fig.  2 ). Each lesson has multiple steps where participants completed tasks focused on content, skill building, assessments, practice, and challenges. An overview of CS topics, duration, target blocks, and deliverables is provided in Table  1 .

Lesson 9 instructional example

Mini project lesson 17 student example

7.1 Research design

This was an action research study, which consists of a “systematic inquiry conducted by educators for the purpose of gathering information about how their particular schools operate, how they teach, and how their students learn” (Mertler, 2020 , p. 29). Action research seeks to address a problem of practice within the scope of an educator’s professional practice (Anderson et al., 2001 ; Arslan-Ari et al., 2020 ; Johnson, 2008 ). Findings of action research lead to evidence-based changes to improve processes of teaching and learning. Action research was an appropriate design for this study, which aimed to address a problem of practice related to CS education within the first author’s instructional setting. Specifically, this action research study was designed to collect standardized data from middle school students to investigate the impact of Code.org’s curriculum on their CT skills and their attitudes toward CT and CS. The ultimate goal was to make data-driven decisions to enhance CS education for these students based on study findings. This action research used a triangulation mixed methods design by combining qualitative and quantitative data sources. The integration of both types of data produces insights beyond the information provided by one type of data alone (Creswell & Creswell, 2018 ; Mertler, 2020 ).

7.2 Setting and participants

This study was conducted in an urban Title 1 middle school in the Southeast of the United States. Participants were recruited from a CS course taught to 25 middle school students enrolled in 8th grade and attending the CS course in the spring of 2022. This was an advanced course that offered high school credit to middle schoolers. A total of 16 students agreed to join the study. They were 13.75 years old on average. Nine were female and seven were male. Eight identified as African American, four as White, one as Asian, and three as multiracial. One was an English as Second Language student. A total of eight participants were considered gifted students in the course. Eight participants had previously enrolled in a CS course.

7.3 Data collection

Institutional Review Board approval, school district approval, parental consent, and participant assent were obtained prior to data collection. Four different data sources were adopted to assess participants’ CT skills and attitudes toward CT and CS: an attitudinal survey, pre- and post-tests, participant artifacts, and participant interviews.

7.3.1 Pre- and post-survey

To assess participants’ attitudes toward CT and CS before the intervention, a 5-point Likert scale attitudinal survey with 19 items was administered (Appendix A). Eight items were borrowed from three of the six subscales in Korkmaz et al.’s ( 2017 ) Computational Thinking Scale: creativity (8 items), algorithmic thinking (6 items), and critical thinking (5 items). Sample items include I like Computer Science and It is fun to try to solve complex problems . Minor adjustments were performed to adapt survey items to lower grade students. For example, the item I believe that I can easily catch the relation between the two pictures was adapted to I believe that I can easily catch the relation between the two pictures or two programs. The added excerpt was for improved clarity. Korkmaz et al. ( 2017 ) confirmed that the items had adequate or good reliability ranging from 0.79 to 0.87.

7.3.2 Pre- and post-tests

The pre-test was administered to 16 participants to establish their baseline CT skills prior to the intervention. The test had 29 questions (Appendix A). Three questions were selected from Rachmatullah et al.’s ( 2020 ) Middle Grades Computer Science Concept Inventory, and all questions presented an internal consistency higher than 0.80. Minor changes were made to these questions: a screenshot of a block-based program was added to the question so participants could visualize the target programming concepts. Seventeen questions were designed by the CS teacher. Construct validity and reliability for the teacher-created questions were established by piloting them with a similar population of middle school students enrolled in the same course prior to data collection. The questions were also reviewed by an expert on CS education and research methods. Minor changes were performed to the questions. Moreover, nine questions were adopted from the Code.org curriculum (see Table  2 ), and these have been created and widely used by CS educators. Twenty-one questions were multiple-choice, two were true/false, and six were short answer. The test assessed algorithmic thinking (14 questions), abstraction (five questions), debugging (five questions), and pattern recognition (five questions). The test was delivered via Google Forms. An identical post-test was administered after the intervention.

7.3.3 Participant artifacts

Participants used block-based programming to create games in Code.org’s online platform at the end of each lesson throughout the intervention. Artifacts, or any type of performance assessment or student projects, are used to systematically evaluate the attainment of learning targets (McMillan, 2013 ). These block-based programming games served as a tool to assess participants’ CT skills based on the extent to which they were able to apply such skills into game design.

7.3.4 Participant interviews

A semi-structured interview protocol (Appendix B) was designed to collect qualitative data on participants’ CT skills and attitudes toward CT and CS to allow triangulation of findings with quantitative data sources. Four participants were randomly chosen for a 20-min, in-person, one-on-one interview after the intervention. Sample questions include When you encountered a problem in your code without an obvious answer, what steps did you take to solve it? (debugging skill) and Do you see yourself as a computer scientist? (attitudes toward CS). The code that participants used to create games was demonstrated during the interview to prompt descriptions about their “strategy on designing video games using block-based programming” (Tang et al., 2020 , p. 4). Follow-up prompts were included to encourage elaboration on responses (Chalmers, 2018 ). Interviews were audio recorded.

8 Data analysis

This study included quantitative and qualitative data analysis methods to develop a better understanding of the phenomenon being investigated (Creswell & Creswell, 2018 ). These methods consisted of descriptive statistics, paired samples t-tests, inductive and thematic analysis as displayed in the research matrix (Table  3 ).

8.1 Descriptive statistics

Data from pre- and post-tests as well as pre- and post-surveys was descriptively analyzed with JASP, a free computer-based statistics software. Measures of central tendency (mean) were used to summarize the central position of the quantitative data set distribution, and measures of dispersion (standard deviation) were used to assess the variability within the same data set (Hanneman et al., 2012 ; Mertler, 2020 ). Descriptive statistics were important to synthesize a large amount of quantitative data and facilitate interpretations about trends and patterns in participants’ CT skills and attitudes towards CT and CS.

8.2 Paired-samples T-tests

Pre- and post-survey data was first computed with Microsoft Excel, then JASP to analyze composite scores for participant attitudes. All reliability coefficients for the composite subscales fall within the range of 0.74 to 0.86. According to DeVellis ( 2016 ), reliability coefficients of 0.70 and above have acceptable reliability. Subsequently, the Shapiro–Wilk test for normality revealed that all subscales from the pre- and post-tests as well as pre- and post-surveys were normally distributed. Thus, the parametric paired samples t-tests (Hanneman et al., 2012 ) were performed to test the hypothesis of a statistically significant increase in CT skills and attitudes towards CT and CS.

8.3 Participant artifacts

A rubric created by Code.org was used to assess the four CT skills (algorithmic thinking, debugging, abstraction, and pattern recognition) using participants’ artifacts, that is, the games created with block-based programs. The rubric contains 7 criteria: program development, program readability, use of functions, background and variables, interactions and controls, position and movement, and variables. Each criterion is assessed based on four levels of achievement ranging from no evidence (0 points) to extensive evidence (7–10 possible points) as presented in Table  4 . The first author, who individually coded participant artifacts, had used the rubric previously to assess student artifacts with a similar population attending the same class. Results from artifact scoring were entered into an Excel spreadsheet and then analyzed with JASP to generate descriptive statistics about participants’ performance. The full rubric with descriptions about each level of achievement is provided in Appendix C.

8.4 Inductive thematic analysis

Inductive thematic analysis was used to identify and organize data into codes and categories to construct a framework to present qualitative findings (Mertler, 2020 ). Open coding and in vivo coding were administered to identify patterns or similarities in the data set (Saldaña, 2016 ). Iterative rounds of coding were performed using the computer-based qualitative data analysis tool Delve. The first author read the coded data multiple times and used Delve’s retrieval features to select and visualize all excerpts assigned the same code. This facilitated visualization of patterns across participants. Then authors held peer debriefing meetings to assign codes into categories and jointly craft qualitative themes that describe participants’ experiences (Braun & Clarke, 2006 ; Clarke & Braun, 2017 ). Themes were probed against coded excerpts for relevance. Thick and rich descriptions with quotes were used to support themes (Mertler, 2020 ).

9.1 Quantitative results

9.1.1 computational thinking skills.

Results showed a noticeable increase in participants’ CT skills between the overall pre-test ( M  = 11.38, SD  = 3.32) and post-test scores ( M  = 18.69, SD  = 4.81) (see Table  5 ). The largest improvement was in algorithmic thinking ( M  = 9.25, SD  = 2.38) followed by pattern recognition ( M  = 3.31, SD  = 1.40) and debugging ( M  = 3.06, SD  = 0.77). The smallest increase was in abstraction ( M  = 3.38, SD  = 0.96). The average artifact score was ( M  = 75.06, SD  = 22.63), with subscale scores for algorithmic thinking ( M  = 24.88, SD  = 8.84), debugging ( M  = 14.75, SD  = 3.43), abstraction ( M  = 20.31, SD  = 6.48) and pattern recognition ( M  = 15.38, SD  = 4.78). Roughly 87% of participants scored 60 or higher in their game designs (see Fig.  3 ).

Artifact scores

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. Paired samples t-tests revealed a statistically significant improvement in three CT skills: algorithmic thinking ( M  = 6.20, SD  = 2.65, t (14) = -3.11, p  = 0.004), debugging ( M  = 3.06, SD  = 0.77, t (14) = -4.22, p  < 0.001), and pattern recognition ( M  = 1.40, SD  = 0.74, t (14) = -4.50, p  < 0.001) (see Table  6 ). Participants’ abstraction skills were not statistically significantly different ( M  = 3.38, SD  = 0.96, t (14) = -1.20, p  = 0.035). A large effect size was found for algorithmic thinking ( d  = 0.80), debugging ( d  = 1.09), pattern recognition ( d  = 1.16), and the overall test ( d  = 1.20), while a medium effect size was found for abstraction skills ( d  = 0.51).

9.1.2 Participant attitudes

There was a marginal increase in participants’ attitudes toward CT and CS from the pre-survey ( M  = 25.31, SD  = 4.90) to the post-survey ( M  = 25.75, SD  = 5.46) (Table  7 ). Moreover, there was a small positive increase in participants’ CT beliefs from the pre-survey ( M  = 24.25, SD  = 3.98) to the post-survey ( M  = 25.95, SD  = 6.01). The overall survey scores showed a similar increase from the pre-survey ( M  = 49.56, SD  = 8.51) to the post-survey ( M  = 51.69, SD  = 11.23).

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. It was determined that paired-samples t-tests would be the most appropriate method to analyze the data inferentially (Gibbons & Chakraborti, 2021 ). Paired samples t-tests revealed no statistically significant differences between the pre- and the post-surveys for CS attitudes ( M  = 25.75, SD  = 5.46, t (15) = -0.43, p  = 0.34), CT beliefs ( M  = 25.94, SD  = 6.01, t (15) = -1.41, p  = 0.09), and the overall survey ( M  = 51.69, SD  = 11.23, t (15) = -1.05, p  = 0.16) (see Table  7 ). A small effect size was found for the attitudes ( d  = 0.11), CT beliefs ( d  = 0.35), and overall survey ( d  = 0.26).

9.2 Qualitative results

9.2.1 configuration of game elements as the foundation to understand and apply ct.

Designing and programming games exposed participants to basic programming concepts which in turn supported learning of CT skills. Participants in this study grounded application of CT skills on their experience with video game elements. Particularly, participants used configuration of game elements such as rewards and points, character movement around the scene, player-character interactions, character-character interactions, and games rules as the foundation to understand and apply CT skills.

Participants who were interviewed mentioned several elements of games to explain programming concepts. For example, when Joe was asked about what he wanted to happen if the score was below zero, he said “I wanted the game to end because if somebody has something like negative one and if they continue playing, then the game will never stop”. Similarly, when May was asked why she looped her sprites she stated, “the loop I used is that I wanted when the player touches the sprites, the sprites had to go in a different place.” Participants also referred to game scores to discuss variables, counter pattern, and character interactions. When asked about how variables work, participants mentioned scores as a variable which can be added or subtracted to for winning or losing a game. Mary stated that a “variable is a number that is subject to change. So, um, in scoring, you could say every time your sprite touches an object, you will gain a point”. May similarly said, “if the player touches the enemy, the [player’s] health would go down, but if you [the player] touch[es] like a candy, your scoring will go up.”

When prompted to define conditional statements, several participants described them as a tool that allows the game designer to create situations and outcomes. For instance, Connie said “if the score is higher than 10”, or “if the right arrow is pressed.” Along these lines, Mary added that “a conditional statement asks if a certain, uh, aspect is true or false and based off of whether that aspect is true or false, it will perform a certain action”. Participants connected game characters with sprites, which are two-dimensional images that represent a character and/or background element. Participants liked the word sprite, thought it was funny, and used it to describe all images adopted in their programs. Further, participants learned how to move sprites with the arrow keys using if statements, the counter pattern, and loops. John stated, “the loop will allow the fish sprite to reset back to the left of the screen”. In summary, most interviewed participants relied on game elements to articulate an understanding of CT concepts and explain how they applied CT for game design.

9.2.2 Collaboration promoted debugging of participants’ own programs

Collaboration during block-based programming involves working with peers to plan, revise, and complete a program. Participants were encouraged to collaborate with their peers if they were stuck and did not know what to do before asking the teacher for help. This allowed participants to take ownership of their work and understand that collaboration is not cheating. During interviews, participants emphasized that collaboration was beneficial. May stated, “I asked one of my friends, because she's very helpful for me. She helped me a bit for the velocity stuff. And then the rest I was able to figure out on my own.” Along these lines, Mary stated that “it was very helpful” collaborating with a classmate. Being able to work with peers and review others’ programs helped participants feel at ease, identify errors in the program, and understand CT concepts more independently without the instructor.

Throughout the module, participants were given faulty programs in which they had to identify and fix the bug. When asked about the debugging process, Joe stated that “I try to look back at the code and make sure that there's no spelling errors.” This is a first step in the debugging process to find the bug location. However, when participants were not successful, they were encouraged to work with peers. When prompted to talk about peer collaboration, May explained, “what she did is she showed me a bit of hers and that showed me how to deal with mine.” These statements indicate that reviewing a peer’s program and comparing it against their own program helped participants create insights about where the bug was without asking the teacher for help.

9.2.3 Balancing creative freedom and structure led to enjoyment of programming

The block-based programming module offered an optimal combination of structure and creative freedom so participants could design their own game. The structure comes in the form of guided planning for using required programs and CT skills while the freedom comes in the form of making decisions about the game environment, characters, and rules in ways that were personally relevant. This balance ensured participants mastered target CT skills but also enjoyed taking ownership over the design of game content or characters. As May said, “I liked choosing my sprites and then deciding how they would interact”. Similarly, John showed excitement when asked about his game sprites. He said “you see, I made that sprite, I drew it and it looks so good.”

The balance between creative freedom and structure made programming a fun activity, which appears to have promoted positive attitudes. Participants seemed to enjoy programming their own games which led them to being very engaged and working hard on game design. May was curious about creating games prior to the study, and then game design helped her perceive programming as a fun activity. May stated “I'd just say creating your own games and websites is very fun. I've always wanted to learn how to create those (…) I thought it wasn't gonna be, but it's very, I'm having a lot of fun doing it.” Along these lines, Joe confirmed that he likes CS and that the experience helped him understand the mechanisms that make programmed artifacts function. Joe said “I just like coding in general. It helps me understand how like video games and stuff like that work.” Understanding the functionality of games was also something Mary enjoyed. She stated, “I like to be able to create code and then watch it actually work”, and “I enjoy creating games once I know how to make them.”

10 Discussion

CT is a fundamental skill for the twenty-first century workforce within and beyond the computing industry (Wing, 2019 ). And yet, middle schoolers in the U.S. have limited exposure to CS education (Google & Gallup, 2020 ), which prevents them from developing CT skills (Brown et al., 2014 ). CS education made the leap from teaching students to consume technology to teaching students to be technology creators within the past 10 years (Kafai, 2016 ; Runciman, 2011 ). But despite the development of many new initiatives and curricula, students still think CS concepts are difficult to learn (Mladenović et al., 2018 ). One way to develop middle school students’ CT skills is to use Code.org’s CS Discoveries game design curriculum. The first author, who is a CS school teacher, led this action research study by collecting standardized data in the form of pre- and post-tests, interviews, and participant artifacts to assess the impact of the curriculum on middle school students’ CT skills and attitudes towards CT and CS. A discussion of results follows.

10.1 CT skills

Quantitative results showed a statistically significant improvement in three of the four CT skills: algorithmic thinking, debugging, and pattern recognition. A total of 14 out of 16 participants had higher overall post-test scores. Participants’ games also showed development of CT skills. These results align with previous research that found the use of game design to benefit learning to program and development of CT skills (Ouahbi et al., 2015 ; Scherer et al., 2020 ).

Algorithmic thinking is the ability to think in steps to solve problems (Chuechote et al., 2020 ). Both quantitative and qualitative results showed improved algorithmic thinking skills. Participants were able to “solve tasks demanding thinking, not only using the rules and algorithms they had learned” (Harangus & Kátai, 2018 , p. 1037) but also creating solutions for complex problems (Durak, 2020 ) related to their own game design. Previous studies that adopted the Code.org curriculum also showed an improvement in participants’ algorithmic thinking skills (Chuechote et al., 2020 ; Lockwood et al., 2016 ; Oluk & Çakir, 2021 ; Peel & Friedrichsen, 2018 ; Tonbuloğlu & Tonbuloğlu, 2019 ). Block-based programming in Code.org allows one to build a plan and think algorithmically as part of their CT learning experience (Kale et al., 2023 ). Further, block-based programming is an effective tool to prepare students for future CS courses as it helps students focus on the process of learning CS concepts without the syntax from text-based programming (Weintrop, 2019 ). The use of block-based programs to control game mechanics offered an “interactive learning environment centered on problem-solving” (Wang et al., 2023 , p. 1506) which encouraged participants to break down character behaviors and game animations into smaller, more manageable tasks. Hence, the game design approach was conducive of algorithmic thinking throughout the multiple lessons implemented in this study.

Debugging is very much part of problem solving within and beyond CS. In this study, finding and fixing program errors was critical for successful game design. Practicing error debugging can lead to improved understanding of programming concepts (Kim et al., 2018 ). Both quantitative and qualitative data showed that participants improved debugging skills. The CS teacher who taught the module encouraged participants to engage in collaborative debugging by reviewing each other’s code and helping each other find and fix errors, which has been found beneficial in the CS education literature. In Papavlasopoulou et al.’ ( 2019 ) study, participants who collaborated more on programming tasks “had a higher level of shared understanding and could communicate better during the coding activity” (p. 421). Kim et al. ( 2022 ) reported that students who were given scaffolds to help debug errors and students who worked with collaborators were more successful at programming. Collaborative debugging seems to promote increased persistence and engagement in programming and problem solving, which in turn contributes to the development of CT skills (Margulieux et al., 2020 ; Tonbuloğlu & Tonbuloğlu, 2019 ; Turchi et al., 2019 ). From a game design standpoint, participants in this study improved their debugging skills due to the required creation and testing of algorithmic sequences to refine the actions to occur within their game and based on visual feedback. In fact, visual feedback has been identified as one of the most effective factors leading to positive learning outcomes in game-based learning (Dehghanzadeh et al., 2024 ; Noroozi et al., 2016 , 2023 ; Sailer & Sailer, 2021 ). In this study, visual feedback helped participants identify a discrepancy between the intended and the actual performance (Carless, 2006 ) of game characters on the screen, which prompted debugging.

In pattern recognition, one sees similarities between program elements that can be matched with previous tasks (Qian & Choi, 2023 ) and then apply those patterns from one problem to the next (Barrón-Estrada et al., 2022 ; Yasin & Nusantara, 2023 ). Results from this study showed an improvement in participants’ pattern recognition. The Code.org curriculum repeatedly referred participants back to previous tasks so they could identify similarities and develop a plan for reusing parts of a program. For example, after learning to program a game score, participants reused that program chunk in every lesson afterwards in increasingly challenging activities. Improved pattern recognition through block-based programming was also found in other studies in which participants successfully synthesized multiple repeated blocks into a more efficient use of loops (Hernández-Zavaleta et al., 2021 ). We argue that the game design curriculum supported study pattern recognition as “participants transferred their learning of block-based programming from previous coding challenges to the new ones when they located any similarities (…) across the challenges” (Umutlu, 2022 , p. 761). The game design approach was crucial in engaging participants in programming similar patterns to control game character behaviors.

Abstraction, or the process of simplifying information, is applicable through the use of functions to hide parts of the program that are reused and reactivated when needed. For example, it is not necessary to display the actual code that controls the addition of points to the game score. This function can be found when needed, but it was often hidden in the code. Results of this study showed an improvement in abstraction skills, but the difference was not statistically significant. One possible explanation for the lack of statistical significance is that the tasks may have been too advanced for participants at this age. In another study with children enrolled in first through sixth grade, “older students were found to do better on the abstraction task than students in the youngest age group” (Rijke et al., 2018 , p. 86). Jean Piaget’s theory of cognitive development states that children are still forming schemas until the age of 12, which makes abstract reasoning difficult for them (Piaget & Cook, 1952 ). Another possibility is that abstraction is not an explicit focus of the Code.org curriculum and perhaps it should be integrated earlier into those lessons. Kale and Yuan’s ( 2021 ) study implemented the same curriculum and found that improvement in participants’ abstraction was lower than in pattern recognition and algorithmic thinking, which aligns with findings from this study. While it was expected that game design with block-based programming would be beneficial for abstraction skills, perhaps participants in this study needed more support. One possibility for future research is the inclusion of causal maps so participants can produce visual illustrations that show relationships among variables of interest (Akcaoglu & Green, 2019 ; Öllinger et al., 2015 ) and relationships between planned programming concepts and game character behaviors.

10.2 Attitudes toward CT and CS

Participants’ attitudes toward CT and CS were moderately high after the study, but they did not statistically significantly differ. We argue that these results could be due to several factors. First, these participants were taking an elective CS course, so perhaps they already had positive attitudes toward CT and CS prior to the study. Second, some programming concepts, such as conditionals, are quite challenging for middle schoolers. In Brennan and Resnick’s ( 2012 ) study, not every student could correctly describe how a conditional statement works or why it was used in the program. One of the interviewees in this study had difficulty answering questions about conditionals even though they knew how to complete the task. Challenges with more advanced programming concepts such as conditionals may have contributed to lowered self-efficacy. Limited ability to apply CT skills can be the reason for less positive attitudes toward programming and CT. Similar to this study, Lambić et al. ( 2021 ) found no significant difference in participants’ attitudes after implementing Code.org’s curriculum while Hsu and Tsai’s ( 2023 ) study found no significant difference in participants’ attitudes after an intervention that combined block-based programming, physical computing, and game design.

Despite the lack of statistical significance, qualitative data suggests improvements in CT skills through game design and revealed that participants enjoyed block-based programming. This aligns with Kalelioglu’s ( 2015 ) study, in which participants reported that they liked using the Code.org site and desired to learn more about programming. Other studies have found that block-based programming contributed to positive attitudes such as interest in taking more programming courses (Weintrop & Wilensky, 2017 ), and positive attitudes towards CS (Bastug & Kircaburun, 2017 ; Kalelioglu, 2015 ). As Gunbatar and Karalar ( 2018 ) stated, “visual programming environments can increase students’ self-efficacy perceptions and attitudes toward programming” (p. 931) because those environments offer a lower level of challenge but also the possibility to increase the level of complexity in the program. Previous research has found that integrating block-based programming into game design yielded positive attitudes such as higher interest in programming (Hromkovic & Staub, 2019 ) and enhanced self-efficacy (Tsai et al., 2023 ).

Participants’ moderately positive attitudes may also be due to the use of games for educational purposes. Game-based learning is an approach through which students learn either by playing games or creating them (Denner et al., 2012 ; Kafai & Burke, 2015 ; Topalli & Cagiltay, 2018 ; Turchi et al., 2019 ). Research has identified the positive effects of game-based learning on student motivation and engagement (e.g., Barreto et al., 2018 ; Breien & Wasson, 2021 ; Hwang et al., 2014 ; Partovi & Razavi, 2019 ; Sharma et al., 2021 ). In this study, participants not only played games, but they were also engaged in game design. We argue that two key components of the constructionist game design experience, personalization and collaboration, contributed to participants’ enjoyment of block-based programming as well as positive attitudes toward CT and CS. Specifically, participants were free to design a game that was personally relevant and they were encouraged to help each other during programming, and these are critical to foster student motivation and engagement in CT (Barman & Kjällander, 2022 ; Hava & Ünlü, 2021 ; Sharma et al., 2021 ; Turchi et al., 2019 ).

11 Limitations

Study limitations should be considered. First, a larger sample size would have been beneficial to achieve more robust quantitative results. Future research should consider implementing the study across multiple middle school classrooms to increase the sample size. Second, the study did not provide insights on fluctuations in participants’ attitudes over time. Adding more timepoints of data collection about attitudes toward CT and CS throughout such a long intervention is recommended. Third, relying exclusively on self-reported data from surveys and interviews with young participants presents issues related to response accuracy and social desirability bias because participants’ self-reported perceptions may not accurately reflect their actual learning experience (Noroozi et al., 2024 ). Strategies to minimize bias from self-reported data should be included in future research. For example, alternative data sources such as participant observations, user data from the game design environment (Banihashem et al., 2023 ), and alternative approaches such as a longitudinal study of attitudes and engagement over time. Finally, only the first author scored the participant artifacts. In the future, inviting another CS teacher to individually score the artifacts and compare results would enhance the validity of results.

12 Conclusion and implications

K-12 students need meaningful CS learning opportunities that foster development of CT skills and positive attitudes toward CT and CS. Significant gains were found in participants’ algorithmic thinking, pattern recognition, and debugging skills but not in abstraction. These results are partly explained by the use of a block-based programming language that lowers that difficulty to learn programming concepts, the use of game design as a strategy to promote freedom of expression and creativity, and the incentive for collaborative problem solving. These elements of the module should be taken into consideration for CS education that aims to teach CT skills.

Student attitudes towards CT and CS did not improve as expected. And yet, students stated that they enjoyed designing and programming their own game. Strategies for motivational support that extend beyond the adopted curriculum and the adopted programming software should be included. Specifically, future initiatives can normalize struggle as part of problem solving and CT, expose students to career paths and tasks that do not necessarily involve programing, and introduce them to successful role models in the field that they can identify with.

The design of this study focused on middle school students, so it is important to consider the results with caution before generalizing them to other contexts and populations. The intervention, which combines game design with block-based programming to foster CT, can and should be implemented and evaluated across other contexts of CS education (e.g., informal learning environments) and other levels of education (e.g., high school students, preservice teacher preparation).

Data availability

Data collected from this study are available from the corresponding author on reasonable request.

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Cafarella, L., Vasconcelos, L. Computational thinking with game design: An action research study with middle school students. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13010-5

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