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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.

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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, 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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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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

14 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

This post is really helpful.

how to cite this page

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

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

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

This is very helpful and very useful!

Wow! This post has an awful explanation. Appreciated.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

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?

• Hire an expert from ResearchProspect today!
• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

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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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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What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

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Basic Research Design

What is research design.

• Definition of Research Design : A procedure for generating answers to questions, crucial in determining the reliability and relevance of research outcomes.
• Importance of Strong Designs : Strong designs lead to answers that are accurate and close to their targets, while weak designs may result in misleading or irrelevant outcomes.
• Criteria for Assessing Design Strength : Evaluating a design’s strength involves understanding the research question and how the design will yield reliable empirical information.

The Four Elements of Research Design (Blair et al., 2023)

• The MIDA Framework : Research designs consist of four interconnected elements – Model (M), Inquiry (I), Data strategy (D), and Answer strategy (A), collectively referred to as MIDA.
• Theoretical Side (M and I): This encompasses the researcher’s beliefs about the world (Model) and the target of inference or the primary question to be answered (Inquiry).
• Empirical Side (D and A): This includes the strategies for collecting (Data strategy) and analyzing or summarizing information (Answer strategy).
• Interplay between Theoretical and Empirical Sides : The theoretical side sets the research challenges, while the empirical side represents the researcher’s responses to these challenges.
• Relation among MIDA Components: The diagram above shows how the four elements of a design are interconnected and how they relate to both real-world and simulated quantities.
• Parallelism in Design Representation: The illustration highlights two key parallelisms in research design – between actual and simulated processes, and between the theoretical (M, I) and empirical (D, A) sides.
• Importance of Simulated Processes: The parallelism between actual and simulated processes is crucial for understanding and evaluating research designs.
• Balancing Theoretical and Empirical Aspects : Effective research design requires a balance between theoretical considerations (models and inquiries) and empirical methodologies (data and answer strategies).

Research Design Principles (Blair et al., 2023)

• Integration of Components: Designs are effective not merely due to their individual components but how these components work together.
• Focus on Entire Design: Assessing a design requires examining how each part, such as the question, estimator, and sampling method, fits into the overall design.
• Importance of Diagnosis: The evaluation of a design’s strength lies in diagnosing the whole design, not just its parts.
• Strong Design Characteristics: Designs with parallel theoretical and empirical aspects tend to be stronger.
• The M:I:D:A Analogy: Effective designs often align data strategies with models and answer strategies with inquiries.
• Flexibility in Models: Good designs should perform well even under varying world scenarios, not just under expected conditions.
• Broadening Model Scope: Designers should consider a wide range of models, assessing the design’s effectiveness across these.
• Robustness of Inquiries and Strategies: Inquiries should yield answers and strategies should be applicable regardless of variations in real-world events.
• Diagnosis Across Models: It’s important to understand for which models a design excels and for which it falters.
• Specificity of Purpose: A design is deemed good when it aligns with a specific purpose or goal.
• Balancing Multiple Criteria: Designs should balance scientific precision, logistical constraints, policy goals, and ethical considerations.
• Diverse Goals and Assessments: Different designs may be optimal for different goals; the purpose dictates the design evaluation.
• Early Planning Benefits: Designing early allows for learning and improving design properties before data collection.
• Avoiding Post-Hoc Regrets: Early design helps avoid regrets related to data collection or question formulation.
• Iterative Improvement: The process of declaration, diagnosis, and redesign improves designs, ideally done before data collection.
• Adaptability to Changes: Designs should be flexible to adapt to unforeseen circumstances or new information.
• Expanding or Contracting Feasibility: The scope of feasible designs may change due to various practical factors.
• Continual Redesign: The principle advocates for ongoing design modification, even post research completion, for robustness and response to criticism.
• Improvement Through Sharing: Sharing designs via a formalized declaration makes it easier for others to understand and critique.
• Enhancing Scientific Communication: Well-documented designs facilitate better communication and justification of research decisions.
• Building a Design Library: The idea is to contribute designs to a shared library, allowing others to learn from and build upon existing work.

The Basics of Social Science Research Designs (Panke, 2018)

Deductive and inductive research.

• Starting Point: Begins with empirical observations or exploratory studies.
• Development of Hypotheses: Hypotheses are formulated after initial empirical analysis.
• Case Study Analysis: Involves conducting explorative case studies and analyzing dynamics at play.
• Generalization of Findings: Insights are then generalized across multiple cases to verify their applicability.
• Application: Suitable for novel phenomena or where existing theories are not easily applicable.
• Example Cases: Exploring new events like Donald Trump’s 2016 nomination or Russia’s annexation of Crimea in 2014.
• Theory-Based: Starts with existing theories to develop scientific answers to research questions.
• Hypothesis Development: Hypotheses are specified and then empirically examined.
• Empirical Examination: Involves a thorough empirical analysis of hypotheses using sound methods.
• Theory Refinement: Results can refine existing theories or contribute to new theoretical insights.
• Application: Preferred when existing theories relate to the research question.
• Example Projects: Usually explanatory projects asking ‘why’ questions to uncover relationships.

Explanatory and Interpretative Research Designs

• Definition: Explanatory research aims to explain the relationships between variables, often addressing ‘why’ questions. It is primarily concerned with identifying cause-and-effect dynamics and is typically quantitative in nature. The goal is to test hypotheses derived from theories and to establish patterns that can predict future occurrences.
• Definition: Interpretative research focuses on understanding the deeper meaning or underlying context of social phenomena. It often addresses ‘how is this possible’ questions, seeking to comprehend how certain outcomes or behaviors are produced within specific contexts. This type of research is usually qualitative and prioritizes individual experiences and perceptions.
• Explanatory Research: Poses ‘why’ questions to explore causal relationships and understand what factors influence certain outcomes.
• Interpretative Research: Asks ‘how is this possible’ questions to delve into the processes and meanings behind social phenomena.
• Explanatory Research: Relies on established theories to form hypotheses about causal relationships between variables. These theories are then tested through empirical research.
• Interpretative Research: Uses theories to provide a framework for understanding the social context and meanings. The focus is on constitutive relationships rather than causal ones.
• Explanatory Research: Often involves studying multiple cases to allow for comparison and generalization. It seeks patterns across different scenarios.
• Interpretative Research: Typically concentrates on single case studies, providing an in-depth understanding of that particular case without necessarily aiming for generalization.
• Explanatory Research: Aims to produce findings that can be generalized to other similar cases or populations. It seeks universal or broad patterns.
• Interpretative Research: Offers detailed insights specific to a single case or context. These findings are not necessarily intended to be generalized but to provide a deep understanding of the particular case.

Qualitative, Quantitative, and Mixed-method Projects

• Definition: Qualitative research is exploratory and aims to understand human behavior, beliefs, feelings, and experiences. It involves collecting non-numerical data, often through interviews, focus groups, or textual analysis. This method is ideal for gaining in-depth insights into specific phenomena.
• Example in Education: A qualitative study might involve conducting in-depth interviews with teachers to explore their experiences and challenges with remote teaching during the pandemic. This research would aim to understand the nuances of their experiences, challenges, and adaptations in a detailed and descriptive manner.
• Definition: Quantitative research seeks to quantify data and generalize results from a sample to the population of interest. It involves measurable, numerical data and often uses statistical methods for analysis. This approach is suitable for testing hypotheses or examining relationships between variables.
• Example in Education: A quantitative study could involve surveying a large number of students to determine the correlation between the amount of time spent on homework and their academic achievement. This would involve collecting numerical data (hours of homework, grades) and applying statistical analysis to examine relationships or differences.
• Definition: Mixed-method research combines both qualitative and quantitative approaches, providing a more comprehensive understanding of the research problem. It allows for the exploration of complex research questions by integrating numerical data analysis with detailed narrative data.
• Example in Education: A mixed-method study might investigate the impact of a new teaching method. The research could start with quantitative methods, like administering standardized tests to measure learning outcomes, followed by qualitative methods, such as conducting focus groups with students and teachers to understand their perceptions and experiences with the new teaching method. This combination provides both statistical results and in-depth understanding.
• Research Questions: What kind of information is needed to answer the questions? Qualitative for “how” and “why”, quantitative for “how many” or “how much”, and mixed methods for a comprehensive understanding of both the breadth and depth of a phenomenon.
• Nature of the Study: Is the study aiming to explore a new area (qualitative), confirm hypotheses (quantitative), or achieve both (mixed-method)?
• Resources Available: Time, funding, and expertise available can influence the choice. Qualitative research can be more time-consuming, while quantitative research may require specific statistical skills.
• Data Sources: Availability and type of data also guide the methodology. Existing numerical data might lean towards quantitative, while studies requiring personal experiences or opinions might be qualitative.

References:

Blair, G., Coppock, A., & Humphreys, M. (2023).  Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign . Princeton University Press.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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

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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design? Research design is the structure of research methods and techniques selected to conduct a study. It refines the methods suited to the subject and ensures a successful setup. Defining a research topic clarifies 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:

1. Data Collection

2. Measurement

3. Data Analysis

Elements of Research Design 

Now that you know what is research design, it is important to know the elements and components of research design. Impactful research minimises bias and enhances data accuracy. Designs with minimal error margins are ideal. Key elements include:

1. Accurate purpose statement

2. Techniques for data collection and analysis

3. Methods for data analysis

4. Type of research methodology

5. Probable objections to research

6. Research settings

7. Timeline

8. Measurement of analysis

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Characteristics of Research Design

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

1. Reliability

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

2. Validity

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

3. Neutrality

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

4. Generalizability

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

Research Design Process

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

1. Consider Aims and Approaches

Define the research questions and objectives, and establish the theoretical framework and methodology.

2. Choose a Type of Research Design

Select the suitable research design, such as experimental, correlational, survey, case study, or ethnographic, according to the research questions and objectives.

3. Identify Population and Sampling Method

Determine the target population and sample size, and select the sampling method, like random, stratified random sampling, or convenience sampling.

4. Choose Data Collection Methods

Decide on the data collection methods, such as surveys, interviews, observations, or experiments, and choose the appropriate instruments for data collection.

5. Plan Data Collection Procedures

Create a plan for data collection, detailing the timeframe, location, and personnel involved, while ensuring ethical considerations are met.

6. Decide on Data Analysis Strategies

Select the appropriate data analysis techniques, like statistical analysis, content analysis, or discourse analysis, and plan the interpretation of the results.

What are the Types of Research Design?

A researcher must grasp various types to decide which model to use for a study. There are different research designs that can be broadly classified into quantitative and qualitative.

Qualitative Research

Qualitative research identifies relationships between collected data and observations through mathematical calculations. Statistical methods validate or refute theories about natural phenomena. This research method answers "why" a theory exists and explores respondents' perspectives.

Quantitative Research

Quantitative research is essential when statistical conclusions are needed to gather actionable insights. Numbers provide clarity for critical business decisions. This method is crucial for organizational growth, with insights from complex numerical data guiding future business decisions.

Qualitative Research vs Quantitative Research

While researching, it is important to know the difference between qualitative and quantitative research. Here's a quick difference between the two:

The research types can be further divided into 5 categories:

1. Descriptive Research

Descriptive research design focuses on detailing a situation or case. It's a theory-driven method that involves gathering, analysing, and presenting data. This approach offers insights into the reasons and mechanisms behind a research subject, enhancing understanding of the research's importance. When the problem statement is unclear, exploratory research can be conducted.

2. Experimental Research

Experimental research design investigates cause-and-effect relationships. It’s a causal design where the impact of an independent variable on a dependent variable is observed. For example, the effect of price on customer satisfaction. This method efficiently addresses problems by manipulating independent variables to see their effect on dependent variables. Often used in social sciences, it involves analysing human behaviour by studying changes in one group's actions and their impact on another group.

3. Correlational Research

Correlational research design is a non-experimental technique that identifies relationships between closely linked variables. It uses statistical analysis to determine these relationships without assumptions. This method requires two different groups. A correlation coefficient between -1 and +1 indicates the strength and direction of the relationship, with +1 showing a positive correlation and -1 a negative correlation.

4. Diagnostic Research

Diagnostic research design aims to identify the underlying causes of specific issues. This method delves into factors creating problematic situations and has three phases: 

• Issue inception
• Issue diagnosis
• Issue resolution

5. Explanatory Research

Explanatory research design builds on a researcher’s ideas to explore theories further. It seeks to explain the unexplored aspects of a subject, addressing the what, how, and why of research questions.

Benefits of Research Design

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

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

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

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

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

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

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

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

Frequently Asked Questions

What are the key components of a research design, how can i choose the best research design for my study, what are some common pitfalls in research design, and how can they be avoided, how does research design impact the validity and reliability of a study, what ethical considerations should be taken into account in research design.

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• v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

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

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

How to cite this article

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

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

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

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

Longitudinal vs Transversal Studies

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

Forward vs Backward Directed Studies

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

Prospective vs Retrospective Studies

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

CLASSIFICATION OF STUDY DESIGNS

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

Descriptive Studies

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

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

Analytical (Inferential) Studies

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

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

Cross-sectional Studies

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

Case–control Studies

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

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

Cohort Studies

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

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

Interventional Studies

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

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

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

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

Source of support: Nil

Conflict of interest: None

What is Research Design? Elements, Types, Examples

Appinio Research · 06.09.2023 · 24min read

Ever wondered what lays the foundation for successful research studies? It all starts with a well-crafted research design. In the world of inquiry, research design is the guiding compass that shapes the entire process, helping you navigate complexities and unlock the doors to meaningful insights. Whether you're embarking on your first research journey or seeking to refine your skills, understanding the art and science of research design is the key to unlocking the true potential of your investigations.

What is Research Design?

Research design is like the roadmap for your research journey. Imagine planning a cross-country trip: you wouldn't hit the road without a clear route, right? Similarly, research design provides the structure and strategy you need to navigate your way through the complexities of a study.

It's the blueprint that outlines the steps you'll take, the methods you'll use, and the goals you aim to achieve.

At its core, research design is all about making smart decisions. It's about choosing the best tools to answer your questions and gather information. Whether you're exploring the effects of a new drug, understanding the habits of a specific demographic, or investigating the behaviors of animals, a well-designed research plan sets the stage for success.

In a nutshell, research design is your guide, helping you collect data, draw conclusions, and make meaningful contributions to your field.

Why is Research Design Important in the Research Process?

Research design plays a crucial role in ensuring the success of your research study. A well-designed research plan:

• Provides structure and direction to your study.
• Helps in clearly defining research objectives and questions.
• Guides the choice of appropriate methodologies and data collection methods .
• Ensures that ethical considerations are addressed.
• Enhances the validity and reliability of your findings.

How Research Design Affects Study Outcomes

Your research design has a direct impact on the outcomes of your study. A well-crafted research plan:

• Increases the likelihood of obtaining accurate and reliable results.
• Enables you to draw valid conclusions and make meaningful interpretations.
• Enhances the credibility and generalizability of your findings.
• Guides the implementation of research procedures in a consistent and organized manner.

Key Elements of a Research Study

A well-designed research study is like a puzzle where every piece fits perfectly to reveal a clear picture. These fundamental elements ensure that your research is structured, meaningful, and capable of generating credible insights.

Clear Research Objectives

Think of research objectives as your guiding stars. They define what you aim to achieve with your study. Clear goals keep you on track, guiding your research questions, methods, and analysis.

Precise Research Questions and Hypotheses

Research questions and hypotheses are the compass that points you in the right direction. They provide focus by outlining what you want to explore and predict. Well-crafted questions and hypotheses make your study purposeful and relevant.

Appropriate Methodology Selection

Choosing a suitable methodology is like selecting the best tool for the job. Quantitative methods are your go-to for measurable data, while qualitative methods help you dive deep into complex human experiences. Mixed methods offer the best of both worlds.

Thoughtful Participant Selection

Selecting the right participants is like assembling a diverse team for a project. Your sample should represent the population you're studying. Choose appropriate sampling techniques and determine the sample size that strikes the right balance between accuracy and feasibility.

Effective Data Collection Strategies

Data collection is like gathering puzzle pieces. Choose methods that align with your research goals. Surveys, interviews, observations, and experiments are just a few of the tools at your disposal.

Reliable Research Instrument Development

Research instruments are your tools for collecting data. Whether it's a questionnaire or an interview guide, they need to be well-constructed, unbiased, and capable of capturing the information you need.

Thoughtful Research Procedure Design

Your research procedure is the timeline that ensures everything happens in the proper order. From recruiting participants to data analysis, a well-structured procedure keeps your study organized and efficient.

Rigorous Data Analysis and Interpretation

Data analysis is where you piece the puzzle together. Applying the right techniques to your data—whether quantitative or qualitative —reveals patterns, relationships, and insights that answer your research questions.

Validity and Reliability Considerations

Validity and reliability are the quality checks of your study. Validity ensures that your measurements are accurate, while reliability guarantees consistency. Addressing these ensures your findings hold true and can be trusted.

Ethical Considerations

Ethical considerations are the foundation of responsible research. Protect participants' rights, ensure their consent, and follow ethical guidelines to conduct your study with integrity.

A well-designed research study brings all these elements together harmoniously, resulting in a comprehensive, credible, and impactful exploration of your chosen research topic.

Types of Research Design

Research design comes in various flavors, each tailored to answer different types of questions and explore diverse aspects of your research topic. Let's dive into the main types of research designs to help you choose the one that aligns with your objectives.

Quantitative Research Designs

Quantitative research is all about numbers and measurements. If you're interested in uncovering patterns, relationships, and trends through numerical data, these designs are your go-to options:

• Experimental Design: This design allows you to manipulate variables to establish cause-and-effect relationships. Think of it as a controlled experiment where you change one thing to see how it impacts another.
• Survey Research: Surveys are your ticket to collecting a lot of data from a wide range of people. Structured questionnaires help gather standardized responses, making it easy to analyze patterns.
• Longitudinal Studies : Imagine tracking a group of people over years to see how they change. Longitudinal studies dive deep into understanding development, behaviors, or changes within a specific group.

Qualitative Research Designs

Qualitative research focuses on understanding the complexities of human experiences, behaviors, and contexts. If you're intrigued by narratives and in-depth insights, consider these designs:

• Case Study: Dive deep into a single subject, exploring it from every angle. It's like zooming in on a single puzzle piece to understand its intricate details.
• Ethnographic Study : If you want to immerse yourself in a culture or community, ethnography is your tool. Live among the people you're studying to grasp their worldviews and practices.
• Grounded Theory: This design is all about building theories from scratch based on the data you collect. It's like letting the information guide you toward new insights and concepts.

Mixed Methods Research

Sometimes, one approach just isn't enough. Mixed methods research combines both quantitative and qualitative methods to give you a comprehensive view of your research topic. It's like using wide-angle and macro lenses together to capture the big picture and the tiny details.

Each research design has its strengths and shines in different situations. The type you choose will depend on your research questions, goals, and the kind of insights you aim to uncover.

How to Define Research Objectives and Questions?

At the heart of every research study are clear and focused objectives, along with well-crafted research questions or hypotheses. We'll dive into the process of formulating these crucial components, ensuring that your study remains on track and purposeful.

1. Formulate Clear Research Objectives

Research objectives outline the specific goals you aim to achieve through your study. Clear and concise (SMART) objectives provide direction and purpose to your research. Here's how to formulate well-crafted research objectives:

• Be Specific: Clearly state what you intend to accomplish.
• Be Measurable: Define outcomes that can be quantified or observed.
• Be Achievable: Set realistic goals within the scope of your study.
• Be Relevant: Ensure that your objectives align with the research problem.
• Be Time-Bound: Specify a timeframe for achieving your objectives.

2. Develop Research Questions and Hypotheses

Research questions and hypotheses guide your study and direct your research efforts. They should be focused, relevant, and provide a clear framework for investigation.

• Research Questions: These are open-ended queries that help you explore a particular topic. They often start with words like "what," "how," or "why." For example: "What are the factors that influence consumer purchasing decisions?"
• Hypotheses: Hypotheses are statements that propose a specific relationship between variables. They are testable predictions about the outcomes of your study. For example: "Increasing the price of a product will result in decreased sales."

3. Ensure Alignment Between Objectives and Questions

It's essential to ensure that your research objectives and questions are well-aligned. Your research questions should directly address your objectives, helping you fulfill the purpose of your study.

By formulating clear research objectives and crafting well-structured questions or hypotheses, you'll establish a strong foundation for your research study.

How to Select Research Participants?

The participants in your research study form the foundation upon which your findings rest. Proper participant selection is crucial for obtaining relevant and reliable data.

Sampling Techniques

Sampling involves selecting a subset of individuals from a larger pool to represent the whole. The choice of sampling technique depends on the research goals and the nature of the population.

• Probability Sampling: Probability sampling ensures that each member of the population has an equal chance of being selected. Common methods include simple random sampling, stratified sampling, and cluster sampling .
• Non-Probability Sampling: Non-probability sampling methods do not guarantee equal representation. These methods include convenience sampling, purposive sampling, and snowball sampling.

Sample Size Determination

Determining the appropriate sample size  is essential to ensure the reliability of your findings. An inadequate sample size might lead to biased results, while an excessively large sample might be wasteful.

Ethical Considerations in Participant Selection

Respecting the rights and well-being of your participants is paramount. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and minimizing potential harm.

By selecting the right participants and adhering to ethical guidelines, you'll lay the groundwork for collecting meaningful and trustworthy data.

Research Data Collection Strategies

Collecting data is a fundamental step in the research process. The strategies you choose for data collection directly influence the quality and validity of your findings.

Quantitative Data Collection

Quantitative data collection involves gathering numerical information that can be analyzed statistically. Here are some common strategies:

• Surveys and Questionnaires: Surveys and questionnaires allow you to collect standardized responses from a large number of participants. They are useful for obtaining quantitative data on attitudes, preferences, and behaviors.
• Experiments: Experimental design involves manipulating variables to observe their effects. Controlled experiments provide insights into causal relationships, and random assignment helps minimize bias.
• Observations and Secondary Data Analysis: Direct observations of subjects or behaviors can provide valuable data. Additionally, analyzing existing datasets (secondary data) can save time and resources.

Qualitative Data Collection

Qualitative data collection focuses on capturing rich, context-specific information. Here are some effective methods:

• Interviews: Interviews involve direct interaction with participants to gather in-depth insights. Types include structured, semi-structured, and unstructured interviews, each offering a different level of flexibility.
• Focus Groups : Focus groups bring together a small group of participants to discuss a specific topic. This method encourages open discussions and the exploration of diverse perspectives.
• Participant Observation: Participant observation involves immersing yourself in the research setting to understand behaviors, interactions, and dynamics. It's particularly beneficial in ethnographic studies.

Data Validity and Reliability Across Methods

Ensuring the validity and reliability of collected data is crucial for drawing accurate conclusions. Validity refers to the accuracy of measurements, while reliability is the consistency of results. Across quantitative and qualitative methods, these principles apply:

• Quantitative: Ensure survey questions are straightforward, and measures are accurate and consistent.
• Qualitative: Maintain consistency in data collection procedures, and use techniques like member checking and triangulation to enhance validity.

Enhance your data collection strategies with the power of modern research technology. Appinio offers a comprehensive platform designed to streamline both quantitative and qualitative data collection. With user-friendly survey and questionnaire tools and in-depth interview capabilities, Appinio empowers researchers to gather high-quality data effortlessly.

Elevate the validity and reliability of your research with our cutting-edge platform. Book a demo today to explore how Appinio can transform your data collection process and help you achieve more accurate research outcomes!

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How to Develop Research Instruments?

Research instruments, such as surveys, interview protocols, and observation guides, are tools that help you collect data from participants. Developing effective instruments requires careful planning and attention to detail.

How to Construct Survey Instruments?

Surveys are a standard method for collecting data from many participants. To construct an effective survey instrument:

• Define Your Variables: Clearly define the variables you're measuring and ensure they align with your research questions.
• Use Clear Language: Write clear and concise questions using simple language to avoid confusion.
• Avoid Bias: Avoid leading or biased questions that could influence participant responses.
• Include Validity Checks: Incorporate validation questions to ensure respondents are providing accurate information.

How to Create Interview Protocols?

Interviews offer an opportunity to gather in-depth insights directly from participants. To create effective interview protocols:

• Structure Questions: Organize questions logically and flow from general to specific topics.
• Open-Ended Questions: Include open-ended questions encouraging participants to share their thoughts and experiences.
• Probing Questions: Develop probing questions to dig deeper into participant responses and gain deeper insights.

Pre-testing and Piloting Research Instruments

Before launching your research, pre-test or pilot your instruments with a small group of participants. This helps identify issues with clarity, wording, or question order and allows you to refine the instruments for maximum effectiveness.

By investing time in constructing well-designed research instruments, you'll collect accurate and relevant data that contribute to the success of your study.

How to Design the Research Procedure?

The research procedure outlines the step-by-step plan for conducting your study. A well-designed procedure ensures consistency, reliability, and efficiency in data collection.

To design an effective research procedure:

1. Sequence Research Activities

Sequencing research activities involves arranging the order in which different tasks will be carried out. Consider the following when creating your sequence:

• Logical Flow: Ensure that activities are organized in a logical order, from participant recruitment to data analysis.
• Dependencies: Identify tasks that depend on the completion of others and plan accordingly.
• Flexibility: Allow for some flexibility to accommodate unexpected challenges or opportunities.

2. Establish a Data Collection Timeline

Creating a timeline for your research helps you stay on track and manage your resources efficiently. Consider the following when establishing your timeline:

• Breakdown of Tasks: Divide the research process into manageable tasks and allocate time for each.
• Realistic Deadlines: Set realistic deadlines that consider the complexity of each task and potential delays.
• Buffer Periods: Include buffer periods to account for unforeseen delays or revisions.

3. Ensure Consistency in Data Collection Procedures

Consistency is crucial in obtaining reliable and valid data. Establish standardized procedures for data collection:

• Training: Train researchers involved in data collection to follow consistent procedures and protocols.
• Detailed Instructions: Provide clear and detailed instructions for each data collection method.
• Monitoring: Regularly monitor data collection to ensure adherence to procedures and address any issues.

By designing a well-structured research procedure, you'll ensure that your study progresses smoothly, data is collected consistently, and timelines are met. The next step is moving on to the crucial phase of data analysis and interpretation.

Research Data Analysis and Interpretation

Data analysis is the process of transforming raw data into meaningful insights. It's where you draw conclusions and make sense of the information you collected.

Quantitative Data Analysis Techniques

Quantitative data analysis involves processing numerical data to identify patterns and relationships. Here are some common techniques:

• Descriptive Statistics : Descriptive statistics, such as mean, median, and standard deviation, summarize and describe the main features of a dataset.
• Inferential Statistics : Inferential statistics help you draw conclusions about a population based on a sample. Techniques include t-tests , ANOVA , and regression analysis.
• Regression Analysis : Regression analysis helps you understand the relationships between variables and predict outcomes. Linear and logistic regressions are widely used.

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Qualitative Data Analysis Approaches

Qualitative data analysis involves interpreting non-numerical data to uncover themes and patterns. Here are some common approaches:

• Thematic Analysis : Thematic analysis involves identifying recurring themes or patterns in qualitative data. It helps you discover meaningful insights and concepts.
• Content Analysis: Content analysis is used to systematically analyze textual or visual content to identify specific patterns, themes, or trends.
• Constant Comparative Method: The constant comparative method involves comparing data points throughout the analysis to uncover patterns and relationships.

Validity and Reliability in Data Analysis

Ensuring the validity and reliability of your data analysis is essential for producing accurate findings:

• Triangulation: Use multiple data sources, methods, or analysts to validate your findings.
• Member Checking: Share your findings with participants to confirm that your interpretations align with their experiences.

By carefully analyzing and interpreting your data, you'll uncover insights that address your research questions and contribute to the overall understanding of your topic.

Validity and Reliability in Research Design

Validity and reliability are essential concepts in research design that ensure the credibility and trustworthiness of your study. In this section, we'll delve into these concepts and explore how they impact the quality of your research.

Internal Validity: Controlling for Confounding Variables

Internal validity refers to the degree to which your study accurately measures the cause-and-effect relationship you intend to study without interference from extraneous variables. To enhance internal validity:

• Control Groups : Use control groups in experimental designs to compare the effects of variables.
• Randomization: Randomly assign participants to groups to ensure unbiased distribution of characteristics.
• Eliminate Confounding Variables: Identify and control for factors that could influence your results but are not part of your research question.

External Validity: Generalizability of Findings

External validity refers to the extent to which your findings can be generalized to a broader population or real-world settings. To enhance external validity:

• Random Sampling: Use random sampling to ensure that your sample is representative of the larger population.
• Ecological Validity: Design your study to mirror real-world situations as closely as possible.
• Replication: Replicate your study with different populations or settings to validate your findings.

How to Ensure Research Reliability and Reproducibility?

Reliability refers to the consistency and stability of your measurements over time and across different conditions. To ensure research reliability:

• Consistent Procedures: Use standardized procedures for data collection and analysis.
• Inter-Rater Reliability: Have multiple researchers analyze data independently to assess agreement.
• Test-Retest Reliability: Repeat measurements on the same subjects to evaluate consistency.

Ethical Considerations in Research Design

Ethical guidelines are a fundamental aspect of research design. Respecting the rights and well-being of participants is paramount. These include:

• Informed Consent: Obtain informed consent from participants, ensuring they understand the study's purpose, procedures, and risks.
• Confidentiality: Protect participant privacy by safeguarding their personal information.
• Institutional Review Board (IRB): Obtain ethical approval from an IRB before conducting research involving human participants.
• Minimizing Harm: Ensure participants are not subjected to unnecessary physical, emotional, or psychological harm.

By addressing these validity, reliability, and ethical considerations, you'll ensure that your research study is rigorous, credible, and contributes meaningfully to the field.

As you progress, it's crucial to communicate your findings effectively. Let's explore how to do that next.

How to Report and Present Research Findings?

Effectively reporting and presenting your research findings is essential for sharing your insights with the academic community and beyond.

1. Structure the Research Report

A well-structured research report communicates your study clearly and concisely. The typical structure includes:

• Title: A clear and informative title that captures the essence of your study.
• Abstract: A brief summary of your research question, methods, findings, and conclusions.
• Introduction: Introduce the research problem, objectives, and significance of the study.
• Literature Review: Review existing research and theories relevant to your topic.
• Methodology: Describe your research design, participants, data collection, and analysis methods.
• Results: Present your findings using tables, charts, and statistical analysis .
• Discussion: Interpret your results, relate them to existing literature, and address implications.
• Conclusion: Summarize your study, restate findings, and suggest future research directions.
• References: Cite sources you've referenced throughout the report.

2. Create Visual Representations of Data

Visual representations, such as graphs, charts, and tables, help convey complex information more easily. Use appropriate visuals to illustrate trends, patterns, and relationships in your data.

3. Write Clear and Compelling Research Summaries

In addition to your full research report, consider creating concise and engaging summaries that capture the essence of your study. These summaries help share findings with a broader audience, such as policymakers or the general public.

By effectively reporting and presenting your research findings, you contribute to disseminating knowledge and ensuring that your study's insights are accessible and impactful.

In conclusion, research design is like the blueprint of your investigation. It's the plan that makes sure everything fits together just right. By choosing the proper methods, asking the right questions, and following ethical guidelines, you're setting yourself up for success. Remember, research design isn't just for the experts—it's a powerful tool anyone can use to uncover knowledge and make informed decisions. So, whether you're analyzing economic trends or trying to understand your customers' preferences, a solid research design will guide you on your path to discovery.

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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

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Outline Components of Research Design

Please note: Not all of the components below will be included in your research project.  Details of which components are necessary for your subject discipline and level of study will be outlined by your supervisor.

Research Design

A research design is a detailed plan that outlines how you propose to answer your research question. It should detail your research aims and how you plan to achieve them (objectives). It may include details of your

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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Research Methodologies

Research design, external validity, internal validity, threats to validity.

• What are research methodologies?
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According to Jenkins-Smith, et al. (2017), a research design is the set of steps you take to collect and analyze your research data.  In other words, it is the general plan to answer your research topic or question.  You can also think of it as a combination of your research methodology and your research method.  Your research design should include the following: 

• A clear research question
• Theoretical frameworks you will use to analyze your data
• Key concepts
• Your hypothesis/hypotheses
• Independent and dependent variables (if applicable)
• Strengths and weaknesses of your chosen design

There are two types of research designs:

• Experimental design: This design is like a standard science lab experiment because the researcher controls as many variables as they can and assigns research subjects to groups.  The researcher manipulates the experimental treatment and gives it to one group.  The other group receives the unmanipulated treatment (or not treatment) and the researcher examines affect of the treatment in each group (dependent variable).  This design can have more than two groups depending on your study requirements.
• Observational design: This is when the researcher has no control over the independent variable and which research participants get exposed to it.  Depending on your research topic, this is the only design you can use.  This is a more natural approach to a study because you are not controlling the experimental treatment.  You are allowing the variable to occur on its own without your interference.  Weather experiments are a great example of observational design because the researcher has no control over the weather and how it changes.

When considering your research design, you will also need to consider your study's validity and any potential threats to its validity.  There are two types of validity: external and internal validity.  Each type demonstrates a degree of accuracy and thoughtfulness in a study and they contribute to a study's reliability.  Information about external and internal validity is included below.

External validity is the degree to which you can generalize the findings of your research study.  It is determining whether or not the findings are applicable to other settings (Jenkins-Smith, 2017).  In many cases, the external validity of a study is strongly linked to the sample population.  For example, if you studied a group of twenty-five year old male Americans, you could potentially generalize your findings to all twenty-five year old American males.  External validity is also the ability for someone else to replicate your study and achieve the same results (Jenkins-Smith, 2017).  If someone replicates your exact study and gets different results, then your study may have weak external validity.

Questions to ask when assessing external validity:

• Do my conclusions apply to other studies?
• If someone were to replicate my study, would they get the same results?
• Are my findings generalizable to a certain population?

Internal validity is when a researcher can conclude a causal relationship between their independent variable and their dependent variable.  It is a way to verify the study's findings because it draws a relationship between the variables (Jenkins-Smith, 2017).  In other words, it is the actual factors that result in the study's outcome (Singh, 2007).  According to Singh (2007), internal validity can be placed into 4 subcategories:

• Face validity: This confirms the fact that the measure accurately reflects the research question.
• Content validity: This assesses the measurement technique's compatibility with other literature on the topic.  It determines how well the tool used to gather data measures the item or concept that the researcher is interested in.
• Criterion validity: This demonstrates the accuracy of a study by comparing it to a similar study.
• Construct validity: This measures the appropriateness of the conclusions drawn from a study.

According to Jenkins-Smith (2017), there are several threats that may impact the internal and external validity of a study:

Threats to External Validity

• Interaction with testing: Any testing done before the actual experiment may decrease participants' sensitivity to the actual treatment.
• Sample misrepresentation: A population sample that is unrepresentative of the entire population.
• Selection bias: Researchers may have bias towards selecting certain subjects to participate in the study who may be more or less sensitive to the experimental treatment.
• Environment: If the study was conducted in a lab setting, the findings may not be able to transfer to a more natural setting.

Threats to Internal Validity

• Unplanned events that occur during the experiment that effect the results.
• Changes to the participants during the experiment, such as fatigue, aging, etc.
• Selection bias: When research subjects are not selected randomly.
• If participants drop out of the study without completing it.
• Changing the way the data is collected or measured during the study.
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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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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.

#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.

#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.

#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.

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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How to choose your study design

Affiliation.

• 1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
• PMID: 32479703
• DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

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How to... Design a research study

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

On this page

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

The following are some definitions of research design by researchers:

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

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

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

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

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

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

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

What is your theoretical and epistemological perspective?

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

What type of study are you undertaking?

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

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

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

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

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

What is your research question?

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

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

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

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

What sample size will you base your data on?

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

See  Sampling techniques .

What research methods will you use and why?

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

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

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

How will you triangulate your research?

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

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

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

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

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

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

How will you ensure the reliability of your research?

Reliability

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

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

Generalisability

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

Transferability

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

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

Some basic definitions

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

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

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

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

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

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

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

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

Probability sampling

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

Probability sampling can be:

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

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

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

Non probability sampling

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

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

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

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

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

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

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

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

Calculating the sample size

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

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

The response rate

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

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

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

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

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

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

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

The following studies compare variables:

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

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

They can also look at cause and effect:

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

What is the hypothesis?

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

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

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

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

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

What are the appropriate measures to use

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

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

Data can be categorised as follows:

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

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

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

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

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

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

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

How many holidays do you take in a year?

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

Tick the option which most agrees with your views.

Navigating my way around the CD was:

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

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

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

How will I analyse the data?

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

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

What are the instruments used in quantitative research?

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

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

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

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

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

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

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

What are the advantages and drawbacks of quantitative research?

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

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

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

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

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

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

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

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

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

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

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

To what types of research questions is qualitative research relevant?

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

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

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

What is the relationship of qualitative research to hypotheses?

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

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

How will you analyse the data?

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

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

What are the main instruments of qualitative research?

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

Ethnographic methods

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

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

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

Historical analysis

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

Grounded theory

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

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

Action research

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

Narrative methods

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

Discourse theory

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

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

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

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

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

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

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

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

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

How rigorous is qualitative research?

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

Rigour in qualitative research is greatly aided by:

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

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

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

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

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

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

Can qualitative research be used in with quantitative research?

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

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

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

Over what time period will you collect your data?

This refers to two types of issues:

Type of study

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

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

Methodology

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

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

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

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

Doing the plan

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

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

Timetabling

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

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

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

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

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

Research Design Canvas

• First Online: 10 June 2024

Cite this chapter

• Rashina Hoda   ORCID: orcid.org/0000-0001-5147-8096 2  

This chapter focuses on describing how to go about designing a research project. First, we will learn about framing the research study as a project . Then, we will be introduced to the research design canvas . While the details are specific to socio-technical grounded theory (STGT) studies, the research design canvas or template can be used for research project design in general. Next, we will learn about the 10 elements of the research design canvas, including the forming of the research team , identifying the domain and actors , selecting the phenomenon and topic to investigate, carefully assessing research ethics and considering the research values , formulating the guiding research questions , acknowledging the team’s research philosophy , deciding on the initial research protocols including data, techniques, and tools, and listing the desirable research impact . The chapter concludes by describing a pilot study to apply and refine the above elements of the research project design.

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Hoda, R. (2024). Research Design Canvas. In: Qualitative Research with Socio-Technical Grounded Theory. Springer, Cham. https://doi.org/10.1007/978-3-031-60533-8_4

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Published : 10 June 2024

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