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Research Design – Types, Methods and Examples

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Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

11 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

This post is really helpful.

how to cite this page

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

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

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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Types of Research Designs Compared | Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

Read more about creating a research design

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

Updated: July 23, 2024

Published: January 18, 2023

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

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

Table of Contents

What is research design?

The four types of research design, research design examples.

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

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

Marketers use different types of research design when conducting research .

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

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

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

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Experimental

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

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

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

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

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

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

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

Correlational

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

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

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

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

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

Descriptive

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

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

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

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

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

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

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

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

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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Types of research design: An overview

Research design constitutes the blueprint  for your study. This guide  will help make research design easier for you to understand and choose from.

The pursuit of knowledge has always taken the form of research, regardless of the subject matter. There are times when research results in breakthroughs in science, technology, or discovering new species.

Due to the current dire plight when Coronavirus is ravaging the world, scientists carried out a tremendous amount of research in order to discover a vaccine for the disease. Through this blog post, we will examine the types of research designs and their various aspects. 

What is research design?

When we define research as a collection of information, taking the methodologies into account, we can see that research contains significant information about the subject.

It is a set of facts gathered through formulating a thesis and followed up with structured findings based on the same hypothesis. You can do academic research or scientific research. Let’s take a closer look at what a research design is. Research challenges faced by organizations often influence research designs rather than the other way around. When designing a research project, the tools for use are determined as well as the way in which they are applied.

In research design, you outline a way of incorporating all elements of the study into a comprehensive and sequential framework. Through this, you ensure that your research will address the desired problem effectively. 

Essentially, it lays the foundation for data collection, evaluation, and reporting. Keep in mind that the design you choose depends on the research problem!

Elements of research design: The framework

A researcher can use a research design to embark on a quest into the world of mystery, while using a structured approach along the way.

Similar decisions are made by researchers when choosing approaches from a variety of methodologies to determine the type of research to be performed, a lot like an architect determining the different design of a building.

Thus, let’s look at what are the elements which are the most important considerations that every researcher should include in their research design.

These elements are essential:

• A clear statement of purpose
• Methodology used to analyze collected details
• Types of research methods
• A list of potential obstacles to research
• Collection and analysis of research data: methods to be implemented
• An overview of the objectivity’s timeline
• An assessment of the analysis
• An overview of the study’s settings

With the elements listed above, one can develop an overall perspective of the research, as it establishes a logical structure.

Characteristics of research design: The features

Validity of any research design depends on robustness of the results. It is imperative to achieve utmost neutrality and ensure your research results against biased interpretation in order to get accurate data.

It is important that the results of your study are beneficial to a broad range of people, not just a minority. In order to verify this, you should make sure your sample size is high enough and, just to be safe, allow some room for error. 

Your study’s success depends on a proper research design. Accurate and unbiased insights are provided by thorough and well-designed research studies. Four key characteristics of good research design include:

Neutrality: In the course of designing your research, it may be necessary to speculate about the type of data you are expecting to collect. Your research should produce neutral results that are free from preconceived notions ( Unbiased results ). Consider opinions regarding the final evaluation outcome and the derived results coming from a variety of sources and whether they are in agreement with the findings. By doing this, the research findings will be neutral, making the study more valid and reliable.

Reliability: After conducting the same research on a regular basis, results are expected to remain the same. Hence, for a high standard of results, your data collection design should include the method of developing research questions. If your design is reliable, then you can generate the anticipated results.

Validity: We have a variety of measurement tools at our disposal. Only those tools are appropriate for measuring effectiveness, which allow a research participant to determine results in accordance with the research objective. Assessments derived from this approach will then be credible and valid.

Generalization : You should not limit your study to a small group of people, but rather apply what you learned from your design to a large population. By generalizing, we mean that you can conduct your research survey at any time on any demographic group with the same degree of accuracy.

In order to create an effective research design, the factors mentioned above must be balanced among the respondents. By doing so, a more comprehensive and accurate study will be produced, one that is understandable to a larger audience. 

The major categories of research design 

To choose the best model for a study, it is vital that the researcher understands the different types of research design. Your design can be categorized broadly, along with research as a whole, into qualitative and quantitative, flexible and fixed.

Qualitative: research based on mathematical calculations that determine correlations between data and observations. The statistical method can be employed to prove or disprove theories that are related to phenomena that occur organically. For example, researchers use qualitative methods to determine “why” certain theories are valid, as well as “what” respondents think about it. Such information enables a researcher to come to a final understanding with sufficient evidence.

Quantitative: research that gathers empirical data in order to generate information that can be used to guide decisions. The numbers offer an enhanced perspective on making important organizational decisions. It is imperative that organizations conduct quantitative research in order to progress. Evaluation and meta-analysis of data mainly rely on graphs, figures, and pie diagrams. Analysis and information derived from precise numerical data and statistics can be extremely useful in determining the future direction of the operation. 

Fixed and Flexible Research Design

A distinction can also be made between fixed and flexible research designs. Study designs fall into two above categories: quantitative (fixed design) and qualitative data gathering (flexible design). 

When you use a fixed study design, you understand the design even before the data collection process begins. There almost are no randomly selected outcomes. 

On the other hand, flexible designs allow for greater freedom of response, for instance, respondents are required to provide their own answers instead of selecting from predetermined answers.

Therefore, research designs can be categorized further into five types.

1. Descriptive research design

In Descriptive Research Design, the researcher provides an in-depth explanation/description of what he or she is researching. The data collection, analysis, preparation and presentation of data is purely theoretical in this type of research design. 

A theory-based approach is one in which the researcher is particularly concerned with the topic the research is focused on. The approach is used for various studies such as case studies, in-depth observations, and surveys.

It gives a researcher a logical way to state the problem so that others will be able to better comprehend the rationale for conducting this sort of research. 

When you don’t have a clear problem statement, your research is exploratory rather than descriptive. A good example of this type of research design would be: What is the prevalence of Covid disease among population XYZ?

2. Correlational research design

A correlational research design focuses on the correlation between various factors without enabling the researcher to alter any of them. This type of study involves at least two different groups of data rather than an experiment. 

While evaluating the association between two variables, no sweeping generalizations are made; statistical analysis is used to determine that relationship. Positive, negative or zero correlations could result from correlational study designs. 

For instance, case-control studies and prospective studies can be conducted with this technique.

Data is more easily collected from the real world when using correlational research designs. Thus, your results can be applied externally in a valid way to actual scenarios. 

Several scenarios lend themselves to correlational studies research, such as:

• To examine non-cause-and-effect relationships
• To investigate cause-and-effect relationships among fixed entities

3. Experimental research design

In experimental research, two sets of variables are used to determine the outcome of the study. 

To gauge differences between the first and second sets, the first set serves as a constant. Field experiments, controlled experiments, or quasi-experiments are all examples of this type of research design that establish a relationship between two variables.

As a result, the investigator examines how an independent variable influences a dependent variable. For example, you can explore the relationship between price (an independent variable) and brand loyalty (a dependent variable). 

Typically, a research design of this type aids in answering a research question by controlling the independent variables and studying how they affect the dependent variables.

4. Diagnostic research design

Among the different types of research design, diagnostic research is designed to discover the root cause of a particular condition or occurrence. 

Finding out how specific issues or challenges are caused by other factors can help you gain a more profound understanding of your prospects’ problems.

There are usually three steps in this design – (1) inception of the problem, (2) diagnosis of it, and (3) solution of it.

5. Explanatory research design

Again, the name speaks for itself. Using an explanation research design, the researcher is able to elaborate, investigate, and explain their concepts and theories. 

Research designs of this type are used to examine the unknown facets of a specific topic and uncover the answers. In a nutshell, this application provides us with information on how to locate the smallest information fragments. 

By using this approach, researchers can obtain a broad idea and use that information as a way to identify future issues more rapidly. Explanatory research methods include:

• Literature analysis
• An in-depth interview
• Having a focus group
• Analyses of case studies

A researcher who replicates an existing market study expects the same results. Make a list of the types of research concerns you will ask your participants through the survey and include them in your research design. 

Setting this standard will help to ensure the outcome of your research. The only way to attain the results you want is to make sure your design is reliable. When you have analyzed the topic and its innovativeness, you can decide what type of research design you want. (Read our guide on how to write a research paper )

Types of research design grouped by categories of participants

The quality of the grouping of participants can also be used to categorize research design types. The sample size and how the participants are grouped depend on the research hypothesis.

There is usually at least one experimental and one control group in a research study based on experimental design. Imagine that, in a study for Covid vaccine, one group would receive treatment and another would not. You get the idea.

There are four types of research design based on participant grouping:

1. Cohort study

Participants in a cohort study are drawn from a group of individuals with similar characteristics, and they are studied at a predetermined time intervals. There is a common characteristic(the same disease or gene) among the participants in a panel study.

2. Cross-sectional study

Research in social science, medical science, and biology often uses cross-sectional studies. Data in this design is either collected from people as a whole, or from a statistically significant sample of people at a particular interval.

3. Longitudinal study

In longitudinal research, the same variables are observed repeatedly over a short or long period of time. In most cases, they are observational studies, although longitudinal randomized experiments may also be conducted.

4. Cross-sequential study

Cross-sequential research design integrates both longitudinal and cross-sectional approaches. In this way, the two previously mentioned designs can be enhanced for some of their fundamental shortcomings.

How does it affect your work?

Research designs define a disciplined approach or method to accomplish various tasks in a research study. Designing a research project is meant to help the researcher achieve his or her objectives without deviating from the plan. The process is designed in a comprehensive manner.

The components of a high-quality research design work together harmoniously. Research objectives and outcomes must be aligned with the theoretical and conceptual framework.

• When a researcher draws up a research design (experiment design), he or she can easily formulate the experiment’s objectives.
• The goal of a good research design is to help the researcher reach the objectives in a timely manner and to enable getting the most effective solution for the research problem.
• Researchers can accomplish all the tasks they need to do with limited resources more effectively with the use of design strategy.
• As a result of a good research design, a study is likely to be accurate, reliable, consistent, and legitimate.
• From the outset of the research project, the researcher is satisfied and confident, as well as feeling successful.
• Errors are reduced, and bias is eliminated.
• The high level of detail provided at every stage of the research process makes the study more informative and effective.
• Making the correct decisions at every stage of a study is easier with a research plan.
• In this way, it can be determined which tasks are major and minor.
• Furthermore, the design enables the researcher to find an answer to the unknown and achieve a good outcome. Publication and exposure can then occur. 

An excellent research design, appropriate methods and accurate data collection are essential to your study’s success. Additionally, the sources you use for your analysis should be credible. 

Only then can you make valid, reliable inferences. There’s no doubt that research has the potential to lead to solutions to virtually every problem in the world. 

Understanding types of research design is essential to conducting your thesis as it will help you to gain a greater insight into any topic you are researching.

It is important to remember that the research design constitutes the blueprint for your study. All the necessary foundations for the research will be laid in this design and it will bring out more positive results. Write all the questions, objectives and audience that will take part in the discussion. 

Here’s hoping that this guide made research design easier for you.

Yet guess what, it’s much easier to start working with a template, right? In fact, Mind the graph is your one-stop solution. We provide templates, graphical illustrations, and everything you will need for your scientific endeavors. To know more find us here .

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About Fabricio Pamplona

Fabricio Pamplona is the founder of Mind the Graph - a tool used by over 400K users in 60 countries. He has a Ph.D. and solid scientific background in Psychopharmacology and experience as a Guest Researcher at the Max Planck Institute of Psychiatry (Germany) and Researcher in D'Or Institute for Research and Education (IDOR, Brazil). Fabricio holds over 2500 citations in Google Scholar. He has 10 years of experience in small innovative businesses, with relevant experience in product design and innovation management. Connect with him on LinkedIn - Fabricio Pamplona .

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5 Research design

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

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

Key attributes of a research design

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

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

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

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

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

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

Improving internal and external validity

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

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

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

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

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

Popular research designs

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

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

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

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

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

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

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

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

Selecting research designs

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

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

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

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• Published: 12 August 2024

Evaluation of didactic units on historical thinking and active methods

• Pedro Miralles-Sánchez   ORCID: orcid.org/0000-0002-2436-3012 1 ,
• Jairo Rodríguez-Medina   ORCID: orcid.org/0000-0002-6466-5525 2 &
• Raquel Sánchez-Ibáñez 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1032 ( 2024 ) Cite this article

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The purpose of this study is to evaluate the effects of an implementation of eight didactic units on historical thinking and active methods as part of a teacher training programme. All this with four specific objectives that try to find out changes in the methodology, motivation, satisfaction and learning of the students. To this end, the research is carried out by means of a mixed method using quantitative data, obtained from a pretest/posttest, and qualitative data, obtained from a focus group and interviews. The target groups of the teaching units are secondary and high school students aged between 13 and 18 years. A total of 114 students of these students participated in the data collection with a pretest/posttest, six master students in the focus group, and three teachers and three secondary and high school students were interviewed. The results obtained indicated that significant differences of medium effect were found in the pre and post phase factor in learning and satisfaction, and of large effect in methodology and motivation. As for the gender factor, significant differences of small effect were found in motivation and satisfaction, with higher values for women. The positive statements of both master’s students and high school students and teachers were quite striking, although the limitations and difficulties must be highlighted. It is concluded that the design of this type of didactic units has meant a significant improvement, achieving that the students have developed a notorious improvement in their perception of the objectives studied.

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Introduction.

Research in history didactics has distinguished two types of historical content. On the one hand, substantive or first-order content. These are those which refer both to concepts or principles and to specific historical dates and events. On the other hand, strategic, second-order content or historical meta-concepts as methodological concepts. These are related to the historian’s skills, the search for, selection and treatment of historical sources, empathy or historical perspective, related to the definition of historical thinking (Sáiz and Gómez, 2016 ). This didactic approach aims for students to learn to think historically by deploying different strategies and competences to analyse and respond to different historical questions and to understand the past in a more complex way. These competences and strategies are related to the search for, selection and treatment of historical sources, empathy, multi-causal explanation, or historical perspective; in short, the functions of a historian (Peck and Seixas, 2008 ; Seixas and Morton, 2013 ). These concepts are variable and do not form a closed and invariable list, but each author gives greater importance to certain aspects (Gómez Carrasco et al., ( 2017 )).

Since the late 1980s, an effort has been made in the British field to analyse second-order concepts in students’ argumentation. Here the Concepts of History and Teaching Approaches project (Lee et al. 1996 ) stands out, which investigated the historical concepts that students should acquire. At the same time, in the USA, through Wineburg ( 2001 ), work began with cognitive psychology techniques (experts and novices) to investigate the skills that students should acquire, with the well-known historical thinking and its competences finally being developed by mainly Canadian and American authors (Ercikan and Seixas, 2015 ; Seixas and Morton, 2013 ; VanSledright, 2014 ; Wineburg et al., 2013 ). For their part, the work of Chapman ( 2011 ) and the Constructing History 11–19 project (Cooper and Chapman, 2009 ) delve deeper into this line of reasoning in the use of sources, a thematic field also addressed in other countries such as the Netherlands (Van Drie and Van Boxtel, 2008 ) and Chile (Henríquez and Ruíz, 2014 ).

The importance of teaching historical thinking in the classroom lies in the fact that historical thinking does not develop naturally, but needs explicit teaching (Wineburg, 2001 ). To develop these competences, the introduction of the historian’s method and techniques and historical awareness are key elements, with appropriate techniques and instruments to assess them (Domínguez, 2015 ). To develop them, a methodological change in the classroom is necessary, as is already being proposed and discussed in countries such as Portugal (Gago, 2018 ), Spain (Navarro and De Alba, 2015 ) or the United Kingdom (Smith, 2019 ). This change implies moving from the current dominance of expository teaching strategies to a greater presence of enquiry strategies that help to promote the development of independence, critical thinking, and autonomous learning in students.

Working with historical sources, which can begin even earlier, is valued positively by students in upper secondary education, as it promotes a research experience in which students construct their knowledge about the past (Prieto, Gómez and Miralles, 2013 ), however, this type of experience is not usually abundant in classrooms at this stage in Spain. The abuse of the lecture and the passive role reserved for students ends up making them, for the most part, limit themselves to studying what is offered in class by not seeking information from other sources and memorising the information they receive (Sáiz and López-Facal, 2015 ). Consequently, it is very difficult to create critical citizenship in students, as they may believe everything the teacher tells them, as they are not familiar with enquiry (Guirao, 2013 ).

When it comes to identifying teaching models, it is worth highlighting the line of research developed by Trigwell and Prosser ( 2004 ) based on interviews with teachers and a questionnaire called Approaches to Teaching Inventory (ATI) (Trigwell et al., 2005 ). They identified four different conceptions of teaching and three methodologies, establishing five approaches which can be grouped into three broad models or ways of teaching. In the first model, the role of the teacher is greater, since the importance lies in the transmission of content, students assume a passive role, limiting themselves to receiving and memorising the knowledge transmitted by teachers, thus establishing a unidirectional relationship, without considering their experience, previous knowledge, characteristics or context. The most used methodological strategy is the master class and the main resources used are the textbook and class notes. In addition, a final examination of the learning contents is usually established (Hernández et al., 2012 ; Guerrero-Romera et al., 2022 ).

On the other hand, there is learner-centred teaching which differs from the previous one in that the teacher’s intention is to provoke conceptual change and intellectual growth in the learner. Thus, the teacher acts as a guide, guiding students in the process of constructing their own knowledge, encouraging their conceptions, and providing them with opportunities to interact, debate, investigate and reflect. The aim of this model is for students to learn content by questioning and reflecting on it. The strategies employed are active and inquiry based. In contrast to the previous model, which encourages competitiveness and individualism, this approach favours interaction and cooperation between the individuals involved in the teaching and learning process and prioritises continuous assessment (Vermunt and Verloop, 1999 ; Kember and Kwan, 2000 ; Trigwell et al., 2005 ; Henze and van Driel, 2011 ). Finally, there is a third, intermediate model based on teacher-student interaction, although it should be noted that there is a hierarchical relationship between the different approaches, with each including elements of the previous one (Guerrero-Romera et al., 2022 ).

Evaluative studies of formative processes such as this one are seeing an increase in the field of history education especially in terms of changing the conceptual model of history teaching (Carretero et al., 2017 ; Metzger and Harris, 2018 ). Some work, such as that being carried out in the Netherlands, focuses on evaluative research that is more focused on teaching practice (De Groot-Reuvekamp et al., 2018 ; Van Straaten et al., 2018 ). Regarding the evaluation of historical thinking effects, we can recently highlight Tirado-Olivares et al. ( 2024 ) relating it to academic performance, or Bartelds et al. ( 2020 ) highlighting the importance of historical empathy. It is also worth highlighting the research carried out by the University of Murcia (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), which implemented training units focused on historical thinking skills and changes in the way of teaching. This research therefore seeks to be a significant improvement compared to traditional methods used in the teaching of social sciences, as it seeks to develop essential skills for critical thinking and citizenship training, and to evaluate its effectiveness through rigorous methods and a scientific approach. All this to encourage a critical spirit and autonomous learning and therefore the formation of critical and independent citizens who know how to judge for themselves the vicissitudes that civic life in democracy demands of them.

The main objective of this article is to detect if there are significant changes in students after the design and implementation of eight didactic units (DU from now on) to promote the learning of historical thinking skills through active teaching methods. To achieve the objective, it has been divided into the following specific objectives:

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods Table 1 .

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 2 .

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 3 .

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods.

Research design

This is an evaluative type of DU research of historical thinking and active methods with a mixed explanatory approach and a quasi-experimental A-B design. The research method is therefore mixed, qualitative, and quantitative data have been collected and analysed in a rigorous way in response to the research objective, organising them into specific research objectives and integrating the two forms of data and their results into conclusions framed in the theory and scientific production studied (Creswell & Plano Clark, 2017 ). The selection of the eight DU was made at random, as we have worked with the students who have been tutored by us during the internship period. On one hand, a quantitative analysis of the data obtained by means of a Likert-type questionnaire (1–5) was carried out. Questionnaire designs are extremely common in the field of education, as they can be applied to a multitude of problems and allow data to be collected on many variables and outcomes to be measured (Sapsford & Jupp, 2006 ). On the other hand, the decision was to apply a qualitative exploratory method through a focus group with master’s students who applied the DU and interviews with practising teachers and students who witnessed these units (supplementary material, Figs. 1 – 3 ). Interviews are useful when you want subjects to describe complex phenomena and facts that are the object of study (Pérez-Juste et al., 2012 ), as well as focus groups. The focus group was recorded via an online Zoom meeting (Archibald et al., 2019 ) and then transcribed using artificial intelligence (Notta AI), while the interviews were answered on the spot individually in writing.

The quantitative analysis (R Core Team, 2023 ), a repeated measures mixed factorial design with one within-subjects factor (the time of assessment) and one between-subjects factor (gender) was used. The within-subject factor has two levels (pretest and posttest) and the between-subject factor has three levels (female and male). The dependent variables were the scores obtained in each of the subscales of the questionnaires Secondary school students’ assessment of History teaching and Secondary school students’ opinion of the implementation of the History training unit (supplementary material Figs. 4 and 5 ). For the qualitative analysis, a descriptive analysis was carried out using the qualitative research software Atlas.Ti 23, which is widely used in research in the field of Social Science Didactics (Rüssen, 1997 ; Sánchez-Ibáñez, Martínez-Nieto ( 2015 )). As a complement to this software, the ChatGPT tool has also been used to improve the accuracy of the codes and data analysis, as an aid both in designing the codes of the transcripts, organising the main conclusions obtained from the coding of the participants’ responses (Lopezosa & Codina, 2023 ), and finding out the percentage of occurrence of words. All codes are open and non-exclusive, so that the same response can be associated with more than one code.

Participants

This is a non-probabilistic convenience sample composed in the quantitative analysis of 114 young people aged between 12 and 20 years (M = 15.63, SD = 1.54). Fifty-one males (44%) and 65 females (56%) participated in the pre-test. In the post-test 50 males (44%) and 64 females (56%) participated. Of these, 14 men and 10 women were from the first year of high school, 5 men and 18 women were from the second year of high school, 11 men and 8 women were from the second year of ESO, 14 men and 21 women from the third year of ESO and 7 men and 10 women from the fourth year of ESO (Fig. 1 ). As for the focus group, 6 students of the master’s degree in teaching, 2 men and 4 women aged between 22–45 years, participated. The interviews were conducted with 3 secondary school teachers, 2 men and 1 woman aged 40–60 and 3 pupils aged 13–17 respectively.

Distribution by Gender and Grade.

Instruments

For the collection of quantitative data, two closed-response questionnaires based on a Likert-type scale (1–5) were used. The questionnaires given to pupils were entitled Assessment of Secondary School pupils on the teaching of History (pretest) and Opinion of Secondary School pupils on the implementation of the History unit (posttest). The questionnaires have 37 items divided into four categories corresponding to each of the specific research objectives: Assessment of the implementation of the DU in the teaching/learning process; Assessment of student motivation in an innovative DU; Analysis of student satisfaction with an innovative DU; Analysis of student learning and its results to check whether the DU has been effective (supplementary material Figs. 4 and 5 ). For its part, the qualitative analysis was used to complement the quantitative research by relating its questions to the objectives and thus elucidating the impact of the OD. It consists of both a focus group with trainee teachers consisting of nine questions and interviews with classroom tutors and students with a total of sixteen questions (supplementary material Figs. 1 – 3 ).

Validation of these instruments has been essential to ensure that the data collected are accurate and reliable, through peer review and pilot testing on a small group of participants to assess the effectiveness and relevance of the questions and observation procedures (Gómez et al., 2021 a; Rodríguez et al., 2020 ; Miralles-Sánchez et al., 2023 ).

This research is based on a research project consisting of four phases: prior observation of the classroom (December 2022-February 2023), design of training units (March-April 2023), implementation of training units (May-July 2023) and evaluation of results (September 2023-July 2024). The design of the DU and the data collection were thanks to a training programme implemented during the academic year 2022/23 in a Spanish university for students of the Master’s degree in teacher training in the speciality of Geography, History and History of Art. Held from 10 January to 17 March 2023, the duration of the activity involved a total of 18 face-to-face hours where students attended a series of lectures given by expert lecturers in Didactics of Social Sciences with the aim of helping students to carry out a Master’s Final Project (MFP) based on the implementation and evaluation of a didactic DU on historical thinking and active methods during the internship period of the Master’s. The activity consisted of 6 sessions: presentation and approach of the MFP, concepts of historical thinking, teaching methods and active evaluation processes, quantitative and qualitative analysis of data in educational research, and guidelines for the presentation and bibliography of the MFP.

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of methodology scores. No significant differences were observed between sexes (MH = 35.93, SD = 5.60; MM = 36.43, SD = 5.83) in the initial (pre) assessment (F (1,112 = 5.83). 83) at baseline (pre) assessment (F (1,112) = 0.21, p = 0.64) and no gender differences between groups (MH = 43.32, SD = 6.91; MM = 44.53, SD = 7.58) were observed at posttest (F (1,112) = 0.77, p = 0.38).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.08, p = 0.77). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 91.88, p < 0.01) with a large effect size (partial η2 = 0.26). Figure 2 shows the result graphically.

Differences in Methodology Scores by Gender and Phase.

The master’s students emphasise that none of them were previously familiar with the theory of historical thinking, having recently learned it in class, although some had experience of teaching with active methods. They emphasise the importance of interactive and participatory methods, as well as the crucial role of the teacher in the educational experience, recognising positive changes in current teaching, although with divergent opinions on the influence of students on methodology. The positive experience with students and the inclusion of relevant points in teaching are highlighted, but the persistence of traditional methods that are not very active and the resistance of some students to participatory methods are criticised, representing a challenge in contemporary teaching Fig. 3 .

Changes and improvements in DU according to master’s students.

Significant statements

“So I think that the figure of the teacher will always be…. All that helps, all the technique, everything we learn and all that, but I think that the figure of the teacher is fundamental, it is important.” - He emphasises the importance of the role of the teacher and the relationship that the teacher establishes with the students.

“I think it’s changing a lot because before you went to class and the teacher would give you a lecture or whatever and the students were very dispersed, but I think that is changing now, and as we bring in new generations, I think it’s going to change a bit more.” - He sees a positive change in the way history teaching is approached.

“No, I think so, in a certain sense it has changed, because it is true that at secondary school, when you are a teenager you see two types of teachers, a teacher who practically limits himself to lecturing you and that’s it, and others who question you more.” - He expresses that teaching has not changed completely, suggesting that there are still teachers who adopt fewer interactive approaches.

“I’ve had bad history teachers all my life, you know, the kind that came in and talked to me unfunnily about things that had happened and that was it.” - Reflects a past negative experience with less committed history teachers.

“So, it’s true that when I was a student, I felt that sometimes history classes were very theoretical and so on, but it’s true that when I came to class as a non-student, I saw that sometimes teachers have to adopt this methodology because otherwise it’s impossible.” - She acknowledges that sometimes teachers are forced to adopt fewer interactive methods due to student resistance.

“My internship tutor said that students are not used to any of this and that in reality many are comfortable in this role of going to the institute like someone who goes to the cinema, to see the teacher or tell the story and then I’ll study and do the exam and that’s it.” - He points to the resistance of some students to more participatory methods as a challenge in today’s teaching.

On the other hand, they stress the crucial role of an active and engaging methodology to enhance the learning experience, with the consideration that there is no single methodology effective for all groups. However, they also mention the importance of dosing or reducing content to avoid information overload, as well as the need for continuous observation and analysis to determine the most effective methods, with a willingness to adapt according to the results. While some participants emphasise the relevance of methodology over content, others argue that both are crucial and should be tailored to each group. In general, there is convergence on the difficulty in achieving active student participation, attributing this to a lack of empathy or resistance towards interactive activities, recognising the importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness. The need to simplify teaching and focus on relevant aspects of the curriculum is mentioned, as well as the need to face technological challenges with alternative plans. Their commitment to quality teaching, willingness to learn and adapt is also highlighted, although areas for improvement such as more detailed planning, time and classroom management are mentioned.

Literal and derived mentions of relevant words in the code “Changes and improvements in interventions”: Methodology: 34 times (5.53%), Activities: 21 times (3.43%), Technology: 21 times (3.43%), Content: 18 times (2.94%), Plan: 10 times (1.63%), Topic: 6 times (0.98%), Participate: 6 times (0.98%), Exam: 5 times (0.82%), Adapt: 5 times (0.82%).

As far as secondary school students are concerned, in general, there is a diversity of opinions among students regarding the methodology of teaching history. Some prefer more dynamic and visual approaches, while others are happy with the traditional way of teaching. The perception of motivation also highlights the importance of active participation and discussion in the learning process. This variability may be attributable to personal experiences, levels of interest in the subject or perceptions about the purpose of history education. To gain a deeper understanding, it would be useful to further explore the reasons behind students’ responses. Students’ ratings of the current teacher’s experience suggest that teaching experience and ability are considered important factors in teaching effectiveness.

While Teacher 1 and Teacher 3 recognise aspects of the competence-based approach to historical thinking in teaching practice, Teacher 2 is not familiar with the specific term. Regarding the development of historical competences in pupils, Teacher 1 highlights the importance of adapting materials to children’s understanding from an early age, while Teacher 2 suggests interdepartmental collaboration and family involvement to improve outcomes. Teacher 3 recognises the need for continuous improvement and stresses the importance of learning from mistakes. In relation to teaching perspectives and approaches, Teacher 3 emphasises the connection between historical events and social, economic and political contexts over time, highlighting the importance of ‘historical empathy’. Finally, teachers agree on the challenges and complexities of teaching historical competences, highlighting the need to make them understandable for students and to avoid reducing them to mere memorisation.

Regarding active learning methodologies such as project or problem-based learning, there are differences in its implementation between Teacher 1, who uses it more in lower grades due to exam preparation, and Teacher 2, who offers a short answer. Teacher 3 shows experience in educational innovation projects, indicating a predisposition towards more innovative approaches. The commitment and dedication required is highlighted, as well as the lack of detail on implementation by Teacher 1, which may limit its wider application due to the associated stress and workload. Several challenges and limitations in the implementation of active teaching methodologies are highlighted. These challenges include existing workload, loneliness among colleagues, lack of digital resources both at school and at home for students, limited time in the classroom, language barrier in understanding concepts, lack of teacher training, distrust of new methodologies, and the complexity of catering for diversity in the classroom. In addition, it is stressed that the impact of the methodology on student learning requires adequate assessment and collaborative work to generate significant changes.

Finally, it should be noted that the three teachers agree that active methodologies and historical thinking are not widespread in secondary classrooms. The reasons mainly point to lack of training, time constraints, lack of resources and mistrust on the part of teachers. Inertia in the education system, resistance to changing traditional pedagogical practices and a preference for safe and rote approaches are also mentioned. We can see that resistance to change seems to be a significant barrier. Lack of training and institutional support is highlighted as a key problem. The importance of satisfying studious learners through traditional methods is mentioned as a potential barrier to adopting more creative and reflective approaches.

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of the motivation scores. No significant differences were observed between sexes (MH = 22.45, SD = 4.86; MM = 23.33 SD = 5.40) in the initial (pre) assessment (F (1,112) = 0.82, p  = 0.36). However, significant differences were observed at the posttest as a function of gender (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) with a small effect size (partial η2 = . Significant differences were observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

Repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 1.08, p  = 0.30). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 48.83, p < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Figure 4 shows the result graphically. Therefore, motivation increased in both groups after the intervention, but especially in the female group.

Differences in Motivation Scores by Gender and Phase.

Master students highlight a higher motivation (8 positive occurrences in the code “Improvements and difficulties in the DU” 1.23%) and satisfaction (4 positive occurrences in this code 0.61%) among students despite facing difficulties. Some participants noted an improvement in their teaching skills after applying the DU, highlighting the importance of practical experience and the application of theoretical concepts in lesson planning and execution. The implementation of gamification and flipped classroom was mentioned to make teaching more attractive, showing the ability to adapt to challenging situations and look for alternative solutions. The importance of the teacher in the learning experience was highlighted and difficulties related to the implementation of technology in the classroom and the resistance of some students to participate in interactive activities were pointed out.

“Overall it did increase a lot of satisfaction and their motivation regarding the subject.”

“In general what I planned worked and it worked more than anything else in the time I had planned.”

“Well, I think that yes, it worked for them, that it was something they had never given before and it was totally different and they liked it.”

“I mean, yes there are digital whiteboards, yes there are projectors, but it’s complicated, especially to apply, in this case, a didactic unit.”

“So, the cooperative work part is fine, the inverted classroom, fatal.”

“But I also think that it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when they were in the classroom, it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when it was two hours, it was noticeable because just before break time I was already tired”.

On the other hand, in general, the perception of the secondary school students interviewed on the effectiveness of the trainee teachers’ teaching method is ambiguous and could benefit from more specific details on the perceived changes. As an analysis we can indicate that the introduction of these DU seems to have had a positive impact on students’ attention and motivation, the use of audio-visual methods and interactivity are prominent aspects of the new methodology that students appreciate. The relationship between the way of teaching and the retention of information for exams is highlighted as an important point for student satisfaction, and resources such as slides, and short videos are specific elements that students find useful. Therefore, the new way of working of the trainee teacher seems to have generated a positive experience for the students, improving participation, motivation, and information retention.

Teachers in this regard highlight positive results, such as improved motivation and reduced student boredom, as well as increased class participation. However, they recognise that the effectiveness of techniques may vary and that training in new active learning methodologies is needed to address student diversity and to keep up to date. In addition, they highlight a shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. The importance of adaptability of methodologies is emphasised, as their effectiveness depends on factors such as the subject matter, the group of learners and the resources available. It is pointed out that student motivation can influence their adaptation to the methodologies, and the use of visual and playful techniques to engage less motivated students is suggested. In addition, it is emphasised that the aim of teaching history is to enable students to interpret the world today, thus encouraging critical thinking. The effectiveness of diversity intervention programmes is acknowledged, highlighting the importance of making the content relevant to each learner.

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of satisfaction scores is observed. No significant differences were observed between sexes (MH = 21.98, SD = 3.72; MM = 22.13 SD = 3.43) in the initial (pre) assessment (F (1,112) = 0.05, p  = 0.83). However, significant differences were observed at the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 3.04, p  = 0.08). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). That is, the intervention had a significant effect on students’ satisfaction with the subject. Figure 5 shows the result graphically.

Differences in Satisfaction Scores by Gender and Stage.

As a general observation we can indicate that all three secondary school pupils interviewed have positive perceptions of the usefulness of history. The definitions of history are varied, but they share the central idea of past events, and the pupils’ responses show a basic understanding of the importance of history in understanding the present and developing critical skills. Their interest in learning about the past is highlighted and it is noted that the content of lessons and the amount of work for exams are important considerations for some students. Students’ comments suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve student motivation. It would be beneficial to delve deeper into the responses to better understand the underlying reasons behind their perceptions and to gain a more complete picture of their experience with the subject.

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of perceived learning scores is observed. Table 4 presents the results for perceived learning on a scale of 13 to 65. No significant gender differences were observed (MH = 40.27, SD = 5.40; MM = 40.67, SD = 5.14) at the initial (pre) assessment (F (1,112) = 0.16, p  = 0.69). There were also no significant sex differences at posttest (MH = 43.94, SD = 6.32; MM = 45.39, SD = 6.38) (F (1,112) = 1.46, p  = 0.23).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.82, p  = 0.37). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Fig. 6 shows the result graphically.

Differences in Perceived Learning Scores by Gender and Stage.

Master’s students recognise the usefulness of the theory of historical thinking in the planning and execution of classes, as well as the importance of the ethical dimension of history and the need to connect history with citizenship education. The use of primary sources and active methodology to involve students in historical analysis is highlighted. Furthermore, the importance of contextualising history teaching in the immediate environment and addressing social, cultural, and political issues to develop critical thinking in students is emphasised. However, there are divergences among the participants in terms of the perceived novelty of the theory of historical thinking, the depth of ethical exploration in the historical context and the inclusion of themes. Finally, the importance of connecting history with current affairs is mentioned, although this may present challenges in the handling of sensitivities and emotions during the teaching of certain historical topics.

For their part, teachers seem to agree that history teaching should not be limited to the transmission of historical facts, but should also encourage critical thinking, reflection and active participation in social problems. Citizenship education is seen as a process that goes beyond the acquisition of knowledge, including the development of analytical skills and the ability to question and criticise social and political reality.

Discussion and conclusions

If we look at the first objective, we can see that a significant effect of the phase factor (Pre vs Post) was observed in the methodology (F (1,108) = 91.88, p  < 0.01) with a large effect size (partial η2 = 0.26). In turn, we can see corroboration of this change as master’s students highlight in their statements the importance of interactive and participatory methods, as well as the role of the teacher in the educational experience. They recognise positive changes in current teaching, highlighting the positive experience with children and the inclusion of relevant points, but they criticise the persistence of traditional methods that are not very active and the resistance of some students to participatory methods. This represents a challenge in contemporary teaching, with difficulties in achieving active student participation attributed to a lack of empathy or resistance to interactive activities. The importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness is therefore highlighted, although some also point out the need to dose the content and adapt according to the results.

For their part, high school students emphasise the importance of visual resources, discussions and the connection between past and present in history teaching, as well as teaching experience and skill, reflecting diversity in preferences and learning styles. The effectiveness of the trainee teachers’ teaching methods is ambiguously perceived and may need more specific details on perceived changes. On the other hand, high school teachers recognise the need for training in new methodologies to address student diversity and to keep up to date, highlighting a shift towards a more active and participatory approach to learning. This coincides with the results of Sánchez et al. ( 2020 ) where they note an advance in teachers’ perception of a methodology oriented towards fostering historical and critical thinking in students. However, these teachers face various difficulties and limitations in the implementation of these methodologies, such as workload, lack of digital resources and the language barrier. The impact of the methodologies on learning requires adequate assessment and collaborative work to generate significant changes, being one of the main challenges for education in the future. Consequently, we believe it is crucial that educational administrations encourage the motivation and training of both new and old teachers in order to achieve the necessary methodological improvement in the teaching of history. Teachers suggested that the use of visual and playful techniques engage less motivated students, and the aim of fostering critical thinking through history teaching is highlighted, so the effectiveness of the intervention programmes for diversity is recognised, emphasising the relevance of the content for each student.

This may lead us to see that the generalised perception of students in the pre-test denotes the persistence of the traditional teaching model with the absence of active methods, digital resources, and historical thinking skills. Monteagudo-Fernández et al. ( 2020 ) obtain similar results in a study with secondary education and baccalaureate students, confirming the existence of a traditional model in the teaching of history that excludes cooperative and inquiry-based methodologies. This reality must point towards a didactic model that prioritises competence learning and student activism in their learning process, highlighting advocates such as Carretero et al., ( 2017 ) or Metzger & Harris, ( 2018 ), who are committed to a methodological change that moves away from the predominant conceptual model for teaching history.

In terms of motivation, we can see that a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 48.83, p  < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Thus, motivation increased in both groups after the intervention, but especially in the female group. The master’s students corroborate this by highlighting a higher motivation and satisfaction among students despite facing difficulties, while for high school students, in general, the new way of working of the trainee teacher seems to have generated a positive experience, improving participation, motivation and retention of information. The importance of active participation and discussion in the learning process is particularly emphasised by the high school students. Teachers highlight positive results, such as improved motivation and reduced student boredom, as well as increased participation in class. However, there is no significant statement regarding a difference in motivation with respect to gender, which may suggest that this is a change that is little perceived by teachers and students, but which is present and should be considered when applying these active and historical thinking methods.

These results are similar to those presented by several authors (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), who also highlight as the most important factor that motivation is due to the use of resources other than the school textbook, which is very good news for continuing to take steps towards methodological complementarity, so that the students themselves are aware that by using all kinds of resources to learn, they can and should be more motivated. In these studies (Gómez et al., 2021a ; Gómez et al., 2021b ), they also found that the item with the lowest score in their pretest is the one that states that students are motivated because they can contribute their points of view and knowledge, something that clearly does not occur in traditional classes where the students’ role as receivers predominates. For his part, Singer ( 1996 ) considers gender to be one of the most significant predictors in relation to teaching approaches. In this sense, Maquilón, Sánchez and Cuesta ( 2016 ), in their study of active Primary School teachers, point out that men tend to opt for an approach based on the transmission and reproduction of information, while women are inclined towards a more student-centred approach.

In satisfaction, significant differences were also observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06), as for motivation (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) (partial η2 = 0.04). However, repeated measures analysis of variance did not produce a significant result of interaction effect between sex and phase (F (1,108) = 3.04, p  = 0.08). A significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). In other words, the intervention had a significant effect on students’ satisfaction with the subject, in agreement with what was stated by the master’s students and teaching staff on the improvement of student motivation and satisfaction. They highlight the relationship between the way of teaching and the retention of information for the exams as an important point for their satisfaction. High school students highlight that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and comprehension of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve pupils’ motivation.

Finally, on learning, a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Master’s students highlight the importance of the teacher in the learning experience and difficulties related to the implementation of technology in the classroom and the reluctance of some students to participate in interactive activities were noted, although the crucial role of this methodology in enhancing the learning experience is highlighted, with the consideration that there is no single methodology effective for all groups. Students suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns. Teachers for their part highlight the shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. However, this requires adequate assessments and collaborative work to generate significant changes, as well as continuous training in active learning methodologies and strategies, considered essential nowadays.

There is still an overuse of textbooks and the expository strategy by teachers who teach History (Carretero and Van Alphen, 2014 ; Colomer et al., 2018 ). However, more and more teachers in Spain are in favour of a teaching model in which the student acquires a greater role through the implementation of innovative resources (heritage, written and oral sources, new technologies) and educational strategies that encourage the active participation of students in the teaching and learning process (project-based learning, gamification, flipped classroom) (Gómez et al., 2018 ; Gómez et al., 2021a ; Sánchez et al., 2020 ). It is therefore important to be aware of developments in the incorporation of competence-based social sciences teaching and a learner-centred model at all levels of education.

We can conclude from the above that the programme was quite effective in the objectives studied. In the quantitative data we observed an improvement in the students’ perception of all the variables studied after the intervention, especially the change in methodology and the improvement in motivation had a large effect size. Moreover, it can be noted that the DOMs applied most of the methods, techniques, and resources we proposed in the training programme (supplementary material Fig. 6 ). On the other hand, we found quite positive statements about the programme from both master’s students and high school students and teachers as we have seen in the different points. However, it is important to point out the limitations and difficulties reported by teachers and students when implementing this type of unit, as well as the fact that there were some weaknesses in this study, such as the small quantitative and qualitative sample group. As a possible future improvement when carrying out the interviews and organising the focus group, it is possible to point out that it could be organised with more time and written commitment from the participants, as the initial intention was for 8 teachers, secondary school students and Master’s students to participate, respectively, one for each unit applied. The limitations of their availability played a negative role in the collection of more qualitative data, as participation was voluntary and, in the case of high school students, parental approval was required.

Data availability

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

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RSI and JR-M: conceived and designed the project and doctoral thesis of which this study is part. PMS and JR-M.: have made methodology, data collection and formal analysis. PM-S and JR-M have co-written the manuscript and RSI contributed to revisions, having read and approved the submitted manuscript. All authors have read and agreed to the published version of the manuscript.

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This study was performed in line with the principles of the Declaration of Helsinki. It is part of grant PRE2021-097619, funded by MCIN/AEI/10.13039/501100011033 and ESF + . It is part of the research project “La enseñanza y el aprendizaje de competencias históricas en bachillerato: un reto para lograr una ciudadanía crítica y democrática” (PID2020-113453RB-I00), funded by the Agencia Estatal de Investigación (AEI/10.13039/501100011033). This project was granted favourable by Ethics Research Committee of the University of Murcia 8/03/2021.

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Miralles-Sánchez, P., Rodríguez-Medina, J. & Sánchez-Ibáñez, R. Evaluation of didactic units on historical thinking and active methods. Humanit Soc Sci Commun 11 , 1032 (2024). https://doi.org/10.1057/s41599-024-03546-9

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• Indian J Anaesth
• v.60(9); 2016 Sep

Types of studies and research design

Mukul chandra kapoor.

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

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

INTRODUCTION

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

Triad of evidence-based medicine

EVIDENCE-BASED MEDICINE

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

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

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

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

MEDICAL RESEARCH

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

General principles of medical research

Types of study design

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

Classification of types of medical research

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

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

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

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

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

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

Secondary research

Narrative review.

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

Systematic review

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

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

Meta-analysis

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

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

The evidence-based medicine pyramid

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

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

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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

Published on 16.8.2024 in Vol 13 (2024)

Rationale, Design, and Intervention Development of a Mobile Health–Led Primary Care Program for Management of Type 2 Diabetes in Rural Thailand: Protocol for a SMARThealth Diabetes Study

Authors of this article:

• Methee Chanpitakkul 1 , MBBS, MD   ; 
• Devarsetty Praveen 2, 3, 4 , MBBS, MD, PhD   ; 
• Renu John 2, 4 , BDS, MPH   ; 
• Arpita Ghosh 2, 3, 4 , PhD   ; 
• Salyaveth Lekagul 1 , MBBS, MD   ; 
• Malulee Kaewhiran 5 , MBBS, MD   ; 
• Kriang Tungsanga 1, 6 , MBBS, MD   ; 
• Vivekanand Jha 2, 3, 4, 7 , MBBS, MD, DM  

1 Bhumirajanagarindra Kidney Institute Hospital, Bangkok, Thailand

2 The George Institute for Global Health, Hyderabad, India

3 University of New South Wales, Sydney, Australia

4 Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India

5 Kamphaeng Phet Provincial Health Office, Kamphaeng Phet, Thailand

6 Chulalongkorn University, Bangkok, Thailand

7 School of Public Health, Imperial College, London, United Kingdom

Corresponding Author:

Devarsetty Praveen, MBBS, MD, PhD

The George Institute for Global Health

#401, 4th Floor, Shangrila Plaza

Plot No.14, Road No. 2, Banjara Hills

Hyderabad, 500034

Phone: 91 9959777623

Email: [email protected]

Background: Noncommunicable diseases (NCDs), particularly diabetes and chronic kidney diseases, pose a significant health burden in Thailand, especially among socioeconomically disadvantaged populations. The existing primary health care system faces challenges in providing optimal care for NCDs due to inadequate primary care workforce. The SMARThealth program offers a technology-based solution to enhance NCD management through task-sharing among nonphysician health care workers.

Objective: This study aims to adapt and implement the SMARThealth Diabetes program in rural Thailand to improve diabetes management. The main objectives are to (1) adapt, validate, and integrate the SMARThealth Diabetes program for improving the management of type 2 diabetes mellitus at the primary health care level; and (2) to determine the feasibility and acceptability of the SMARThealth Diabetes program in rural communities of Thailand.

Methods: A pragmatic, type 2 hybrid effectiveness or implementation, parallel-group cluster randomized controlled trial of 12 months duration and involving 51 subdistrict health offices in rural communities of Kamphaeng Phet province, Thailand, will be conducted. The intervention arm will receive the SMARThealth Diabetes program, including workforce restructuring, clinical decision support system, and continuous performance monitoring, while the control arm will continue with usual practice. Data will be collected using the SMARThealth platform and will be stored on a server in Thailand. The primary outcome measure will be the change in mean hemoglobin A 1c (HbA 1c ) measured at randomization and 12 months from randomization between the intervention and control clusters. Secondary outcomes will include the difference in change in albuminuria status, estimated glomerular filtration rate, systolic blood pressure, and low-density lipoprotein cholesterol level. The analysis for change in HbA 1c between baseline and end of study will be performed using linear mixed models. Any imbalances between the 2 arms will be addressed by sensitivity analyses. Additionally, a mixed methods process evaluation will be conducted to assess the implementation process, that will include in-depth interviews and focus group discussions, in addition to the quantitative data collected during the implementation process. The qualitative data will be thematically analyzed to explore factors that promote or inhibit the implementation and maintenance of the program.

Results: The data collection commenced in November 2022, and the results will be ready for publication by the first quarter of 2025. Effectiveness of the intervention package will be assessed by change in mean HbA 1c measures, and detailed feasibility, barriers, and enablers for the implementation of the intervention will be documented through a detailed process evaluation.

Conclusions: The study protocol outlines a novel approach to enhancing diabetes management in rural Thailand through digital technology–based interventions that will facilitate task-sharing among health care workers. This can help inform future strategies for improving NCD care in low-resource settings globally.

Trial Registration: Thai Clinical Trials Registry TCTR20200322006; https://www.thaiclinicaltrials.org/show/TCTR20200322006

International Registered Report Identifier (IRRID): DERR1-10.2196/59266

Introduction

South-East Asian countries (including Thailand) have been experiencing a steady rise in the burden of noncommunicable diseases (NCDs). The Global Burden of Disease Study 2015 reported diabetes and chronic kidney diseases (CKD) as Thailand's third and fifth leading causes of death [ 1 ]. According to the 5th Thai National Health Examination survey conducted in 2014 [ 2 ], Thailand has 4.63 million adults with diabetes (9.9% of the adult population), which is expected to grow to 5.2 million by 2035. In addition, 7 million adults in Thailand have prediabetes due to increasing childhood and adolescent obesity and is estimated that 25%-48% of people with diabetes will develop CKD [ 3 ]. According to the Microalbuminuria Prevalence Study survey in 2002 [ 4 ] across 10 Asian countries, it was found that 45% of the population in Thailand with diabetes had microalbuminaria and 15% had macroalbuminaria. Both conditions increase the risk for the development and progression of CKD, which will eventually lead to the need for dialysis or a kidney transplant. According to data from the Thai Renal Registry, diabetes was responsible for 34.6% cases of kidney failure in 2007 [ 5 ], increasing to 41.5% in 2020 [ 6 ]. Suboptimal treatment of diabetes leads to other macro- and microvascular complication such as cardiovascular disease, neuropathy, and retinopathy, all of which are associated with the use of more resources [ 7 ] and have grave economic and health impact.

The risk of suboptimal care and poor outcomes is worse among the socioeconomically disadvantaged. According to the World Bank reports, about 4.9 million people in Thailand live below poverty lines, with catastrophic health care cost being one of the factors contributing to relapse into poverty [ 8 ]. However, the launch of the universal health coverage program in Thailand that provided a comprehensive benefits package and zero copayment for health services was followed by a progressive decrease in the number of households (6% in 1996 to 2% in 2015) that experienced financial catastrophic events (defined as out-of-pocket payment for health exceeding 10% of household total consumption expenditure) due to health care [ 9 ].

Fortunately, the adverse outcomes, including the development of microvascular complications and progression to kidney failure can be prevented by optimal glycemic and blood pressure (BP) control and institution of effective interventions for CKD prevention. Healthy lifestyle (maintaining ideal body weight, physical activity, healthy diet, and smoking cessation), and pharmaceutical interventions including glycaemia management, BP control, use of renin-angiotensin system blockers, sodium-glucose cotransporter inhibitors, mineralocorticoid antagonists, and statins can substantially prevent the development of cardiovascular diseases and progression of kidney disease [ 10 - 12 ].

The key to early detection and prevention of renal and other complications in diabetes is the provision of effective and affordable primary care. However, major challenges undermine these efforts, especially among developing countries. For example, the size of the primary care workforce in developing countries is generally inadequate to meet the rising needs of giving care to those with diabetes and its complications. Maldistribution of health care workforce occurs in Thailand, similar to other developing countries. Due to the disproportionate doctor-population ratio, especially in the rural areas, there are not enough medical doctors to provide regular and optimum out-patient care to all patients with diabetes. The doctor-to-population ratio in Bangkok metropolitan area is as high as 1:515, whereas such ratio in the rural area is as low as 1:1723 to 1:2761 in the central and north-eastern regions, respectively [ 13 ]. In rural areas, it is commonly observed that those without serious complications could see a doctor in person only once or twice a year.

Health System in Thailand

Under the government's universal health coverage policy implemented in 2002, all Thai citizens are eligible to receive essential health care services with support from public health funding [ 14 ]. The Ministry of Public Health (MOPH) is the country’s largest public-sector agency, controlling over two-thirds of all hospitals and beds. The MOPH divides the country into 12 health administrative regions exclusive of Bangkok metropolitan area. Altogether, there are 165 tertiary, quaternary, or specialty hospitals (300-1000 beds) at the regional and provincial levels, 743 secondary and tertiary care hospitals (10 to 250 beds) at a district level, and about 9800 subdistrict health offices (SDHOs) [ 15 ]. The SDHOs provide basic clinical care, as well as health promotion and primary disease prevention, to more than 44 million (80%) Thais living in rural areas [ 15 ]. Each SDHO employs 1-2 community nurses, 1-2 public health officers, and 1 dental therapist to care for 3000-6000 residents’ households. Though there is a well-connected system of patient referral from SDHO to its respective district hospital, most primary care services at SDHO are provided by its community nurses and public health officers.

In response to the growing burden of NCDs among the rural Thai population, the Thai MOPH launched the Thailand healthy lifestyle strategy 2018-2037 with the goal of improving care of diabetes and hypertension in public sector hospitals [ 16 ]. Each public hospital and SDHO must report the percentage of population screened for and found to have diabetes or hypertension and those having CKD. Though clinical practice guidelines have been developed, guideline-based clinical care is still far from reaching the targets. Only a minority of individuals with diabetes receiving continuous care could achieve recommended treatment targets for BP control (29.8%) and glycemic control (35%) [ 17 ].

Successful prevention initiatives after diabetes screening have been reported recently [ 14 ]. A cluster randomized study examining the effectiveness of integrated care on delaying the progression of stage 3-4 CKD in rural Thailand (ESCORT study) [ 18 ], evaluated integrated CKD care involving group counseling from a multidisciplinary team of district hospital staff and quarterly home visits by the community CKD care network (consisting of subdistrict health care officers and village health volunteers).  The intervention was able to slow the decline in estimated glomerular filtration rate (eGFR) over a 2- to 3-year period (difference 2.74, 95% CI 0.60-4.50 mL/min/1.73 m²; P=.009), showing the feasibility of using nonphysician workforce to augment primary care delivery and underscoring the need to develop effective strategies to deliver low-cost, evidence-based treatments for the prevention of diabetes and diabetic kidney disease that can be implemented in the primary health care systems [ 18 ]. In the subsequent prospective follow-up study, ESCORT II study [ 19 ], the integrated care model was extended to 5 district hospitals without a parallel control group. This expanded approach, which encompassed hospital multidisciplinary care and community home visits, resulted in a consistent mean eGFR decline of –0.92 mL/min/1.73 m²/year over the 36-month duration [ 19 ]. These findings further underscore the effectiveness of the integrated care model at the community level in effectively delaying the progression of CKD.

SMARThealth

The SMARThealth program, based on a technology platform developed by the George Institute for Global Health, enables communities and health care providers to prevent and manage NCDs using guideline-based care, and continuous quality control [ 20 ]. This initiative is based on principles of “task-sharing,” in which routine clinical procedures are conducted by nonphysician health care workers, including village health volunteers, community nurses, and public health officers to increase access to quality health care and reduce costs. SMARThealth for cardiovascular diseases was integrated with primary health care services and evaluated in 8 villages in Malang district, East Java, Indonesia (2016-2018). It was associated with an 8.3 (95% CI –10.1 to –6.6) mmHg reduction in BP [ 21 ]. The strategy has been adopted by the Malang district government for scale-up and integration with existing electronic medical record system (e-Puskesmas) in over 390 villages (2020-2023) and is being evaluated as part of the Global Alliance for Chronic Diseases scale-up funding round. Digital technologies, including affordable and innovative solutions, allow patients and health care providers to make evidence-based decisions.

The SMARThealth program [ 20 ] has been adapted for optimal management of patients with diabetes and kidney disease in the context of Thailand’s health system and comprises the following elements. First, a platform for community nurses to assess the disease risk using a clinical decision support system (CDSS) app on an Android tablet device. The app allows community nurses to collect essential health-related information from patients, inform them of their risk status, provide lifestyle advice relating to physical activity, diet and tobacco and alcohol, and refer high-risk patients to the specialist doctor at the district hospital. In addition, the app provides decision support to community nurses for providing guideline-based recommendations and adjusting medication prescriptions during follow-up. Second, a 2-day training induction and ongoing support from the research team for community nurses. Third, data collected by the community nurses are asynchronously uploaded to a shared electronic medical record (OpenMRS) via the Sana Mobile Dispatch Server and stored on a centralized server in Thailand. Finally, availability of a central support team to visit the community nurses periodically and provide support such as re-training, co-ordinating, and solving IT issues. The quality of intervention is ensured by supervisor field visits.

The primary study hypothesis is that compared with usual care, adapting, and implementing a multifactorial intervention (SMARThealth) that strengthens the existing primary health care system and addresses known constraints for diabetes management in rural Thailand will improve blood glucose levels and thereby reduce renal complications among adults with type 2 diabetes.

Main Study Objectives

The study objectives are to (1) adapt, validate, and integrate the SMARThealth Diabetes program for improving the management of type 2 diabetes mellitus at the primary health care level; and (2) determine if the SMARThealth Diabetes program is feasible for implementation and acceptable in the rural communities of Thailand.

Study Design

The overall study design is presented in Figure 1 .

Objective 1: Intervention Development

Algorithm development and validation, updating of decision support tools.

The existing global guideline-based algorithms [ 22 ] for the screening and management of diabetes were updated with recommendations consistent with Thai guidelines [ 23 ]. These included evidence-based guidance on monitoring BP and blood glucose, lifestyle changes (smoking cessation, weight loss, improved diet, exercise, alcohol, and sodium restriction), and adherence to medical treatments, including renin angiotensin aldosterone system blockade. The draft algorithm underwent expert review by leading diabetes and kidney disease specialists to assess consistency with global and local guidelines. Drugs for diabetes, hypertension, and hyperlipidemia were identified according to their availability at the SDHOs. The recommendations were converted into “pseudo-code” rules and developed into detailed software specifications. After programming, the algorithms underwent independent clinical and statistical validation using established methods [ 20 ].

Deployment of the Algorithms on the Mobile Platform

The algorithms were integrated and deployed using the existing Java-based SMARThealth platform, which can be accessed directly on low-cost Android devices. The existing platform was modified to accommodate additional inputs and outputs based on the updated algorithm. The recorded input information was ensured to be securely transferred (synchronous or asynchronous) to a shared electronic record located on a central server in Thailand. Output providing referral and monitoring recommendations for the nurses are generated based on the parameters entered. Nurses can adjust medications through the platform, and if a referral to a higher center is needed, referral advice with details of the referral center is also provided.

Iterative Testing, Refinement, and Validation of the Platform

The platform was rigorously validated in 2 steps. The first is to ensure that the local guidelines are correctly interpreted by the algorithm, and the second is to ensure that it is accurately deployed in the Java-based mobile platform. The outputs from the SMARThealth platform of about 100 patients were run through 2 clinicians to estimate the accuracy of the recommendations. Minor changes, as suggested by clinicians were considered, and the algorithms were modified till both clinicians were satisfied with the final recommendations.

Integration of the SMARThealth Platform With the Primary Health Care System in Thailand

Rapid health service assessment.

An audit of health service capacity was conducted using qualitative methods, including in-depth interviews and focus group discussions (FGDs) with community members, health care providers and district administrators in five primary catchments at all levels of health care. The interviews were conducted in local language (Thai) and led by a moderator trained in interview and group facilitation techniques. A total of 20 in-depth interviews and 2 FGDs were conducted with a total sample of 36 participants in 5 primary health care catchment areas, till no new information was obtained from the interviews. This assessment identified barriers and facilitators for the multifaceted system intervention implementation and identified modifications that will be required to maximize the likelihood of successful integration and implementation. Findings of the rapid health service assessment will be published separately.

Workflow Integration

Through the inputs from the health service assessment and ongoing discussions with nurses from SDHOs and doctors at health facilities, the SMARThealth platform was adapted to suit the existing workflow of the health offices. For example, the paper registration forms were digitized and integrated with the platform, the existing printers at the SDHOs are used to print treatment advice and referral cards, available laboratory records at SDHO are used for input into the platform, and mobile communication of doctors and nurses are retained through use of this platform.

Addition of a New Workforce Training Module

A SMARThealth Diabetes training program was developed. Inputs from the rapid health system assessment helped in the planning of the training program in terms of location of training, content, and timings. The training package is delivered in an initial workshop format and continuously reinforced through the program’s digital platform. The training program delivery is supported by training manuals to nurses and doctors to ensure local relevance.

Objective 2: Evaluation of the Platform

The intervention will be evaluated using a pragmatic, type 2 hybrid effectiveness or implementation, parallel-group cluster randomized controlled trial of 12 months duration. A total of 51 SDHOs will be randomized 1:1 into intervention and control arms. As the intervention is directed at the health system involving the nurses, doctors, and other staff working as teams, effectiveness is best evaluated through cluster randomized controlled trial with clustering at the SDHO level.

Study Setting

The study is conducted in rural communities in Kamphaeng Phet province in Thailand through the involvement of SDHOs selected in consultancy with the ministry. To be eligible, the SDHO personnel must be willing to participate in the study, have interest in using digital technology, and not participating in any competing research study.

Eligibility Criteria

Inclusion criteria.

Aligning with the Thai public health screening policies, and in discussion with MOPH, patients attending SDHOs will be eligible to participate if they are (1) aged 30 to 70 years; (2) known cases of type 2 diabetes currently on oral hypoglycemic medications or identified to have diabetes in the initial screening by the community nurses; (3) have CKD-EPI eGFR of > 60 mL/min/1.73 m² [ 24 ]; and (4) undergoing treatment at the SDHOs by a community nurse.

Exclusion Criteria

Participants with the following characteristics will not be eligible: (1) individuals who decline participation; (2) patients with a diagnosis of nondiabetic kidney disease (for example, ADPKD, lupus nephritis, obstructive uropathy, renal stone, nephrotic syndrome, or primary glomerular disease); (3) patients with expected life expectancy of less than 2 years; (4) patients with AIDS; (5) patients with known pregnancy; and (6) patients having communication problems, for example, migrant workers and people with dementia.

Informed Consent

Consent will be obtained based on a professional-cluster design approach proposed by Hemming and Eldridge [ 25 ]. Consent will be collected at three levels: (1) at the cluster level —verbal approval from each SDHO to participate in the trial; (2) from the health workers—verbal approval from nurses and doctors to participate in the study and provide information during and after the study; and (3) from individual participants—consent from patients with diabetes interested in participating in the study for data collection during the study.

Intervention Group

SDHOs randomized to the intervention group will receive the SMARThealth Diabetes program described earlier.

First, a workforce restructuring and training program to increase the involvement of community nurses in routine aspects of diabetes and kidney disease care. Using the module developed in objective 1, training will be provided to community nurses to promote awareness of lifestyle determinants, use of the CDSS to record risk factor information from the laboratory records available at the health center, guidance in the interpretation of the CDSS output, and processes to refer high-risk individuals to the district hospitals and training to monitor and promote adherence to prescribed medications. The training will be imparted for 2 days. After training and certification, community nurses will each be provided with a 7-inch tablet preloaded with the SMARThealth software. Second, the Thai CDSS will incorporate components related to referral, follow-up, and personalized recommendations about blood glucose control, BP lowering, pharmacotherapy including ACE inhibition, statin, and eye and foot care. Third, continuous performance monitoring. The performance of community health nurses will be monitored using standard, automated reports generated in real time and based on certain key performance indicators (eg, the proportion of individuals with diabetes on recommended combination treatment). These reports will be provided to health administrators in each district.

Criteria for Discontinuing or Modifying Allocated Interventions

There will be no predefined criteria for stopping the allocated intervention. In case new national clinical guidelines are published during the trial, this may justify modification of the content of the intervention without changing the form of the intervention. The decision for modification will be made by the steering committee.

Participants may withdraw from the trial at any time. Withdrawal will not negatively affect their ability to access usual care from the SDHO.

Control Group

SDHOs randomized to the control group will continue with usual practice, without access to the training and support package, the CDSS and associated tools.

Primary Outcome

The primary outcome will be the difference in change in mean hemoglobin A 1c (HbA 1c ) measured at the SDHO between randomization and 12 months from randomization, between the intervention and control clusters. HbA 1c has been chosen as the primary outcome as it is recommended by local clinical guidelines as a treatment target.

Secondary Outcomes

Secondary outcomes will include the difference in change in albuminuria status; change in eGFR, systolic blood pressure, and low-density lipoprotein cholesterol level; change in the proportion of patients with HbA 1c <7%, and patients with systolic blood pressure<140 mm Hg; measured at randomization and 12 months from randomization, between the intervention and control clusters.

SDHOs will be recruited and randomized before individual participant recruitment commences. After randomization, community nurses in the intervention clusters will receive the training program (as described earlier). Eligible patients with diabetes registered within the SDHOs will be invited by the community nurses to participate in this study. After obtaining written informed consent, demographic and additional data, baseline parameters will be collected using previously validated, structured questionnaires. At the end of the study (12 months), data and biological samples will again be collected at the laboratory in SDHOs by trained laboratory technicians, along with completing the endline questionnaires by community nurses to allow analyses for all outcomes.

Statistical Considerations

Randomization of 51 SDHOs with an average cluster size of 40 individuals with diabetes at baseline will provide 90% power (2α=0.05) to detect an absolute mean HbA 1c difference of 0.5%. A change of 0.5% in HbA 1c is considered clinically meaningful [ 26 ]. Intracluster correlation coefficients of 0.05 for HbA 1c are assumed. By assuming a 10% dropout rate in our study, the sample size calculation indicated that a total of 1540 participants will be required to be recruited in both arms, with 770 in each group ( Figure 2 ).

The analysis of change in HbA 1c between baseline and the end of study (primary end point) will be performed using a linear mixed model including treatment arm and baseline HbA 1c as fixed effects as well as SDHO as a random effect to account for clustering of individuals by village. In case of baseline imbalances between the 2 arms at the cluster or individual level, unbalanced characteristics will be added to the models as a sensitivity analysis. The same approach will be applied for continuous secondary end points.

Data Collection and Management

Data collection will commence at baseline (0 months; randomization) and will be conducted quarterly at 3, 6, 9, and 12 months after randomization by community nurses. All data will be captured on tablet devices using the SMARThealth app adapted for the clinical research forms (CRFs). The CRFs collect information on anthropometric measurements, medical history, and current medication for hypertension, diabetes, and hyperlipidemia, in addition to laboratory test results. These CRFs will be completed at months 0, 3, 6, 9, and 12. The primary outcome variable (HbA 1c ) and other laboratory tests will only be recorded at months 0, 6, and 12. Data will be deidentified, saved, and stored in a secure server located in Bangkok, Thailand. Published, validated questionnaires are used in the CRFs and tested using the SMARThealth platform before data collection. About 10% of all CRFs will be checked for accuracy against source data by the project team.

SMARThealth platform will be used for digitally collecting all baseline and outcome data. Data access will be restricted to delegated study staff and will require multifactor identification with a digital log kept of all logins. The project manager and data managers will be responsible for data quality. The project manager will ensure quality by interacting periodically with community nurses and checking for completeness of data, and data validation.

Confidentiality

Participant confidentiality will be maintained throughout the study. Each participant in the study will be assigned a 10-digit unique alphanumeric patient ID. The first 2 digits are the district code, the following 5 digits are the SDHO code, and the last 3 digits indicate the individual patient ID. This unique ID ensures that their personal identifiers remain anonymized throughout the research process.

Our data management system employs 3 robust security layers to safeguard patient data. First, password protection is implemented to regulate access, preventing unauthorized usage by individuals. Second, local data protection measures, including encrypted data communication and role-based access, are implemented to ensure the security of the locally stored data at the device level. Finally, at the server level, additional measures such as firewall protection, reliable maintenance, monitoring, and support services are implemented to safeguard the database.

Process Evaluation

A detailed awareness of local contextual factors will be undertaken to understand the impact of SMARThealth and barriers to its implementation and scale-up. The process evaluation will be designed in line with recommendations by the UK Medical Research Council in its “guidelines for developing and evaluating complex interventions” [ 27 , 28 ]. Evaluation data, data collected during intervention delivery, and data collected through qualitative methods will be used to investigate the process evaluation framework that includes identifying factors that promote or inhibit the implementation and maintenance of the program [ 29 ]. A maximum variation sampling technique will ensure diverse opinions are captured from participants and health care workers. The process evaluation will be informed by normalization process theory (NPT) [ 30 ] to assess the extent to which the new system fits within the normal processes of the current service provision in the villages and SDHOs. The 4 main concepts of NPT namely, coherence, cognitive participation, collective action, and reflective monitoring will be identified in the process of implementation of the SMARThealth diabetes program in the intervention SDHOs.

Key evaluation areas that the study will explore are: (1) how the health workers use the intervention, (2) what effects the intervention might have on doctor practices, and (3) participant experiences of receiving the intervention.

Qualitative data will be collected using semistructured, in-depth interviews and focused group discussions [ 31 ]. NPT will inform the semistructured interview guides and will also be used as a framework for analyzing the data. All interviews and group discussions will be audio-recorded, and then transcribed verbatim. The qualitative data will be thematically analyzed (from codes to categories and finally themes) to identify and interpret patterns and themes within the data. Data will be analyzed contemporaneously, and data collection will be stopped when thematic saturation is reached. Two researchers who are trained in qualitative methodology will be involved in coding the interview and FGD transcripts. In the event of a disagreement, a consensus meeting will be held with the principal investigator to reach an agreement on interpretation of the data. Intercoder reliability will be reported using qualitative assessments of agreement and consistency among the coders using processes described by O’Connor and Joffe [ 32 ]. Process evaluation data will be analyzed independently of the outcome evaluation data and then the 2 sets of data will be combined and triangulated to increase the reliability of the results.

Ethical Considerations

This program will be conducted, evaluated, and reported in compliance with all local regulatory requirements in Thailand. Ethical approval has been obtained from the Ethical Review Committee for Research in Human Subjects at MOPH in Thailand (25/2562) and the Oxford Tropical Ethics Research Committee (21/19).

All potential participants for both the qualitative and quantitative component of the study will be provided with a participant information sheet prior to their recruitment into this study.

Participation in the study is voluntary, and no financial compensation will be provided to the participants. Data collected using the SMARThealth Diabetes platform will be stored securely in a central server located in Bangkok, Thailand. Personal identifiable information will be stored securely; electronic data will be deidentified and analyzed. Findings will be written up as means and published without identifying information. Electronic audio files from interviews will be stored electronically on a local network server at the researcher’s organization, under both firewall and password protection. Access will be limited to study investigators. All the data collected in the study will be managed by the central research team. The data collection will be routinely monitored for completeness and quality control checks will be introduced. The physical documents will be stored securely and only accessible by the study researcher.

The study was funded by the Medical Research Council, United Kingdom in April 2018 with additional funding received from Medical Research Council and Imperial College London in September 2022. We had to delay the recruitment of the participants into the trial between the period 2020-2021 due to the unprecedented COVID-19 pandemic. As of May 2024, we have enrolled 1599 patients with type 2 diabetes, who are managed and followed up by community nurses at 51 SDHOs. Final data analysis and results are expected to be published in the first quarter of 2025.

Expected Findings

This study will address the growing epidemic of diabetes leading to CKD and kidney failure in Thailand. To our knowledge, the SMARThealth Diabetes study is the first randomized trial to use technology as a health system intervention to support the delivery of optimal management of diabetes at a primary health care level in Thailand. This intervention is designed to address several gaps in the treatment of diabetes and kidney disease, including inadequate workforce, disproportionate focus on physician-centric care models not aligned with the values and preferences of communities in which they are implemented, variation in the quality of care, and its high cost.

The SMARThealth Diabetes intervention relies on primary health care workforce reengineering and an electronic decision support with an emphasis on minimizing variations on quality of care. These components are embedded in the public health care system, enhancing the adaptation and sustainability of the intervention. Similar primary health care interventions have already been successfully implemented in primary health care settings in Indonesia and India for detection, prevention, and management of cardiovascular diseases [ 21 , 33 ]. Hence, there is a need to extend the platform to similar primary health care settings in other low- and middle-income countries. The evidence generated through this study will have substantial potential to inform policymakers and system planners to include high-quality primary health care for common NCD conditions.

Strengths and Limitations of the Study

There are many strengths in this study, including the use of a novel multifaceted mobile health strategy to engage communities, physicians as well as nonphysician health care providers in learning how to cope with diabetes and, as a result, strengthen the overall health system. Additionally, a comprehensive process evaluation will allow the assessment of the feasibility, scalability, and sustainability of such a strategy in the local health care system.

The main limitation is that it is conducted in selected rural areas, which might not represent the wider population in other districts and provinces in Thailand. However, integrating the intervention into the health system, which is similar throughout the country will help to learn from this project and support future scale-up if successful. Further, although this project will be deployed in rural Thailand, the components of the intervention are generic, and this study will generate important evidence that will inform the adaptation of similar interventions in other health care settings. Aligning to the study design, the study will need to collect information on HbA 1c and other laboratory tests from participants in both arms leading to better than usual care in the control arm. We have streamlined the timing of the data collection to the routine follow-up visit of patients in the control arm. Still, we need to acknowledge there could be additional communication with these patients to ensure follow-up visits are happening as per the need of the study.

Conclusions

In conclusion, we present a study protocol for a pragmatic, parallel group cluster randomized hybrid effectiveness or implementation trial to test the SMARThealth Diabetes platform in rural Thailand. The study aligns with guidelines developed by the Medical Research Council for developing and evaluating complex interventions. The platform will support the delivery of optimal management of diabetes at a primary health care level and we hope this trial will demonstrate the effectiveness of the platform in addressing the gaps in the treatment of diabetes and kidney diseases.

Acknowledgments

The authors acknowledge the support of the study team, health workers, nurses, patients, subdistrict health office staff, district medical officers, and other district officials in the study districts, and Dr Pittala Vara Prasad and Dr Priyakanta Nayak for managing various aspects of the study. The publication fees were paid by the Imperial College Open Access policy.

Data Availability

The data sets generated or analyzed during this study will not be publicly available in accordance with the data management plan but will be made available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

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Abbreviations

Edited by D Khajeei; The proposal for this study was peer reviewed by the UK-Thailand Joint Health Research Call - Non-Communicable Diseases 2017 - Medical Research Council (United Kingdom).submitted 08.04.24; accepted 18.06.24; published 16.08.24.

©Methee Chanpitakkul, Devarsetty Praveen, Renu John, Arpita Ghosh, Salyaveth Lekagul, Malulee Kaewhiran, Kriang Tungsanga, Vivekanand Jha. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 16.08.2024.

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

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Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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