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What Is Research Methodology? Types, Process, Examples In Research Design

Research methodology is the backbone of any successful study, providing a structured approach to collecting and analysing data. It encompasses a broad spectrum of methods, each with specific processes and applications, tailored to answer distinct research questions.

This article will explore various types of research methodologies, delve into their processes, and illustrate with examples how they are applied in real-world research.

Understanding these methodologies is essential for any researcher aiming to conduct thorough and impactful studies.

Types Of Research Methodology

Research methodology contains various strategies and approaches to conduct scientific research, each tailored to specific types of questions and data.

Think of research methodology as the master plan for your study. It guides you on why and how to gather and analyse data, ensuring your approach aligns perfectly with your research question.

This methodology includes deciding between qualitative research, which explores topics in depth through interviews or focus groups, or quantitative research, which quantifies data through surveys and statistical analysis.

There is even an option to mix both, and approach called the mixed method.

If you’re analysing the lived experiences of individuals in a specific setting, qualitative methodologies allow you to capture the nuances of human emotions and behaviours through detailed narratives.

Quantitative methodologies would enable you to measure and compare these experiences in a more structured, numerical format.

Choosing a robust methodology not only provides the rationale for the methods you choose but also highlights the research limitations and ethical considerations, keeping your study transparent and grounded.

It’s a thoughtful composition that gives research its direction and purpose, much like how an architect’s plan is essential before the actual construction begins.

Qualitative Research Methodology

Qualitative research dives deep into the social context of a topic. It collects words and textual data rather than numerical data.

Within the family, qualitative research methodologies can be broken down into several approaches: 

Ethnography: Deeply rooted in the traditions of anthropology, you immerse yourself in the community or social setting you’re studying when conducting an ethnography study.

Case Study Research:  Here, you explore the complexity of a single case in detail. This could be an institution, a group, or an individual. You might look into interviews, documents, and reports, to build a comprehensive picture of the subject.

Grounded Theory:  Here, you try to generate theories from the data itself rather than testing existing hypotheses. You might start with a research question but allow your theories to develop as you gather more data.

Narrative Research:  You explore the stories people tell about their lives and personal experiences in their own words. Through techniques like in-depth interviews or life story collections, you analyse the narrative to understand the individual’s experiences.

Discourse Analysis: You analyse written or spoken words to understand the social norms and power structures that underlie the language used. This method can reveal a lot about the social context and the dynamics of power in communication. 

These methods help to uncover patterns in how people think and interact. For example, in exploring consumer attitudes toward a new product, you would likely conduct focus groups or participant observations to gather qualitative data.

This method helps you understand the motivations and feelings behind consumer choices.

Quantitative Research Methodology

Quantitative research relies on numerical data to find patterns and test hypotheses. This methodology uses statistical analysis to quantify data and uncover relationships between variables.

There are several approaches in quantitative research:

Experimental Research:  This is the gold standard when you aim to determine causality. By manipulating one variable and controlling others, you observe changes in the dependent variables.

Survey Research: A popular approach, because of its efficiency in collecting data from a large sample of participants. By using standardised questions, you can gather data that are easy to analyse statistically. 

Correlational Research: This approach tries to identify relationships between two or more variables without establishing a causal link. The strength and direction of these relationships are quantified, albeit without confirming one variable causes another.

Longitudinal Studies: You track variables over time, providing a dynamic view of how situations evolve. This approach requires commitment and can be resource-intensive, but the depth of data they provide is unparalleled.

Cross-sectional Studies: Offers a snapshot of a population at a single point in time. They are quicker and cheaper than longitudinal studies. 

Mixed Research Methodology

Mixed methods research combines both approaches to benefit from the depth of qualitative data and the breadth of quantitative analysis.

You might start with qualitative interviews to develop hypotheses about health behaviours in a community. Then, you could conduct a large-scale survey to test these hypotheses quantitatively.

This approach is particularly useful when you want to explore a new area where previous data may not exist, giving you a comprehensive insight into both the empirical and social dimensions of a research problem.

Factors To Consider When Deciding On Research Methodology

When you dive into a research project, choosing the right methodology is akin to selecting the best tools for building a house.

It shapes how you approach the research question, gather data, and interpret the results. Here are a couple of crucial factors to keep in mind.

Research Question Compatibility

The type of research question you pose can heavily influence the methodology you choose. Qualitative methodologies are superb for exploratory research where you aim to understand concepts, perceptions, and experiences.

If you’re exploring how patients feel about a new healthcare policy, interviews and focus groups would be instrumental.

Quantitative methods are your go-to for questions that require measurable and statistical data, like assessing the prevalence of a medical condition across different regions.

Data Requirements

Consider what data is necessary to address your research question effectively. Qualitative data can provide depth and detail through:

• images, and

This makes qualitative method ideal for understanding complex social interactions or historical contexts. 

Quantitative data, however, offers the breadth and is often numerical, allowing for a broad analysis of patterns and correlations.

If your study aims to investigate both the breadth and depth, a mixed methods approach might be necessary, enabling you to draw on the strengths of both qualitative and quantitative data.

Resources and Constraints

While deciding on research methodology, you must evaluate the resources available, including:

• funding, and

Quantitative research often requires larger samples and hence, might be more costly and time-consuming.

Qualitative research, while generally less resource-intensive, demands substantial time for data collection and analysis, especially if you conduct lengthy interviews or detailed content analysis.

If resources are limited, adapting your methodology to fit these constraints without compromising the integrity of your research is crucial.

Skill Set and Expertise

Your familiarity and comfort level with various research methodologies will significantly affect your choice.

Conducting sophisticated statistical analyses requires a different skill set than carrying out in-depth qualitative interviews.

If your background is in social science, you might find qualitative methods more within your wheelhouse; whereas, a postgraduate student in epidemiology might be more adept at quantitative methods.

It’s also worth considering the availability of workshops, courses, or collaborators who could complement your skills.

Ethical and Practical Considerations

Different methodologies raise different ethical concerns.

In qualitative research, maintaining anonymity and dealing with sensitive information can be challenging, especially when using direct quotes or detailed descriptions from participants.

Quantitative research might involve considerations around participant consent for large surveys or experiments.

Practically, you need to think about the sampling design to ensure it is representative of the population studied. Non-probability sampling might be quicker and cheaper but can introduce bias, limiting the generalisability of your findings.

By meticulously considering these factors, you tailor your research design to not just answer the research questions effectively but also to reflect the realities of your operational environment.

This thoughtful approach helps ensure that your research is not only robust but also practical and ethical, standing up to both academic scrutiny and real-world application.

What Is Research Methodology? Answered

Research methodology is a crucial framework that guides the entire research process. It involves choosing between various qualitative and quantitative approaches, each tailored to specific research questions and objectives.

Your chosen methodology shapes how data is gathered, analysed, and interpreted, ultimately influencing the reliability and validity of your research findings.

Understanding these methodologies ensures that researchers can effectively write research proposal, address their study’s aims and contribute valuable insights to their field.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

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10 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

how to cite this page

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

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

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Research Methodology & Design

Research methodology.

• Seminal Authors
• Qualitative Research Methods
• Quantitative Research Methods
• Mixed Methods
• Action Research
• Case Study Design
• Constructive Research
• Ethnography
• Phenomenology
• Research Ethics & Quality
• Dissertations
• Organizing Research & Citations This link opens in a new window

More NU Resources

More research method & design help from across National University

• NU Dissertation Center - Chapter 3
• NU Applied Doctoral Center - Section 2
• ASC - Research Tools

Once you are well into your literature review, it is time to start thinking about the study you will design to answer the gap you identified. Which methodology will you use to gather the data for your research? Will you use a qualitative, quantitative, or mixed methods methodology? You will choose a research method that best aligns with your research question.

To evaluate which type of methodology will be most appropriate, you will work closely with your Dissertation Chair. However, as you are reading the literature, take a look at past studies that focus on your topic, or a similar topic. What kind of research methodology do you see being used most often? Once you have an idea about the general methodology type that would suit your research, consult with your Dissertation Chair on the possibility of using that methodology.

Finding a research design strategy is similar to the research process as a whole: first, locate general information on research design and methodologies, then gain background knowledge on the methodology you feel would most appropriately address the type of data you will be collecting, and finally choose a methodology and test/measurement to use in your research. The following techniques outline how to locate information about research methodology from reference books, scholarly articles and dissertations.

• NU Definition of Terms In order to support your understanding and progress as a scholar, the School of Education has established the following definitions for each of these terms. These definitions are informed by the SAGE Resource Methods Map.
• Books & Videos
• Scholarly Articles

Content: Books, reference works, journal articles, and instructional videos on research methods and design. 

Purpose: Use to learn more about qualitative, quantitative, and mixed methods research. 

Special Features: Includes a methods map, project planner, and "which stats" test

• SAGE Research Methods Title List

The SAGE Research Methods database may be used to locate information about research design and methodology. It includes over 175,000 pages of content from the following sources: encyclopedias, dictionaries, books, journal articles, videos, and major works--resources that bring together the seminal articles about that particular methodology. For a complete list of titles in SAGE Research Methods, please see the SAGE Research Methods Title List.

Using the menu bar, you may browse SAGE Research Methods content by topic, discipline, or content type .

For additional guidance, see the SAGE Research Methods workshop .

After you have located background information about your research design, you may want to locate scholarly journal articles on your research topic that use a particular type of methodology. By looking at research articles that use a particular methodology you can learn a lot about your field. What types of research studies are prevalent? What methodologies are appropriate for a specific research question? How do you construct a research study? What methodologies should you consider for your dissertation research? Few databases allow you to limit your search by research methodology. APA PsycArticles  and APA PsycInfo  are the exceptions; these databases do allow you to limit your search results to show articles that use a particular methodology. 

Content: APA database that offers full-text for journals published by APA, the Canadian Psychological Association, Hogrefe Publishing Group and APA's Educational Publishing Foundation. View the  APA PsycArticles Journal History  for a complete coverage list.

Purpose: Important database for psychology, counseling, and education students.

Special Features: The database is updated bi-weekly all content is available in PDF and HTML formats.

Help using this database.

Content: Journal article database from the American Psychological Association that indexes over 2,500 journals along with book chapters and dissertations.

Purpose: Provides a single source of vetted, authoritative research for users across the behavioral and social sciences.

Special Features: citations in APA Style®, updated bi-weekly, spans 600 years of content

Content: Scholarly journals, e-books, videos and more. 

Purpose: A key multidisciplinary database for most topics. It is one of the library’s main search engines and the most comprehensive single search. 

Note: Certain library databases and publisher content are not searchable in NavigatorSearch, and individual databases may need to be searched to retrieve information due to unique content. NavigatorSearch can be found at https://resources.nu.edu .

Content: National University & NCU student dissertations and literature reviews.

Purpose: Use for foundational research, to locate test instruments and data, and more. 

Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature.

Content: Global student dissertations and literature reviews.

Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature

The ProQuest Dissertations & Theses database (PQDT) is the world's most comprehensive collection of dissertations and theses. It is the database of record for graduate research, with over 2.3 million dissertations and theses included from around the world.

Finally, you may use the ProQuest Dissertations & Theses database to discover graduate and doctoral-level research that has already been conducted on your topic. A similar study may have employed a research methodology appropriate for use in your own dissertation. Check dissertation abstracts to see if the author mentions which methodology was used.

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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.

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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