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Essentials of research design and methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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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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• Fully updated for the 7th edition of the Publication Manual of the American Psychological Association.
• More inclusive and supportive language throughout helps readers better see themselves in the research process.
• Learning Objectives provide additional structure and clarity to the reading process.
• The latest information on participatory research, evaluating literature for quality, using software to design literature maps, and additional statistical software types is newly included in this edition.
• Chapter 4: Writing Strategies and Ethical Considerations now includes information on indigenous populations and data collection after IRB review.
• An updated Chapter 8: Quantitative Methods now includes more foundational details, such as Type 1 and Type 2 errors and discussions of advantages and disadvantages of quantitative designs.
• A restructured and revised Chapter 10: Mixed Methods Procedures brings state-of-the-art thinking to this increasingly popular approach.
• Chapters 8, 9, and 10 now have parallel structures so readers can better compare and contrast each approach.
• Reworked end-of-chapter exercises offer a more straightforward path to application for students.
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• Current references and additional readings are included in this new edition.
• Compares qualitative, quantitative, and mixed methods research in one book for unparalleled coverage.
• Highly interdisciplinary examples make this book widely appealing to a broad range of courses and disciplines.
• Ethical coverage throughout consistently reminds students to use good judgment and to be fair and unbiased in their research.
• Writing exercises conclude each chapter so that readers can practice the principles learned in the chapter; if the reader completes all of the exercises, they will have a written plan for their scholarly study.
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Research Design and Methodology

There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods. The last part illustrates the general research framework. The purpose of this section is to indicate how the research was conducted throughout the study periods.

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This section presents an introduction of the study and review of the theories that are adopted in the study. These underpin the theoretical perspectives adopted in the process, the Statement of the problem, objectives and the scope of the study.

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Test-Negative Study Designs for Evaluating Vaccine Effectiveness

• 1 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
• Original Investigation Association Between 3 Doses of mRNA COVID-19 Vaccine and Symptomatic Infection Caused by Omicron and Delta Variants Emma K. Accorsi, PhD; Amadea Britton, MD; Katherine E. Fleming-Dutra, MD; Zachary R. Smith, MA; Nong Shang, PhD; Gordana Derado, PhD; Joseph Miller, PhD; Stephanie J. Schrag, DPhil; Jennifer R. Verani, MD, MPH JAMA

The evaluation of vaccines continues long after initial regulatory approval. Postapproval observational studies are often used to investigate aspects of vaccine effectiveness (VE) that clinical trials cannot feasibly assess. These includes long-term effectiveness, effectiveness within subgroups, effectiveness against rare outcomes, and effectiveness as the circulating pathogen changes. 1 Policymakers rely on these data to guide vaccine recommendations or formulation updates. 2

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Dean N , Amin AB. Test-Negative Study Designs for Evaluating Vaccine Effectiveness. JAMA. Published online June 12, 2024. doi:10.1001/jama.2024.5633

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Inversely engineered metasurfaces for independent manipulation of transmitted and reflected light fields

• Published: 13 June 2024
• Volume 67 , article number  274213 , ( 2024 )

Cite this article

• Yongjian Liu 1 , 2 , 3   na1 ,
• Yu Luo 1 , 2 , 4   na1 ,
• Fei Zhang 1 , 2 , 4 ,
• Mingbo Pu 1 , 2 , 3 , 4 ,
• Hanlin Bao 1 , 2 , 3 ,
• Mingfeng Xu 1 , 2 , 3 , 4 ,
• Yinghui Guo 1 , 2 , 3 , 4 ,
• Lanting Li 5 ,
• Xiong Li 1 , 2 , 3 ,
• Xiaoliang Ma 1 , 2 , 3 &
• Xiangang Luo 1 , 2 , 3  

Independent manipulation of transmitted and reflected light fields is a key technology for the realization of multifunctional optical applications, which can be implemented based on multilayered plasmonic or supercell subwavelength structures. However, the former is not suitable for the optical bands, while the latter is insufficient in generating large phase gradients. Here, an adjoint-optimization-based inverse design methodology is proposed, which utilizes the polarization-selective local interference between individual meta-atoms and enables monolayer dielectric metasurfaces to decouple the wavefront of transmitted and reflected optical fields. Moreover, this methodology serves to mitigate the aperiodic electromagnetic crosstalk inherent between adjacent meta-atoms, consequently leading to a significant enhancement in the performance of meta-devices. We analyzed the physical mechanism of adjoint optimization and proposed the concept of phase factors, highlighting their importance in the rapid inverse design of meta-devices-an aspect often overlooked in previous research. To demonstrate the feasibility and robustness of our method, we optimize monolayer metasurfaces with different initial structures. These devices efficiently focus and deflect x -linearly and y -linearly polarized incident light in transmission and reflection spaces, respectively. Overall, this methodology holds immense potential for designing multifunctional, high-performing metasurfaces that meet multiple constraints, opening up broad prospects for applications.

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M. Khorasaninejad, W. T. Chen, R. C. Devlin, J. Oh, A. Y. Zhu, and F. Capasso, Science 352 , 1190 (2016).

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Authors and Affiliations

National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, 610209, China

Yongjian Liu, Yu Luo, Fei Zhang, Mingbo Pu, Hanlin Bao, Mingfeng Xu, Yinghui Guo, Xiong Li, Xiaoliang Ma & Xiangang Luo

State Key Laboratory of Optical Technologies on Nano-Fabrication and Micro-Engineering, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China

College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

Yongjian Liu, Mingbo Pu, Hanlin Bao, Mingfeng Xu, Yinghui Guo, Xiong Li, Xiaoliang Ma & Xiangang Luo

Research Center on Vector Optical Fields, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China

Yu Luo, Fei Zhang, Mingbo Pu, Mingfeng Xu & Yinghui Guo

Tianfu Xinglong Lake Laboratory, Chengdu, 610299, China

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This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFB2805800), the National Natural Science Foundation of China (Grant Nos. 62175242, and U20A20217), and the Sichuan Science and Technology Program (Grant No. 2021ZYCD002) .

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Liu, Y., Luo, Y., Zhang, F. et al. Inversely engineered metasurfaces for independent manipulation of transmitted and reflected light fields. Sci. China Phys. Mech. Astron. 67 , 274213 (2024). https://doi.org/10.1007/s11433-024-2375-8

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DOI : https://doi.org/10.1007/s11433-024-2375-8

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