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Research Design 101

Everything You Need To Get Started (With Examples)

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

Research design for qualitative and quantitative studies

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

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Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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example of a research study design

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

example of a research study design

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

example of a research study design

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 .

example of a research study design

Psst... there’s more!

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

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10 Comments

Wei Leong YONG

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

Solly Khan

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

hetty

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

Belz

This was really helpful. thanks

Imur

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

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

Sam Msongole

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

Robyn Pritchard

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

kelebogile

how to cite this page

Peter

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

ali

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

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Research Method

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

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

Research DesignResearch Methodology
The plan and structure for conducting research that outlines the procedures to be followed to collect and analyze data.The set of principles, techniques, and tools used to carry out the research plan and achieve research objectives.
Describes the overall approach and strategy used to conduct research, including the type of data to be collected, the sources of data, and the methods for collecting and analyzing data.Refers to the techniques and methods used to gather, analyze and interpret data, including sampling techniques, data collection methods, and data analysis techniques.
Helps to ensure that the research is conducted in a systematic, rigorous, and valid way, so that the results are reliable and can be used to make sound conclusions.Includes a set of procedures and tools that enable researchers to collect and analyze data in a consistent and valid manner, regardless of the research design used.
Common research designs include experimental, quasi-experimental, correlational, and descriptive studies.Common research methodologies include qualitative, quantitative, and mixed-methods approaches.
Determines the overall structure of the research project and sets the stage for the selection of appropriate research methodologies.Guides the researcher in selecting the most appropriate research methods based on the research question, research design, and other contextual factors.
Helps to ensure that the research project is feasible, relevant, and ethical.Helps to ensure that the data collected is accurate, valid, and reliable, and that the research findings can be interpreted and generalized to the population of interest.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

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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.

A visual diagram describing the following. Two types of epidemiological studies are descriptive and analytical. Types of descriptive studies are case reports, case series, descriptive surveys. Types of analytical studies are observational or experimental. Observational studies can be cross-sectional, case-control or cohort studies. Types of experimental studies can be lab trials or field trials.

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

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

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Very informative and easy understandable

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

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Wow very erll explained and easy to understand

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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

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well understood,thank you so much

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Well understood…thanks

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Simply explained. Thank You.

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

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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.

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it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

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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.

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

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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!

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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

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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 ;)

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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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Methodology

  • Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

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 categorize 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 analyze
  • 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, other interesting articles.

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

Type of research What’s the difference? What to consider
Basic vs. applied Basic research aims to , while applied research aims to . Do you want to expand scientific understanding or solve a practical problem?
vs. Exploratory research aims to , while explanatory research aims to . How much is already known about your research problem? Are you conducting initial research on a newly-identified issue, or seeking precise conclusions about an established issue?
aims to , while aims to . Is there already some theory on your research problem that you can use to develop , or do you want to propose new theories based on your findings?

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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.

Type of research What’s the difference? What to consider
Primary research vs secondary research Primary data is (e.g., through or ), while secondary data (e.g., in government or scientific publications). How much data is already available on your topic? Do you want to collect original data or analyze existing data (e.g., through a )?
, while . Is your research more concerned with measuring something or interpreting something? You can also create a research design that has elements of both.
vs Descriptive research gathers data , while experimental research . Do you want to identify characteristics, patterns and or test causal relationships between ?

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?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Type of research What’s the difference? What to consider
allows you to , while allows you to draw conclusions . Do you want to produce  knowledge that applies to many contexts or detailed knowledge about a specific context (e.g. in a )?
vs Cross-sectional studies , while longitudinal studies . Is your research question focused on understanding the current situation or tracking changes over time?
Field research vs laboratory research Field research takes place in , while laboratory research takes place in . Do you want to find out how something occurs in the real world or draw firm conclusions about cause and effect? Laboratory experiments have higher but lower .
Fixed design vs flexible design In a fixed research design the subjects, timescale and location are begins, while in a flexible design these aspects may . Do you want to test hypotheses and establish generalizable facts, or explore concepts and develop understanding? For measuring, testing and making generalizations, a fixed research design has higher .

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your 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

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.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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What is research design? Types, elements, and examples

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

example of a research study design

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

   
Deals with subjective aspects, e.g., experiences, beliefs, perspectives, and concepts.  Measures different types of variables and describes frequencies, averages, correlations, etc. 
Deals with non-numerical data, such as words, images, and observations.  Tests hypotheses about relationships between variables. Results are presented numerically and statistically. 
In qualitative research design, data are collected via direct observations, interviews, focus groups, and naturally occurring data. Methods for conducting qualitative research are grounded theory, thematic analysis, and discourse analysis. 

 

Quantitative research design is empirical. Data collection methods involved are experiments, surveys, and observations expressed in numbers. The research design categories under this are descriptive, experimental, correlational, diagnostic, and explanatory. 
Data analysis involves interpretation and narrative analysis.  Data analysis involves statistical analysis and hypothesis testing. 
The reasoning used to synthesize data is inductive. 

 

The reasoning used to synthesize data is deductive. 

 

Typically used in fields such as sociology, linguistics, and anthropology.  Typically used in fields such as economics, ecology, statistics, and medicine. 
Example: Focus group discussions with women farmers about climate change perception. 

 

Example: Testing the effectiveness of a new treatment for insomnia. 

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

example of a research study design

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

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  • v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

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

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

How to cite this article

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

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

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

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

Longitudinal vs Transversal Studies

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

Forward vs Backward Directed Studies

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

Prospective vs Retrospective Studies

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

CLASSIFICATION OF STUDY DESIGNS

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

Descriptive Studies

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

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

Analytical (Inferential) Studies

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

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

Cross-sectional Studies

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

Case–control Studies

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

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

Cohort Studies

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

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

Interventional Studies

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

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

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

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

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Conflict of interest: None

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  • Knowledge Base
  • Methodology

Types of Research Designs Compared | Examples

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

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

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

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

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

Table of contents

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

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

Type of research What’s the difference? What to consider
Basic vs applied Basic research aims to , while applied research aims to . Do you want to expand scientific understanding or solve a practical problem?
vs Exploratory research aims to , while explanatory research aims to . How much is already known about your research problem? Are you conducting initial research on a newly-identified issue, or seeking precise conclusions about an established issue?
aims to , while aims to . Is there already some theory on your research problem that you can use to develop , or do you want to propose new theories based on your findings?

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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.

Type of research What’s the difference? What to consider
Primary vs secondary Primary data is (e.g., through interviews or experiments), while secondary data (e.g., in government surveys or scientific publications). How much data is already available on your topic? Do you want to collect original data or analyse existing data (e.g., through a )?
, while . Is your research more concerned with measuring something or interpreting something? You can also create a research design that has elements of both.
vs Descriptive research gathers data , while experimental research . Do you want to identify characteristics, patterns, and or test causal relationships between ?

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?

Type of research What’s the difference? What to consider
allows you to , while allows you to draw conclusions . Do you want to produce knowledge that applies to many contexts or detailed knowledge about a specific context (e.g., in a )?
vs Cross-sectional studies , while longitudinal studies . Is your research question focused on understanding the current situation or tracking changes over time?
Field vs laboratory Field research takes place in , while laboratory research takes place in . Do you want to find out how something occurs in the real world or draw firm conclusions about cause and effect? Laboratory experiments have higher but lower .
Fixed vs flexible In a fixed research design the subjects, timescale and location are begins, while in a flexible design these aspects may . Do you want to test hypotheses and establish generalisable facts, or explore concepts and develop understanding? For measuring, testing, and making generalisations, a fixed research design has higher .

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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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

  • Data type required for research
  • Research resources
  • Participants required for research
  • Hypothesis based upon research question(s)
  • Data analysis  methodologies
  • Variables (Independent, dependent, and confounding)
  • The location and timescale for conducting the data
  • The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Steps of research design

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

  • Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
  • Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
  • Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

  • Do you have enough time to gather data and complete the write-up?
  • Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
  • Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

The researcher collects the primary data from first-hand sources with the help of different data collection methods such as interviews, experiments, surveys, etc. Primary research data is considered far more authentic and relevant, but it involves additional cost and time.
Research on academic references which themselves incorporate primary data will be regarded as secondary data. There is no need to do a survey or interview with a person directly, and it is time effective. The researcher should focus on the validity and reliability of the source.

Qualitative Vs. Quantitative Data

This type of data encircles the researcher’s descriptive experience and shows the relationship between the observation and collected data. It involves interpretation and conceptual understanding of the research. There are many theories involved which can approve or disapprove the mathematical and statistical calculation. For instance, you are searching how to write a research design proposal. It means you require qualitative data about the mentioned topic.
If your research requires statistical and mathematical approaches for measuring the variable and testing your hypothesis, your objective is to compile quantitative data. Many businesses and researchers use this type of data with pre-determined data collection methods and variables for their research design.

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Methods What to consider
Surveys The survey planning requires;

Selection of responses and how many responses are required for the research?

Survey distribution techniques (online, by post, in person, etc.)

Techniques to design the question

Interviews Criteria to select the interviewee.

Time and location of the interview.

Type of interviews; i.e., structured, semi-structured, or unstructured

Experiments Place of the experiment; laboratory or in the field.

Measuring of the variables

Design of the experiment

Secondary Data Criteria to select the references and source for the data.

The reliability of the references.

The technique used for compiling the data source.

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

This article is a step-by-step guide to how to write statement of a problem in research. The research problem will be half-solved by defining it correctly.

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

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Understanding Research Study Designs

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In order to find the best possible evidence, it helps to understand the basic designs of research studies. The following basic definitions and examples of clinical research designs follow the “ levels of evidence.”

Case Series and Case Reports

Case control studies, cohort studies, randomized controlled studies, double-blind method, meta-analyses, systematic reviews.

These consist either of collections of reports on the treatment of individual patients with the same condition, or of reports on a single patient.

  • Case series/reports are used to illustrate an aspect of a condition, the treatment or the adverse reaction to treatment.
  • Example : You have a patient that has a condition that you are unfamiliar with. You would search for case reports that could help you decide on a direction of treatment or to assist on a diagnosis.
  • Case series/reports have no control group (one to compare outcomes), so they have no statistical validity.
  • The benefits of case series/reports are that they are easy to understand and can be written up in a very short period of time.

example of a research study design

Patients who already have a certain condition are compared with people who do not.

  • Case control studies are generally designed to estimate the odds (using an odds ratio) of developing the studied condition/disease. They can determine if there is an associational relationship between condition and risk factor
  • Example: A study in which colon cancer patients are asked what kinds of food they have eaten in the past and the answers are compared with a selected control group.
  • Case control studies are less reliable than either randomized controlled trials or cohort studies.
  • A major drawback to case control studies is that one cannot directly obtain absolute risk (i.e. incidence) of a bad outcome.
  • The advantages of case control studies are they can be done quickly and are very efficient for conditions/diseases with rare outcomes.

example of a research study design

Also called longitudinal studies, involve a case-defined population who presently have a certain exposure and/or receive a particular treatment that are followed over time and compared with another group who are not affected by the exposure under investigation.

  • Cohort studies may be either prospective (i.e., exposure factors are identified at the beginning of a study and a defined population is followed into the future), or historical/retrospective (i.e., past medical records for the defined population are used to identify exposure factors).
  • Cohort studies are used to establish causation of a disease or to evaluate the outcome/impact of treatment, when randomized controlled clinical trials are not possible.
  • Example: One of the more well-know examples of a cohort study is the Framingham Heart Study, which followed generations of residents of Framingham, Massachusetts.
  • Cohort studies are not as reliable as randomized controlled studies, since the two groups may differ in ways other than the variable under study.
  • Other problems with cohort studies are that they require a large sample size, are inefficient for rare outcomes, and can take long periods of time. 

Cohort studies

This is a study in which 1) There are two groups, one treatment group and one control group. The treatment group receives the treatment under investigation, and the control group receives either no treatment (placebo) or standard treatment. 2) Patients are randomly assigned to all groups. 

  • Randomized controlled trials are considered the “gold standard” in medical research. They lend themselves best to answering questions about the effectiveness of different therapies or interventions.
  • Randomization helps avoid the bias in choice of patients-to-treatment that a physician might be subject to. It also increases the probability that differences between the groups can be attributed to the treatment(s) under study.
  • Having a  control group allows for a comparison of treatments – e.g., treatment A produced favorable results 56% of the time versus treatment B in which only 25% of patients had favorable results.
  • There are certain types of questions on which randomized controlled studies cannot be done for ethical reasons, for instance, if patients were asked to undertake harmful experiences (like smoking) or denied any treatment beyond a placebo when there are known effective treatments.

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A type of randomized controlled clinical trial/study in which neither medical staff/physician nor the patient knows which of several possible treatments/therapies the patient is receiving.

  • Example : Studies of treatments that consist essentially of taking pills are very easy to do double blind – the patient takes one of two pills of identical size, shape, and color, and neither the patient nor the physician needs to know which is which.
  • A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects, which reduces the risk of bias, it can eliminate or minimize the placebo effect which is a further challenge to the validity of a study.

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Meta-analysis is a systematic, objective way to combine data from many studies, usually from randomized controlled clinical trials, and arrive at a pooled estimate of treatment effectiveness and statistical significance.

  • Meta-analysis can also combine data from case/control and cohort studies. The advantage to merging these data is that it increases sample size and allows for analyses that would not otherwise be possible.
  • They should not be confused with reviews of the literature or systematic reviews. 
  • Two problems with meta-analysis are publication bias (studies showing no effect or little effect are often not published and just “filed” away) and the quality of the design of the studies from which data is pulled. This can lead to misleading results when all the data on the subject from “published” literature are summarized.

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A systematic review is a comprehensive survey of a topic that takes great care to find all relevant studies of the highest level of evidence, published and unpublished, assess each study, synthesize the findings from individual studies in an unbiased, explicit and reproducible way and present a balanced and impartial summary of the findings with due consideration of any flaws in the evidence. In this way it can be used for the evaluation of either existing or new technologies and practices.   

A systematic review is more rigorous than a traditional literature review and attempts to reduce the influence of bias. In order to do this, a systematic review follows a formal process:

  • Clearly formulated research question
  • Published & unpublished (conferences, company reports, “file drawer reports”, etc.) literature is carefully searched for relevant research
  • Identified research is assessed according to an explicit methodology
  • Results of the critical assessment of the individual studies are combined
  • Final results are placed in context, addressing such issues are quality of the included studies, impact of bias and the applicability of the findings
  • The difference between a systematic review and a meta-analysis is that a systematic review looks at the whole picture (qualitative view), while a meta-analysis looks for the specific statistical picture (quantitative view). 

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R esearch Process in the Health Sciences  (35:37 min): Overview of the scientific research process in the health sciences. Follows the seven steps: defining the problem, reviewing the literature, formulating a hypothesis, choosing a research design, collecting data, analyzing the data and interpretation and report writing. Includes a set of additional readings and library resources.

Research Study Designs in the Health Sciences  (29:36 min): An overview of research study designs used by health sciences researchers. Covers case reports/case series, case control studies, cohort studies, correlational studies, cross-sectional studies, experimental studies (including randomized control trials), systematic reviews and meta-analysis.  Additional readings and library resources are also provided.

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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

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  • Rothman KJ, Greenland S, Lash T. Modern Epidemiology. 3 rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.

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example of a research study design

How to... Design a research study

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

On this page

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

The following are some definitions of research design by researchers:

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

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

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

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

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

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

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

What is your theoretical and epistemological perspective?

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

What type of study are you undertaking?

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

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

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

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

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

What is your research question?

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

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

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

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

What sample size will you base your data on?

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

See  Sampling techniques .

What research methods will you use and why?

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

The two main approaches to methodology
 
typically use  typically use 
are  are 
involve the researcher as ideally an  require more   and   on the part of the researcher.
may focus on cause and effect focuses on understanding of phenomena in their social, institutional, political and economic context
require a   require a 
have the   that they may force people into categories, also it cannot go into much depth about subjects and issues. have the   that they focus on a few individuals, and may therefore be difficult to generalise.

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

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

How will you triangulate your research?

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

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

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

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

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

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

How will you ensure the reliability of your research?

Reliability

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

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

Generalisability

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

Transferability

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

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

Some basic definitions

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

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

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

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

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

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

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

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

Probability sampling

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

Probability sampling can be:

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

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

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

Non probability sampling

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

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

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

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

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

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

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

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

Calculating the sample size

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

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

The response rate

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

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

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

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

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

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

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

The following studies compare variables:

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

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

They can also look at cause and effect:

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

What is the hypothesis?

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

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

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

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

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

What are the appropriate measures to use

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

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

Data can be categorised as follows:

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

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

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

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

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

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

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

How many holidays do you take in a year?

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

Tick the option which most agrees with your views.

Navigating my way around the CD was:

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

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

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

How will I analyse the data?

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

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

What are the instruments used in quantitative research?

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

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

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

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

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

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

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

What are the advantages and drawbacks of quantitative research?

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

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

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

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

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

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

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

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

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

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

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

To what types of research questions is qualitative research relevant?

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

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

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

What is the relationship of qualitative research to hypotheses?

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

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

How will you analyse the data?

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

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

What are the main instruments of qualitative research?

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

Ethnographic methods

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

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

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

Historical analysis

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

Grounded theory

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

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

Action research

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

Narrative methods

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

Discourse theory

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

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

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

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

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

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

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

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

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

How rigorous is qualitative research?

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

Rigour in qualitative research is greatly aided by:

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

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

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

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

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

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

Can qualitative research be used in with quantitative research?

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

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

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

Over what time period will you collect your data?

This refers to two types of issues:

Type of study

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

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

Methodology

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

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

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

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

Doing the plan

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

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

Timetabling

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

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

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

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

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

What is Research Design? Characteristics, Types, Process, & Examples

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What is Research Design? Characteristics, Types, Process, & Examples

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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? Think of it as your roadmap, ensuring you don't end up lost in a sea of confusing data. So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design in research? Well, research design is the plan that shows how the study will be done. This plan covers everything from how data will be collected to how it will be analysed. A good research design has a clear question to answer, a detailed plan for gathering information, and a way to make sense of the findings. A good research design has three key ingredients:

1. A clear question: What exactly are you trying to learn? ‍

2. Data collection: How will you gather information (surveys, interviews, experiments)?

3. Analysis: How will you make sense of the data you collect?

Elements of Research Design 

Now that you know what is research design, it is important to know the elements. The elements or components of research design help to ensure that it is reliable, valid and can yield meaningful results. They also provide a guide for the research process, helping the researcher from the initial stages of formulating the research question to the final stages of interpreting the findings. 

1. Purpose Statement: This is a clear and concise statement of the research objectives and the specific goals the research aims to achieve.

2. Research Questions: These are the specific questions the research aims to answer.

3. Research Methodology: This refers to the overall approach and specific methods used to collect and analyse data.

4. Data Collection Methods: These are the specific techniques used to gather data for the research.

5. Data Analysis Techniques: These are the methods used to analyse and interpret the collected data.

6. Units of Analysis: These are the specific entities (e.g., individuals, groups, organisations) that the research focuses on.

7. Linking Data to Propositions: This involves connecting the data collected to the research questions or hypotheses.

8. Interpretation of Findings: This involves making sense of the data and drawing conclusions based on the research objectives.

9. Possible Obstacles to the Research: This involves identifying potential challenges or issues that may arise during the research process.

10. Settings for Research Study: This refers to the context or environment in which the research is conducted.

11. Time of the Research Study: This refers to the timeframe of the research, whether it’s cross-sectional (at one specific point in time) or longitudinal (over an extended period).

Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability: A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity: A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality: A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability: A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

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The Process of Research Design

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

Step 1: Establish Priorities for Research Design: 

Before conducting any research study, you must address an important question: "what is research design and how to create one?" For example, if you're researching the impact of remote learning on student performance, your priority might be to establish a clear research question and objectives.

Step 2: Choose your Data Type you Need for Research

One of the best features of research design is to decide on the type of data you need for your research. For instance, if you’re studying the effects of a new drug, you might need quantitative data like clinical trial results.

There are lots of ways to answer your research questions. Think about what you want to achieve before you decide how to do your research. The first thing, do you know what is qualitative research design and what is quantitative research design? Here's a quick difference between the two:

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Aspect Qualitative Research  Quantitative Research
Data Type Non-numerical data such as words, images, and sounds. Numerical data that can be measured and expressed in numerical terms.
Purpose To understand concepts, thoughts, or experiences. To test hypotheses, identify patterns, and make predictions.
Data Collection Common methods include interviews with open-ended questions, observations described in words, and literature reviews. Common methods include surveys with closed-ended questions, experiments, and observations recorded as numbers.
Data Analysis Data is analyzed using grounded theory or thematic analysis. Data is analyzed using statistical methods.
Outcome Produces rich and detailed descriptions of the phenomenon being studied, and uncovers new insights and meanings. Produces objective, empirical data that can be measured.

What is Research Design in Quantitative Research?

There are 4 main types of quantitative research design- 

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Type of Design  Purpose  Characteristics
Experimental To assess the causal impact of one or more experimental manipulations on a dependent variable. Involves manipulation of an independent variable and measurement of its effect on a dependent variable.
Other variables are controlled so they can’t impact the results.
Allows drawing of conclusions about the causal relationships among variables.
Quasi-Experimental To test causal relationships, often when it is not feasible to randomly assign participants to conditions. Similar to experimental design but lacks random assignment.
It involves questions about differences—often the difference between an outcome measured in the experimental and control groups.
Descriptive To create a snapshot of the current state of affairs. Provides a relatively complete picture of what is occurring at a given time.
Allows the development of questions for further study.
Does not assess relationships among variables.
Correlational To assess the relationships between and among two or more variables. Measures variables without manipulating any of them.
Can test whether variables change together, but can’t be sure that one variable caused a change in another.
Allows testing of expected relationships between and among variables and the making of predictions.

What are Research Design Examples?

1. Experimental Research Methods: 

Drug Efficacy Study: A pharmaceutical company wants to test the effectiveness of a new drug. They randomly assign participants to two groups: one group receives the new drug (experimental group), and the other group receives a placebo (control group). The company then measures the health outcomes of the two groups.

2. Quasi-Experimental Research Methods:

Teaching Method Evaluation: A researcher is interested in the impact of a new teaching method. A group of students are taught using the new method, while another group is taught using the traditional method. The researcher then compares the academic performance of the two groups.

3. Descriptive Research Methods:

Consumer Behavior Survey: A company wants to understand the shopping habits of their customers. They conduct a survey asking customers about their shopping frequency, preferred products, and reasons for their preferences.

4. Correlational Research Methods:

Health and Lifestyle Study: A health researcher is interested in the relationship between physical activity levels and heart disease. They collect data on the physical activity levels and heart health of a large group of people over several years. The researcher then analyses the data to see if there is a correlation between physical activity and heart disease

What is Qualitative Research Design?

Qualitative research designs are more flexible and open-ended. They're all about deeply understanding a particular situation or topic, and you have room to be imaginative and adaptable in planning your study. Below, you'll find a list of typical qualitative research designs.

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Type of Design  Purpose  Characteristics
Case Study To provide an in-depth analysis of a specific case, such as an individual, group, or event. Involves detailed, intensive knowledge about a single ‘case’, or bounded system.
Data collection can involve multiple forms to provide the in-depth case picture
Ethnography To understand and describe the cultural behaviors of a particular social group. Involves extensive fieldwork, including participant observation and interviews. More emphasis on observations.
Grounded Theory To develop a theory grounded in data from the field. Involves systematic, yet flexible guidelines for collecting and analyzing qualitative data to construct theories ‘grounded’ in the data themselves.
More emphasis on interviews
Phenomenology To understand the lived experiences of individuals around a certain phenomenon. Involves studying a small number of subjects through extensive and prolonged engagement to develop patterns and relationships of meaning.
Data collection is typically limited to interviews

Step 3: Decide your Data Collection Techniques

Now that you understand what is research design in research, you should also know the types of what are the different types of research design techniques. Choose the methods you’ll use to gather your data. If you’re surveying consumer behaviour, for example, you might use questionnaires or interviews.

Survey methods

Surveys are like questionnaires or interviews where you ask people about what they think, do, feel, or are like. They help you gather information straight from the source. So, when you're planning a research project, you can pick either questionnaires or interviews as your main way to get data. Research design is just the plan you make for how you're going to do your research, including what methods you'll use, like surveys.

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Aspect Questionnaires  Interviews
Form
Written Oral
Nature Objective Subjective
Questions Close-Ended Open-Ended
Information Provided Factual Analytical
Order of Questions Cannot be changed, as they are written in an appropriate sequence Can be changed as per need and preference
Cost Economical Expensive
Time Informant’s own time Real time
Communication One to many One to one
Non-response High Low
Identity of Respondent Unknown Known

Observation methods

Observational studies are a way to gather information without bothering anyone. You just watch and note down what you see, like people's actions or how they interact, without asking them directly. You can do this right then and there, jotting down stuff, or you can record videos to check out later. Depending on what you're studying, these observations can focus on describing things or counting them up.

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Aspect Quantitative Observation Qualitative Observation
Nature of Data Numerical and statistical  Words, images, and sounds
Purpose To test or confirm theories and assumptions To understand concepts, thoughts, or experiences
Data Collection Surveys with closed-ended questions, experiments, observations recorded as numbers Interviews with open-ended questions, observations described in words, literature reviews
Analysis Statistical methods Grounded theory or thematic analysis
Outcome Establish generalizable facts about a topic Gather in-depth insights on topics that are not well understood

Secondary Data

If you can't gather data yourself, you can use info already collected by other researchers, like from government surveys or past studies. You can then analyse this data to explore new questions. This can broaden your research because you might access bigger and more diverse samples. But, since you didn't collect the data yourself, you can't choose what to measure or how, which limits your conclusions.

In simple terms, research design is about how you plan to gather and analyse data to answer your research questions. If you can't collect data directly, you might use data already gathered by others, known as secondary data, to still answer your questions.

Step 4: Sort Out your Data Analysis

When you find what research design in research, just having a bunch of raw data isn't enough to answer your questions. You also need to figure out how you're going to make sense of that data. This is where research design comes in.

If you're working with quantitative research, you'll probably use statistics to analyse your data. Statistics help you understand things like how your data is spread out, what the average is, and how different groups compare. For example, you might use tests to see if there's a connection between two things or if one group is different from another.

But if you're dealing with more qualitative research, you'll need a different approach. Instead of crunching numbers, you'll be diving deep into your data, looking for patterns and meanings. You might use methods like thematic analysis or discourse analysis to make sense of it all.

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Aspect Thematic Analysis Discourse Analysis
Purpose To identify patterns or themes within qualitative data. To study how language is used in different situations to understand what people really mean and what messages they are sending.
Focus Emphasizes temporality and its relationship with how people tell their stories. More concerned about the semiotics of personal narratives and how those personal discourses relate to the real world, and interpret that world.
Data Analysis Involves identifying, analyzing, and reporting patterns (themes) within data. Examines language use in various forms of communication such as spoken, written, visual or multi-modal texts, and focuses on how language is used to construct social meaning and relationships.
Outcome Provides a rich and detailed, yet complex account of data. Helps us understand how language is used to create social relationships and cultural norms.

Sampling Procedures

Choosing the right way to pick people for your study is important. But it's not just about that. You also need a solid plan for how you'll reach out and get those people to join in.

Here's what you need to think about:

1. How many people do you need to join to make sure your study is good?

2. What rules will you use to decide who can join and who can't?

3. How will you get in touch with them—by mail, online, phone, or meeting them in person?

4. If you're picking people randomly, it's crucial that everyone who gets chosen actually takes part. How can you make sure most of them do?

If you're not picking people randomly, how will you ensure that your study is unbiased and represents different kinds of people? 

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the 4 types of research design, what are the important concepts of research design, what are the 5 components of a research, what are different types of research, what are the 4 major elements of a research design.

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25 Types of Research Designs

25 Types of Research Designs

Chris Drew (PhD)

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 types and examples, explained below

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.

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

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.

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

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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Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

Independent Measures Design 2

  • Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
  • Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
  • Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
  • Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

  • Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
  • Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
  • Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
  • Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

counter balancing

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

matched pairs design

  • Con : If one participant drops out, you lose 2 PPs’ data.
  • Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
  • Con : Very time-consuming trying to find closely matched pairs.
  • Pro : It avoids order effects, so counterbalancing is not necessary.
  • Con : Impossible to match people exactly unless they are identical twins!
  • Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Research Design: Definition, Types, Characteristics & Study Examples

Research design

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

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

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

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

What Is a Research Design: Definition

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

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

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

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

Research design example

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

What Makes a Good Study Design? 

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

A good research design should be:

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

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

Research Study Design Structure

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

A typical design includes 5 main components:

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

Research Design Essential Characteristics

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

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

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

Key Concepts in Research Design

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

Primary concepts

An is hypothesized to have an impact on a . Researchers record the alterations in the dependent variable caused by manipulations in the independent variable.

An is an uncontrolled factor that may affect a dependent variable in a study.

Researchers hold all variables constant except for an independent variable to attribute changes to it, rather than other factors.

A is an educated guess about a causal relationship between 2 or more variables.

Types of Approaches to Research Design

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

Quantitative Research  

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

Qualitative Research 

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

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Types of Research Designs & Examples

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

  • Experimental
  • Correlational
  • Descriptive
  • Diagnostic/explanatory.

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

1. Experimental

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

Its core characteristics include:

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

2. Correlational

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

Correlational studies encompass such features: 

  • Data collection from natural settings
  • No intervention by the researcher
  • Observation over time.
A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

3. Descriptive 

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

Descriptive studies are characterized by such features:

  • Random and convenience sampling
  • Observation
  • No intervention.
A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

4. Diagnostic

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

Among essential hallmarks of explanatory studies are: 

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

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

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

1. Determine Your Aims 

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

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

2. Select a Type of Research Design

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

3. Define Your Population and Sampling Methods

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

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

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

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

4. Decide on Your Data Collection Methods

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

Data collection methods in various studies

Experiments, controlled trials

Surveys, observations

Direct observation, video recordings, field notes

 

Medical or psychological tests, screening, clinical interviews

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

5. Arrange Your Data Collection Process

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

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

Measures and scales 

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

Operationalization 

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

Operationalization in study design example

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

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

6. Choose Data Analysis Techniques

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

Qualitative data may require thematic analysis or content analysis.

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

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

Research Design Checklist

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

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

Bottom Line on Research Design & Study Types

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

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

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FAQ About Research Study Designs

1. what is a study design.

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

2. What is the purpose of a research design?

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

3. What is the importance of research designs?

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

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

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

4. What are the 4 types of study designs?

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

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

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Descriptive Research

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

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  • Open access
  • Published: 02 July 2024

Contextualising and challenging under-representation in research in light of Cultural Trauma: a qualitative focus group and interview study

  • Kate Fryer 1 ,
  • Isobel Hutt 2 ,
  • Habiba Aminu 1 ,
  • Emma Linton 3 ,
  • Johanna White 4 ,
  • Josie Reynolds 3 &
  • Caroline Mitchell 5  

Research Involvement and Engagement volume  10 , Article number:  69 ( 2024 ) Cite this article

Metrics details

Although underserved populations— including those from ethnic minority communities and those living in poverty—have worse health and poorer healthcare experiences, most primary care research does not fairly reflect these groups. Patient and public involvement (PPI) is usually embedded within research studies in the United Kingdom (UK), but often fails to represent underserved populations. This study worked with patient and public contributors and local community leaders, situated in a socio-economically deprived and ethnically diverse urban area, to explore under-representation in primary healthcare research.

We undertook a focus group with a purposive sample of 6 members of a Patient and Public Involvement Group (PPIG), and interviews with 4 community leaders (representing Black, South Asian, Roma and socio-economically deprived communities). An iterative analysis process based on template analysis was used. Focus group 1 was rapidly analysed, and a template created. Findings were presented in focus group 2, and the template further developed. The Cultural Trauma concept was than applied to the template to give a wider theoretical lens. In-depth analysis of focus groups and interviews was then performed based on the template.

Wider societal and historical influences have degraded trust in academic and healthcare institutions within underserved populations. Along with more practical considerations, trust underpins personal motivations to engage with research. Researchers need to invest time and resources in prolonged, mutually beneficial engagement with communities of importance to their research, including sharing power and influence over research priorities. Researcher reflexivity regarding differential power and cultural competencies are crucial. Utilising participatory methodologies including co-production demonstrates a commitment to inclusive study design.

Conclusions

Re-framing evidence-based medicine to be more useful and relevant to underserved populations with the highest burden of ill health is urgently needed. Lack of representation in primary healthcare research reflects wider societal inequalities, to which Cultural Trauma provides a useful lens. However, there are actions that researchers can take to widen representation. This will ultimately help achieve the goal of increased health equity by enhancing scientific rigour and research generalizability.

Plain English summary

People living in poverty, and people from ethnic minority communities may be referred to as ‘underserved’. Underserved communities benefit less from health services, and along with other factors, this leads to health inequalities. Primary care research does not include enough people from these communities. This makes the health inequalities  worse.

This study looks at why people from underserved communities are not included in research. It also looks at what might help. We had focus group discussions with members of a Patient and Public Involvement Group (PPIG). These are individuals who do not have research expertise, but use their lived experience as patients to influence the research process. This group was formed in 2017, from areas where more people live with social disadvantage. We also interviewed local community leaders. Interviews and focus groups ask open questions, so are a good way to explore what people think about an issue. We found a useful theory about how cultural history affects what people can do. We used this to help us to understand how our findings could improve and widen participation in research within underserved communities.

We found that trust is very important. There needs to be trust between people and organisations. There are also practical reasons people from underserved communities might not be able to get involved in research. Researchers need to be aware of these things, and work with people from these communities throughout all stages of research. Long term relationships need to develop between institutions and people doing research. Understanding each other’s culture and history makes it easier to work together.

Peer Review reports

Research suggests that people from underserved populations, including persons living in poverty and people from ethnic minority communities, have greater risk of poor health, and poorer experiences of using health services [ 1 , 2 , 3 ] with the two characteristics combining to create compounded inequity. This was clearly demonstrated during the COVID-19 pandemic, where the impact on minority populations was disproportionate [ 4 ]. The majority of primary healthcare research does not have adequate representation from people of these communities [ 3 , 5 ].

‘Evidence based medicine’ across a range of common health conditions is derived from studies which commonly exclude certain populations within their design, thereby potentially perpetuating health inequalities [ 6 , 7 ]. The UK National Institute for Health and Care Research (NIHR) produced NIHR-INCLUDE guidance [ 8 ] to improve inclusion of underserved groups in research. The importance of building trust with communities already subjected to structural and institutional discrimination is highlighted, but there is a dearth of research about how to build trust and mutual understanding to inform inclusive research practise. Many studies describe how they have addressed this issue in their own studies, but fewer have resulted in explicit learning which can be applied by other researchers [ 9 , 10 ]. One example is a toolkit developed by Farooqi et al. [ 11 ] to help researchers maximise ethnic minority recruitment to health research: The final toolkit recommendations were considering the communities which the research needs to involve; effective patient and public involvement; effective recruitment; cultural competence of research procedures; and effective feedback. Recommendations were also made as the result of a systematic review by Bonevski et al. [ 12 ], who Identified barriers to involvement in health research for socio-economically disadvantaged groups, and strategies to overcome these. Barriers included mistrust of research within some communities and reliance on technology to collect data. They then developed a set of strategies covering the entire research process from sampling to retention.

A recently published paper by Mitchell et al. 2023 [ 13 ] outlines steps to move from tokenism to citizen control in the research process, to increase representation in research:

1. Build trust and dialogue by exchange of ideas in a community setting, led by community members.

2. Include knowledge sharing about the topic of interest with patients and communities, for example, producing lay summaries of a literature review and bringing in a topic expert for a ‘question and answer’ session.

3. Support the development of research skills in communities where it is desired (capacity building).

4. Co-create from the outset and at every stage of the research process to include generating and prioritising research questions relevant to the public.

Despite ample research evidence demonstrating that lack of engagement not only resides in under-represented communities but also in research teams and institutions [ 9 ], the lack of progress suggests the need for reflective research practice to find solutions to this challenge. We also suggest that the issue needs to be seen in the context of wider societal issues. Local drivers for this work include clearly evidenced health inequalities across the city, and GP practises wishing to be ‘research active’ but finding that studies available to them are not suitably designed for their patient population.

The idea of Cultural Trauma as a mechanism to disrupt health and create disparities is proposed by Subica and Link [ 10 ], drawing on Fundamental Cause theory which is a key concept within the Social Determinants of Health literature [ 10 ]. Subica and Link suggest that Cultural Trauma, the impact of a dominant oppressive group on the resources of another group, damages three health-protective cultural resources: modes, institutions, and lands, as the underlying cause for health inequalities. Modes encompass “ the languages, norms, customs, values, and artefacts ” that create the internal and external worlds of group members. Cultural Trauma disrupts healthy functioning as modes help people to self-regulate when faced with external stressors.

Institutions refers to the systems that define social and community life such as family, educational, religious, and health systems. These institutions protect against stress and support health when individuals have a positive status within them. However, when institutions relegate individuals into lower statuses, this creates stress and leads to poorer health. It may be perpetuated through policies which promote inequality and deny access to flexible resources. Lands is used to describe material resources such as property, housing, food and transport, that are necessary to maintain health within the particular society within which a person lives. This can be through physical dislocation from native lands via force, genocide, or disease or discriminatory policies that strip groups of material resources.

Several papers addressing under-representation in research have been able to extrapolate experiences with a particular study to the wider research landscape, using theoretical concepts. For example, Rai and colleagues [ 14 ] reflect on what it would take to meaningfully attend to ethnicity and race in health research by drawing on experiences in their own study. Their conclusions are strongly informed by a theoretical understanding of issues around race and ethnicity, for example using the phrase, ‘methodological whiteness’.

A paper about reducing social and racial inequalities in obesity by Rosas and Stafford [ 15 ], discusses how to engage people from ethnic minority and socio-economically disadvantaged groups in health research, and focusses on the importance of understanding underlying mechanisms, as a building block for addressing the issue. A paper by Asare [ 16 ] discusses the concept of social determinants of health (SDH) framework (health and education, the built environment and social and community life) as an explanation for minority patients being less likely to partake in cancer research. This paper proposes a framework which nurses can use to identify where support is needed. Both SDH and Cultural Trauma use Fundamental Cause theory to explain current inequalities, but Cultural Trauma draws together the collective impact of lost resources as a result of oppression. The emphasis in this theory is not on the impact of trauma on their psychological-wellbeing, but on the erosion of health-protective factors within their culture. We suggest that this can be further applied to under-representation of certain groups in health research, which may contribute further to health inequalities.

Working with patients from a Patient and Public Involvement Group (PPIG) based in a low socio-economic area with an ethnically diverse population, and with local community groups that support underserved groups, this study aimed to explore how researchers and research institutions can address inequalities in research, from the perspective of underserved groups. Using Cultural Trauma as a theoretical lens helps to contextualise our findings in the wider social context.

We collected data from a focus group with a PPIG, and interviews with community leaders from four voluntary care organisations serving Roma, South Asian, Black, and socio-economically deprived populations. This provides a rich understanding of the barriers and facilitators of participation in research from patient and public perspectives.

Focus groups and interviews (setting and participants)

A focus group methodology was chosen to collect data from the PPIG group, as focus groups are a well-accepted method in social science and the group process allows participants to identify and clarify their views [ 17 ]. Moreover, this was a pre-existing group, who were already comfortable in communicating their views in a group setting. This group was asked to participate as all members live in socio-economically deprived areas, and the group has members from range of ethnic backgrounds and with a range of educational levels.

The decision to do group or individual interviews with community leaders was a pragmatic one, based on the busy schedules of community leaders. Potential participants were purposively sampled via community organisations serving ethnic minority and socio-economically deprived populations.

All participants were contacted via email with a brief description of the research, and asked to contact the researcher (KF) if they would like to take part. They were then sent an information sheet with the opportunity to contact the researcher to ask questions, before returning a signed consent form.

The topic guide for the focus group (see appendix 1) was developed using NIHR-INCLUDE [ 8 ] recommendations, and stakeholder consultation.

Focus groups and interviews were facilitated by KF. KF is a female post-doctoral Research Associate, with over 20 years’ experience of qualitative health research, including conducting focus groups and interviews. KF is white British, and a practising Muslim.

In the first focus group, six participants were recruited, with informed consent, from the PPIG. The participants were two men and four women, age between 40 and 75. Three participants were White British, one Black African and two South Asian. All live in socio-economically deprived neighbourhoods.

A focus group lasting 75 min was conducted in a University building, which was easily accessible by both car and public transport. It was audio-recorded, transcribed verbatim, and then identifying information was removed for analysis. KF took field notes to supplement the analysis.

For the interviews, 4 community leaders were recruited from 4 community organisations, to represent a variety of underserved communities (Roma, South Asian, Black, and socio-economically deprived). The aims of the research were discussed with the participants when inviting them. All who were invited, agreed to take part. The participants were 3 women and 1 man, ranging from age 40–70, and their ethnic backgrounds were Roma, South Asian, Black and White British.

Community leaders were presented with and asked to comment on the findings of the rapid analysis from the PPI focus group (see Table  1 ). Participants were also asked how academics and clinicians may be able to work with community groups to increase participation.

One group interview (three participants) and an additional single interview took place online, as this was preference of the community leaders. The group interview participants were Black Caribbean, South Asian, and Roma, and provided leadership in organisations serving people from those backgrounds. The individual interviewee was White British, leading a community organisation within a socio-economically deprived area. The researcher (KF) and the participants were the only people in the interviews. The group interview was 69 min long, and the single interview was 40 min long. KF took field notes to supplement the analysis.

KF continually checked her understanding of what was being said in the focus groups and interviews with the participants, which was decided as preferable to sharing the transcripts afterwards, which would have been extremely time consuming for the attendees to read through.

The focus group and interviews did not aim to reach data saturation, but to gather enough data from a variety of perspectives to provide conceptual depth.

Analysis- template analysis applying cultural trauma

Template analysis involves [ 18 ] the development of a coding ‘template’, which summarises themes identified by the researcher(s) as important in a data set, and organises them in a meaningful and useful manner. We used template analysis to allow us to bring in wider social and cultural factors to our understanding of our findings, and we achieved this by applying the Cultural Trauma concept. Figure 1 illustrates how we created the template.

figure 1

Analysis process

The template for analysis was created by rapidly analysing the focus group and interview transcripts to create initial templates (see Tables 1 and 2 ).

This was carried out by three members of the research team (KF, EL, JW). The themes were then applied to the three elements of Cultural Trauma (modes, institutions, lands), which helped to give a wider context to the findings (by KF, IH, CM). It was important to develop the initial templates from the study findings, to ensure data was not forced into the theoretical concept.

A detailed analysis of the focus group and interviews was then carried out according to this final model (by IH, KF), using NVivo software. The final model was presented to the PPIG, who confirmed their views were adequately represented and the model made sense to them.

Figure  2 shows how the themes were developed. The three elements of Cultural Trauma [ 10 ] are shown below in the photo on the pink Post-It notes, and key concepts from the background literature are shown on orange Post-It notes. We then took the themes from our rapid analysis (shown on the small square Post-It notes) and arranged them with or between the three elements of Cultural Trauma. This process started to form a picture. There was a cluster of themes between cultural modes and institutions, indicating that the interaction between these two elements is important. Other themes formed a circle around them, indicating factors that were influential in creating this interaction, bringing us to the final model. Where these are presented in the egg-shaped diagram in Fig.  3 , the top half of the egg contains factors relating to institutions, and the bottom half shows factors related to communities.

figure 2

Applying Cultural Trauma to the template

figure 3

The process of research involvement in underserved communities

An in-depth analysis based on this template deepened our understanding of how themes related to each other, and this is represented in Fig.  3 .

Trust and understanding

Our findings from the interviews were clustered around the Cultural Trauma elements of modes and institutions , showing tensions between these, and the importance of trust and understanding. An exploration of themes clustered between ‘Modes’ and ‘Institutions’ suggested that trust between under-served communities and institutions (in this case health and academic institutions) was of utmost importance regarding research participation and was often absent.

Research happens within a wider social context. This context will influence the research process, and the research process cannot be understood without reference to the wider social context. Historical and societal issues will influence communities and therefore how individuals act. Institutions and their relationship with communities will be influenced by these same issues, and will influence how researchers act.

Trust and understanding is therefore for an over-arching theme, which relates to each of the subthemes: ‘Values and Cultural Competence’ and ‘Research priorities, design and recruitment’ which allow us to explore this issue from the point of view of the researcher and their institutions; and, ‘Historical and current social & community context’ and ‘Personal motivations’ in which we understand this from the point of view of under-served communities.

Values and cultural competence

Our findings suggest that values and cultural competence are crucial to building trust and understanding between academic institutions and under-served communities. This was both true at a larger societal scale, and a more local and project specific level. At a larger scale for example, the need for decolonising of the curriculum was mentioned, reflecting the feeling that contributions from non-white academics, and other ways of knowing or cultural interpretations of health had been removed from our understanding of many subjects taught today:

“All the contributions from those previous civilisations, they have contributed to what we know as knowledge today…So people like, different people, so everyone can see that their ancestors did something. It gives them confidence that we too, we can” (PPI contributor - PPIC)

This is an example of the ‘institutions’ aspect of cultural trauma, where policies and denied access have impacted upon the current perceptions of under-served communities within academic institutions.

An observation was made in both focus groups and interviews that research teams are predominantly white and middle class. Where those being researched are from socio-economically deprived or ethnic minority backgrounds, this may reinforce established power dynamics. Diversifying research teams was seen as desirable, but it was acknowledged that people from underserved communities may be less likely to establish academic careers, creating a circular problem:

“if a community member doesn’t feel that they can go to university, or that that’s even an option for them, the last thing is… I don’t see that link between them contributing to research” (Community Leader- CL)

This relates to both modes and institutions within Cultural Trauma – historically, access to educational institutions has been explicitly denied to under-represented groups, and is currently denied implicitly. The process of this has also devalued the modes of under-served groups (e.g. languages and artefacts). Engaging with community leaders for research recruitment was suggested as a solution to overcome this issue. In the light of Cultural Trauma, the mechanism behind this may be to increase positive regard of the institution towards the under-served group, and facilitate access to the under-served group.

However, the community leaders themselves recognised the limitations of this approach from their perspective, and the difficulties this can cause for community organisations:

“it is very difficult for us to keep being asked to participate […] it’s just like, oh, can you get us so and so, can you get us so and so, can you get so and so? We’ll give you a little budget for it, and that’s it. And then we go out. And it costs a lot more of our time” (CL)

It was recognised that ‘helicoptering in’ could be very damaging. This issue relates to lands and can be seen as a result of indirectly discriminating policies impacting upon the distribution of flexible resources. Community leaders unanimously wanted ongoing relationships between community organisations and Universities, which were not only transactional:

“ To make it a good process, there should be some kind of good partnership going”(CL)

In the longer term, capacity building in communities was suggested as a way of breaking the cycle and ensuring that people understand the role and value of health research:

“And that’s the education, it’s the awareness, it’s as much getting that out there to start that little pea on the roll until it becomes bigger and bigger and bigger and the information, but there’s so much information and education about services that needs to still happen” (PPIC)

This would impact upon the lands and institutions elements of Cultural Trauma, by redirecting resources and building positive regard within academic and health institutions, for the under-served group.

The values of the researcher and research team, and their cultural competence, are essential in enabling inclusive research. The first step in this process is to have researchers who genuinely see that engaging collaboratively with communities can enhance the research process:

“So it’s just this kind of how can we see people as humans that have the capacity to find the solutions and offer solutions for a lot of the things that are the problems? They might not have the confidence, they might not have the resources or the skills really to… But they know what they need and they just need a little bit of scaffolding and support to get there” (CL)

Where this is successfully achieved, it may impact upon modes , by valuing and highlighting the knowledge and skills present within under-served communities, and increase positive regard within institutions. This involves an awareness of the historical and cultural issues relating to specific communities. This awareness is necessary for understanding how certain elements of the research process may be triggering for particular groups, relating to the modes element of Cultural Trauma, which acknowledges that self-regulation against stress can be damaged:

“What might look very innocent for us, and it is about respecting them, might trigger some trauma for them” (CL)

This awareness was seen as something that would result from extended engagement with communities, and would not be achieved in a one-off training session:

“If we think of these things as an event as opposed to a process, we’ve got it wrong. It’s not going to be… It’s taken years to get to the point where we are, so we’re not going to turn the ship around overnight. So I think it is about continuing to make those networks, to make those connections, to make real the community partnerships that universities are attempting to do now. But not to take your eye off the ball and think, well, we’ve done that now.”(CL).

This statement has implications for institutions, in how they may control or impact resources available to communities, speaking to the ‘lands’ element of CT.

Historical and current social & community context

The significance of historical cultural factors and their impact on trust and understanding was striking during the focus group with the PPIG. These are issues of race and class in general, which may have contributed to issues of stigma and ‘otherness’ that have embedded a culture in which willingness to participate in research is unlikely. Historical exclusion from and exploitation in health research caused disengagement and therefore a present lack of awareness. This is clearly embedded in the modes aspect of Cultural Trauma, and directly impacts upon how people relate to academic and health institutions currently. In addition, community leaders spoke about specific scandals around research, such as the Tuskagee study in America involving Black men, as being very much in the forefronts of people’s minds when they were asked to participate in health research:

“they’ve not been reassured that the current ethics and current standards make that kind of thing less likely to happen” (CL)

Not only do policies need to change to protect under-served groups, but this needs to be demonstrated and communicated. More general health injustices also had an ongoing influence over people’s response to research, even if they occurred in other countries:

“If we’re talking about in Slovakia, they still sterilise women, so of course there’s going to be a mistrust of doctors here in the UK. It’s not that far removed for them” (CL)

Practical issues such as access to technology, and literacy levels, also impact upon individual capacity to take part, fitting with the lands element of Cultural Trauma. However, more time was spent discussing more abstract concepts, such as valuing health and awareness of research. which was felt to be lacking or very basic:

“yes, there is an awareness of research but I think only what they see on the television and that’s people with the pipettes and putting things in test tubes” (PPIC)

Not only do communities need an awareness of research, but more specifically, how it may affect, and therefore relate to them:

“I think most people, I mean, if you talk to them about research, they probably think that research is not something that affects them. Like you say, it’s like doctors and, you know, people who are educated, that’s something they’ll do, so it’s nothing got to do with them. So they won’t really be interested or want to join in” (PPIC).

This statement demonstrates the impact of denied access over time.

Personal motivations

The factors described above will influence people’s responses to research invitations. During the focus group with the PPIG, we explored what factors had motivated them to become involved in research. Most stated that they valued the opportunity to improve services, and understood that the patient perspective was necessary in addition to the expertise of the researchers:

“That expertise, we public and members of the public, our common knowledge enhances their expertise. And if that common knowledge is not there from our perspective, that expertise is limited.” (PPIC)

This suggests that the people in the PPIG group had overcome CT to some degree, in order to realise the value of their perspective, and how it may have a positive impact. Contributing in this way gave them a feeling of doing something to improve society in general:

“I just thought if I could make one tiny, tiny, tiny bit of difference, then, you know, I’d feel better” (PPIC)

It may also be, in itself, a way of addressing Cultural Trauma. The statement above suggests that the PPIC is aware of the size of the problem faced by under-served communities in regard to health inequality. By contributing, they may be attempting to repair damage to the mode element, by learning to value their own contribution.

Research priorities, design and recruitment

The values and cultural competence of the research team was seen as manifesting in the research process. The PPIG, who are experienced in engaging with researchers, talked about knowing when they are part of a ‘tick box exercise’ as opposed to when engagement is genuine. A ‘tick box exercise’, where engagement is superficial and has no real influence on the research process, is an example of how institutions may continue to deny access, despite updated policies which aim to enhance participation.

There was a tension between valuing lived experience, and the risk of re-traumatising people through the discussion of difficult life events:

“Increasingly, I’m not approaching the people we work with, to say, share your lived experience, because it’s just I’ve seen it re-traumatise.” (CL)

Here we see the cumulative effect of Cultural Trauma, where past trauma and its impact on self-regulation against stress, prevents active participation in the here and now.

Awareness of the language needs of the people that researchers want to involve in research is essential, including literacy levels and different forms of communication. It was suggested that researchers need to be more creative in the way they undertake the information and consent process around research, relying less on technology, and more on traditional forms of communication, approaching communities in their own areas and environments:

“the people that you want really are not going to sit checking their emails. It’s more conversations, the traditional forms of communication that we need to fall back on.” (PPIC)

This both suggests learning to value the modes of particular under-served groups, and also their institutions .

A key part of moving away from the ‘helicoptering in’ approach, was to ensure that people participating in research had feedback regarding the study findings, and impact of their input on the research process:

“I don’t think people would mind if they didn’t go ahead or there were some problems. But as long as they got feedback so they feel appreciated, they feel that next time they want to do more to help, be involved”. (PPIC).

The fact that this is not seen as common practise is yet more evidence of denied access to institutions. Long term relationships with communities were seen as the ideal model, in which this would happen naturally:

“if you’re doing this kind of research, you’re going to have to come back to the community and it should be an iterative process. And that isn’t always the model. And I understand it’s resources and time and time is currency. However, I do feel that when you invest, and if you’re really committed into finding and gathering quality data, then you need to invest that time in trying to get an understanding of the community.” (CL).

This CL directly refers to the flexible resources alluded to in lands. ‘Resources and time and time is currency’ acknowledges that academic and health institutions often choose not to invest limited resources in under-served communities. There was a more general message coming through, that the necessary actions would come from a mind-set which genuinely valued and respected people’s experience, the factors encompassed by the modes aspect of Cultural Trauma:

“no matter who you come across in life, even the guys that live on the street, when you interact with that person, you enrich one another consciously and unconsciously. You learn from that person, that person learns from you. Whether you accept it or not, consciously and unconsciously.” (PPIC).

Reimbursement of community member’s time was problematic. Community leaders and PPI members noted issues with delays in reimbursement, and the limitations of offering vouchers, which have negative connotations within some communities, such as being associated with being in need, or on a low income. This falls within the lands elements of Cultural Trauma, but also overlaps with modes in that there are implications for particular communities of means of payments, in relation to their values and customs. These issues cause inconvenience and undermine the relationship between researchers and communities:

“no-one should have to wait six to eight weeks to be paid for something. And that’s what we always say, well, because of the paperwork or… But that’s not fair, we wouldn’t wait six to eight weeks to be paid on a salary, so why should we allow freelancers or community connectors or organisations, expect them to do that?” (CL)

While paying community organisations for their input was recognised as important, the amount given was often insufficient to cover the actual time and resources of the community group in engaging with the research, putting further stress on the flexible resources covered in the lands element of Cultural Trauma. Community leaders made it clear that any genuine engagement means involvement from the earliest stages, and in on ongoing relationship:

“We’d like to be part of shaping and implementing as well and be part of… To make it a good process, there should be some kind of good partnership going on with the university and communities” (CL)

Summary of findings

The ability for research to be inclusive depends upon trust between academic and health institutions and the communities that research hopes to engage. The impact of Cultural Trauma was evident in the four subthemes, demonstrating the wider context in which this trust needs to be established. The concept of Cultural Trauma aids us in understanding how the lack of representation of under-served communities in research relates to wider aspects of these community’s experiences. We are able to see how damage to modes, institutions and lands directly impacts upon the current situation, from the point of view of researchers and their institutions, and under-served communities.

The barriers to involvement in research by people from underserved communities that we found in our focus groups support those present in other studies, particularly in Bonevski et al.’s systematic review [ 12 ]. Further, we found that the socio-cultural context was extremely influential in how particular groups may respond to research, and that it is important that researchers be aware of this. Similarly, Rosas and Stafford [ 15 ] emphasised the importance of understanding underlying mechanisms, and Farooqi et al. [ 11 ] emphasised the importance of cultural competence of research teams, in addition to the more practical strategies needed. Our findings also suggested that the predominantly ‘white’ nature of research teams was itself a barrier to inclusive research, a problem described by Rai et al. [ 14 ] as ‘methodological whiteness’.

Taken as a whole, our findings emphasise the importance of seeing the research process within a wider context, and we found Cultural Trauma to be a useful framework for understanding the issue. The Cultural Trauma concept suggests damage to three health-protective cultural resources: modes, institutions, and lands , as resulting in Cultural Trauma, an underlying cause of health inequalities [ 10 ]. In this paper, we have demonstrated how this can be further applied to under-representation of certain groups in health research, by using the model as a lens by which to understand our data. This enables us to see the circular impact created by under-representation of underserved groups in research, which further exacerbates health inequalities. The concept of Cultural Trauma helps to frame the under-representation of certain groups in research in relation to broader societal issues, without which it cannot be properly understood.

Strengths and limitations

In the focus groups and interviews we engaged directly with people from socio-economically deprived areas, and from a variety of ethnic backgrounds. We also engaged with community leaders who had a wider view of the relevant issues.

While all of the participants involved in the focus group were from socio-economically deprived backgrounds, half were White British, and therefore could be seen as forming part of the ‘dominant group’ by other people in the group that were from ethnic minority backgrounds. This could have influenced what people felt able to say in the group. However, the effect may have been somewhat mitigated by it being a pre-established group, who had many conversations about class, race and inequality, and therefore are likely to have been more comfortable expressing their views with each-other.

This study was undertaken in a single urban setting, but with people from socio-economically deprived and ethnic minority backgrounds which makes a contribution to a growing body of literature in this area. However, we need to acknowledge that those who participated are likely to be among the most literate and health aware within their communities.

The lead researcher (KF) who facilitated the focus groups, is well known to the PPIG (and present at most of their meetings), and this may have influenced the responses they felt able to give. However, the group has an established ethos of challenging the idea but not the person, and the group appeared comfortable with disagreeing with her. This relationship may also have influenced the data as some things may have been implicitly assumed and so not verbalised.

Lack of representation in primary healthcare research is part of wider societal inequity but there are actions that researchers can take to improve inclusivity in primary healthcare research, thereby improving the quality of data and making evidence-based practise more accurate and valuable within all communities. This could contribute significantly to the reduction of health inequalities.

Whilst the NIHR-INCLUDE Roadmap [ 8 ] provides a good structure for an inclusive research process, prolonged engagement between academic institutions and communities over time and across multiple research projects, and individual researcher reflexivity with respect to differential power and cultural competencies are also crucial to widening participation in research.

Study data collection is the end point of a long process of prioritisation, design and funding procurement, and change needs to occur at all points in that process for real change to happen. If the organisations which currently ‘own’ research, wish research to be representative of the UK population, those organisations need to be willing to disrupt their philosophies and processes.

Recommendations

Research institutions and those who represent them should:

Take responsibility for lack of diverse representation in primary healthcare research.

Build trust and understanding with communities by:

Developing cultural competence

Diversifying research teams

Engaging with community leaders

Invest time and money in long-term reciprocal relationships.

Recognize untapped potential within communities and build capacity for engaging in research.

Ensure that research is not ‘exclusive by design’, for example, considerations around language, digital exclusion of ways of recruiting.

It is also essential to embed PPI which represents and includes those from underserved communities throughout the research cycle, and strategies need to be in place to ensure this happens in a genuine way rather than a ‘tick-box exercise’. Utilising participatory methodologies demonstrates a commitment to inclusive study design from the outset.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Patient and Public Involvement

United Kingdom

Patient and Public Involvement Group

National Institute for Health Research

Patient and Public Contributor

Community Leader

Social determinants of health

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Acknowledgements

We would like to acknowledge the patient and public contributors, and community leaders, who took part in this study and play a key role in our work. We are withholding names due to confidentiality.

This study was funded by the National Institute for Health Research Yorkshire and Humber Clinical Research Network.

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Contributions

KF and CM designed the study. IH was involved in the analysis process. EL and JH were involved in the focus group analysis process. JR and HA were involved in reviewing and writing up the study. CM was the lead applicant on the study and involved in reviewing and writing up the study.

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Competing interests

The lead author (KF) and other authors have ongoing professional relationships with the study participants (non-financial). KF leads the PPIG, and collaborates with the community leaders on research studies.

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Focus group topic guide PPI group

1. Why do you think health research is important?

2. Do you think that most people understand the importance of health research?

3. What motivated you to get involved?

4. What my motivate others in your area/community?

5. What are the barriers to people getting involved in health research?

Prompts: different ethnic backgrounds, levels of education, struggling financially.

1. What things can researchers do, to encourage/make it easier for people to get involved?

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Fryer, K., Hutt, I., Aminu, H. et al. Contextualising and challenging under-representation in research in light of Cultural Trauma: a qualitative focus group and interview study. Res Involv Engagem 10 , 69 (2024). https://doi.org/10.1186/s40900-024-00600-3

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A bibliometric and systematic review of scientific publications on metaverse research in architecture: web of science (WoS)

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  • Güneş Mutlu Avinç   ORCID: orcid.org/0000-0003-1049-2689 1 &
  • Aslı Yıldız 2  

The global trends related to the concept of Metaverse in architecture have significantly expanded in recent years, thanks to the increasing number of scientific publications. Systematically examining the literature on this topic and identifying research trends and potential directions provides comprehensive data maps, thus charting a roadmap for researchers interested in working in this field. In this context, the research aims to identify the trends and tendencies of the concept of the Metaverse in the scientific literature over time at the primary analysis levels, such as countries, institutions, resources, articles, authors, and research topics. The research conducted with this aim involves a dynamic, visual, and systematic examination of the academic literature on academic publishing using data accessed without year limitations from the Web of Science Core Collection-Citation database. In the research conducted without year limitations, a sample comprising 334 articles published/planned to be published between 2005 and 2024 is analyzed. The bibliometrix R-Tool was used to enhance the analysis, and metadata was obtained from the WoS database. This analysis analyzed publications, citations, and information sources, including the most published journals, the most used keywords, the most cited and leading articles, the most cited academics, and the most contributing institutions and countries. In conclusion, this study aims to define the profile of international academic publishing in the field of the Metaverse, present its development, identify research fronts, detect emerging trends, and uncover the working themes and trends in the Metaverse specific to architecture. This study describes the profile of international academic publishing on the metaverse, presents its development, identifies research frontiers, identifies emerging trends, and reveals metaverse study themes and trends in architecture. As a result, education, virtual perception of space, building operation and maintenance, building evacuation, BIM (Building Information Modeling), cultural heritage, physical environment, built environment/planning, smart home, design and creativity, universal design/accessibility, sustainability, smart city/GIS, urban transportation systems, and in-use evaluation are identified as themes that have been studied in relation to the metaverse concept in architecture and design disciplines.

Avoid common mistakes on your manuscript.

Introduction

Information and communication technologies are undergoing rapid change and transformation every day. The concept of technology, which developed with computers in the 1990s, evolved into the Web in the 2000s, Web 2.0, the second stage of the evolution of the Web in the 2004s (Murugesan, 2007 ), telecommunications in the 2010s, and the metaverse in the 2020s (Lee, 2021 ).

Until the 1990s, Web 1.0 emerged as the first version. In this process, access to information was only one-way and there was no interaction. The Web 1.0 era was focused on making connections and obtaining information on the internet. Web 2.0, which emerged in 2004, includes forums, comments, blogs and social networks. However, Web 2.0 allowed users to share information as well as read. Thus, people started to communicate, collaborate and interact in another way (Alby, 2007 ). The concept of Web 3.0, known as the semantic web, which became active in 2010, made it possible to conduct a content search using keywords. Here, instead of humans, computers have come to the forefront to produce new information and think. Web 4.0, which started in 2016, focuses on access and intelligence (Latorre, 2018 ). Using machine learning technologies and artificial intelligence, users started to interact with data. Interpreted as an iteration of the Internet, the metaverse brings together a large number of different virtual spaces that provide access to various projects and entertainment environments using the full spectrum of augmented reality. In summary, the metaverse is the latest in a long line of emerging technologies (Nath, 2022 ). All these definitions are presented in the graph in Fig.  1 .

figure 1

The Changing Face of the Internet: Journey from Web 1.0 to Web 3.0 (Nath, 2022 )

The concept of Metaverse (fictional universe), which first emerged in Neal Stephenson's cyberpunk novel "Snow Crash" in 1992 (Ağırman & Barakalı, 2022 ), has seen a significant increase in studies on this subject after Marc Zuckerberg changed the name of Facebook and announced it as "Meta" in 2021 (see Figure 1 ). The word Metaverse is a combination of the prefix "meta" (meaning "beyond") and the suffix "verse" (short for "universe") (Dionisio et al., 2013 ). In this context, the Metaverse is defined as a virtual structure that allows participants to participate in real life or recreate real life through self-created avatars in a virtual metaphorical environment, independent of temporal and spatial constraints (Díaz et al., 2020 ). The concept of a metaverse or virtual world is a social and economic universe beyond commerce and entertainment, where digital users or avatars represent the individual (Lee, 2021 ). The Metaverse offers a potential environment in various fields, such as culture, education, design, and entertainment.

In the literature, there are studies on metaverse conducted with various keywords in different databases. For example, Abbate et al, ( 2022 ) conducted a study with the keyword “metaverse” in the title, abstract and keywords in the Scopus database, regardless of the time period studied, and the aim of the study was to review the studies on the Metaverse using bibliometric analysis. Feng et al. ( 2024 ), (2024) was conducted in the WOS database with the keywords “metaverse” and “Non-Fungible Token” between 2000 and 2023. Tas and Bolat, ( 2022 ), unlike these studies, conducted a bibliometric analysis of studies on the use of metaverse in education. Similarly, Bızel ( 2023 ) conducted a bibliometric analysis on the concept of “metaverse” and “education” in education between 2004 and 2022. Zhou et al. ( 2023 ) analyzed articles on the concept of metaverse in different databases between 1992 and 2022 and examined the concept of metaverse, its technical features, user behaviors and their theoretical foundations. Studies in the literature have examined the concept of metaverse in general or in relation to education. The scope of this study is the relationship between metaverse and architecture.

Given its inherent connection with architecture and design, this study primarily focuses on the Metaverse universe. Therefore, this study conducts a bibliometric examination of research related to the Metaverse and performs content analysis specifically within architecture. Bibliometrics is a quantitative analysis method that uses mathematical and statistical tools to measure the interrelation and impact of publications within a specific research field (Lee et al., 2020 ). Bibliometric research encompasses empirical methods focusing on quantitative literature studies (Ding et al., 2001 ). As a powerful tool for analyzing the information domain and revealing the cognitive-epistemological structure of the field (Van Eck & Waltman, 2010 ), it provides a macroscopic view across numerous academic studies. This method highlights influential research, authors, journals, institutions, and countries within a specific domain (Mora et al., 2019 ). Based on this premise, this research chose the WoS database for bibliometric analysis. Indeed, in every article indexed in WoS, research data such as authors, sources, cited references, keywords, and more can be accessed (Wang et al., 2022a , 2022b , 2022c ). Furthermore, the Metaverse literature obtained from the WoS database is systematically analyzed using the Bibliometrix and Biblioshiny software packages available in the R program. The following scientific questions guide this research.

RQ1: What is the development trend of publications in the Metaverse field over the years?

RQ2: How are the relationships among stakeholders in this field, such as authors, institutions, and countries?

RQ3: What are the main keywords in the study domain, and how are they clustered?

RQ4: What are the key elements, trends, and themes that characterize the global development of the Metaverse literature?

This bibliometric and content analysis conducted within this framework presents structured information and a comprehensive examination of the Metaverse field. Gaps, significant, and emerging points in the area are identified for researchers in this domain. The significance and contribution of this study lie in its examination of the literature related to the Metaverse and architectural design up to the present day.

Methodology

Data collection.

This study uses the Web of Science (WoS) database for bibliometric analysis. Indeed, the Web of Science database is preferred due to its wide range of tools for manipulating search results and its general, cited reference, and advanced search features (Norris & Oppenheim, 2007 :163; Merigó et al., 2015 ; Gaviria-Marin, 2019 ). On April 29, 2024, a comprehensive search was conducted across all fields in the Web of Science database using the following search query: "metaverse" AND "architect*" or "design" or "architectural studio" or "architectural education" or "building" or "architectural space" or "built environment" AND "virtual space" or "mixed reality" or "augmented reality" or "extended reality" or "cyberspace" or "virtual reality" or "virtual environment" or "virtual worlds" or "digital world" as keywords (Fig.  2 ). This search resulted in the identification of 513 studies.

figure 2

WoS search screen

The flow chart of the study is shown in Fig.  3 .

figure 3

Flowchart of the study

Bibliometric analysis

Bibliometric analysis, a quantitative approach to analyzing academic literature using bibliographies to identify, evaluate, and monitor published research, first used in 1969 (Broadus, 1987 ; Lee et al., 2020 ), is employed in this study to analyze trends and potentials in the field of the Metaverse. Bibliometric analysis distills a comprehensive overview of a specific area by processing a large volume of literature. In this context, bibliometric study provides a broad perspective on extensive research literature and enables the quantitative and objective delineation of research topics from the past to the present (Chen et al., 2021 ). The bibliometric analysis method analyzes development trends in various scientific research fields (Li & Ye, 2016 ). It aids researchers in creating knowledge maps that represent information structure in a particular area and examine their characteristics using statistical and mathematical methods (Ding et al., 2001 ; Godin, 2006 ).

In this study, the graphical web interface Biblioshiny, based on Bibliometrix 3.0 (URL-1), is used within the R software and RStudio environment to create knowledge maps. R software is noted as a dynamically writable and interpretable programming language for statistics and data analysis (Diez-Vial & Montoro-Sanchez, 2017 ; Donoho, 2021 ; Khan et al., 2016 ; Xu & Marinova, 2013 ).

The Bibliometrix R package plays an essential role in scientific methodology by providing a set of tools for quantitative research. This package is developed within the R programming language, an open-source environment and ecosystem. The R language offers substantial opportunities in scientific computation due to its multitude of practical statistical algorithms, access to high-quality numerical data, and integrated data visualization tools (Aria & Cuccurullo, 2017 ; Xie et al., 2020 ). Within the scope of this study, the literature data obtained from the WoS database was analyzed using the bibliometric analysis method within the Bibliometrix software environment. The knowledge maps and data obtained are presented in the findings section.

Distribution of annual documents

To reflect the trends in literature research, it is essential to analyze the accelerations that studies have demonstrated over time (Xie et al., 2020 ). In this context, it was determined that this research, without a time constraint, covers the relevant data from the years 2005 to 2023. It is seen that the number of research studies published on the Metaverse from 2005 to 2023 shows a similar trend with a small number of publications until 2021 but shows a significant increase starting from 2022 (Fig.  4 ). Based on the graphical data, it is anticipated that research in the Metaverse field will rapidly increase.

figure 4

Number of documentations by year

According to the analysis data from the Web of Science category, the top five categories prominently featured are Engineering Electrical Electronic (103), Computer Science Information Systems (102), Telecommunications (86), Computer Science Artificial Intelligence (58), and Computer Science Interdisciplinary Applications (58) (Fig.  5 ).

figure 5

Number of documentations by WoS Categories

When the classification of the retrieved studies is examined in the context of Sustainable Development Goals, it is determined that 168 studies were conducted for Quality Education, 61 for Good Health and Well-being, 32 for Sustainable Cities and Communities, 30 for Industry Innovation and Infrastructure, 17 for Responsible Consumption and Production, 3 for Affordable and Clean Energy, 1 for Gender Equality, 1 for Decent Work and Economic Growth, 1 for Reduced Inequality and 1 for Life on Land (Fig.  6 ).

figure 6

Number of documentations by Sustainable Development Goals

As a result of the studies on the Metaverse, 291 articles, 128 proceeding papers, 46 review articles, 37 early access, 5 book chapters and 4 editorial material were identified (Fig.  7 ).

figure 7

Number of document types

Data screening and inclusion criteria

As a result of the search conducted in the WoS database on April 29, 2024, a total of 513 sources potentially suitable for this study were identified. In this research, 323 documents were retrieved from 199 sources. In this study, which did not apply any time constraints, the retrieved documents spanned the period from 2005 to 2023. The data file downloaded in Bibtex format from the WoS database was uploaded to the Bibliometrix software (URL-1). In this step, publications written in English, accessible, and peer-reviewed were preferred for examination. In this context, the document type was limited to 'Article/Article; Book Chapter/Article; Early Access/Article; Review; Early Access/Review.' As a result of these restrictions, a total of 190 studies were excluded from the analysis. Figure  8 provides general information about these data.

figure 8

Primary data information in Bibliometrix software

Most relevant sources

The retrieved articles have been published in a total of 289 different sources. The sources with the most publications are the ‘IEEE Access’ (N = 13), ‘IEEE Network’ (N = 11), ‘Sustainability’ (N = 10), ‘Internet Research’ (N = 8) and ‘Electronics’ (N = 7) publications. Figure  9 presents the top 20 sources with the highest number of publications.

figure 9

Top 20 most relevant sources

Most frequent words

Keywords ensure the general comprehensibility of a research topic and its content. The analysis of high-frequency keywords reflects important and current topics in the Metaverse field. In this context, Fig.  9 , which includes the ‘TreeMap’, illustrates the tree structure of the 50 most frequently used keywords. In this representation, the size of the rectangle indicates the frequency of usage of the term within the rectangle. In this context, 'virtual-reality (50), augmented reality (30), design (28), system (24), technology (22)' are identified as the top five prominent terms. Additionally, this situation can also be observed through the created 'WordCloud,' which represents the frequency of usage of key terms in a word cloud (Fig.  10 ).

figure 10

Treemap chart of the 50 most frequently occurring "KeyWords Plus" terms and World cloud of the 50 most frequently occurring "KeyWords Plus" terms

Most relevant countries

Figure  11 shows the collaborations between co-authors and their countries. In this context, it is understood that the most co-authors are commonly found in China. In terms of the number of articles published by a single country; China (N = 51), USA (N = 27), Korea (N = 26), Italy (N = 12) and the United Kingdom (N = 9) are ranked as the top five. Similarly, for multiple-authored articles, the top five countries are as follows: China (N = 35), United Kingdom (N = 11), India (N = 8), USA (N = 6), Malaysia (N = 6), Singapore (N = 6), Korea (N = 5), Italy (N = 5) and USA (N = 5). When looking at Fig.  11 , the turquoise bars represent single-country publications (SCP), while the orange bars represent multi-country publications (MCP).

figure 11

Corresponding Author's Countries

Most relevant authors

Figure  12 presents a list of the top 20 authors with the highest number of publications. In this context, it is observed that Niyato has the highest number of publications (10). Following Niyato (N = 11), Xiong (7), Wang (6), Bibri (5), Kim (5), Li (5), Liu (5) and Wang (5) respectively.

figure 12

Most relevant Authors

Authors' production over time

The article dataset related to Metaverse publications includes a total of 1044 authors. Figure  13 displays a diagram depicting the top 20 most productive Metaverse authors during the study period. The size of the dots in this diagram represents the number of articles, while the colors' dimensions represent the annual total citation counts. Regarding the number of articles published during the study period, the top three most productive authors are Niyato (10 articles), followed by Xiong (6 articles), Wang (5 articles), Bibri (2 articles), and Kim (4 articles).

figure 13

The top 20 authors featured in research on the Metaverse

Most relevant affiliations

When evaluating the institutions where publications on the Metaverse have been conducted, Nanyang Technology University (Number of Articles = 20), Singapore University of Technology and Design (N = 15), Norwegian University (N = 12), Sungkyunkwan University (N = 11), and Gachon Univercity (N = 11) are listed as the top institutions (Fig.  14 ).

figure 14

Most relevant Affiliations

Country scientific production

The countries with the highest number of publications in the field of Metaverse are shown in Fig.  15 . The number of research articles is represented by the blue color intensity on the map. According to this graph, China (134), USA (130), Korea (87), United Kingdom (72), India (45), and Singapore (48) are ranked as leading countries in terms of publications.

figure 15

Country Scientific Production

Country collaboration map

International research collaboration for articles on Metaverse is depicted in Fig.  16 . The number of published articles is indicated by the intensity of the blue color on the map. The thickness of brown lines represents the intensity of collaboration based on frequency. While China stands out as the country with the strongest collaboration compared to other countries, the most collaborative countries are ranked as USA, Australia, and the United Kingdom, respectively.

figure 16

Country Collaboration Map for Metaverse Articles

Most cited countries

According to the graph shown in Fig.  17 , the top five countries receiving the most citations are ranked as follows: China (810), USA (689), Korea (659), France (275), and United Kingdom (207).

figure 17

Graph of Countries Receiving the Most Citations

Three-field plot

A three-field graph illustrating the relationship between keywords, countries, and journals is presented in Fig.  18 . The height of the rectangular nodes within the graph represents the frequency of author keywords, keywords plus, and authors. The thickness of the lines between nodes represents the number of connections (Wang et al., 2022a , 2022b , 2022c ).

figure 18

Three-Field Plot showing the relationship between authors keywords (left), keywords plus (middle) and authors (right)

Thematic map

To comprehensively capture the theme map of big data research, author keywords and index keywords from bibliographic records were clustered and classified by dividing them into two using the k-means and naive Bayes algorithms (Parlina et al., 2020 ). The revealed thematic map consists of thirteen clusters. For the clustering of metaverse topics, four categories are represented as clusters in different colors. In the first cluster represented by the green color, the terms ‘virtual reality’, ‘augmented reality’, and ‘design’ take prominence. In the blue cluster, terms such as ‘impact’, ‘experience’, and ‘virtual worlds’' are observed. As seen in Fig.  19 , the red cluster encompasses significant research topics.

figure 19

Thematic map generated using author's keywords

Trend topics

The Trend topics chart presented in Fig.  20 is created based on Keywords Plus. In this context, the size of the circles within the graph indicates the frequency of the term, while the length of the lines represents how long this concept has been studied. In this context, the results of the analysis conducted to identify trend topics with Keywords Plus are presented in Fig.  20 . Accordingly, virtual reality (f = 50), augmented reality (f = 30), technology (f = 24), design (f = 28), model (f = 22), and environments (f = 10) are determined as trend topics.

figure 20

Trend topics with Keywords Plus

Examination of publications in the field of architecture through content analysis

Among these studies, a total of 76 research papers were subjected to content analysis within the context of the relationship between architecture and the Metaverse, and the obtained data are presented in Table  1 . Each study has been categorized according to thematic areas within the fields of architecture and design, revealing that the studies were produced in a total of 15 categories: education, building operation and maintenance, building evacuation, Building Information Modelling (BIM), physical environment, virtual space perception, cultural heritage, built environment/planning, smart city/GIS, smart home, design and creativity, universal design/accessibility, sustainability, urban transportation systems, and post-occupancy evaluation. In addition, brief information about the specific topics each study within the field of architecture and design focused on under each study theme has been provided. According to the content analysis conducted, it is evident that education and virtual space perception themes are the most prevalent subjects of scientific research within disciplines related to architecture and design. The fact that the metaverse universe is a virtual fiction of the real world brings to the fore the question of how this universe is perceived by individuals. Consequently, the perception of virtual spaces becomes significant in studies related to the Metaverse. Furthermore, Table  1 indicates that the concept of the Metaverse is gaining importance in various themes related to architecture. It has the potential to be a promising research area in different fields such as different age groups, disability conditions, construction systems and technologies, cultural heritage, and transportation. The density and relevance of research themes in the fields of architecture and design align with the sustainable development goals related to the Metaverse. Themes such as education quality and sustainable cities and communities, as well as community objectives, are prominent in architectural studies as well.

Discussion and conclusion

This research, in general, reveals the overall trends in scientific research related to the Metaverse and specifically within the disciplines of architecture and design. The number of studies analyzed within the scope of the research has increased rapidly since 2021 and this momentum is expected to increase in the coming years. WoS categories such as Electrical-Electronic Engineering, Computer Science Information, and Telecommunications are prominent areas where the concept of the Metaverse is emphasized. In terms of sustainable development goals, topics like education quality, good health and well-being, and sustainable cities and communities are the focal points of research related to the Metaverse. When the document types are evaluated, it is seen that articles come to the forefront. The most frequently used keywords related to the Metaverse concept include virtual reality, augmented reality, design, system, and technology. In terms of the countries where the most studies are produced, China takes the lead, followed by countries such as the USA, Korea, and the United Kingdom. Additionally, the study provides data related to authors, citations, institutions, and journals.

The results of the bibliometric analysis conducted within the scope of the questions guiding the research are as follows.

This study, which evaluates the research conducted between 2005 and 2023, shows that there is a similar trend with a small number of publications until 2021, but there has been a significant increase since 2021. It is predicted that the research on this subject increased rapidly in 2023 and will increase rapidly in the coming years.

In the study, Nanyang Technology University, Singapore University of Technology and Design, Norwegian University, Sungkyunkwan University, and Gachon University stand out as research institutions related to the metaverse. In the study, Niyato was found to be the author with the highest number of publications. He is followed by Xiong Wang, Bibri, Kim, Li, Liu and Wang. In the study, China, the USA, Korea, the United Kingdom, India and Singapore are ranked as the leading countries in terms of publications. However, when the cooperation potentials of the countries are analyzed, China stands out as the country with the strongest cooperation compared to other countries, while the countries with the most cooperation are the USA, Australia and the UK, respectively.

In the publications examined in the study, it was determined that the keywords metaverse, virtual reality, augmented reality, extended reality, artificial intelligence, blockchain, reality, mixed reality, virtual were frequently used.

The prominent themes clustered in the study are “impact, experience, virtual worlds”, “internet, challenges, blockchain”, “integration, rehabilitation, walking” and “virtual-reality, augmented reality, design”. The global collaborative network framework of Metaverse literacy and research needs to be strengthened. In the future, it is envisioned that this research area will further develop in sectors such as education, health, arts, commerce and entertainment. The results of this study are intended to serve as a reference for future applied research on the metaverse.

The particular focus of this study is to examine the themes in which the concept of the Metaverse has been explored within the disciplines of architecture and design. The content of architectural publications addressing the Metaverse has been analyzed in the study, revealing research trends in this field. Within this context, various themes related to the concept of the Metaverse have been explored in the disciplines of architecture and design. These themes include education, building operation and maintenance, building evacuation, Building Information Modelling (BIM), physical environment, virtual space perception, cultural heritage, built environment/planning, smart cities/GIS, smart homes, design and creativity, universal design/accessibility, sustainability, urban transportation systems, and evaluation in the usage process. Among these themes, education and virtual space perception stand out as the most researched areas.

In conclusion, this study aims to provide guidance for researchers by demonstrating how the concept of the Metaverse has shaped a research landscape within the disciplines of architecture and design over time.

This study presents a bibliometric and content analysis of a research conducted in the WoS database with the keywords “metaverse” AND “architect*” or “design” or “architectural studio” or “architectural education” or “building” or “architectural space” or “built environment” AND “virtual space” or “mixed reality” or “augmented reality” or “extended reality” or “cyberspace” or “virtual reality” or “virtual environment” or “virtual worlds” or “digital world”. The study covers the use of metaverse in the field of architecture and design.

Although the concrete reality of the Metaverse has not materialized due to its conceptual novelty, its future prospects are interpreted as promising (Piñeiro-Chousa et al., 2024 ). In this context, researchers can contribute to this point by analyzing the theoretical foundations of the Metaverse in depth. In future studies, in order to obtain more systematic quantitative results on the metaverse, it is planned to expand the keywords, search different databases and include various contents in the study. In addition, it is planned to examine architectural content produced on digital architecture platforms related to metaverse. In order to expand the scope of the study, the relationship of the metaverse with different disciplines will be discussed.

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Reduced Auditory Perception and Brain Response with Quiet TMS Coil

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BACKGROUND: Electromagnetic forces in transcranial magnetic stimulation (TMS) coils generate a loud clicking sound that produces confounding auditory activation and is potentially hazardous to hearing. To reduce this noise while maintaining stimulation efficiency similar to conventional TMS coils, we previously developed a quiet TMS double containment coil (qTMS-DCC). OBJECTIVE: To compare the stimulation strength, perceived loudness, and EEG response between qTMS-DCC and a commercial TMS coil. METHODS: Nine healthy volunteers participated in a within-subject study design. The resting motor thresholds (RMTs) for qTMS-DCC and MagVenture Cool-B65 were measured. Psychoacoustic titration matched the Cool-B65 loudness to qTMS-DCC pulsed at 80, 100, and 120% RMT. Event-related potentials (ERPs) were recorded for both coils. The psychoacoustic titration and ERPs were acquired with the coils both on and 6 cm off the scalp, the latter isolating the effects of airborne auditory stimulation from body sound and electromagnetic stimulation. The ERP comparisons focused on a centro-frontal region that encompassed peak responses in the global signal. RESULTS: RMT did not differ significantly between the coils, with or without the EEG cap on the head. qTMS-DCC was perceived to be substantially quieter than Cool-B65. For example, qTMS-DCC at 100% coil-specific RMT sounded like Cool-B65 at 34% RMT. The general ERP waveform and topography were similar between the two coils, as were early-latency components, indicating comparable electromagnetic brain stimulation in the on-scalp condition. qTMS-DCC had a significantly smaller P180 component in both on-scalp and off-scalp conditions, supporting reduced auditory activation. CONCLUSIONS: The stimulation efficiency of qTMS-DCC matched Cool-B65, while having substantially lower perceived loudness and auditory-evoked potentials.

Competing Interest Statement

D.L.K.M., L.M.K, S.M.G, and A.V.P. are inventors on patents and patent applications on quiet TMS. L.M.K. has received patent royalties from Nexstim and consulting fees from Ampa Health. N.B.P serves on the medical and scientific advisory board of the Dystonia Medical Research Foundation. S.M.G. is inventor on patents on brain stimulation technology and has received research funding, royalties, or consulting fees from Magstim, Rogue Research, and Ampa Health. A.V.P. is an inventor on patents on TMS technology and has received equity options, scientific advisory board membership, and consulting fees from Ampa Health, patent royalties and consulting fees from Rogue Research, consulting fees from Magnetic Tides and Soterix Medical, equipment loan from MagVenture, and research funding from Motif. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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