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Introducing Research Designs

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• Stefan Hunziker 3 &
• Michael Blankenagel 3  

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We define research design as a combination of decisions within a research process. These decisions enable us to make a specific type of argument by answering the research question. It is the implementation plan for the research study that allows reaching the desired (type of) conclusion. Different research designs make it possible to draw different conclusions. These conclusions produce various kinds of intellectual contributions. As all kinds of intellectual contributions are necessary to increase the body of knowledge, no research design is inherently better than another, only more appropriate to answer a specific question.

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Hunziker, S., Blankenagel, M. (2021). Introducing Research Designs. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_1

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

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

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

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

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

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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RESEARCH DESIGN: QUANTITATIVE, QUALITATIVE, MIXED METHODS, ARTS-BASED, AND COMMUNITY-BASED PARTICIPATORY RESEARCH APPROACHES

This user-friendly book provides a step-by-step guide to using the five major approaches to research design: quantitative, qualitative, mixed methods, arts-based, and communitybased participatory research. Chapters on each approach follow a unique format—they present a template for a research proposal and explain in detail how to conceptualize and fill in every section. Terminology commonly used within each approach is identified, and key moments of ethical decision making are flagged. Interdisciplinary research examples draw on current events and social justice topics. Unique coverage includes hot topics: replication studies and data sharing, tailoring proposals to different audiences, and more. The book also includes a general introduction to social research; an in-depth, practical discussion of ethics; and a chapter on how to begin a research study, from planning a topic to developing a research question via a literature review. Includes Pedagogical Features 

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Director's Messages

Partners in research: communities and nih working together to promote health for all.

This Director’s Message was written in collaboration with the  NIH Common Fund  Community Partnerships to Advance Science for Society (ComPASS) Program .

Have you heard about NIH’s innovative research program designed to create lasting change by addressing the underlying structural factors that shape health? Through the Community Partnerships to Advance Science for Society (ComPASS) Program , NIH is directly funding research projects led by community organizations. 

Instead of tackling health disparities one disease at a time, the ComPASS program addresses structural factors that contribute to multiple diseases and health conditions. The projects study interventions that focus on factors that can promote health, such as improving economic stability, employment opportunities, access to healthy food, safe spaces, transportation, and quality healthcare. Communities and researchers work together as equal partners throughout the research process — from developing and launching structural interventions to sustaining and disseminating findings.

To receive ComPASS program announcements and information about funding opportunities, join the ComPASS listserv . 

How ComPASS is advancing prevention research

ODP has enthusiastically partnered with ComPASS to support and shape this innovative program. ODP helped develop and promote ComPASS funding opportunities and provided technical assistance to applicants and awardees. Our office remains actively involved in promoting ComPASS and the success of community-led research programs to advance prevention research and health equity.

ComPASS embodies several priorities and cross-cutting themes from ODP’s Strategic Plan for Fiscal Years 2024–2028: Creating a Healthier Future for All , including promoting collaborative research, addressing health disparities and social determinants of health (SDOH), developing and testing interventions, and building research capacity. We are excited to highlight a few ways in which ComPASS is integrating the principles of prevention and transforming research into sustainable solutions that promote health equity and create lasting change in communities nationwide.

• Unlike traditional research studies, ComPASS projects are led by community organizations that collaborate with academic researchers and other community partners. 
• ComPASS researchers study ways to reduce health disparities and improve health by addressing structural factors within communities.
• Indigenous healthy homes and healthy communities: A community-led initiative to improve health and support Indigenous Resilience in the US Southwest  
• Reducing Health Disparities through Enhanced Mobility Support and Access
• Cancer in Your Community: Strategies to Reduce Cancer and Chronic Disease in the Arkansas Delta

Synergies between ComPASS and ODP initiatives

ComPASS is one of many recent NIH initiatives to address SDOH and foster health equity. ODP is contributing to this area by leading additional funding opportunities emphasizing multilevel preventive intervention research. 

The Developing and Testing Multi-level Physical Activity Interventions to Improve Health and Well-Being Notice of Special Interest (NOSI) , which is open until November 14, 2027, focuses on social and structural interventions and includes 10 other NIH Institutes, Centers, and Offices (ICOs). The NOSI seeks research applications on new ideas that work at two or more levels of influence (e.g., changing the physical environment and public policy or impacting individual behavior and community resources) to help people increase their physical activity levels. 

Our Multi-Sectoral Preventive Interventions Research Network   funding opportunity will support projects to test preventive interventions involving multiple service sectors (like education, housing, and transportation) and community-based organizations that address SDOH in populations that experience health disparities. ODP issued this funding opportunity with 13 participating NIH ICOs as part of the ADVANCE: Advancing Prevention Research for Health Equity initiative , an NIH-wide effort to develop and evaluate preventive interventions that address health disparities. We expect to announce the awarded projects next spring. 

To cultivate a diverse workforce of scientists who can appropriately design and evaluate preventive interventions, the ODP-coordinated ADVANCE initiative published a Predoctoral T32 Training Program funding opportunity . The awarded training grants will train graduate students across three linked areas: (1) health disparities and health equity research, (2) development and implementation of multilevel preventive interventions, and (3) methods for the design and analysis of studies to evaluate multilevel preventive interventions. Programs are also strongly encouraged to collaborate with and involve community partners in their training activities.

With the help of ComPASS and ODP’s aligned initiatives, we look forward to progressing toward a healthier and more equitable future. Subscribe to ODP’s listserv to get updates on all these funding opportunities and upcoming awards!

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AQR and its partners, employees, directors, officers, advisors, successors, and agents are not responsible for any conduct by unauthorized parties and channels, nor are they responsible for the services or information provided by such unauthorized parties and channels. If you have any questions or wish to ascertain whether you are communicating with an authorized representative of AQR, please contact us at https://www.aqr.com/Contact-Us . If you believe that you or someone you know has been a victim of fraud, please report the matter to your local law enforcement or other relevant authorities. For India residents, you may lodge a report at the national cybercrime reporting portal ( https://cybercrime.gov.in ).

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Alternative Investing

Broad Strategic Asset Allocation

Sizing allocations to liquid and illiquid alternatives alongside traditional assets.

September 18, 2024 - Portfolio Solutions Group

Topics - Alternative Investing Asset Allocation

Traditional strategic asset allocation (SAA) involves determining allocations to stocks, bonds, and cash that are appropriate to an investor’s risk appetite and investment horizon. Where do alternatives fit into this framework? How should their risk and return characteristics be estimated, and how can realistic constraints be modelled? Which alternatives deliver the biggest incremental benefit, and what is an appropriate strategic allocation?

This paper presents one justifiable set of inputs and finds that alternatives earn themselves a sizable strategic allocation. Investors are encouraged to compare these results with their own assumptions, constraints, and allocations as they look to build a resilient portfolio for long-term investment success.

About the Portfolio Solutions Group The Portfolio Solutions Group provides thought leadership to the broader investment community and custom analyses to help AQR clients achieve better portfolio outcomes.

We thank Alfie Brixton, Pete Hecht, Antti Ilmanen, Thomas Maloney, and Nick McQuinn for their work on this paper.

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This document is not intended to, and does not relate specifically to any investment strategy or product that AQR offers. It is being provided merely to provide a framework to assist in the implementation of an investor’s own analysis and an investor’s own view on the topic discussed herein.

This document has been provided to you solely for information purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and AQR Capital Management, LLC (“AQR”) to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. This document is not to be reproduced or redistributed to any other person. The information set forth herein has been provided to you as secondary information and should not be the primary source for any investment or allocation decision. Past performance is not a guarantee of future performance. Diversification does not eliminate the risk of experiencing investment losses.  This material is not research and should not be treated as research. This paper does not represent valuation judgments with respect to any financial instrument, issuer, security or sector that may be described or referenced herein and does not represent a formal or official view of AQR. The views expressed reflect the current views as of the date hereof and neither the author nor AQR undertakes to advise you of any changes in the views expressed herein.  The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons. Charts and graphs provided herein are for illustrative purposes only. The information in this presentation has been developed internally and/or obtained from sources believed to be reliable; however, neither AQR nor the author guarantees the accuracy, adequacy or completeness of such information. Nothing contained herein constitutes investment, legal, tax or other advice nor is it to be relied on in making an investment or other decision. There can be no assurance that an investment strategy will be successful. Historic market trends are not reliable indicators of actual future market behavior or future performance of any particular investment which may differ materially, and should not be relied upon as such. Diversification does not eliminate the risk of experiencing investment losses. The information in this paper may contain projections or other forward-looking statements regarding future events, targets, forecasts or expectations regarding the strategies described herein, and is only current as of the date indicated. There is no assurance that such events or targets will be achieved, and may be significantly different from that shown here. The information in this document, including statements concerning financial market trends, is based on current market conditions, which will fluctuate and may be superseded by subsequent market events or for other reasons. 

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