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  • What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

  • Your overall research objectives and approach
  • Whether you’ll rely on primary research or secondary research
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

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

Table of contents

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

  • Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

  • Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
  • Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

  • Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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components of a research design

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

  • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
  • Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

  • Your research questions and/or hypotheses
  • Your overall approach (e.g., qualitative or quantitative )
  • The type of design you’re using (e.g., a survey , experiment , or case study )
  • Your data collection methods (e.g., questionnaires , observations)
  • Your data collection procedures (e.g., operationalization , timing and data management)
  • Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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  • Knowledge Base
  • Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

  • Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 4 March 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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Research Design 101

Everything You Need To Get Started (With Examples)

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

Research design for qualitative and quantitative studies

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Free Webinar: Research Methodology 101

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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components of a research design

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

components of a research design

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

components of a research design

Recap: Key Takeaways

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

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

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

components of a research design

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This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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

Solly Khan

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

hetty

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

Belz

This was really helpful. thanks

Imur

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

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

Sam Msongole

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

Robyn Pritchard

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

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

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What is a Research Design? Definition, Types, Methods and Examples

By Nick Jain

Published on: September 8, 2023

What is Research Design?

Table of Contents

What is a Research Design?

12 types of research design, top 16 research design methods, research design examples.

A research design is defined as the overall plan or structure that guides the process of conducting research. It is a critical component of the research process and serves as a blueprint for how a study will be carried out, including the methods and techniques that will be used to collect and analyze data. A well-designed research study is essential for ensuring that the research objectives are met and that the results are valid and reliable.

Key elements of research design include:

  • Research Objectives: Clearly define the goals and objectives of the research study. What is the research trying to achieve or investigate?
  • Research Questions or Hypotheses: Formulating specific research questions or hypotheses that address the objectives of the study. These questions guide the research process.
  • Data Collection Methods: Determining how data will be collected, whether through surveys, experiments, observations, interviews, archival research, or a combination of these methods.
  • Sampling: Deciding on the target population and selecting a sample that represents that population. Sampling methods can vary, such as random sampling, stratified sampling, or convenience sampling.
  • Data Collection Instruments: Developing or selecting the tools and instruments needed to collect data, such as questionnaires, surveys, or experimental equipment.
  • Data Analysis: Defining the statistical or analytical techniques that will be used to analyze the collected data. This may involve qualitative or quantitative methods , depending on the research goals.
  • Time Frame: Establishing a timeline for the research project, including when data will be collected, analyzed, and reported.
  • Ethical Considerations: Addressing ethical issues, including obtaining informed consent from participants, ensuring the privacy and confidentiality of data, and adhering to ethical guidelines.
  • Resources: Identifying the resources needed for the research , including funding, personnel, equipment, and access to data sources.
  • Data Presentation and Reporting: Planning how the research findings will be presented and reported, whether through written reports, presentations, or other formats.

There are various research designs, such as experimental, observational, survey, case study, and longitudinal designs, each suited to different research questions and objectives. The choice of research design depends on the nature of the research and the goals of the study.

A well-constructed research design is crucial because it helps ensure the validity, reliability, and generalizability of research findings, allowing researchers to draw meaningful conclusions and contribute to the body of knowledge in their field.

Types of Research Design

There are several types of research designs, each tailored to answer specific research questions and achieve particular objectives. The choice of research design depends on the nature of the research problem and the goals of the study. Here are several typical types of research designs:

1. Experimental Research Design

Randomized Controlled Trial (RCT): In a randomized controlled trial (RCT), individuals are assigned randomly to either an experimental group or a control group. This design is often used to assess the impact of a treatment or intervention.

2. Quasi-Experimental Research Design

Non-equivalent Group Design: In this design, two or more groups are compared, but participants are not randomly assigned. This is common when random assignment is not feasible or ethical.

3. Observational Research Design

Cross-Sectional Study: In a cross-sectional study, data is collected from a single point in time to examine relationships or differences between variables. It does not involve follow-up over time.

Longitudinal Study: This design involves collecting data from the same group of participants over an extended period to study changes and trends over time.

4. Descriptive Research Design

Survey Research: Surveys involve collecting data from a sample of individuals through questionnaires or interviews to describe characteristics, attitudes, or opinions.

Case Study: Case studies involve an in-depth examination of a single individual, group, or phenomenon. They are often used to gain a deep understanding of a unique case.

5. Correlational Research Design

Correlational Study: This design examines the relationships between two or more variables to determine if they are associated. However, it does not establish causation.

6. Ex Post Facto Research Design

In this design, researchers examine existing conditions or behaviors and look for potential causes retrospectively. It’s useful when it’s not feasible to manipulate variables.

7. Exploratory Research Design

Pilot Study: A pilot study is a small-scale preliminary investigation conducted before a full-scale research project to test research procedures and gather initial data.

8. Cohort Study

Cohort studies follow a group of individuals (cohort) over a period of time to assess the development of specific outcomes or conditions. They are common in epidemiology.

9. Action Research

Action research is often used in educational or organizational settings. Researchers work collaboratively with practitioners to address practical problems and make improvements.

10. Meta-Analysis

A meta-analysis involves the statistical synthesis of data from multiple studies on the same topic to provide a more comprehensive overview of research findings.

11. Cross-Sequential Design

This design combines elements of both cross-sectional and longitudinal research to examine age-related changes while comparing different cohorts.

12. Grounded Theory

Grounded theory is a qualitative research approach that focuses on developing theories or explanations grounded in the data collected during the research process.

Each of these research designs has its strengths and weaknesses, and the choice of design depends on the research question, available resources, ethical considerations, and the nature of the data needed to address the research objectives. Researchers often select the design that best aligns with their specific research goals and constraints..

Learn more: What is Research?

Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields:

1. Experimental Method

Controlled Experiments: In controlled experiments, researchers manipulate one or more independent variables and measure their effects on dependent variables while controlling for confounding factors.

2. Observational Method

Naturalistic Observation: Researchers observe and record behavior in its natural setting without intervening. This method is often used in psychology and anthropology.

Structured Observation: Observations are made using a predetermined set of criteria or a structured observation schedule.

3. Survey Method

Questionnaires: Researchers collect data by administering structured questionnaires to participants. This method is widely used for collecting quantitative research data.

Interviews: In interviews, researchers ask questions directly to participants, allowing for more in-depth responses. Interviews can take on structured, semi-structured, or unstructured formats.

4. Case Study Method

Single-Case Study: Focuses on a single individual or entity, providing an in-depth analysis of that case.

Multiple-Case Study: Involves the examination of multiple cases to identify patterns, commonalities, or differences.

5. Content Analysis

Researchers analyze textual, visual, or audio data to identify patterns, themes, and trends. This method is commonly used in media studies and social sciences.

6. Historical Research

Researchers examine historical documents, records, and artifacts to understand past events, trends, and contexts.

7. Action Research

Researchers work collaboratively with practitioners to address practical problems or implement interventions in real-world settings.

8. Ethnographic Research

Researchers immerse themselves in a particular cultural or social group to gain a deep understanding of their behaviors, beliefs, and practices.

9. Cross-sectional and Longitudinal Surveys

Cross-sectional surveys collect data from a sample of participants at a single point in time.

Longitudinal surveys collect data from the same participants over an extended period, allowing for the study of changes over time.

Researchers conduct a quantitative synthesis of data from multiple studies to provide a comprehensive overview of research findings on a particular topic.

11. Mixed-Methods Research

Combines qualitative and quantitative research methods to provide a more holistic understanding of a research problem.

A qualitative research method that aims to develop theories or explanations grounded in the data collected during the research process.

13. Simulation and Modeling

Researchers use mathematical or computational models to simulate real-world phenomena and explore various scenarios.

14. Survey Experiments

Combines elements of surveys and experiments, allowing researchers to manipulate variables within a survey context.

15. Case-Control Studies and Cohort Studies

These epidemiological research methods are used to study the causes and risk factors associated with diseases and health outcomes.

16. Cross-Sequential Design

Combines elements of cross-sectional and longitudinal research to examine both age-related changes and cohort differences.

The selection of a specific research design method should align with the research objectives, the type of data needed, available resources, ethical considerations, and the overall research approach. Researchers often choose methods that best suit the nature of their study and research questions to ensure that they collect relevant and valid data.

Learn more: What is Research Objective?

Research Design Examples

Research designs can vary significantly depending on the research questions and objectives. Here are some examples of research designs across different disciplines:

  • Experimental Design: A pharmaceutical company conducts a randomized controlled trial (RCT) to test the efficacy of a new drug. Participants are randomly assigned to two groups: one receiving the new drug and the other a placebo. The company measures the health outcomes of both groups over a specific period.
  • Observational Design: An ecologist observes the behavior of a particular bird species in its natural habitat to understand its feeding patterns, mating rituals, and migration habits.
  • Survey Design: A market research firm conducts a survey to gather data on consumer preferences for a new product. They distribute a questionnaire to a representative sample of the target population and analyze the responses.
  • Case Study Design: A psychologist conducts a case study on an individual with a rare psychological disorder to gain insights into the causes, symptoms, and potential treatments of the condition.
  • Content Analysis: Researchers analyze a large dataset of social media posts to identify trends in public opinion and sentiment during a political election campaign.
  • Historical Research: A historian examines primary sources such as letters, diaries, and official documents to reconstruct the events and circumstances leading up to a significant historical event.
  • Action Research: A school teacher collaborates with colleagues to implement a new teaching method in their classrooms and assess its impact on student learning outcomes through continuous reflection and adjustment.
  • Ethnographic Research: An anthropologist lives with and observes an indigenous community for an extended period to understand their culture, social structures, and daily lives.
  • Cross-Sectional Survey: A public health agency conducts a cross-sectional survey to assess the prevalence of smoking among different age groups in a specific region during a particular year.
  • Longitudinal Study: A developmental psychologist follows a group of children from infancy through adolescence to study their cognitive, emotional, and social development over time.
  • Meta-Analysis: Researchers aggregate and analyze the results of multiple studies on the effectiveness of a specific type of therapy to provide a comprehensive overview of its outcomes.
  • Mixed-Methods Research: A sociologist combines surveys and in-depth interviews to study the impact of a community development program on residents’ quality of life.
  • Grounded Theory: A sociologist conducts interviews with homeless individuals to develop a theory explaining the factors that contribute to homelessness and the strategies they use to cope.
  • Simulation and Modeling: Climate scientists use computer models to simulate the effects of various greenhouse gas emission scenarios on global temperatures and sea levels.
  • Case-Control Study: Epidemiologists investigate a disease outbreak by comparing a group of individuals who contracted the disease (cases) with a group of individuals who did not (controls) to identify potential risk factors.

These examples demonstrate the diversity of research designs used in different fields to address a wide range of research questions and objectives. Researchers select the most appropriate design based on the specific context and goals of their study.

Learn more: What is Competitive Research?

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

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

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

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

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

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

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

Steps of research design

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

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

Need help with a thesis chapter?

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Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

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Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

Not sure how to approach a company for your primary research study? Don’t worry. Here we have some tips for you to successfully gather primary study.

Repository of ten perfect dissertation research question examples will provide you a better perspective about how to create dissertation research questions.

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Research Design: What it is, Elements & Types

Research Design

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 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:

Characteristics of Research Design

  • 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:

types of research design

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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

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Organizing Your Social Sciences Research Paper

  • Types of Research Designs
  • Purpose of Guide
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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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Major Components of Research Design

The primary intent behind the research design is to help avoid the situation in which the evidence doesn’t address the primary research questions. A research design is concerned with a logical problem and not a logistical problem. Five major components of research design  are:

1. Research study’s questions 2. Study propositions 3. Unit(s) of analysis 4. Linking data to propositions 5. Interpreting a study’s findings

Major Components of Research Design in Research Methodology

The research design components  apply to all types of qualitative, deductive research, whether in the physical or social sciences.

Research study’s questions : This first component suggests the type of the question-in terms of “who,” “what,” “where,” “how,” and “why”-provides an crucial clue concerning the most relevant research method to be used. Use three stages: In the first, make use of the literature to narrow your interest to a key topic or two. In the 2nd, take a look at closely-even dissect-a few key studies on your topic of interest. Identify the questions in those few studies and whether they conclude with new questions for future research. In the last phase, examine another group of scientific studies on the same topic. They might provide support for your potential questions or even suggest means of sharpening them.

Study propositions:  Each proposition directs focus on something which needs to be examined within the scope of study. Only if you are forced to state some propositions will you move in the right direction. For example, you may think that businesses collaborate as they gain mutual benefits. This proposition, apart from highlighting a crucial theoretical issue (that other incentives for collaboration don’t exist or are unimportant), also starts to tell you where you can search for related proof (to define and determine the extent of certain advantages to each business).

Figure: Key Components

Unit of analysis: It is associated with the fundamental problem of defining what the “case” is-a problem which has affected many researchers at the beginning of case studies. Take example of clinical patients. In this situation, an individual is being studied, and the individual is the key unit of analysis. Information regarding the appropriate individual will be collected, and several such individuals  could be part of a multiple-case study. You would need study questions and propositions to help find out the appropriate information to be collected relating to this individual or individuals. Without such questions and propositions, you could be lured to cover “everything” with regards to the individual(s), which is not possible.

Linking data to propositions:  Methods of linking data to propositions are pattern matching, explanation building, time-series analysis, logic models, and cross-case synthesis. The actual analyses will demand that you merge or compute your study data as a direct reflection of your initial study propositions.

Read Also: Elements of Research Design

Interpreting a study’s findings:  A statistical analysis determines if the results of the study support the hypothesis. A number of statistical tests, for example T-tests (that determine if two groups are statistically distinct from one another), Chi-square tests (where data are compared to an anticipated outcome) and one-way analysis of variance (provides for the comparison of multiple groups), are carried out according to the type of data, number and types of variables and data categories. Statistical analysis offer some explicit criteria for interpretations. For example, by convention, social science views a p level of less than .05 to indicate that observed differences were “statistically important.” On the other hand, much case study analysis is not going to depend on the use of statistics and so focuses on other methods of thinking about such criteria.

Read Also: Definition of Research Design

In this article, I have discussed about the major components of research design in research methodology . A research design should include the above listed five components. A research design must suggest what data should be gathered, its propositions, units of analysis, it must tell you what is to be done after the data have been gathered.

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10 Essential Components of Research Design And Solid Methodology

Sep 12, 2023 | Research FAQs

How to Craft a Research Study with a Robust Methodology: A Guide

Embarking on a research journey is a fascinating endeavour that requires a solid roadmap to ensure meaningful and accurate results. Central to this roadmap is the intricate interplay between research design and research methodology. These terms might seem interchangeable, but they encompass distinct aspects of the research process.

Research design encompasses the blueprint that guides the overall structure and organisation of your study, while research methodology involves the techniques, procedures, and tools you use to collect and analyse data. In this guide, we will delve into the essential components of creating a research study with a robust methodology that stands up to scrutiny and advances knowledge in your chosen field.

The 10 Essential Components of Creating a Research Study

#1  defining research design and research methodology.

  • Research Design : At its core, research design refers to the plan that outlines how you will conduct your study, from the formulation of research questions or hypotheses to data collection methods and analysis techniques. It’s the architecture that shapes the overall trajectory of your research.
  • Research Methodology : Research methodology, on the other hand, comprises the techniques and tools you employ to gather and interpret data. It involves decisions about sampling, data collection instruments, data analysis methods, and even ethical considerations. Methodology breathes life into the design, enabling you to address your research questions effectively.

Research Design serves as the strategic blueprint that outlines the systematic approach you intend to take in your study. It’s not merely a series of steps, but a comprehensive plan that encompasses every facet of your research, from the fundamental research questions or hypotheses you’ll explore, down to the precise data collection methods and analytical techniques that will be employed. Think of research design as the architect’s rendering of the entire research process, guiding your journey with a clear roadmap.

In contrast, Research Methodology is the toolbox that equips you with the instruments to carry out the plan delineated by your research design. These methodologies involve a vast array of decisions – from selecting the most appropriate sampling techniques to choosing data collection instruments that perfectly align with the nature of your research questions. Furthermore, it extends to the methods you will employ to analyse the collected data and, importantly, how ethical considerations will be woven into every step. Essentially, research methodology is the engine that drives your research design, infusing it with life and enabling you to extract meaningful insights from the data.

#2 The Crucial Relationship Between Research Questions and Design

The research design hinges on well-defined research questions. Clarity in your research questions helps you choose the appropriate design to address them, whether exploratory, descriptive, causal-comparative, or experimental.

Imagine research design as a tailored suit and research questions as its measurements. A well-fitted suit starts with accurate measurements, just as a well-crafted research design begins with well-structured research questions. The clarity and precision of your research questions directly influence the design you select. A research question that aims to understand patterns might lead you to a descriptive design, while a question exploring causal relationships could guide you toward an experimental design. Thus, the foundational connection between research questions and design choices cannot be overstated.

research design

#3 Types of Research Designs

  • Descriptive Designs : Ideal for capturing the “what” of a phenomenon, descriptive designs involve observing and describing behaviours, attributes, or characteristics. They are often preliminary and offer insights for more in-depth studies.
  • Experimental Designs : These designs allow researchers to establish cause-and-effect relationships by manipulating variables. They involve control groups, experimental groups, and random assignment.
  • Correlational Designs : These designs examine the relationships between variables, helping researchers understand how changes in one variable might relate to changes in another.
  • Longitudinal Designs : Suitable for studying changes over time, longitudinal designs involve observing the same participants at multiple points, revealing developmental trends.

Descriptive Designs : Descriptive research acts as a reconnaissance mission, capturing the essence of a phenomenon. These designs excel at answering “what” questions, laying the groundwork for further in-depth studies. For example, a descriptive study might explore the characteristics of a newly discovered species, providing the basic data required for more extensive research.

Experimental Designs : Experimental designs are akin to scientific laboratories, allowing researchers to manipulate variables to establish cause-and-effect relationships. By employing control and experimental groups, researchers can isolate the influence of specific factors, providing robust evidence for causal claims. These designs are particularly useful in fields like medicine, where testing the efficacy of new treatments is paramount.

Correlational Designs : These designs delve into relationships between variables, revealing how changes in one variable correspond to changes in another. For instance, a correlational study might uncover a strong connection between sleep patterns and academic performance, shedding light on potential influences.

Longitudinal Designs : Longitudinal designs are like time-lapse photography, capturing changes in individuals or groups over an extended period. By observing the same participants at various points, researchers gain insights into developmental trends, making them essential for studying human growth, societal changes, or educational trajectories.

# 4 Sampling Techniques and Considerations

Using the right sample is pivotal for research validity. Techniques like random sampling, stratified sampling, and convenience sampling impact the generalisability of findings. Considerations like sample size and representativeness influence the strength of your conclusions.

Selecting the right sample is like choosing the ingredients for a recipe – it determines the flavour of your study. Random sampling is like drawing ingredients blindly, ensuring a representative mix. Stratified sampling is akin to selecting specific categories of ingredients to ensure a balanced dish, while convenience sampling is comparable to using the ingredients at hand.

The size of your sample impacts the quality of your dish, with larger samples offering more robust conclusions. Just as a well-cooked dish pleases the palate, a well-selected sample pleases the academic appetite.

research design sampling

#5  Data Collection Instruments

Questionnaires, interviews, surveys, observations, and existing records are common data collection tools. The choice depends on your research questions, design, and available resources. Validity and reliability are essential in ensuring that your instruments measure what they intend to and yield consistent results.

Picture data collection instruments as a diverse ensemble of tools in a researcher’s toolkit. The choice of instrument depends on the research design, questions, and resources at hand. Questionnaires act as surveys, gathering structured information efficiently. Interviews are like in-depth conversations, unearthing nuanced insights. Observations become windows into behaviour, revealing hidden patterns. Existing records act as historical archives, offering glimpses into the past. Regardless of the instrument, their validity and reliability are akin to a chef’s precision – crucial for a satisfying outcome.

# 6 Data Analysis Methods

Quantitative studies often involve statistical analysis techniques like regression, ANOVA, and chi-square tests. Qualitative studies use methods like thematic analysis, content analysis, and grounded theory to extract meaningful patterns from textual or visual data.

In the research kitchen, data analysis methods are the secret recipes that transform raw ingredients into delectable dishes. Quantitative studies employ statistical techniques like regression, ANOVA, and chi-square tests, transforming numbers into meaningful insights.

Qualitative studies, however, are like the art of pairing flavours, using thematic analysis, content analysis, and grounded theory to extract the essence from textual or visual data. Just as a culinary masterpiece marries ingredients in perfect harmony, data analysis methods bring coherence to the research findings.

research design studies

#7 Ethical Considerations

Ethical research demands safeguarding participants’ rights, obtaining informed consent, and ensuring confidentiality. Adhering to ethical guidelines builds trust and credibility in your study.

Ethics in research is like the dining etiquette at a grand banquet – it ensures that participants are treated with respect and integrity. Safeguarding their rights is the equivalent of offering them a seat at the table. Obtaining informed consent is akin to explaining the menu before serving a dish, ensuring participants are aware of what’s involved. Confidentiality acts as the vault that protects their privacy, building trust and integrity in the research process. Adhering to ethical guidelines is as crucial as adhering to table manners – it cultivates an environment of professionalism and mutual respect.

# 8 Validity and Reliability

Validity assesses whether your study measures what it intends to, while reliability gauges the consistency of your measurements. Triangulation, member checks, and inter-rater reliability enhance the robustness of your study.

In research, validity and reliability are like the sturdy foundation of a well-built structure. Validity ensures that your study measures what it intends to, just as a sturdy pillar supports the weight it’s designed for. Reliability is the consistency that underpins your measurements – like the uniformity of bricks in a wall. Just as an architect cross-checks plans, triangulation and member checks enhance the robustness of your research. Inter-rater reliability acts as the seal of approval, ensuring that multiple perspectives align harmoniously.

#9 Pilot Studies

Conducting a pilot study helps identify potential issues with your research design, methodology, or instruments before the main study. It’s a crucial step in refining your approach.

A pilot study is akin to a dress rehearsal before a grand performance. Imagine your research as a stage production – a pilot study is the practice run that unveils potential glitches, ensuring that the main event goes off without a hitch. Like trying on costumes and testing lighting cues, a pilot study uncovers issues with design, methodology, and instruments before the curtains rise. This prelude to the main research endeavour can be likened to fine-tuning an orchestra before the concert – a crucial step in refining your approach and ensuring the spotlight shines on your findings.

research design testing

Key Takeaways

  • Research design is the blueprint that guides your study, while research methodolog y involves the techniques for data collection and analysis.
  • Research design is closely linked to well-defined research questions, shaping the direction of the study.
  • Types of research designs include descriptive, experimental, correlational, and longitudinal.
  • Sampling techniques impact the representativeness of your findings, and data collection instruments should be valid and reliable.
  • Quantitative studies use statistical analysis, while qualitative studies employ techniques like thematic analysis.
  • Ethical considerations are paramount, and maintaining validity and reliability enhances study credibility.
  • Pilot studies help identify and rectify issues before the main study.
  • Research is iterative and involves refining your approach based on insights.

#1 0 Iterative Nature of Research

Research is seldom linear; it often involves refining, revisiting, and adapting your approach based on emerging insights and challenges.

Picture research as a sculptor moulding clay – it’s an iterative process that requires shaping and reshaping until the desired form emerges. Just as an artist revisits their canvas, research often demands revisiting and refining your approach. As you uncover insights, challenges, and unexpected discoveries, adapting your strategy becomes essential. Embracing this iterative nature is akin to sailing with the wind – it allows you to navigate uncharted waters and reach new horizons of understanding.

Keep in mind that research design and methodology work in harmony, guiding your journey while equipping you with the tools to explore uncharted territories. Clarity in your research questions sets the tone, with design choices echoing the path you chart. From choosing data collection tools to adhering to ethical considerations, every step contributes to the symphony of a meticulously crafted research study.

Contributing To Advancements in Your Field

A robust research study hinges on the symbiotic relationship between research design and research methodology. The design serves as the guiding framework, while the methodology equips you with the tools to explore your research questions rigorously. The journey from formulating clear research questions to selecting appropriate designs, sampling methods, data collection instruments, and analysis techniques demands meticulous planning, ethical considerations, and a commitment to validity and reliability.

Embrace the iterative nature of research, welcoming the opportunity to refine and adapt your approach as you uncover new dimensions of understanding within your chosen field. Remember, a well-crafted research study not only contributes to academia but also shapes the larger discourse in your area of interest.

Useful Resources

  • Research Methods Knowledge Base – An extensive repository of information on research methods and design.
  • Way With Words – Professional academic research transcription services that enhance the accuracy and clarity of your research interviews and recordings.

Engagement Questions

  • How do you envision the relationship between research design and research methodology in optimising the quality of your study?
  • Can you identify a real-world scenario where choosing the right research design significantly impacted the study’s outcomes?
  • In what ways can a well-thought-out pilot study contribute to the success of your main research endeavour?
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The Importance of Research Design: A Comprehensive Guide

Morten Pedersen

Research design plays a crucial role in conducting scientific studies and gaining meaningful insights. A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. This comprehensive guide will provide an in-depth understanding of research design, its key components, different types, and its role in scientific inquiry. Furthermore, it will discuss the necessary steps in developing a research design and highlight some of the challenges that researchers commonly face.

Table of Contents

Understanding research design.

Research design refers to the overall plan or strategy that outlines how a study is conducted. It serves as a blueprint for researchers, guiding them in their investigation, and helps ensure that the study objectives are met. Understanding research design is essential for researchers to effectively gather and analyze data to answer research questions.

When embarking on a research study, researchers must carefully consider the design they will use. The design determines the structure of the study, including the research questions, data collection methods, and analysis techniques. It provides clarity on how the study will be conducted and helps researchers determine the best approach to achieve their research objectives. A well-designed study increases the chances of obtaining valid and reliable results.

Definition and Purpose of Research Design

Research design is the framework that outlines the structure of a study, including the research questions, data collection methods, and analysis techniques. It provides a systematic approach to conducting research and ensures that all aspects of the study are carefully planned and executed.

The purpose of research design is to provide a clear roadmap for researchers to follow. It helps them define the research questions they want to answer and identify the variables they will study. By clearly defining the purpose of the study, researchers can ensure that their research design aligns with their objectives.

Key Components of Research Design

A research design consists of several key components that influence the study’s validity and reliability. These components include the research questions, variables and operational definitions, sampling techniques, data collection methods, and statistical analysis procedures.

The research questions are the foundation of any study. They guide the entire research process and help researchers focus their efforts. By formulating clear and concise research questions, researchers can ensure that their study addresses the specific issues they want to investigate.

components of a research design

Variables and operational definitions are also crucial components of research design. Variables are the concepts or phenomena that researchers want to measure or study. Operational definitions provide a clear and specific description of how these variables will be measured or observed. By clearly defining variables and their operational definitions, researchers can ensure that their study is consistent and replicable.

Sampling techniques play a vital role in research design as well. Researchers must carefully select the participants or samples they will study to ensure that their findings are generalizable to the larger population. Different sampling techniques, such as random sampling or purposive sampling, can be used depending on the research objectives and constraints.

Data collection methods are another important component of research design. Researchers must decide how they will collect data, whether through surveys, interviews, observations, or experiments. The choice of data collection method depends on the research questions and the type of data needed to answer them.

Finally, statistical analysis procedures are used to analyze the collected data and draw meaningful conclusions. Researchers must determine the appropriate statistical tests or techniques to use based on the nature of their data and research questions. The choice of statistical analysis procedures ensures that the data is analyzed accurately and that the results are valid and reliable.

Types of Research Design

Research design encompasses various types that researchers can choose depending on their research goals and the nature of the phenomenon being studied. Understanding the different types of research design is essential for researchers to select the most appropriate approach for their study.

When embarking on a research project, researchers must carefully consider the design they will employ. The design chosen will shape the entire study, from the data collection process to the analysis and interpretation of results. Let’s explore some of the most common types of research design in more detail.

Experimental Design

Experimental design involves manipulating one or more variables to observe their effect on the dependent variable. This type of design allows researchers to establish cause-and-effect relationships between variables by controlling for extraneous factors. Experimental design often relies on random assignment and control groups to minimize biases.

Imagine a group of researchers interested in studying the effects of a new teaching method on student performance. They could randomly assign students to two groups: one group would receive instruction using the new teaching method, while the other group would receive instruction using the traditional method. By comparing the performance of the two groups, the researchers can determine whether the new teaching method has a significant impact on student learning.

Experimental design provides a strong foundation for making causal claims, as it allows researchers to control for confounding variables and isolate the effects of the independent variable. However, it may not always be feasible or ethical to manipulate variables, leading researchers to explore alternative designs.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

  • Introduction to experimental methods
  • Respondent management with groups and populations
  • How to set up stimulus selection and arrangement

components of a research design

Non-Experimental Design

Non-experimental design is used when it is not feasible or ethical to manipulate variables. This design relies on naturally occurring variations in data and focuses on observing and describing relationships between variables. Non-experimental design can be useful for exploratory research or when studying phenomena that cannot be controlled, such as human behavior.

For instance, researchers interested in studying the relationship between socioeconomic status and health outcomes may collect data from a large sample of individuals and analyze the existing differences. By examining the data, they can determine whether there is a correlation between socioeconomic status and health, without manipulating any variables.

Non-experimental design allows researchers to study real-world phenomena in their natural setting, providing valuable insights into complex social, psychological, and economic processes. However, it is important to note that non-experimental designs cannot establish causality, as there may be other variables at play that influence the observed relationships.

Quasi-Experimental Design

Quasi-experimental design resembles experimental design but lacks the element of random assignment. In situations where random assignment is not possible or practical, researchers can utilize quasi-experimental designs to gather data and make inferences. However, caution must be exercised when drawing causal conclusions from quasi-experimental studies.

Consider a scenario where researchers are interested in studying the effects of a new drug on patient recovery time. They cannot randomly assign patients to receive the drug or a placebo due to ethical considerations. Instead, they can compare the recovery times of patients who voluntarily choose to take the drug with those who do not. While this design allows for data collection and analysis, it is important to acknowledge that other factors, such as patient motivation or severity of illness, may influence the observed outcomes.

Quasi-experimental designs are valuable when experimental designs are not feasible or ethical. They provide an opportunity to explore relationships and gather data in real-world contexts. However, researchers must be cautious when interpreting the results, as causal claims may be limited due to the lack of random assignment.

By understanding the different types of research design, researchers can make informed decisions about the most appropriate approach for their study. Each design offers unique advantages and limitations, and the choice depends on the research question, available resources, and ethical considerations. Regardless of the design chosen, rigorous methodology and careful data analysis are crucial for producing reliable and valid research findings.

The Role of Research Design in Scientific Inquiry

A well-designed research study enhances the validity and reliability of the findings. Research design plays a crucial role in ensuring the scientific rigor of a study and facilitates the replication of studies by other researchers. Understanding the role of research design in scientific inquiry is vital for researchers to conduct impactful and robust research.

Ensuring Validity and Reliability

Research design plays a critical role in ensuring the validity and reliability of the study’s findings. Validity refers to the degree to which the study measures what it intends to measure, while reliability pertains to the consistency and stability of the results. Through careful consideration of the research design, researchers can minimize potential biases and increase the accuracy of their measurements.

Facilitating Replication of Studies

A robust research design allows for the replication of studies by other researchers. Replication plays a vital role in the scientific process as it helps confirm the validity and generalizability of research findings. By clearly documenting the research design, researchers enable others to reproduce the study and validate the results, thereby contributing to the cumulative knowledge in a field.

Steps in Developing a Research Design

Developing a research design involves a systematic process that includes several important steps. Researchers need to carefully consider each step to ensure that their study is well-designed and capable of addressing their research questions effectively.

Identifying Research Questions

The first step in developing a research design is to identify and define the research questions or hypotheses. Researchers need to clearly articulate what they aim to investigate and what specific information they want to gather. Clear research questions provide guidance for the subsequent steps in the research design process.

Selecting Appropriate Design Type

Once the research questions are identified, researchers need to select the most appropriate type of research design. The choice of design depends on various factors, including the research goals, the nature of the research questions, and the available resources. Careful consideration of these factors is crucial to ensure that the chosen design aligns with the study objectives.

Determining Data Collection Methods

After selecting the research design, researchers need to determine the most suitable data collection methods. Depending on the research questions and the type of data required, researchers can utilize a range of methods, such as surveys, interviews, observations, or experiments. The chosen methods should align with the research objectives and allow for the collection of high-quality data.

One of the most important considerations when designing a study in human behavior research is participant recruitment. We have written a comprehensive guide on best practices and pitfalls to be aware of when recruiting participants, which can be read here.

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

In the realm of scientific studies, especially within human cognitive-behavioral research, the deployment of advanced technologies such as iMotions software and biosensors has revolutionized research design. This chapter delves into how these technologies can be integrated into various research designs, improving the depth, accuracy, and reliability of scientific inquiries.

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

The iMotions platform stands as a pivotal tool in modern research design. It’s designed to integrate data from a plethora of biosensors, providing a comprehensive analysis of human behavior. This software facilitates the synchronizing of physiological, cognitive, and emotional responses with external stimuli, thus enriching the understanding of human behavior in various contexts.

Biosensors: Gateways to Deeper Insights

Biosensors, including eye trackers, EEG, GSR, ECG, and facial expression analysis tools, provide nuanced insights into the subconscious and conscious aspects of human behavior. These tools help researchers in capturing data that is often unattainable through traditional data collection methods like surveys and interviews.

Application in Different Research Designs

  • Eye Tracking : In experimental designs, where the impact of visual stimuli is crucial, eye trackers can reveal how subjects interact with these stimuli, thereby offering insights into cognitive processes and attention.
  • EEG : EEG biosensors allow researchers to monitor brain activity in response to controlled experimental manipulations, offering a window into cognitive and emotional responses.

components of a research design

  • Facial Expression Analysis : In observational studies, analyzing facial expressions can provide objective data on emotional responses in natural settings, complementing subjective self-reports.
  • GSR/EDA : These tools measure physiological arousal in real-life scenarios, giving researchers insights into emotional states without the need for intrusive measures.
  • EMG : In studies where direct manipulation isn’t feasible, EMG can indicate subtle responses to stimuli, which might be overlooked in traditional observational methods.
  • ECG/PPG : These sensors can be used to understand the impact of various interventions on physiological states such as stress or relaxation.

Streamlining Research Design with iMotions

The iMotions platform offers a streamlined process for integrating various biosensors into a research design. Researchers can easily design experiments, collect multimodal data, and analyze results in a unified interface. This reduces the complexity often associated with handling multiple streams of data and ensures a cohesive and comprehensive research approach.

Integrating iMotions software and biosensors into research design opens new horizons for scientific inquiry. This technology enhances the depth and breadth of data collection, paving the way for more nuanced and comprehensive findings.

Whether in experimental, non-experimental, or quasi-experimental designs, iMotions and biosensors offer invaluable tools for researchers aiming to uncover the intricate layers of human behavior and cognitive processes. The future of research design is undeniably intertwined with the advancements in these technologies, leading to more robust, reliable, and insightful scientific discoveries.

Challenges in Research Design

Research design can present several challenges that researchers need to overcome to conduct reliable and valid studies. Being aware of these challenges is essential for researchers to address them effectively and ensure the integrity of their research.

Ethical Considerations

Research design must adhere to ethical guidelines and principles to protect the rights and well-being of participants. Researchers need to obtain informed consent, ensure participant confidentiality, and minimize potential harm or discomfort. Ethical considerations should be carefully integrated into the research design to promote ethical research practices.

Practical Limitations

Researchers often face practical limitations that may impact the design and execution of their studies. Limited resources, time constraints, access to participants or data, and logistical challenges can pose obstacles during the research process. Researchers need to navigate these limitations and make thoughtful choices to ensure the feasibility and quality of their research.

Research design is a vital aspect of conducting scientific studies. It provides a structured framework for researchers to answer their research questions and obtain reliable and valid results. By understanding the different types of research design and following the necessary steps in developing a research design, researchers can enhance the rigor and impact of their studies.

However, researchers must also be mindful of the challenges they may encounter, such as ethical considerations and practical limitations, and take appropriate measures to address them. Ultimately, a well-designed research study contributes to the advancement of knowledge and promotes evidence-based decision-making in various fields.

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3.4: Components of a Research Project

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Learning Objectives

  • Describe useful strategies to employ when searching for literature.
  • Describe why sociologists review prior literature and how they organize their literature reviews.
  • Identify the main sections contained in scholarly journal articles.
  • Identify and describe the major components researchers need to plan for when designing a research project.
  • Describe the importance of a research proposal.

In this section, we’ll examine the most typical components that make up a research proposal and research project, bringing in a few additional components to those we have already discussed. Keep in mind that our purpose at this stage is simply to provide a general overview of research design. The specifics of each of the following components will vary from project to project. Further, the stage of a project at which each of these components comes into play may vary.

Searching for Literature

Familiarizing yourself with research that has already been conducted on your topic is one of the first stages of conducting a research project and is crucial for coming up with a good research design. But where to start? How to start? As you search for literature, you may have to be fairly broad in your search for articles.

I’m guessing you may feel you’ve heard enough about electronic gadget addiction in this chapter, so let’s consider a different example here. On my campus, much to the chagrin of a group of student smokers, smoking was recently banned. These students were so upset by the idea that they would no longer be allowed to smoke on university grounds that they staged several smoke-outs during which they gathered in populated areas around campus and enjoyed a puff or two together.

A student in my research methods class wanted to understand what motivated this group of students to engage in activism centered around what she perceived to be, in this age of smoke-free facilities, a relatively deviant act. Were the protesters otherwise politically active? How much effort and coordination had it taken to organize the smoke-outs? The student researcher began her research by attempting to familiarize herself with the literature on her topic. Yet her search in Sociological Abstracts for “college student activist smoke-outs,” yielded no results. Concluding there was no prior research on her topic, she informed me that she would need an alternative assignment to the annotated bibliography I required since there was no literature for her to review. How do you suppose I responded to this news? What went wrong with this student’s search for literature?

In her first attempt, the student had been too narrow in her search for articles. But did that mean she was off the hook for completing the annotated bibliography assignment? Absolutely not. Instead, she went back to Sociological Abstracts and searched again using different combinations of search terms. Rather than searching for “college student activist smoke-outs” she tried, among other sets of terms, “college student activism.” This time her search yielded a great many articles. Of course, they were not focused on prosmoking activist efforts, but they were focused on her population of interest, college students, and on her broad topic of interest, activism. I suggested that reading articles on college student activism might give her some idea about what other researchers have found in terms of what motivates college students to become involved in activist efforts. I also suggested she could play around with her search terms and look for research on activism centered on other sorts of activities that are perceived by some as deviant, such as marijuana use or veganism. In other words, she needed to be broader in her search for articles.

While this student found success by broadening her search for articles, her reading of those articles needed to be narrower than her search. Once she identified a set of articles to review by searching broadly, it was time to remind herself of her specific research focus: college student activist smoke-outs. Keeping in mind her particular research interest while reviewing the literature gave her the chance to think about how the theories and findings covered in prior studies might or might not apply to her particular point of focus. For example, theories on what motivates activists to get involved might tell her something about the likely reasons the students she planned to study got involved. At the same time, those theories might not cover all the particulars of student participation in smoke-outs. Thinking about the different theories then gave the student the opportunity to focus her research plans and even to develop a few hypotheses about what she thought she was likely to find.

Reviewing the Literature

Developing an annotated bibliography is often one of the early steps that researchers take as they begin to familiarize themselves with prior research on their topic. A second step involves a literature review in which a researcher positions his or her work within the context of prior scholarly work in the area. A literature review addresses the following matters: What sorts of questions have other scholars asked about this topic? What do we already know about this topic? What questions remain? As the researcher answers these questions, he or she synthesizes what is contained in the literature, possibly organizing prior findings around themes that are relevant to his or her particular research focus.

I once advised an undergraduate student who conducted a research project on speciesism, the belief that some species are superior to or have more value and rights than others. Her research question was “Why and how do humans construct divisions between themselves and animals?” This student organized her review of literature around the two parts of her research question: the why and the how. In the “why” section of her literature review, she described prior research that addressed questions of why humans are sometimes speciesist. She organized subsections around the three most common answers that were presented in the scholarly literature. She used the same structure in the “how” section of her literature review, arranging subsections around the answers posed in previous literature about how humans construct divisions between themselves and animals. This organizational scheme helped readers understand what we already know about the topic and what theories we rely on to help make sense of the topic. In addition, by also highlighting what we still don’t know, it helped the student set the stage for her own empirical research on the topic

The preceding discussion about how to organize a review of scholarly literature assumes that we all know how to read scholarly literature. Yes, yes, I understand that you must know how to read. But reading scholarly articles can be a bit more challenging than reading a textbook. Here are a few pointers about how to do it successfully. First, it is important to understand the various sections that are typically contained in scholarly journals’ reports of empirical research. One of the most important and easiest to spot sections of a journal article is its abstract , the short paragraph at the beginning of an article that summarizes the author’s research question, methods used to answer the question, and key findings. The abstract may also give you some idea about the theoretical proclivities of the author. As a result, reading the abstract gives you both a framework for understanding the rest of the article and the punch line. It tells you what the author(s) found and whether the article is relevant to your area of inquiry.

After the abstract, most journal articles will contain the following sections (although exact section names are likely to vary): introduction, literature review, methodology, findings, and discussion. Of course, there will also be a list of references cited, lists of references cited are a useful source for finding additional literature in an area. and there may be a few tables, figures, or appendices at the end of the article as well. While you should get into the habit of familiarizing yourself with articles you wish to cite in their entirety , there are strategic ways to read journal articles that can make them a little easier to digest. Once you have read the abstract and determined that this is an article you’d like to read in full, read through the discussion section at the end of the article next. Because your own review of literature is likely to emphasize findings from previous literature, you should make sure that you have a clear idea about what those findings are. Reading an article’s discussion section helps you understand what the author views as the study’s major findings and how the author perceives those findings to relate to other research.

As you read through the rest of the article, think about the elements of research design that we have covered. What approach does the researcher take? Is the research exploratory, descriptive, or explanatory? Is it inductive or deductive? Idiographic or nomothetic? What claims does the author make about causality? What are the author’s units of analysis and observation? Use what you have learned about the promise and potential pitfalls associated with each of these research elements to help you responsibly read and understand the articles you review. Future chapters of this text will address other elements of journal articles, including choices about measurement, sampling, and research method. As you learn about these additional items, you will increasingly gain more knowledge that you can apply as you read and critique the scholarly literature in your area of inquiry.

Additional Important Components

Thinking about the overarching goals of your research project and finding and reviewing the existing literature on your topic are two of the initial steps you’ll take when designing a research project. Forming a clear research question, is another crucial step. There are a number of other important research design components you’ll need to consider, and we will discuss those here.

At the same time that you work to identify a clear research question, you will probably also think about the overarching goals of your research project. Will it be exploratory, descriptive, or explanatory? Will your approach be idiographic or nomothetic, inductive or deductive? How you design your project might also be determined in part by whether you aim for your research to have some direct application or if your goal is to contribute more generally to sociological knowledge about your topic. Next, think about what your units of analysis and units of observation will be. These will help you identify the key concepts you will study. Once you have identified those concepts, you’ll need to decide how to define them, and how you’ll know that you’re observing them when it comes time to collect your data. Defining your concepts, and knowing them when you see them, has to do with conceptualization and operationalization, the focus of a later chapter. Of course, you also need to know what approach you will take to collect your data. Thus identifying your research method is another important part of research design. You also need to think about who your research participants will be and what larger group(s) they may represent. These topics will be the focus of a later chapter too. Last, but certainly not least, you should consider any potential ethical concerns that could arise during the course of your research project. These concerns might come up during your data collection, but they might also arise when you get to the point of analyzing or sharing your research results.

Decisions about the various research components do not necessarily occur in sequential order. In fact, you may have to think about potential ethical concerns even before zeroing in on a specific research question. Similarly, the goal of being able to make generalizations about your population of interest could shape the decisions you make about your method of data collection. Putting it all together, the following list shows some of the major components you’ll need to consider as you design your research project:

  • Research question
  • Literature review
  • Research strategy (idiographic or nomothetic, inductive or deductive)
  • Research goals (basic or applied)
  • Units of analysis and units of observation
  • Key concepts (conceptualization and operationalization)
  • Method of data collection
  • Research participants (sample and population)
  • Ethical concerns

Research Proposal

At the stage before actually starting the research it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior literature, theories you wish to employ along with hypotheses to be tested (if you are doing deductive research, how measurement will be done, what research method to be employed and why, and desired sampling strategy (or who the subjects are.  Also, do not forget to include a budget.  Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

KEY TAKEAWAYS

  • When identifying and reading relevant literature, be broad in your search for articles, but be narrower in your reading of articles.
  • Writing an annotated bibliography can be a helpful first step to familiarize yourself with prior research in your area of interest.
  • Literature reviews summarize and synthesize prior research.
  • Literature reviews are typically organized around substantive ideas that are relevant to one’s research question rather than around individual studies or article authors.
  • When designing a research project, be sure to think about, plan for, and identify a research question, a review of literature, a research strategy, research goals, units of analysis and units of observation, key concepts, method(s) of data collection, population and sample, and potential ethical concerns.
  • A research proposal is also important to consider.
  • Find and read a complete journal article that addresses a topic that is of interest to you. In four to eight sentences, summarize the author’s research question, theoretical framing, methods used, and major findings. Reread the article, and see how close you were in reporting these key elements. What did you understand and remember best? What did you leave out? What reading strategies may have helped you better recall relevant details from the article?
  • Using the example of students’ electronic gadget addictions, design a hypothetical research project by identifying a plan for each of the nine components of research design that are presented in this section.

Geektonight

What is Research Design? Features, Components

  • Post last modified: 13 August 2023
  • Reading time: 15 mins read
  • Post category: Research Methodology

What is Research Design?

Research design refers to the overall strategy or plan that a researcher outlines to conduct a study and gather relevant data to address a research question or test a hypothesis. It serves as a blueprint for the entire research process, providing a structure and guidance for the collection, analysis, and interpretation of data.

In the field of research, the major purpose of research is to find a solution for a given research problem. The researcher can find a solution to a research problem by ensuring that he/she uses an appropriate research design.

Table of Content

  • 1 What is Research Design?
  • 2 Concept of Research Design
  • 3 Need and Features of Research Design
  • 4.1 Neutrality
  • 4.2 Reliability
  • 4.3 Performance
  • 4.4 General practice
  • 4.5 Qualitative
  • 4.6 Quantitative
  • 5.1 Research questions
  • 5.2 Course suggestions
  • 5.3 Unit analysis
  • 5.4 Data linking and propositions
  • 5.5 Interpretation of findings from the study

The chances of success of a research project depend on how the researcher has taken care to develop a research design that is in line with the research problem. A research design is created or developed when the researcher prepares a plan, structure and strategy for conducting research.

Research design is the base over which a researcher builds his research. A good research design provides vital information to a researcher with respect to a research topic, data type, data sources and techniques of data collection used in the research. In this chapter, you will study about the concept of research design, its need, features, components, etc.

Next, the chapter will describe the types of research design, research design framework, and types of errors affecting research design. Towards the end, you will study about the meaning of experiments and types of experiments.

Concept of Research Design

The research design refers to the framework of research methods and techniques selected by a researcher. The design chosen by the researchers allows them to use appropriate methods to study and plan their studies effectively and in the future. The descriptive research method focuses primarily on defining the nature of a class of people, without focusing on the “why” of something happening.

In other words, it “explains” the topic of research, without covering why “it” happens. Let us study in detail about the concept of research design, its requirements, features or characteristics, designing research framework its related case studies and observations.

Cross-sectional and longitudinal studies, casual research and errors arising while designing the research which are related to improper selection of respondents. This is a framework for determining the research methods and techniques to be used. This design enables researchers to set the research methods that are most relevant to the subject.

The design of the research topic describes the type of research (testing, research, integration, experimentation, review) and its sub-type (test design, research problem, descriptive case study). Research design can also be considered as the blueprint for collection, measurement and analysis of data.

The type of research problem the organisation is facing will determine the structure of the research and not the other way around. The study design phase determines which tools to use and how to use them. Impact studies often create less bias in the data and increase confidence in the accuracy of the data collected. A design that produces a small error limit in test studies is usually considered to be the desired result.

In research, the important things are:

  • A specific statement of intent
  • Strategies used to collect and analyse data
  • Type of research methodology
  • Potential objections to research
  • Research study settings
  • Analysis rating

Need and Features of Research Design

Much of what we do in our daily lives is based on understanding, what we have learned from others, or what we have learned through personal experience or observation. Sometimes, there are conflicting ideas about what is good or what works in a particular situation.

In addition, what works in one situation or situation may be ineffective or even harmful in another, or it may be combined with other measures. Psychological techniques ignore the impact of external factors that can influence what is seen. Even in health care settings, there are gaps in knowledge, ideas about how something can work better and ideas for improvement.

Since health professionals cannot afford to be risky, research is needed. For clinical trials, this is also a legal requirement that pharmaceutical companies cannot obtain marketing authorisation (i.e., permission to sell their new drugs) until they are approved by the relevant authorities.

Another advantage of doing research is that in most studies, the findings can be statistically recorded and statistically evaluated to determine if the findings are significant (meaning how much they can be called with a certain degree of certainty that they are not just a risk factor).

With limited studies, results can usually be performed in a broader population (for example, in people with dementia, caregivers, GPs, or generalised individuals, depending on the study group). This is because steps would be taken to ensure that the group of participants in the study, represented other people in that category, as far as possible.

The advantage of many quality studies is that they allow for a thorough investigation of a particular aspect of the human experience. They give people the opportunity to express in their own words how they feel, what they think, and how they make sense of the world around them.

In some cases, the results may be passed on to others as conditions. However, the advantage of quality studies is that it provides rich, logical and insightful information on the complexity of human experience with all the contradictions, differences and idiosyncrasies. Others discuss topics that have not been researched before and maybe facing issues that are controversial, critical, or illegal.Some courses also work to give voice to vulnerable or small groups

Features of Research Design

Proper research design makes your study a success. Effective research provides accurate and impartial information. You will need to create a survey that meets all the key design features. Key features of a good research design are:

When planning your study, you may need to think about the details you are going to collect. The results shown in the study should be fair and impartial. Understand the ideas about the last scores tested and the conclusions from most people and consider those who agree with the results obtained.

Reliability

With regular research, the researcher involved expects the same results regularly. Research design should be developed in a way that good research questions are developed and quality results are ensured. You will only be able to access the expected results if your design is reliable.

Performance

There are many measuring tools available. However, the only valid measurement tools are those that assist the researcher in measuring results according to the research purpose. The list of questions created from this project will be valid.

General practice

The effect of your design should apply to people and not just to the restricted sample. A comprehensive design means that your survey can be done on any part of the people with the same accuracy. The above factors affect the way respondents respond to research questions and therefore all of the above factors should be balanced in good design. The researcher must have a clear understanding of the different types of study design in order to choose which model to use in the study.

Qualitative

Quality research helps in understanding the problem and to develop hypothesis. Researchers rely on high-quality research methods that conclude “why” a certain idea exists and what “responders” say.

Quantitative

A quantitative study is one of the situations in which statistical conclusions are arrived at on the basis of collected data. Numbers provide a better idea of how to make critical business decisions. Research is needed for the growth of any organisation. The information taken from the data and the analysis of the hard data is very effective in making decisions related to the future of the business.

Components of Research Design

The main purpose behind the design of the study is to help avoid a situation where the evidence does not address the main research questions. The research design is about a logical problem and not a planning problem.

The five main components of a research design are:

Research questions

Course suggestions.

  • Units of analysis
  • Linking data to propositions
  • Interpretation of the findings of the study

The components of research design apply to all types of standardised, extra-terrestrial research, whether physical or social sciences.

This first item raises the type of question – about “who,” “what,” “where,” “how,” and “why” – provides important clues as to the proper research methodology used. Use three paragraphs: First, use the books to reduce your interest in one or two topics. In 2nd paragraph, take a closer look — or cut — a few key lessons from your favorite topic. Find questions in those few studies and conclude with new questions for future research. In the 3rd paragraph, check out another science group on the same topic. They may offer support for your potential questions or suggest ways to sharpen it.

Each suggestion directs the focus to something needed to be tested within the study. Only if you are forced to give some suggestions will you go the right way. For example, you would think that businesses are cooperating as they receive the same benefits. This suggestion, in addition to highlighting an important theoretical issue (that some corporate incentives do not exist or do not matter), also begins to tell you where to look for related evidence (defining and determining the magnitude of specific benefits in each business).

Unit analysis

It is associated with the basic problem of defining what “case” is – a problem that has affected many researchers at the beginning of the study. Take the example of medical patients. In this case, the person is being studied, and that person is an important unit of analysis.

Information about the right person will be collected, and few such people can be part of a multidisciplinary investigation. You will need study questions and suggestions to help you find the right information to collect about this person or people. Without such questions and suggestions, you may be tempted to cover “everything” about the person (s), which is not possible.

Data linking and propositions

Data linking methods and propositions such as pattern, definition structure, time series analysis, logic models and cross-case synthesis. The actual analysis will require you to compile or calculate your study data as a direct indication of your initial study suggestions.

Interpretation of findings from the study

Statistical analysis determines whether the research results support the hypothesis. Several statistical tests, for example, T-tests (determining whether two groups are statistically different from each other), Chi-square tests (where data is compared with the expected result), and oneway analysis of variance (provides multiple group comparisons), are performed by data type, number, and types of variables and data categories.

Statistical analysis provides some clear ways to translate. For example, according to the agreement, social science looks at a level below -55 to show that perceived differences are “statistically significant.” On the other hand, the analysis of many cases will not depend on the use of statistics and therefore focuses on alternative approaches to these approaches.

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components of a research design

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Scientific Research and Methodology

1.6 the components of research.

The research process typically follows the process in Fig. 1.1 . This is not always possible or practical, and the process is not always linear (researchers may jump from step to step as necessary). Nonetheless, following this process is good practice when possible.

The six basic steps in research

FIGURE 1.1: The six basic steps in research

All these steps are discussed in this book:

  • Asking the question: Chap. 2 .
  • Designing the study: Chaps. 3 to 9 .
  • Collecting the data: Chap. 10 .
  • Describing and summarising the data: Chaps. 11 to 14 .
  • Analysing the data: Chaps. 15 to 35 .
  • Reporting the results: Chaps. 36 and 37 .

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  • Aug 24, 2017

FIVE COMPONENTS OF RESEARCH DESIGN

Updated: Mar 21, 2018

Students doing research for the first time face many challenges.  Here are five big ideas that are important to process. Download a pdf here

components of a research design

Goals: Why is your study worth doing? What issues do you want it to clarify, and what practices and policies do you want it to influence? Why do you want to conduct this study, and why should we care about the results?

Conceptual framework: What do you think is going on with the issues, settings, or people you plan to study? What theories, beliefs, and prior research findings will guide or inform your research, and what literature, preliminary studies, and personal experiences will you draw on for understanding the people or issues you are studying?

Research questions: What, specifically, do you want to learn or understand by doing this study? What do you not know about the things you are studying that you want to learn? What questions will your research attempt to answer, and how are these questions related to one another?

Methods: What will you actually do in conducting this study? What approaches and techniques will you use to collect and analyze your data, and how do these constitute an integrated strategy?

Validity: How might your results and conclusions be wrong? What are the plausible alternative interpretations and validity threats to these, and how will you deal with these? How can the data that you have, or that you could potentially collect, support or challenge your ideas about what’s going on? Why should we believe your results?

Adapted from Maxwell, J. (2005)

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Elements of Research Design

Innovator

Conducting research requires a systematic approach, and the design of the research project is one of the most crucial aspects. Research design provides the framework that guides the entire research process, from the formulation of the research problem to the analysis of the collected data.

The purpose of a research study provides the foundation for all other aspects of the study, setting the direction for the entire project. The purpose should noy only be clear, but also well-defined, as well as it should specify both research questions or hypotheses that are going to be addressed in the study.

The purpose of a research study can be classified into two broad categories: exploratory and confirmatory.

  • Exploratory research is conducted when little is known about a topic, or when research questions are both broad or open-ended. This type of research is often used to generate ideas and hypotheses that can be tested in subsequent studies.
  • Confirmatory research, on the other hand, is conducted to test specific hypotheses or theories. This type of research is typically more structured and is focused on collecting data to support or refute the hypotheses.

It is important to clearly define the purpose of a research study, since this will determine the methodology and techniques that should be used for data collection and analysis. The purpose will also influence the study's sample size, time frame, and overall design.

In addition to the type of research, it is also important to consider both the intended audience and the use of results. Will the results be used for academic purposes, to inform policy, or to guide decision-making in a particular field? Knowing this will help to ensure that the study is designed in a manner that will meet the needs of the audience and provide the information they require.

Data collection methods

Data collection is a important step in the research process. The choice of method depends on the research question, the study setting, the type of data required, as well as the available resources. There are several methods for collecting data, which include surveys, interviews, observations, and experiments.

  • Surveys are a common method for collecting data in social science research. Surveys can be administered through a variety of modes, such as mail, telephone, or online. Surveys are an effective method for collecting data on attitudes, beliefs, and behaviors of a large sample of people.
  • Interviews are a qualitative data collection method that allows researchers to collect detailed information about people's experiences, thoughts, and opinions. These can be conducted face-to-face or over the telephone, being a valuable method for collecting data when the researcher wants to gain a deeper understanding of a particular issue.
  • Observations are another method for collecting qualitative data. This involves observing people or events in their natural setting. This method is useful for collecting data on behaviors, activities, and interactions.
  • Experiments are a method for collecting quantitative data. The use of this method involves manipulating one or more variables to observe their effect on a dependent variable. Experiments are useful for testing causal relationships between variables.

Data analysis methods

Data analysis is the process of turning raw data into useful information. There are several methods for analyzing data, which include descriptive and inferential statistics, as well as qualitative data analysis:

  • Descriptive statistics are able to summarize the data, providing a snapshot of the data. Descriptive statistics include measures such as the mean, median, and standard deviation.
  • Inferential statistics allow researchers to make generalizations about a population, based on a sample of data. Inferential statistics include tests of hypothesis and regression analysis.
  • Qualitative data analysis is a method for analyzing qualitative data, such as interview transcripts, field notes, and observation data. Qualitative data analysis involves categorizing, coding, and synthesizing the data to identify patterns and themes.

Time dimension

The time dimension of a study refers to the time frame in which data is collected. There are several time frames to consider, which include cross-sectional, longitudinal, and panel studies:

  • Cross-sectional studies collect data from a sample of people at a single point in time. These studies are useful to collect data on current attitudes, behaviors, and beliefs.
  • Longitudinal studies collect data from the same sample of people over an extended period of time. These studies are useful for observing changes in attitudes, behaviors, and beliefs over time.
  • Panel studies collect data from the same sample of people at multiple points in time. Panel studies are useful to understand changes in attitudes, behaviors, and beliefs over time, as well as the relationships between these changes.

Study setting

The study setting is a critical component of the research design, as it can greatly impact the validity of the results. It refers to the location or environment in which data is collected, and can go from laboratory settings to real-world settings (such as schools, homes, or even communities).

When selecting a study setting, researchers should consider factors such as accessibility, representativeness, and feasibility. Accessibility refers to the ease with which data can be collected in the setting, while representativeness refers to the extent to which the setting is representative of the population of interest. Feasibility refers to the practicality of conducting the study in the selected setting.

It is important to choose a study setting that is appropriate for the research question that is being addressed. For example, if a researcher is interested in exploring the impact of a new educational program, it may be appropriate to conduct the study in a school setting. On the other hand, if a researcher is interested in exploring the impact of a new medication, it may be appropriate to conduct the study in a clinical setting.

In some cases, researchers may need to use multiple study settings in order to obtain a comprehensive understanding of the issue being studied. For example, a study that intends to explore the impact of a new educational program may need to collect data in schools, as well as in homes, in order to obtain a complete picture of the program's impact.

Measurement and operationalization

Measurement and operationalization are considered as critical components of research design, ensuring the validity and reliability of the results. Measurement refers to the process of assigning values to variables, while operationalization refers to the process of defining a variable in terms of the specific methods and procedures used to measure it.

It is important to ensure that the considered variables are accurately and consistently measured, in order to produce valid results. This can be accomplished by using reliable and valid measures, and by consistently applying the same methods and procedures for measuring the variables across all participants in the study.

In addition, it is important to operationalize variables in a clear and consistent manner. This involves the definition of the variable in terms of the specific methods and procedures used to measure it, ensuring that the methods and procedures are consistent across all participants in the study.

For example, if a researcher is interested in measuring stress levels in a study, they may operationalize stress as the score on a commonly used stress questionnaire. In order to ensure consistency in the measurement of stress, the researcher would need to use the same questionnaire for all the participants in the study, scoring the questionnaire in the same manner for all participants.

Research approach

The research approach refers to the overall strategy that is used to conduct the research. There are two main research approaches: quantitative and qualitative :

  • Quantitative research is a deductive approach that involves the collection of numerical data, and the further analysis using statistical methods. This approach is useful for testing hypotheses, as well as to make generalizations about a population.
  • Qualitative research is an inductive approach that involves collecting qualitative data, such as interview transcripts, field notes, and observation data. This approach is useful for gaining a deep understanding of a particular issue and exploring complex phenomena.

In conclusion, the elements of a research design play a crucial role in the success of a research study. A well-designed study can increase the chance of obtaining valid and reliable results, that can be further used not only to answer the research questions, but also to contribute to advances on the knowledge in a particular field.

The purpose of the study provides the foundation for all other aspects of the design and sets the direction for the entire project. The type of research, intended audience, and use of the results should also be considered when defining the purpose.

The study setting, measurement and operationalization, and research approach are also critical elements of research design. The study setting should be chosen based on factors such as accessibility, representativeness, and feasibility, and should be appropriate for the research question being addressed. Measurement and operationalization ensures the validity and reliability of the results, by accurately and consistently measuring variables and defining variables in a clear and consistent manner. The research approach should be chosen based on the research question, the type of research, and the data collection methods that will be used.

In summary, the elements of research design are interdependent and should be carefully considered and implemented, in order to produce valid and reliable results that will help to contribute to advances of knowledge in a particular field.

Header image by Lena_viridis .

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Research Methodology for Allied Health Professionals pp 11–17 Cite as

Components of Research Methodology

  • Animesh Hazari 2  
  • First Online: 01 March 2024

This chapter gives an overall understanding of components used in any research sequentially. These components would be followed and used for conducting as well as writing the research projects. In my personal experience, it would lead the readers to conduct their research projects with accuracy and scientific rigor. Here, we will try to list the components and briefly discuss them. Detailed information will be given in the chapters ahead. These components of research methodology can vary depending on the field of study and the research design, but here are the key components commonly found in most research methodologies:

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Hazari, A. (2023). Components of Research Methodology. In: Research Methodology for Allied Health Professionals. Springer, Singapore. https://doi.org/10.1007/978-981-99-8925-6_3

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DOI : https://doi.org/10.1007/978-981-99-8925-6_3

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Clinical research study designs: The essentials

Ambika g. chidambaram.

1 Children's Hospital of Philadelphia, Philadelphia Pennsylvania, USA

Maureen Josephson

In clinical research, our aim is to design a study which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and governed by ethical clinical principles. The purpose of this review is to provide the readers an overview of the basic study designs and its applicability in clinical research.

Introduction

In clinical research, our aim is to design a study, which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods that can be translated to the “real world” setting. 1 Before choosing a study design, one must establish aims and objectives of the study, and choose an appropriate target population that is most representative of the population being studied. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and is governed by ethical principles. 2

From an epidemiological standpoint, there are two major types of clinical study designs, observational and experimental. 3 Observational studies are hypothesis‐generating studies, and they can be further divided into descriptive and analytic. Descriptive observational studies provide a description of the exposure and/or the outcome, and analytic observational studies provide a measurement of the association between the exposure and the outcome. Experimental studies, on the other hand, are hypothesis testing studies. It involves an intervention that tests the association between the exposure and outcome. Each study design is different, and so it would be important to choose a design that would most appropriately answer the question in mind and provide the most valuable information. We will be reviewing each study design in detail (Figure  1 ).

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Overview of clinical research study designs

Observational study designs

Observational studies ask the following questions: what, who, where and when. There are many study designs that fall under the umbrella of descriptive study designs, and they include, case reports, case series, ecologic study, cross‐sectional study, cohort study and case‐control study (Figure  2 ).

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Classification of observational study designs

Case reports and case series

Every now and then during clinical practice, we come across a case that is atypical or ‘out of the norm’ type of clinical presentation. This atypical presentation is usually described as case reports which provides a detailed and comprehensive description of the case. 4 It is one of the earliest forms of research and provides an opportunity for the investigator to describe the observations that make a case unique. There are no inferences obtained and therefore cannot be generalized to the population which is a limitation. Most often than not, a series of case reports make a case series which is an atypical presentation found in a group of patients. This in turn poses the question for a new disease entity and further queries the investigator to look into mechanistic investigative opportunities to further explore. However, in a case series, the cases are not compared to subjects without the manifestations and therefore it cannot determine which factors in the description are unique to the new disease entity.

Ecologic study

Ecological studies are observational studies that provide a description of population group characteristics. That is, it describes characteristics to all individuals within a group. For example, Prentice et al 5 measured incidence of breast cancer and per capita intake of dietary fat, and found a correlation that higher per capita intake of dietary fat was associated with an increased incidence of breast cancer. But the study does not conclude specifically which subjects with breast cancer had a higher dietary intake of fat. Thus, one of the limitations with ecologic study designs is that the characteristics are attributed to the whole group and so the individual characteristics are unknown.

Cross‐sectional study

Cross‐sectional studies are study designs used to evaluate an association between an exposure and outcome at the same time. It can be classified under either descriptive or analytic, and therefore depends on the question being answered by the investigator. Since, cross‐sectional studies are designed to collect information at the same point of time, this provides an opportunity to measure prevalence of the exposure or the outcome. For example, a cross‐sectional study design was adopted to estimate the global need for palliative care for children based on representative sample of countries from all regions of the world and all World Bank income groups. 6 The limitation of cross‐sectional study design is that temporal association cannot be established as the information is collected at the same point of time. If a study involves a questionnaire, then the investigator can ask questions to onset of symptoms or risk factors in relation to onset of disease. This would help in obtaining a temporal sequence between the exposure and outcome. 7

Case‐control study

Case‐control studies are study designs that compare two groups, such as the subjects with disease (cases) to the subjects without disease (controls), and to look for differences in risk factors. 8 This study is used to study risk factors or etiologies for a disease, especially if the disease is rare. Thus, case‐control studies can also be hypothesis testing studies and therefore can suggest a causal relationship but cannot prove. It is less expensive and less time‐consuming than cohort studies (described in section “Cohort study”). An example of a case‐control study was performed in Pakistan evaluating the risk factors for neonatal tetanus. They retrospectively reviewed a defined cohort for cases with and without neonatal tetanus. 9 They found a strong association of the application of ghee (clarified butter) as a risk factor for neonatal tetanus. Although this suggests a causal relationship, cause cannot be proven by this methodology (Figure  3 ).

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Case‐control study design

One of the limitations of case‐control studies is that they cannot estimate prevalence of a disease accurately as a proportion of cases and controls are studied at a time. Case‐control studies are also prone to biases such as recall bias, as the subjects are providing information based on their memory. Hence, the subjects with disease are likely to remember the presence of risk factors compared to the subjects without disease.

One of the aspects that is often overlooked is the selection of cases and controls. It is important to select the cases and controls appropriately to obtain a meaningful and scientifically sound conclusion and this can be achieved by implementing matching. Matching is defined by Gordis et al as ‘the process of selecting the controls so that they are similar to the cases in certain characteristics such as age, race, sex, socioeconomic status and occupation’ 7 This would help identify risk factors or probable etiologies that are not due to differences between the cases and controls.

Cohort study

Cohort studies are study designs that compare two groups, such as the subjects with exposure/risk factor to the subjects without exposure/risk factor, for differences in incidence of outcome/disease. Most often, cohort study designs are used to study outcome(s) from a single exposure/risk factor. Thus, cohort studies can also be hypothesis testing studies and can infer and interpret a causal relationship between an exposure and a proposed outcome, but cannot establish it (Figure  4 ).

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Cohort study design

Cohort studies can be classified as prospective and retrospective. 7 Prospective cohort studies follow subjects from presence of risk factors/exposure to development of disease/outcome. This could take up to years before development of disease/outcome, and therefore is time consuming and expensive. On the other hand, retrospective cohort studies identify a population with and without the risk factor/exposure based on past records and then assess if they had developed the disease/outcome at the time of study. Thus, the study design for prospective and retrospective cohort studies are similar as we are comparing populations with and without exposure/risk factor to development of outcome/disease.

Cohort studies are typically chosen as a study design when the suspected exposure is known and rare, and the incidence of disease/outcome in the exposure group is suspected to be high. The choice between prospective and retrospective cohort study design would depend on the accuracy and reliability of the past records regarding the exposure/risk factor.

Some of the biases observed with cohort studies include selection bias and information bias. Some individuals who have the exposure may refuse to participate in the study or would be lost to follow‐up, and in those instances, it becomes difficult to interpret the association between an exposure and outcome. Also, if the information is inaccurate when past records are used to evaluate for exposure status, then again, the association between the exposure and outcome becomes difficult to interpret.

Case‐control studies based within a defined cohort

Case‐control studies based within a defined cohort is a form of study design that combines some of the features of a cohort study design and a case‐control study design. When a defined cohort is embedded in a case‐control study design, all the baseline information collected before the onset of disease like interviews, surveys, blood or urine specimens, then the cohort is followed onset of disease. One of the advantages of following the above design is that it eliminates recall bias as the information regarding risk factors is collected before onset of disease. Case‐control studies based within a defined cohort can be further classified into two types: Nested case‐control study and Case‐cohort study.

Nested case‐control study

A nested case‐control study consists of defining a cohort with suspected risk factors and assigning a control within a cohort to the subject who develops the disease. 10 Over a period, cases and controls are identified and followed as per the investigator's protocol. Hence, the case and control are matched on calendar time and length of follow‐up. When this study design is implemented, it is possible for the control that was selected early in the study to develop the disease and become a case in the latter part of the study.

Case‐cohort Study

A case‐cohort study is similar to a nested case‐control study except that there is a defined sub‐cohort which forms the groups of individuals without the disease (control), and the cases are not matched on calendar time or length of follow‐up with the control. 11 With these modifications, it is possible to compare different disease groups with the same sub‐cohort group of controls and eliminates matching between the case and control. However, these differences will need to be accounted during analysis of results.

Experimental study design

The basic concept of experimental study design is to study the effect of an intervention. In this study design, the risk factor/exposure of interest/treatment is controlled by the investigator. Therefore, these are hypothesis testing studies and can provide the most convincing demonstration of evidence for causality. As a result, the design of the study requires meticulous planning and resources to provide an accurate result.

The experimental study design can be classified into 2 groups, that is, controlled (with comparison) and uncontrolled (without comparison). 1 In the group without controls, the outcome is directly attributed to the treatment received in one group. This fails to prove if the outcome was truly due to the intervention implemented or due to chance. This can be avoided if a controlled study design is chosen which includes a group that does not receive the intervention (control group) and a group that receives the intervention (intervention/experiment group), and therefore provide a more accurate and valid conclusion.

Experimental study designs can be divided into 3 broad categories: clinical trial, community trial, field trial. The specifics of each study design are explained below (Figure  5 ).

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Experimental study designs

Clinical trial

Clinical trials are also known as therapeutic trials, which involve subjects with disease and are placed in different treatment groups. It is considered a gold standard approach for epidemiological research. One of the earliest clinical trial studies was performed by James Lind et al in 1747 on sailors with scurvy. 12 Lind divided twelve scorbutic sailors into six groups of two. Each group received the same diet, in addition to a quart of cider (group 1), twenty‐five drops of elixir of vitriol which is sulfuric acid (group 2), two spoonfuls of vinegar (group 3), half a pint of seawater (group 4), two oranges and one lemon (group 5), and a spicy paste plus a drink of barley water (group 6). The group who ate two oranges and one lemon had shown the most sudden and visible clinical effects and were taken back at the end of 6 days as being fit for duty. During Lind's time, this was not accepted but was shown to have similar results when repeated 47 years later in an entire fleet of ships. Based on the above results, in 1795 lemon juice was made a required part of the diet of sailors. Thus, clinical trials can be used to evaluate new therapies, such as new drug or new indication, new drug combination, new surgical procedure or device, new dosing schedule or mode of administration, or a new prevention therapy.

While designing a clinical trial, it is important to select the population that is best representative of the general population. Therefore, the results obtained from the study can be generalized to the population from which the sample population was selected. It is also as important to select appropriate endpoints while designing a trial. Endpoints need to be well‐defined, reproducible, clinically relevant and achievable. The types of endpoints include continuous, ordinal, rates and time‐to‐event, and it is typically classified as primary, secondary or tertiary. 2 An ideal endpoint is a purely clinical outcome, for example, cure/survival, and thus, the clinical trials will become very long and expensive trials. Therefore, surrogate endpoints are used that are biologically related to the ideal endpoint. Surrogate endpoints need to be reproducible, easily measured, related to the clinical outcome, affected by treatment and occurring earlier than clinical outcome. 2

Clinical trials are further divided into randomized clinical trial, non‐randomized clinical trial, cross‐over clinical trial and factorial clinical trial.

Randomized clinical trial

A randomized clinical trial is also known as parallel group randomized trials or randomized controlled trials. Randomized clinical trials involve randomizing subjects with similar characteristics to two groups (or multiple groups): the group that receives the intervention/experimental therapy and the other group that received the placebo (or standard of care). 13 This is typically performed by using a computer software, manually or by other methods. Hence, we can measure the outcomes and efficacy of the intervention/experimental therapy being studied without bias as subjects have been randomized to their respective groups with similar baseline characteristics. This type of study design is considered gold standard for epidemiological research. However, this study design is generally not applicable to rare and serious disease process as it would unethical to treat that group with a placebo. Please see section “Randomization” for detailed explanation regarding randomization and placebo.

Non‐randomized clinical trial

A non‐randomized clinical trial involves an approach to selecting controls without randomization. With this type of study design a pattern is usually adopted, such as, selection of subjects and controls on certain days of the week. Depending on the approach adopted, the selection of subjects becomes predictable and therefore, there is bias with regards to selection of subjects and controls that would question the validity of the results obtained.

Historically controlled studies can be considered as a subtype of non‐randomized clinical trial. In this study design subtype, the source of controls is usually adopted from the past, such as from medical records and published literature. 1 The advantages of this study design include being cost‐effective, time saving and easily accessible. However, since this design depends on already collected data from different sources, the information obtained may not be accurate, reliable, lack uniformity and/or completeness as well. Though historically controlled studies maybe easier to conduct, the disadvantages will need to be taken into account while designing a study.

Cross‐over clinical trial

In cross‐over clinical trial study design, there are two groups who undergoes the same intervention/experiment at different time periods of the study. That is, each group serves as a control while the other group is undergoing the intervention/experiment. 14 Depending on the intervention/experiment, a ‘washout’ period is recommended. This would help eliminate residuals effects of the intervention/experiment when the experiment group transitions to be the control group. Hence, the outcomes of the intervention/experiment will need to be reversible as this type of study design would not be possible if the subject is undergoing a surgical procedure.

Factorial trial

A factorial trial study design is adopted when the researcher wishes to test two different drugs with independent effects on the same population. Typically, the population is divided into 4 groups, the first with drug A, the second with drug B, the third with drug A and B, and the fourth with neither drug A nor drug B. The outcomes for drug A are compared to those on drug A, drug A and B and to those who were on drug B and neither drug A nor drug B. 15 The advantages of this study design that it saves time and helps to study two different drugs on the same study population at the same time. However, this study design would not be applicable if either of the drugs or interventions overlaps with each other on modes of action or effects, as the results obtained would not attribute to a particular drug or intervention.

Community trial

Community trials are also known as cluster‐randomized trials, involve groups of individuals with and without disease who are assigned to different intervention/experiment groups. Hence, groups of individuals from a certain area, such as a town or city, or a certain group such as school or college, will undergo the same intervention/experiment. 16 Hence, the results will be obtained at a larger scale; however, will not be able to account for inter‐individual and intra‐individual variability.

Field trial

Field trials are also known as preventive or prophylactic trials, and the subjects without the disease are placed in different preventive intervention groups. 16 One of the hypothetical examples for a field trial would be to randomly assign to groups of a healthy population and to provide an intervention to a group such as a vitamin and following through to measure certain outcomes. Hence, the subjects are monitored over a period of time for occurrence of a particular disease process.

Overview of methodologies used within a study design

Randomization.

Randomization is a well‐established methodology adopted in research to prevent bias due to subject selection, which may impact the result of the intervention/experiment being studied. It is one of the fundamental principles of an experimental study designs and ensures scientific validity. It provides a way to avoid predicting which subjects are assigned to a certain group and therefore, prevent bias on the final results due to subject selection. This also ensures comparability between groups as most baseline characteristics are similar prior to randomization and therefore helps to interpret the results regarding the intervention/experiment group without bias.

There are various ways to randomize and it can be as simple as a ‘flip of a coin’ to use computer software and statistical methods. To better describe randomization, there are three types of randomization: simple randomization, block randomization and stratified randomization.

Simple randomization

In simple randomization, the subjects are randomly allocated to experiment/intervention groups based on a constant probability. That is, if there are two groups A and B, the subject has a 0.5 probability of being allocated to either group. This can be performed in multiple ways, and one of which being as simple as a ‘flip of a coin’ to using random tables or numbers. 17 The advantage of using this methodology is that it eliminates selection bias. However, the disadvantage with this methodology is that an imbalance in the number allocated to each group as well as the prognostic factors between groups. Hence, it is more challenging in studies with a small sample size.

Block randomization

In block randomization, the subjects of similar characteristics are classified into blocks. The aim of block randomization is to balance the number of subjects allocated to each experiment/intervention group. For example, let's assume that there are four subjects in each block, and two of the four subjects in each block will be randomly allotted to each group. Therefore, there will be two subjects in one group and two subjects in the other group. 17 The disadvantage with this methodology is that there is still a component of predictability in the selection of subjects and the randomization of prognostic factors is not performed. However, it helps to control the balance between the experiment/intervention groups.

Stratified randomization

In stratified randomization, the subjects are defined based on certain strata, which are covariates. 18 For example, prognostic factors like age can be considered as a covariate, and then the specified population can be randomized within each age group related to an experiment/intervention group. The advantage with this methodology is that it enables comparability between experiment/intervention groups and thus makes result analysis more efficient. But, with this methodology the covariates will need to be measured and determined before the randomization process. The sample size will help determine the number of strata that would need to be chosen for a study.

Blinding is a methodology adopted in a study design to intentionally not provide information related to the allocation of the groups to the subject participants, investigators and/or data analysts. 19 The purpose of blinding is to decrease influence associated with the knowledge of being in a particular group on the study result. There are 3 forms of blinding: single‐blinded, double‐blinded and triple‐blinded. 1 In single‐blinded studies, otherwise called as open‐label studies, the subject participants are not revealed which group that they have been allocated to. However, the investigator and data analyst will be aware of the allocation of the groups. In double‐blinded studies, both the study participants and the investigator will be unaware of the group to which they were allocated to. Double‐blinded studies are typically used in clinical trials to test the safety and efficacy of the drugs. In triple‐blinded studies, the subject participants, investigators and data analysts will not be aware of the group allocation. Thus, triple‐blinded studies are more difficult and expensive to design but the results obtained will exclude confounding effects from knowledge of group allocation.

Blinding is especially important in studies where subjective response are considered as outcomes. This is because certain responses can be modified based on the knowledge of the experiment group that they are in. For example, a group allocated in the non‐intervention group may not feel better as they are not getting the treatment, or an investigator may pay more attention to the group receiving treatment, and thereby potentially affecting the final results. However, certain treatments cannot be blinded such as surgeries or if the treatment group requires an assessment of the effect of intervention such as quitting smoking.

Placebo is defined in the Merriam‐Webster dictionary as ‘an inert or innocuous substance used especially in controlled experiments testing the efficacy of another substance (such as drug)’. 20 A placebo is typically used in a clinical research study to evaluate the safety and efficacy of a drug/intervention. This is especially useful if the outcome measured is subjective. In clinical drug trials, a placebo is typically a drug that resembles the drug to be tested in certain characteristics such as color, size, shape and taste, but without the active substance. This helps to measure effects of just taking the drug, such as pain relief, compared to the drug with the active substance. If the effect is positive, for example, improvement in mood/pain, then it is called placebo effect. If the effect is negative, for example, worsening of mood/pain, then it is called nocebo effect. 21

The ethics of placebo‐controlled studies is complex and remains a debate in the medical research community. According to the Declaration of Helsinki on the use of placebo released in October 2013, “The benefits, risks, burdens and effectiveness of a new intervention must be tested against those of the best proven intervention(s), except in the following circumstances:

Where no proven intervention exists, the use of placebo, or no intervention, is acceptable; or

Where for compelling and scientifically sound methodological reasons the use of any intervention less effective than the best proven one, the use of placebo, or no intervention is necessary to determine the efficacy or safety of an intervention and the patients who receive any intervention less effective than the best proven one, placebo, or no intervention will not be subject to additional risks of serious or irreversible harm as a result of not receiving the best proven intervention.

Extreme care must be taken to avoid abuse of this option”. 22

Hence, while designing a research study, both the scientific validity and ethical aspects of the study will need to be thoroughly evaluated.

Bias has been defined as “any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure's effect on the risk of disease”. 23 There are multiple types of biases and so, in this review we will focus on the following types: selection bias, information bias and observer bias. Selection bias is when a systematic error is committed while selecting subjects for the study. Selection bias will affect the external validity of the study if the study subjects are not representative of the population being studied and therefore, the results of the study will not be generalizable. Selection bias will affect the internal validity of the study if the selection of study subjects in each group is influenced by certain factors, such as, based on the treatment of the group assigned. One of the ways to decrease selection bias is to select the study population that would representative of the population being studied, or to randomize (discussed in section “Randomization”).

Information bias is when a systematic error is committed while obtaining data from the study subjects. This can be in the form of recall bias when subject is required to remember certain events from the past. Typically, subjects with the disease tend to remember certain events compared to subjects without the disease. Observer bias is a systematic error when the study investigator is influenced by the certain characteristics of the group, that is, an investigator may pay closer attention to the group receiving the treatment versus the group not receiving the treatment. This may influence the results of the study. One of the ways to decrease observer bias is to use blinding (discussed in section “Blinding”).

Thus, while designing a study it is important to take measure to limit bias as much as possible so that the scientific validity of the study results is preserved to its maximum.

Overview of drug development in the United States of America

Now that we have reviewed the various clinical designs, clinical trials form a major part in development of a drug. In the United States, the Food and Drug Administration (FDA) plays an important role in getting a drug approved for clinical use. It includes a robust process that involves four different phases before a drug can be made available to the public. Phase I is conducted to determine a safe dose. The study subjects consist of normal volunteers and/or subjects with disease of interest, and the sample size is typically small and not more than 30 subjects. The primary endpoint consists of toxicity and adverse events. Phase II is conducted to evaluate of safety of dose selected in Phase I, to collect preliminary information on efficacy and to determine factors to plan a randomized controlled trial. The study subjects consist of subjects with disease of interest and the sample size is also small but more that Phase I (40–100 subjects). The primary endpoint is the measure of response. Phase III is conducted as a definitive trial to prove efficacy and establish safety of a drug. Phase III studies are randomized controlled trials and depending on the drug being studied, it can be placebo‐controlled, equivalence, superiority or non‐inferiority trials. The study subjects consist of subjects with disease of interest, and the sample size is typically large but no larger than 300 to 3000. Phase IV is performed after a drug is approved by the FDA and it is also called the post‐marketing clinical trial. This phase is conducted to evaluate new indications, to determine safety and efficacy in long‐term follow‐up and new dosing regimens. This phase helps to detect rare adverse events that would not be picked up during phase III studies and decrease in the delay in the release of the drug in the market. Hence, this phase depends heavily on voluntary reporting of side effects and/or adverse events by physicians, non‐physicians or drug companies. 2

We have discussed various clinical research study designs in this comprehensive review. Though there are various designs available, one must consider various ethical aspects of the study. Hence, each study will require thorough review of the protocol by the institutional review board before approval and implementation.

CONFLICT OF INTEREST

Chidambaram AG, Josephson M. Clinical research study designs: The essentials . Pediatr Invest . 2019; 3 :245‐252. 10.1002/ped4.12166 [ CrossRef ] [ Google Scholar ]

How practical application of research provided an ‘Aha’ moment for students

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components of a research design

Find out how Kate Lamble, a Nurse Educator in the School of Social Care and Health, created active, applied and authentic (AAA) learning activities to help students in her Diploma of Nursing course, translate theory into clinical practice.

This snapshot shows how RMIT’s signature AAA pedagogy manifests in a vocational education (VE) course through intentionally designed learning activities and assessments that actively engage students, apply 'real-world' problems or scenarios and have an authentic, meaningful purpose

The Context

Kate Lamble was faced with a common challenge - how to help students make the connection between theory, data, and research, and its application in real world practice?

Kate grounded the course learning activities and assessment in a common yet critical clinical practice - hand hygiene, and designed learning activities that built students' understanding of how hand hygiene research translates into clinical policies and practices and informs the decisions they make on a ward.

The Learning Activties

Students actively researched the importance of hand hygiene in clinical practice and were shown how to collate and analyse data on hand hygiene. They put this knowledge and skills into practice by undertaking a practical, real-world work task. Teams of students collaboratively drafted a set of criteria and conducted an observational audit of other students using hand hygiene as they went about clinical work in a simulated work environment.

The Assessment

Each team of students analysed their observations and the data they collected then presented their finding in a poster which was shared with classmates in an online forum, where students asked questions and gave feedback.

nurse handling an iv drip

Active, Applied and Authentic spotlights

Students were active partners in their learning as they analysed hand hygiene data and research together then collaborated to create posters which they presented and discussed with the whole cohort.

Practical learning activities assisted students to develop relevant, transferable skills for applying research knowledge to clinical practice.

Learning activities were challenging, collaborative and involved authentic tasks that scaffolded students’ knowledge and skills for completing the assessment - which was held in a simulated workplace setting to allow students to gain real-world application of skills and knowledge.

How did the AAA pedagogical design impact the student? 

Student growth

According to educator, Kate Lamble, "what I absolutely love about teaching this subject is that you take something that is very theoretically based and you give it a clinical context and you watch the students have these 'aha' moments all the way along - and from my perspective that is what makes it all worthwhile." 

The active collaborative learning activities engaged and connected students and the practical application of knowledge made the learning experience real and built transferable knowledge and skills. According to student, Emily,  "my initial opinion of research was how is this going to be relevant in the clinical environment? But the practical exercises really showed me how it all connects"

What technologies, space and foundational components supported this AAA snapshot?

Note that links may require a RMIT staff login.

The following tools, technologies, spaces and resources enabled and supported the application of AAA Pedagogy:

IPL: Career Development Learning supported by Industry Embedded Activities

Tools : Canvas LMS, SharePoint posters

Digital learning spaces: Canvas, Teams Meetings 

Physical F2F Spaces: Clinical Lab workspace, collaborative meeting rooms, tutorial rooms.

Foundations

The following pedagogical foundational components helped inform the development of this AAA Pedagogy:

Course and Program policies, procedures and instructions

Assessment Framework

Academic Integrity Framework 

IPL Framework and CDL Framework

Click on the links below to hear more about this AAA best practice or access resources to help develop similar active, applied, authentic learning activities and assessments.

VE Teacher Success Stories - Nursing video

Teach VE Hub

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  • ‘You can change your life through sports’—physical activity interventions to improve the health and well-being of adults experiencing homelessness: a mixed-methods systematic review
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  • http://orcid.org/0000-0003-0248-4160 Jo Dawes 1 ,
  • Raphael Rogans-Watson 2 ,
  • Julie Broderick 3
  • 1 Department of Epidemiology & Public Health , UCL , London , UK
  • 2 Elderly Medicine , University Hospitals Sussex NHS Foundation Trust , Worthing , UK
  • 3 Discipline of Physiotherapy, School of Medicine , Trinity College Dublin , Dublin , Ireland
  • Correspondence to Jo Dawes, Department of Epidemiology & Public Health, UCL, London, London, UK; joanna.dawes{at}ucl.ac.uk

Objectives Systematically synthesise evidence of physical activity interventions for people experiencing homelessness (PEH).

Design Mixed-methods systematic review.

Data sources EMBASE, Web of Science, CINAHL, PubMed (MEDLINE), PsycINFO, SPORTDiscus and Cochrane Library, searched from inception to October 2022.

Eligibility criteria PICO framework: population (quantitative/qualitative studies of PEH from high-income countries); intervention (physical activity); comparison (with/without comparator) and outcome (any health/well-being-related outcome). The risk of bias was assessed using Joanna Briggs Institute critical appraisal tools.

Results 3615 records were screened, generating 18 reports (17 studies, 11 qualitative and 6 quantitative (1 randomised controlled trial, 4 quasi-experimental, 1 analytical cross-sectional)) from the UK, USA, Denmark and Australia, including 554 participants (516 PEH, 38 staff). Interventions included soccer (n=7), group exercise (indoor (n=3), outdoor (n=5)) and individual activities (n=2). The risk of bias assessment found study quality to vary; with 6 being high, 6 moderate, 4 low and 1 very low. A mixed-methods synthesis identified physical and mental health benefits. Qualitative evidence highlighted benefits carried into wider life, the challenges of participating and the positive impact of physical activity on addiction. Qualitative and quantitative evidence was aligned demonstrating the mental health benefits of outdoor exercise and increased physical activity from indoor group exercise. Quantitative evidence also suggests improved musculoskeletal health, cardiovascular fitness, postural balance and blood lipid markers (p<0.05).

Conclusion Qualitative evidence suggests that physical activity interventions for PEH can benefit health and well-being with positive translation to wider life. There was limited positive quantitative evidence, although most was inconclusive. Although the evidence suggests a potential recommendation for physical activity interventions for PEH, results may not be transferable outside high-income countries. Further research is required to determine the effectiveness and optimal programme design.

  • Physical activity
  • Physical fitness
  • Public health

Data availability statement

Data are available upon reasonable request.

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

https://doi.org/10.1136/bjsports-2023-107562

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WHAT IS ALREADY KNOWN ON THIS TOPIC

People experiencing homelessness suffer a higher burden of physical and mental health conditions than housed populations.

Limited studies suggest that regular physical activity may address many health conditions prevalent among people experiencing homelessness, although the evidence has not been systematically reviewed.

WHAT THIS STUDY ADDS

A variety of physical activity interventions have been designed and provided to engage people experiencing homelessness, including soccer, outdoor and indoor group activities, and individual activities.

The synthesis of qualitative and quantitative evidence suggests that physical activity can benefit the mental and physical health of people experiencing homelessness with positive translation of benefits to wider life.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Group physical activity interventions seemed to be the most benefitical to people experiencing homelessness, perhaps due to its facilitation of social support and connection.

Qualitative data highlighted the pressure some participants felt in competetive tournament settings. Organisers should recognise this and consider support to ameliorate impacts of pressure experienced.

Consideration should be given to the intensity level of physical activity interventions for this population. Given the high prevalence and poor health of many people experiencing homelessness, lower threshhold activities are likely to be more inclusive for the population.

Introduction

Homelessness is an extreme form of social exclusion 1 2 related to poverty in high-income countries. 3 People experiencing homelessness (PEH) are defined as those who are ‘roofless’ (eg, no fixed abode) and ‘houseless’ (eg, living in hostel, shelter, temporary accommodation) in accordance with the European Federation of National Organisations Working with the Homeless. 4 Prior to the COVID-19 pandemic, homelessness in the UK had increased annually since 2010 5 with estimates of all categories of homelessness in England standing at 280 000 people, 6 of which 4266 were estimated to be sleeping on the streets. 7 The Organisation for Economic Co-operation and Development (OECD) estimates that almost 2 million people are experiencing homelessness in 35 OECD countries. 8

PEH have poorer health than the general population, 9 10 often characterised by a tri-morbidity of mental health diagnoses, chronic physical health conditions and addiction. 9 Poor health is thought to be both precipitated and exacerbated by poor living conditions, lack of resources, social exclusion, stigmatisation and difficulty accessing suitable health services. 11

Physical activity is beneficial for people with disabilities and chronic health conditions, both from a physical health and a social perspective. Guidance suggests that the type and amount of physical activity should be determined by a person’s abilities and the severity of their condition or disability, which may change over time. 12 PEH live with a high burden of physical deficits, 13 falls and frailty, 14 respiratory disease, cardiac problems, stroke and diabetes, 15 which could be positively influenced by physical activity. A recent scoping review found that among PEH, overall levels of physical activity appeared to be low, though the authors recognised that across studies reviewed, physical activity levels varied. 16 Low levels of physical activity could be due to limited opportunities or barriers to accessing physical activity, rather than through choice. Consequently, PEH may miss out on health gains and a reduced risk of harm that physical activity affords people with these conditions. It is important that this population has opportunities for physical activity to stabilise or reverse physical declines associated with homelessness. Given the multiple barriers PEH face accessing services, it may be important that physical activity interventions are specifically tailored to their needs to optimise reach and participation. This perspective is consistent with public engagement activities with PEH and staff who care for them, which took place prior to the commencement of this research. This research poses two research questions: what is the range of physical activity interventions provided to PEH? And, what is the evidence supporting the effectiveness of these interventions?

This review aims to summarise the available evidence for physical activity interventions intended to improve health outcomes of adults experiencing homelessness, focusing on physical activity interventions and their effectiveness in improving health outcomes.

A preliminary scoping review revealed that published literature in the field of physical activity for PEH comprised both quantitative and qualitative research. Therefore, a mixed-methods systematic review was adopted. This allowed for the findings of effectiveness (quantitative evidence) and participant experiences (qualitative evidence) to be brought together, to facilitate a broader understanding of whether and how interventions worked. 17 18 This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines 19 and checklist, 20 and the protocol was registered a priori in PROSPERO database (reference number: CRD42020216716).

Identification

Defining search terms.

Initial search terms were generated by reviewers (JFD, RR-W and JB), who between them, have extensive clinical and research expertise and experience in the health of PEH, physical activity and systematic review methodology. The search terms were refined and tailored for a preliminary search of MEDLINE, and used to test the proof of concept and search strategy. The search syntax ( online supplemental file 1 ) was designed by a professional librarian in collaboration with two reviewers (JFD and RR-W).

Supplemental material

Search terms were refined, adapted and run in MEDLINE, EMBASE, Web of Science, CINAHL, PsycINFO, SPORTDiscus and the Cochrane Library. The searches were conducted on 17 February 2021, including literature from the previous 30 years (1991–2021) and restricted to English language only. The searches were re-run using the original search terms by a specialist librarian at Trinity College Dublin on 19 October 2022 to identify any new reports published between 21 October 2021 and 19 October 2022. All previous databases were searched, except SPORTDiscus, as it was unavailable in the institution’s library databases. Duplicates were removed at this stage. The reference lists of relevant systematic reviews and all included studies were hand-searched for reports to be added for screening. Corresponding authors of records that comprised an abstract only were contacted, where possible, to request full-text reports. Additionally, an expert reviewer suggested a study unidentified by searches, but met the inclusion criteria, so it was put forward for screening.

Title and abstract screening

On completion of the identification process, all report titles and abstracts were uploaded to the online systematic reviewing management system, Covidence. Two pairs of reviewers (JFD/RR-W and JFD/JB) independently performed (a) title and abstract screening and (b) full-text screening, judged against predetermined protocol criteria. In the event of disagreement, the third reviewer (JB or RR-W) was consulted for an additional opinion.

The PICO framework was used to identify inclusion criteria. For inclusion, all the following criteria were to be met:

Studies that included adults who were homeless under the European Typology of Homelessness and housing Exclusion (ETHOS) criteria for homelessness, 4 that is rooflessness, houselessness, living in insecure housing or living in inadequate housing. Age >18 years.

Intervention

Studies that included any physical activity intervention delivered as a stand-alone intervention or part of multimodal intervention, in any setting. Studies undertaken in high-income countries 21 were included, where there is assumed consistency in health and social care infrastructure as well as in family and community support systems, which impact how homelessness is perceived and managed. 22

This mixed-methods review included quantitative studies reporting any measures demonstrating health outcomes, including but not limited to primary measures such as cardiovascular fitness and strength, and qualitative findings describing participant perceptions linking physical activity intervention to health and/or well-being outcomes.

The presence of a comparison group was not required as an inclusion criterion.

Study types

This review considered quantitative, qualitative and mixed-methods studies.

Risk of bias assessment

In recognition of the diverse study designs included in this review, the Joanna Briggs Institute (JBI) critical appraisal tool portfolio was a key resource for judging quality and risk of bias. 23 24 These tools provide a criterion-based checklist for determining presence (yes), absence (no), a lack of clarity (unclear) or a lack of applicability (not applicable) of quality in studies across a variety of methods. 25 To determine the dependability and credibility of qualitative reports, their ConQual ratings were calculated. 26 Although Munn et al discourage cut-off values in determining the quality level in quantitative studies, for clarity and consistency of this mixed-methods review, a pragmatic decision was made to select cut-offs of <25% (very low), <50% (low), <75% (moderate) and >75% (high). Munn et al state that if cut-offs are preferred, these thresholds are best decided by the reviewers themselves. 25 A summary of the quality assessment of all reports is given in online supplemental file 2 .

Protocol deviation

This review was registered on PROSPERO, registration number: CRD42020216716. Found at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216716 . In the PROSPERO protocol, we stated we would use Cochrane and Downs and Black risk of bias tools. However, once the diversity of the final studies was identified, the review team recognised that JBI risk of bias tools were more suited to the studies within our review.

Data extraction

The following data were extracted to an excel spreadsheet: study design, inclusion criteria, participants (description, number, accommodation, age, education, employment, ethnicity, race, biological sex, mental health and physical health), intervention (setting, frequency, intensity, time, type, group or individual, presence of other non-physical activity intervention components), quantitative outcome measures and qualitative themes.

Initially, JFD carried out and collated data extraction from five reports. This was reviewed by RR-W and JB to ensure accuracy and consistency. Once all three team members agreed on the data extraction process, the remaining reports were divided among the team for completion of data extraction. Data from each report were checked for accuracy by another member of the research team. Any inconsistencies in interpretation or reporting were discussed, and consensus was reached.

Strategy for mixed-methods data synthesis

The synthesis followed the JBI methodology for mixed-methods systematic reviews, 27 whereby established convergent, segregated, results-based mixed-methods frameworks for systematic reviewing were employed. 28 29 First, qualitative and quantitative data were meaningfully categorised by JFD and JB, respectively. Each reviewer conducted their analysis separately, independently and concurrently. JFD adopted a reflexive thematic analysis approach to synthesise the qualitative data, by extracting all qualitative results into an excel spreadsheet and following the six processes of thematic analysis, namely: familiarisation; coding; generating initial themes; reviewing and developing themes; refining, defining and naming themes; and writing up. 30 Details of themes are outlined in online supplemental file 3 . Due to the heterogeneity of quantitative studies, it was not possible for JB to carry out a meta-analysis. So narrative synthesis was used. Quantitative findings were then ‘qualitized’ to transform them into a qualitative, descriptive format. Next, quantitative and qualitative evidence were linked and organised to produce an overall ‘configured analysis’ 27 and reported as a series of tables and combined narrative synthesis.

Equality and diversity statement

Our author and librarian team consisted of three women and two men. The author team included early and mid-career researchers and clinicians across two disciplines (medicine and physiotherapy) from two countries (UK and Ireland). This research explores physical activity interventions for PEH, an under-served, often marginalised and excluded population who experience extreme socioeconomic disadvantage. This population is known to have complex and chronic health needs and is an often-overlooked group in physical activity research.

Study selection

13 737 records were identified through searches. After the removal of duplicates (n=10 122), 3615 records were screened by title and abstract, with 3496 records excluded at this stage. 119 reports were sought for full-text review, 4 could not be found, so 115 full-text reports were reviewed. Of these, 97 records were excluded at this stage (exclusions based on: 1 duplicate, 9 population, 59 intervention, 8 non-English language, 19 insufficient data, 1 protocol only). Finally, 18 reports were included for quality checking. Two reports described different aspects of a single study. Therefore, data were extracted from 18 reports describing 17 studies. The full identification, screening and inclusion process are outlined in a PRISMA diagram ( figure 1 ).

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PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analyses.

Quality assessment

The majority of the 11 qualitative studies were high quality, with 8 reporting at least 7 out of 10 quality criteria on the JBI checklist for qualitative studies ( online supplemental file 2 ). One study was of very low quality, 31 with only the statement of researcher positionality being clear, and all other criteria either unreported or unclear. Of the quantitative studies, the one randomised controlled trial (RCT) 32 was assessed as moderate quality due to methodological limitations, for example, lack of clarity regarding blinding of assessor and whether treatment groups were concealed. The analytical cross-sectional study was of moderate quality, and in general, quasi-experimental studies were of high quality.

Description of studies

Eighteen reports, describing 17 studies, were included ( table 1 ). Of these studies, 7 were from the USA, 5 from the UK, 3 from Denmark and 2 from Australia. The variety of designs across these studies comprised 11 qualitative and 6 quantitative reports (4 quasi-experimental, 1 RCT and 1 analytical cross-sectional). The interventions addressed varied, including soccer (n=7); group outdoor exercise (n=5); group indoor multimodal exercise (n=3) and individual multimodal interventions (n=2) ( online supplemental file 4 ).

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Summary of included studies

Study populations

Online supplemental file 5 provides detail of each study population included in this systematic review. Across the 17 studies, 516 PEH were participants. Some studies included women only (n=5), men only (n=5) or mixed cohorts (n=7). Three qualitative studies reported staff/coaches’ perspectives (n=38). The age range of participants who were homeless was 16–65 years. It was specified in the review protocol that only studies with participants >18 years would be included. However, for pragmatic reasons, several studies 33–37 were included despite containing participants from the age of 16 years. In these studies, proportions of participants <18 years were not specified, although one study 38 stated that the ‘majority’ of participants were between the ages of 20 and 24 years. Descriptions of study participants’ experiences of homelessness varied but were mainly focused on: street homeless, living in hostel/shelter, transitional/social service accommodation or ‘homeless at time of intervention’. Studies that focused on Street Soccer and the Homeless World Cup invited participation from PEH and other socially excluded groups, for example, people attending unemployment offices or drug rehabilitation services. Although these studies did not define proportions of participants experiencing homelessness, for pragmatic reasons they were included, as the intervention had been specifically designed for PEH. Only one study specified exclusion criteria, 32 which were based on reading ability and length of time staying in the shelter. In two studies, several participants were eligible but chose not to participate, 39 40 the reasons for which was not specified. The number of study drop-outs was described in three reports/two studies, 32 36 37 but the reasons for drop-out were not specified.

Physical activity interventions and their components

Online supplemental file 4 provides a description of all included interventions. The studies included seven soccer interventions (tournament focused (n=2), group training focused (n=3) and combining group training and tournament participation (n=2)); five group outdoor exercise (adventure training (n=3), running (n=1) and gardening (n=1)); three group indoor multimodal exercise (aerobic-based circuits (n=2) and dance (n=1)) and two individual multimodal interventions (pedometer with step goals and earn-a-bike scheme). Online supplemental file 4 also provides programming variables, including: setting; frequency; intensity; time; type and the presence of other non-physical activity components of multimodal interventions.

Seven studies investigated the impact of soccer for PEH. These studies (eight reports) explored soccer group training (n=4), 39 41–43 tournament participation (n=2) 33 40 and interventions of training for and participating in tournaments (n=2). 44 45 The studies involving tournaments were focused around national or international tournaments such as the Homeless World Cup or Street Soccer USA Cup. 33 40 44 45

Group outdoor exercise

Five studies provided evidence of the value of group outdoor exercise. These included group outdoor adventure (n=3), 34 35 46  women’s running groups (n=1) 38 and women’s gardening groups (n=1). 47 These studies described multimodal interventions, including outdoor adventure interventions which contained multiple activities (eg, archery, rock climbing, hiking), and all studies reported additional support, such as the provision of education, debriefing, opportunities for reflection, childcare, food or clothing.

Group indoor multimodal exercise

All group indoor multimodal exercise studies (n=3) were instructor-led interventions provided to small groups in settings such as leisure centres 36 or shelter recreation rooms. 31 All studies were multimodal as they combined different types of activity, for example, stretching, cardiovascular exercise, dance, aerobic circuits, strength-based exercise to music and meditation.

Individual multimodal interventions

Two studies reported interventions for individuals. 32 48 One involved participants wearing a pedometer and working towards a step goal. This was provided along with an educational newsletter and fruit/vegetable snacks. 32 The other study described cycle training to learn road safety and cycle maintenance, alongside earning a bicycle for individual use. 48

Intervention and outcomes

Findings are described across four tables ( tables 2–5 ). Table 2 shows all synthesised findings relating to mental health and table 3 shows all synthesised findings relating to physical health where the configured analysis identified qualitative and quantitative evidence supporting matched themes. Table 4 shows evidence that was identified in either quantitative or qualitative reports alone. For example, findings, where only quantitative data existed, were related to bone health and blood markers. Whereas qualitative evidence only was identified relating to other important aspects of physical activity, not specifically or directly health-related, such as the benefits carried into wider life, challenges of participation and addiction.

Summary of synthesised findings relating to mental health benefits of physical activity participation

Summary of synthesised findings relating to physical health benefits of physical activity participation

Outcomes where quantitative only or qualitative only findings exist, no mixed-methods synthesis

Summary of available evidence for physical activity interventions categorised by intervention type, findings and evidence quality

The impact of physical activity interventions on the mental health of PEH

There were several domains within mental health where both quantitative and qualitative evidence was synthesised, suggesting physical activity was beneficial (summarised in table 2 ). These included enhanced confidence, empowerment and self-esteem; resilience, coping and hope; independence, self-regulation and personal development; stress and anxiety; and mood and state of mind.

Enhanced confidence, empowerment and self-esteem

There was high quality qualitative evidence that group running, soccer and indoor group exercise, and moderate quality qualitative evidence that group outdoor adventure and earn-a-bike enhanced confidence, empowerment and self-esteem. However, the only quantitative study to assess outcomes in this domain used the Hope scale (agency subscale), finding no significant differences between groups. One soccer player suggested:

… Football gave me confidence and took away feelings of depression as it made me more social. 44

Resilience, coping and hope

There was high quality qualitative evidence that group running, and group outdoor adventure enhanced resilience, coping and hope. However, the only quantitative study to measure relevant outcomes using the Hope Scale (pathway domain) found no significant difference between intervention and control groups. A member of staff involved in delivering group outdoor adventure described changes in a participant’s ability to cope:

… when we went to Coniston, not even 10 min, we was there she wanted to come home, but she didn’t and she learned how to cope… she really enjoyed herself. 35

Independence, self-regulation and personal development

Qualitative evidence suggested that group running, and soccer (both high quality) and group outdoor adventure and earn-a-bike (both moderate quality) enhanced independence, self-regulation and personal development. This was supported by moderate quality quantitative evidence that outdoor adventure improved life functioning. An outdoor adventure participant describes how it impacted them:

when I leave here, I face any challenges… in my life, then I know that I will be able to do them because I’ve become a stronger person from coming here. 35

Stress and anxiety

There was high quality qualitative evidence that group running, indoor group exercise and outdoor adventure and moderate quality qualitative evidence that soccer and earn-a-bike had a positive effect on stress and anxiety. The studies that used quantitative measures to assess stress/anxiety in soccer (moderate quality) and indoor group exercise (low quality) did not conclusively support the qualitative evidence. A participant at a gym-based programme said:

I… didn’t have the confidence to go outside, I felt a lot of like anxiety and this, the gym and stuff helps me with my anxiety really well. 36

Mood and state of mind

There was high quality qualitative evidence that soccer, group running and indoor group exercise and moderate quality qualitative evidence that earn-a-bike enhanced mood and state of mind. This was supported by moderate quality quantitative evidence that group outdoor adventure improved well-being.

The impact of physical activity interventions on the physical health of PEH

Changes were shown in the following physical health domains: body shape and weight loss; fitness levels; physical skills development and physical activity levels. The synthesised findings are summarised in table 3 . Quantitative findings not corroborated by qualitative findings are summarised in table 4 .

Body shape and weight loss

Synthesised findings showed that indoor group exercise and group running (both high quality qualitative evidence) were perceived as improving body shape and facilitating weight loss, while soccer was shown to significantly decrease weight-bearing fat mass and total fat mass (high quality quantitative evidence).

I took my measurements when I started street fit, and I took my measurements now, and I’m a lot more buff. 36

Fitness levels

Synthesised findings for fitness levels showed that group running, group indoor training and earn-a-bike (all high-quality qualitative evidence) significantly improved fitness and endurance levels, a finding backed up by a high-quality quantitative study of soccer. A person who cycled with earn-a-bike described trying to increase fitness:

… after riding, you know, for an hour, two hours, and sometimes I’ll ride for four hours. You know, I really want to make sure that my body is fit. 48

Physical skill development

While moderate quality qualitative evidence for group outdoor adventure was suggestive of positive changes in physical skills development, the quantitative research exploring this domain through measuring postural balance showed no significant difference between intervention and control groups. However, when comparing pre to post values in the intervention group, postural balance improved by 39% (p=0.004) in the right leg and 45% (p=0.006) in the left leg.

Physical activity levels

Synthesised findings showed that group indoor exercise and running groups (both high quality qualitative evidence) and earn-a-bike (moderate qualitative evidence) positively influenced physical activity levels. This was supported by a moderate quality quantitative pedometer and set a step count study. A woman from a running group described how since joining the group she now runs on her own:

I feel so much more body confident … I can actually run for the whole session without nearly dying. I also go out for runs on my own and I definitely think I’ve got faster. 38

Bone health and cholesterol

A high quality study measured markers of bone health 41 and cholesterol levels 39 in PEH who played soccer. Although not all bone markers improved, increases in osteocalcin from pre-intervention to post-intervention were reported and this change was significantly different between controls and intervention groups. With regards to cholesterol markers (low-density lipoprotein-lipid (LDL)/high-density lipoprotein (HDL)) cholesterol was lowered and LDL:HDL ratios increased in the intervention group after 12 weeks of soccer—findings which were significantly different (p=0.05) from the control group.

Other considerations relevant to physical activity interventions for PEH

There were some findings relevant which described the impact of physical activity for PEH described in qualitative literature only. Themes include addiction, self-medication and medication; benefits carried into wider life and challenges to participation in physical activity when homeless (outlined in table 4 ).

Addiction, self-medication and medication

Across several qualitative studies of soccer (high quality) and earn-a-bike (moderate quality), physical activity positively influencing addiction was described. One person who played football stated:

I’m drinking less and do not think I need alcohol as much now… It’s great to feel this way and football is a focus for us. 44

Benefits of physical activity participation carried into wider life

Most of the qualitative studies, including soccer, running groups, earn-a-bike, outdoor adventure, gardening and dance, described benefits to wider life. Subthemes included: development of life and interpersonal skills, improved social connectedness and relationships with others, practical and functional benefits, and physical activity as a catalyst for positive healthy life change. A participant who undertook leisure centre-based group indoor training said:

I’ve noticed a massive improvement in my fitness, and it’s definitely keeping me motivated to live a healthy lifestyle, because you don’t put in all that hard work and then want to ruin it, you know what I mean? 36

… and similarly, how a participant of soccer described life change:

We can go back there and show that homelessness isn’t permanent and that you can change your life through sports. 33

Challenges to participation in physical activity when homeless

Qualitative evidence demonstrated the importance of acknowledging specific challenges related to physical activity PEH faced, which impacted uptake and dropout rates across a variety of interventions. Those who participated in soccer tournaments described heavy defeats impacting on self-worth. 44 Women who participated in running groups described lack of funds for transport or the unpredictability of homelessness as a barrier to attending. 38 There was also worry about loss of donated kit (eg, running clothes) 38 and equipment (eg, bicycle) 48 through theft and staff who led dance groups reported inconsistent attendance among shelter-dwellers. 31

An overall summary of available evidence for physical activity interventions categorised by intervention type, findings and evidence quality is provided in table 5 .

This review identified evidence for diverse physical activity interventions for PEH. The mixed-methods methodology enabled a meaningfully configured synthesis of the breadth of available evidence. This review demonstrated positive impacts of physical activity for PEH in relation to mental and physical health outcomes with translation of benefits to wider life.

Physical activity interventions were heterogeneous, grouped into broad categories of soccer, group outdoor exercise, group indoor multimodal exercise and individualised multimodal interventions. In terms of specific sports, soccer predominated (7/17). This is unsurprising considering its global resonance. 37 The mental health benefits of physical activity participation identified in our review align with research carried out in non-homeless populations, for example, the psychological state of ‘flow’ (where a person feels simultaneously cognitively efficient, motivated and happy) has been found to be increased by soccer training and running. 49 However, the majority (4/7) of soccer interventions included in our review included tournament participation. While benefits to tournament participation exist, negative experiences of pressure and fear of letting down teammates were qualitatively reported. Organisers of soccer tournaments for PEH should consider support to ameliorate impacts of possible pressure experienced, which could negatively impact mental health or self-management of addiction. Moreover, our review highlights that comparing the nuances of benefits and challenges of tournament participation and training warrants further research.

Group exercise appeared to be most beneficial for PEH. It is likely that group activities facilitated social support, which is especially pertinent for PEH whose social networks are often fragmented. 50 Configured qualitative and quantitative findings highlighted most evidence for mental health benefits in group outdoor exercise. Specifically, these benefits related to an improvement in mood and state of mind and increased independence, focus, personal development and ability to foster relationships. This may be related to emerging evidence for optimised benefits of outdoor exercise. 51 Corroboration of qualitative and quantitative evidence indicated that PEH who participated in physical activity interventions increased their physical activity levels. There is inherent difficulty comparing types of interventions for levels of benefit, as interventions and outcome measures were heterogeneous. Many physical activity interventions included additional intervention components such as counselling, food or sports kit. Consequently, it is not known if these additional components, enhanced or diluted the effect of physical activity. Moreover, descriptions of physical activity programme variables such as dosage were often lacking, limiting judgment of interventions.

Programme intensity deserves consideration. Soccer, which predominated, is a vigorous intensity sport (10 metabolic equivalent of task (METs) for competitive soccer and 7 METs for casual soccer) 52 so it is likely this high entry level may be exclusionary for some PEH. It should also be considered that some participants may be content to participate on the field while exerting minimal energy, so a diversity in intensity levels is also possible. Given the high prevalence and early manifestation of non-communicable diseases and poor general physical health in many PEH, 53 specifically focused lower threshold physical activity interventions should be also considered. Some low threshold programmes were identified such as gardening and dance. People designing physical activity interventions for PEH should consider a range of abilities and likely poor physical condition, perhaps offering a choice of low threshold activity, as well as higher intensity options, depending on ability and interest.

Qualitative studies dominated the evidence base, justifying the methodological decision of a mixed-methods review. The quality of evidence of most qualitative studies was judged to be high, with perspectives of staff enhancing credibility to the understanding of intervention impact. Significant changes were reported for the outcomes of weightbearing fat mass and overall fat mass in one soccer study, 41 although changes in muscle mass were not reported. Cardiovascular fitness and endurance also improved significantly in soccer studies. 39 42 While these findings were in small, uncontrolled studies, the implication of even minor changes to outcomes such as cardiovascular fitness and endurance may be of importance to PEH, as this group is significantly more likely than housed people to be hospitalised due to acute trauma. 54 Although not specifically explored in this population, it is likely that higher baseline fitness and strength levels may aid recovery post-hospitalisation, so multifaceted programmes addressing cardiovascular endurance and strength may be most beneficial for this population. A limitation of the evidence identified was that only one quantitative study was an RCT. While RCTs are considered the highest evidence level, this review attests to the usefulness of other study designs in this novel and emerging topic. It is acknowledged that RCTs may be especially difficult to undertake due to possible implementation barriers and complexities within this cohort. We propose that to build the evidence base, forms of controlled trials should be conducted where possible, with a view to including more randomised trials in the future. A further limitation was that feasibility outcomes such as adherence and retention rates were not well described, though challenges to participation were described in several qualitative studies. Feasibility analysis, including assessment of adherence and retention, should be included in future studies. Outcome measures employed were not consistent, for example, cardiovascular fitness was measured in three different ways: the Yo-Yo endurance test, cycle ergometry and maximal treadmill testing. The evidence base is limited in terms of the most suitable outcome measures 55 to use in physical activity interventions for PEH. Future studies should explore the most suitable outcome measures with a view to improving consistency in their use to enable future evidence syntheses.

Strengths of this review were its mixed-methods design and the global spread of identified research including studies from the UK and Europe, North America and Australia. However, only high-income countries were included, as low-income and middle-income settings were considered to have different structural influences on homelessness. So a limitation of this systematic review is that the translation of findings to other settings is not known. With regards to descriptions of exclusion criteria, included studies appeared to be pragmatic with minimal reporting of these. For example, no studies listed addiction status or gender diversity as pre-specified barriers to inclusion. Notably, most studies described the outcomes of ‘real world’ established programmes for PEH. In these cases, study eligibility criteria were dependent on those who engaged with the specific programme in the first instance, the eligibility criteria for which were not described, and were most likely self-selection. Only a small number of drop-outs were reported and there was minimal detail about their characteristics. We recognise that a level of stability in addiction and overall socioeconomic status is required to enable engagement in any type of physical activity intervention. Consequently, conclusions drawn from our review may not be applicable to the full diversity of PEH.

A final strength was that the review team capitalised on expertise in inclusion health, physical activity interventions and evidence synthesis with input from expert medical librarians. Studies were quality assessed using a consistent ‘family’ of critical analysis tools from JBI.

This mixed-methods systematic review demonstrates the value in exploring literature across a wide variety of methodological domains to gain insights into the existence and impact of a variety of physical activity interventions for PEH. To confidently inform policy, more research in this topic is required, however, from a practice and research perspective, our results provide initial justification for the inclusion of this typically under-represented group in targeted physical activity interventions with benefits to multiple aspects of physical and mental health, and positive translation into wider life demonstrated. Future research should include larger-scale high quality quantitative research to provide more robust evidence regarding objective impact.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Acknowledgments.

The authors would like to thank Jacqui Smith, clinical librarian at UCL for sharing her extensive knowledge and supporting the team with their protocol design, searching strategy and carrying out the searches. Thanks also to and David Mockler, librarian, Trinity College Dublin, Dr Cliona Ni Cheallaigh and Professor Andrew Hayward for their advice and support of this work.

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Supplementary materials

Supplementary data.

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Twitter @DawesJo

Contributors JFD, RR-W and JB all contributed to the planning, conduct and reporting of the work described in the manuscript. JFD is responsible for the overall content as guarantor. JFD and RR-W designed and contributed to the initial registration of the research and the identification of literature at the search stage. JFD, RR-W and JB all contributed to the screening, data extraction and reporting. All authors contributed to the writing up, review, editing and finalising of the manuscript.

Funding JFD was funded by a pre-doctoral fellowship from the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR), Grant Reference Number PD-SPH-2015. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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