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• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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• How it works

How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

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

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Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

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• How It Works

Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

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Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

Psst… there’s more (for free)

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

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Designing a Research Question

• First Online: 29 November 2023

Cite this chapter

• Ahmed Ibrahim 3 &
• Camille L. Bryant 3  

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This chapter discusses (1) the important role of research questions for descriptive, predictive, and causal studies across the three research paradigms (i.e., quantitative, qualitative, and mixed methods); (2) characteristics of quality research questions, and (3) three frameworks to support the development of research questions and their dissemination within scholarly work. For the latter, a description of the P opulation/ P articipants, I ntervention/ I ndependent variable, C omparison, and O utcomes (PICO) framework for quantitative research as well as variations depending on the type of research is provided. Second, we discuss the P articipants, central Ph enomenon, T ime, and S pace (PPhTS) framework for qualitative research. The combination of these frameworks is discussed for mixed-methods research. Further, templates and examples are provided to support the novice health scholar in developing research questions for applied and theoretical studies. Finally, we discuss the Create a Research Space (CARS) model for introducing research questions as part of a research study, to demonstrate how scholars can apply their knowledge when disseminating research.

• Research purpose
• Research objective
• Question formation
• Research question
• PICO framework
• PPhTS framework

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Onwuegbuzie A, Leech N. Linking research questions to mixed methods data analysis procedures 1. Qual Rep. 2006;11(3):474–98. https://doi.org/10.46743/2160-3715/2006.1663 .

Article   Google Scholar  

Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches. 4th ed. Thousand Oaks: Sage; 2018.

Google Scholar  

Johnson B, Christensen LB. Educational research: quantitative, qualitative, and mixed approaches. Thousand Oaks: Sage Publications, Inc.; 2020.

White P. Who’s afraid of research questions? The neglect of research questions in the methods literature and a call for question-led methods teaching. Int J Res Method Educ. 2013;36(3):213–27. https://doi.org/10.1080/1743727x.2013.809413 .

Lingard L. Joining a conversation: the problem/gap/hook heuristic. Perspect Med Educ. 2015;4(5):252–3. https://doi.org/10.1007/s40037-015-0211-y .

Article   PubMed   PubMed Central   Google Scholar  

Dillon JT. The classification of research questions. Rev Educ Res. 1984;54(3):327–61. https://doi.org/10.3102/00346543054003327 .

Dillon JT. Finding the question for evaluation research. Stud Educ Eval. 1987;13(2):139–51. https://doi.org/10.1016/S0191-491X(87)80027-5 .

Smith NL. Toward the justification of claims in evaluation research. Eval Program Plann. 1987;10(4):309–14. https://doi.org/10.1016/0149-7189(87)90002-4 .

Smith NL, Mukherjee P. Classifying research questions addressed in published evaluation studies. Educ Eval Policy Anal. 1994;16(2):223–30. https://doi.org/10.3102/01623737016002223 .

Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research methods in psychology. 9th ed. New York: McGraw Hill; 2011.

DeCuir-Gunby JT, Schutz PA. Developing a mixed methods proposal a practical guide for beginning researchers. Thousand Oaks: Sage; 2017.

Book   Google Scholar  

Creswell JW, Guetterman TC. Educational research: planning, conducting, and evaluating quantitative and qualitative research. 6th ed. New York: Pearson; 2019.

Ely M, Anzul M, Friedman T, Ganer D, Steinmetz AM. Doing qualitative research: circles within circles. London: Falmer Press; 1991.

Agee J. Developing qualitative research questions: a reflective process. Int J Qual Stud Educ. 2009;22(4):431–47. https://doi.org/10.1080/09518390902736512 .

Johnson RB, Onwuegbuzie AJ. Mixed methods research: a research paradigm whose time has come. Educ Res. 2004;33(7):14–26. https://doi.org/10.3102/0013189x033007014 .

Creamer EG. An introduction to fully integrated mixed methods research. Thousand Oaks: Sage; 2018.

Swales J. Genre analysis: English in academic and research settings. Cambridge: Cambridge University Press; 1990.

Swales J. Research genres: explorations and applications. Cambridge: Cambridge University Press; 2004.

Kendall PC, Norris LA, Rifkin LS, Silk JS. Introducing your research report: writing the introduction. In: Sternberg RJ, editor. Guide to publishing in psychology journals. 2nd ed. Cambridge: Cambridge University Press; 2018. p. 37–53. https://doi.org/10.1017/9781108304443.005 .

Thomson P, Kamler B. Writing for peer reviewed journals: strategies of getting published. Abingdon: Routledge; 2013.

Lingard L. Writing an effective literature review: Part I: Mapping the gap. Perspectives on Medical Education. 2018;7:47–49.

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Ibrahim, A., Bryant, C.L. (2023). Designing a Research Question. In: Fitzgerald, A.S., Bosch, G. (eds) Education Scholarship in Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-38534-6_4

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Writing about Design

Principles and tips for design-oriented research.

How to define a research question or a design problem

Introduction.

Many texts state that identifying a good research question (or, equivalently, a design problem) is important for research. Wikipedia, for example, starts (as of writing this text, at least) with the following two sentences:

“A research question is ‘a question that a research project sets out to answer’. Choosing a research question is an essential element of both quantitative and qualitative research.” (Wikipedia, 2020)

However, finding a good research question (RQ) can be a painful experience. It may feel impossible to understand what are the criteria for a good RQ, how a good RQ can be found, and to notice when there are problems with some RQ candidate.

In this text, I will address the pains described above. I start by presenting a scenario of a project that has problems with its RQ. The analysis of that scenario allows me then to describe how to turn the situation described in the scenario for a better research or design project.

Scenario of a problematic project

Let us consider a scenario that you are starting a new research or design project. You have already an idea: your work will be related to communication with instant messaging (IM). Because you are a design-minded person, you are planning to design and develop a new IM feature: a possibility to send predefined replies on a mobile IM app. Your idea is that this feature will allow the user to communicate quickly with others in difficult situations where the they can only connect with others through their mobile phone. Your plan is to supply the mobile IM app with messages like “I’m late by 10 minutes but see you soon”, “I can’t answer back now but will do that later today”, and so on.

Therefore, your plan involves designing such an app, maybe first by sketching it and then illustrating its interaction with a prototyping software like Figma or Adobe XD. You may also decide to make your design functional by programming it and letting a selected number of participants to use it. These kinds of activities will let you demonstrate your skills as a designer-researcher.

Although predefined messages for a mobile IM app can be a topic of a great study, there are some problems with this project that require you to think more about it before you start. As the project is currently defined, it is difficult to provide convincing answers to these challenges:

• Challenge 1: Why would this be a relevant topic for research or design? Good studies address topics that may interest also other people than the author only. The current research topic, however, does not do that self-evidently yet: it lacks an explanation why it would make sense to equip mobile IM apps with predefined replies. There is only a guess that this could be useful in some situations, but this may not convince the reader about the ingenuity of this project.
• Challenge 2: How do you demonstrate that your solution is particularly good? For an outsider who will see the project’s outcome, it may not be clear why your final design would be the best one among the other possible designs. If you propose one interaction design for such a feature, what makes that a good one? In other words, the project lacks a yardstick by which its quality should be measured.
• Challenge 3: How does this project lead to learning or new knowledge? Even if you can show that the topic is relevant (point 1) and that the solution works well (2), the solution may feel too “particularized” – not usable in any other design context. This is an important matter in applied research fields like design and human–computer interaction, because these fields require some form of generalizability from their studies. Findings of a study should result in some kind of knowledge, such as skills, sensitivity to important matters, design solutions or patterns, etc. that could be used also at a later time in other projects, preferably by other people too.

All these problems relate to a problem that this study does not have a RQ yet . Identifying a good research question will help clarify all the above matters, as we will see below.

Adding a research question / design problem

RQs are of many kinds, and they are closely tied to the intended finding of the study: what contribution  should the study deliver. A contribution can be, for example, a solution to a problem or creation of novel information or knowledge. Novel information, in turn, can be a new theory, model or hypothesis, analysis that offers deeper understanding, identification of an unattended problem, description about poorly understood phenomenon, a new viewpoint, or many other things.

The researcher or thesis author usually has a lot of freedom in choosing the exact type of contribution that they want to make. This can feel difficult to the author: there may be no-one telling what they should study. In a way, in such a situation, the thesis/article author is the client of their own research: they both define what needs to be done, and then accomplish that work. Some starting points for narrowing down the space of possibilities is offered here.

Most importantly, the RQ needs to be focused on a topic that the author genuinely does not know, and which is important to find out on the path to the intended contribution. In our scenario about a mobile IM app’s predefined replies, there are currently too many alternatives for an intended contribution, and an outsider would not be able to know which one of them to expect:

• Demonstration that mobile IM apps will be better to use when they have this new feature.
• Report on the ways by which people would use the new feature, if their mobile IM apps would have such a feature.
• Requirements analysis for the specific design and detailed features by which the feature should be designed.
• Analysis of the situations where the feature would be most needed, and user groups who would most often be in such situations.

All of these are valid contributions, and the author can choose to focus on any one of them. This depends also on the author’s personal interests. This gives a possibility for formulating a RQ for the project. It is important to notice that each one of the possible contributions listed above calls for a different corresponding RQ:

RQ1: Do predefined replies in mobile IM apps improve their usability?

RQ2: How will users start using the predefined replies in mobile IM apps?

RQ3: How should the interaction in the IM app be designed, and what kind of predefined replies need to be offered to the users?

RQ4: When are predefined replies in IM apps needed?

This list of four RQs, matched with the four possible contributions, shows why the scenario presented in the beginning of this text was problematic. Only after asking these kinds of questions one is able to seek to answer to the earlier-presented three challenges in the end of the previous section. Also, each of the RQs needs a different research or design method, and its own kind of background research.

The choice and fine-tuning of the research question / design problem

Which one of the above RQs should our hypothetical researcher/designer choose? Lists of basic requisites for good RQs have been presented in many websites. They can help identify RQs that will still need refinement. Monash University offers the following kind of helpful list:

• Clear and focused.  In other words, the question should clearly state what the writer needs to do.
• Not too broad and not too narrow.  The question should have an appropriate scope. If the question is too broad it will not be possible to answer it thoroughly within the word limit. If it is too narrow you will not have enough to write about and you will struggle to develop a strong argument.
• Not too easy to answer.  For example, the question should require more than a simple yes or no answer.
• Not too difficult to answer.  You must be able to answer the question thoroughly within the given timeframe and word limit.
• Researchable.  You must have access to a suitable amount of quality research materials, such as academic books and refereed journal articles.
• Analytical rather than descriptive.  In other words, your research question should allow you to produce an analysis of an issue or problem rather than a simple description of it.

If a study meets the above criteria, it has a good chance of avoiding a problem of presenting a “non-contribution” : A laboriously produced finding that nonetheless does not provide new, interesting information. The points 3 and 6 above particularly guard against such studies: they warn the readers from focusing their efforts on something that is already known (3) and only describing what was done or what observations were made, instead of analysing them in more detail (6).

In fine-tuning a possible RQ, it is important to situate it to the right scope. The first possible RQ that comes to one’s mind is often too broad and needs to be narrowed. RQ4 above (“ When are predefined replies in IM apps most needed? ”), for example, is a very relevant question, but it is probably too broad.

Why is RQ4 too broad? The reason is that RQs are usually considered very literally. If you leave an aspect in your RQ unspecified, then it means that you intend that your RQ and your findings will be generalisable (i.e., applicable) to all the possible contexts and cases that your RQ can be applied to. Consider the following diagram:

With a question “ When are predefined replies in IM apps most needed?”, you are asking a question that covers both leisure-oriented and work-oriented IM apps which can be of very different kinds. Some of the IM apps are mobile-oriented (such as WhatsApp) and others are desktop-oriented (such as Slack or Teams). Unless you specify your RQ more narrowly, your findings should be applicable to all these kinds of apps. Also, RQ4 is unspecific also about the people that you are thinking as communication partners. It may be impossible for you to make a study so broad that it applies to all of these cases.

Therefore, a more manageable-sized scoping could be something like this:

RQ4 (version 2): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed?

Furthermore, you can also narrow down your focus theoretically. In our example scenario, the researcher/designer can decide, for example, that they will consider predefined IM replies from the viewpoint of “face-work” in social interaction. By adopting this viewpoint, the researcher/designer can decide that they will design the IM’s replies with a goal that they help the user to maintain an active, positive image in the eyes of others. When they start designing the reply feature, they can now ask much more specific questions. For example: how could my design help a user in doing face-work in cases where they are in a hurry and can only send a short and blunt message to another person? How could the predefined replies help in situations where the users would not have time to answer but they know they should? Ultimately, would the predefined replies make it easier for users to do face-work in computer-mediated communications (CMC)?

You can therefore further specify RQ4 into this:

RQ4 (version 3): In which away-from-desktop leisure life situations are predefined replies in IM apps most needed when it is important to react quickly to arriving messages?

As you may notice, it is possible to scope the RQ too narrowly so that it starts to be close to absurd. But if that does not become a problem, the choice of methods (i.e., the research design ) becomes much easier to do.

The benefit of theoretically narrowed-down RQs (in this case, building on the concept of face-work in RQ4 version 3) have the benefit that they point you to useful background literature. Non-theoretical RQs (e.g., RQ4 version 2), in contrast, require that you identify the relevant literature more independently, relying on your own judgment. In the present case, you can base your thinking about IM apps’ on sociological research on interpersonal interaction and self-presentation (e.g., Goffman 1967) and its earlier applications to CMC (Nardi et al., 2000; Salovaara et al., 2011). Such a literature provides the starting points for deeper design considerations. Deeper considerations, in turn, increase the contribution of the research, and make it interesting for the readers.

As said, the first RQ that one comes to think of is not necessarily the best and final one. The RQ may need to be adapted (and also can be adapted) over the course of the research. In qualitative research this is very typical, and the same applies to exploratory design projects that proceed through small design experiments (i.e., through their own smaller RQs).

This text promised to address the pains that definition of a RQ or a design problem may pose for a student or a researcher. The main points of the answer may be summarized as follows:

• The search for a good RQ is a negotiation process between three objectives : what is personally motivating, what is realistically possible to do (e.g., that the work can be built on some earlier literature and there is a method that can answer to the RQ), and what motivates its relevance (i.e., can it lead to interesting findings).
• The search for a RQ or a design problem is a process and not a task that must be fixed immediately . It is, however, good to get started somewhere, since a RQ gives a lot of focus for future activities: what to read and what methods to choose, for example.

With the presentation of the scenario and its analysis, I sought to demonstrate why and how choosing an additional analytical viewpoint can be a useful strategy. With it, a project whose meaningfulness may be otherwise questionable for an outsider can become interesting when its underpinnings and assumptions are explicated. That helps ensure that the reader will appreciate the work that the author has done with their research.

In the problematization of the scenario, I presented the three challenges related to it. I can now offer possible answers to them, by highlighting why a RQ can serve as a tool for finding them:  

• Why would this be a relevant topic for research or design? Choice of a RQ often requires some amount of background research that helps the researcher/designer to understand how much about the problem has already been solved by others. This awareness helps shape the RQ to focus on a topic where information is not yet known and more information is needed for a high-quality outcome.
• How do you demonstrate that your solution is particularly good? By having a question, it is possible to analyse what are the right methods for answering it. The quality of executing these becomes then evaluatable. The focus on a particular question also will permit that the author compromises optimality in other, less central outcomes. For example, if smoothness of interaction is in the focus, then it is easy to explain why long-term robustness and durability of a prototype may not be critical.
• How does this project lead to learning or new knowledge? Presentation of the results or findings allows the researcher/design to devote their Discussion section (see the IMRaD article format ) to topics that would have been impossible to predict before the study. That will demonstrate that the project has generated novel understanding: it has generated knowledge that can be considered insightful.

If and when the researcher/designer pursues further in design and research, the experience of thinking about RQs and design problems accumulates. As one reads literature , the ability to consider different research questions becomes better too. Similarly, as one carries out projects with different RQs and problems, and notices how adjusting them along the way helps shape one’s work, the experience similarly grows. Eventually, one may even learn to enjoy the analytical process of identifying a good research question.

As a suggestion for further reading, Carsten Sørensen’s text  (2002) about writing and planning an article in information systems research field is a highly recommended one. It combines the question of choosing the RQ with the question on how to write a paper about it.

Goffman, E. (1967). On face-work: An analysis of ritual elements in social interaction. Psychiatry , 18 (3), 213–231.  https://doi.org/10.1080/00332747.1955.11023008

Nardi, B. A., Whittaker, S., & Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW 2000) (pp. 79–88). New York, NY: ACM Press. https://doi.org/10.1145/358916.358975

Salovaara, A., Lindqvist, A., Hasu, T., & Häkkilä, J. (2011). The phone rings but the user doesn’t answer: unavailability in mobile communication. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2011) (pp. 503–512). New York, NY: ACM Press. https://doi.org/10.1145/2037373.2037448

Sørensen, C. (2002): This is Not an Article — Just Some Food for Thoughts on How to Write One. Working Paper. Department of Information Systems, The London School of Economics and Political Science. No. 121.

Wikipedia (2020). Research question. Retrieved from https://en.wikipedia.org/wiki/Research_question (30 November 2020).

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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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

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How to Write a Good Research Question (w/ Examples)

What is a Research Question?

A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning  how to write a hypothesis or research question is the start to composing any thesis, dissertation, or research paper. It is also one of the most important sections of a research proposal . 

A good research question not only clarifies the writing in your study; it provides your readers with a clear focus and facilitates their understanding of your research topic, as well as outlining your study’s objectives. Before drafting the paper and receiving research paper editing (and usually before performing your study), you should write a concise statement of what this study intends to accomplish or reveal.

Research Question Writing Tips

Listed below are the important characteristics of a good research question:

A good research question should:

• Be clear and provide specific information so readers can easily understand the purpose.
• Be focused in its scope and narrow enough to be addressed in the space allowed by your paper
• Be relevant and concise and express your main ideas in as few words as possible, like a hypothesis.
• Be precise and complex enough that it does not simply answer a closed “yes or no” question, but requires an analysis of arguments and literature prior to its being considered acceptable. 
• Be arguable or testable so that answers to the research question are open to scrutiny and specific questions and counterarguments.

Some of these characteristics might be difficult to understand in the form of a list. Let’s go into more detail about what a research question must do and look at some examples of research questions.

The research question should be specific and focused 

Research questions that are too broad are not suitable to be addressed in a single study. One reason for this can be if there are many factors or variables to consider. In addition, a sample data set that is too large or an experimental timeline that is too long may suggest that the research question is not focused enough.

A specific research question means that the collective data and observations come together to either confirm or deny the chosen hypothesis in a clear manner. If a research question is too vague, then the data might end up creating an alternate research problem or hypothesis that you haven’t addressed in your Introduction section .

The research question should be based on the literature 

An effective research question should be answerable and verifiable based on prior research because an effective scientific study must be placed in the context of a wider academic consensus. This means that conspiracy or fringe theories are not good research paper topics.

Instead, a good research question must extend, examine, and verify the context of your research field. It should fit naturally within the literature and be searchable by other research authors.

References to the literature can be in different citation styles and must be properly formatted according to the guidelines set forth by the publishing journal, university, or academic institution. This includes in-text citations as well as the Reference section . 

The research question should be realistic in time, scope, and budget

There are two main constraints to the research process: timeframe and budget.

A proper research question will include study or experimental procedures that can be executed within a feasible time frame, typically by a graduate doctoral or master’s student or lab technician. Research that requires future technology, expensive resources, or follow-up procedures is problematic.

A researcher’s budget is also a major constraint to performing timely research. Research at many large universities or institutions is publicly funded and is thus accountable to funding restrictions. 

The research question should be in-depth

Research papers, dissertations and theses , and academic journal articles are usually dozens if not hundreds of pages in length.

A good research question or thesis statement must be sufficiently complex to warrant such a length, as it must stand up to the scrutiny of peer review and be reproducible by other scientists and researchers.

Research Question Types

Qualitative and quantitative research are the two major types of research, and it is essential to develop research questions for each type of study. 

Quantitative Research Questions

Quantitative research questions are specific. A typical research question involves the population to be studied, dependent and independent variables, and the research design.

In addition, quantitative research questions connect the research question and the research design. In addition, it is not possible to answer these questions definitively with a “yes” or “no” response. For example, scientific fields such as biology, physics, and chemistry often deal with “states,” in which different quantities, amounts, or velocities drastically alter the relevance of the research.

As a consequence, quantitative research questions do not contain qualitative, categorical, or ordinal qualifiers such as “is,” “are,” “does,” or “does not.”

Categories of quantitative research questions

Qualitative research questions.

In quantitative research, research questions have the potential to relate to broad research areas as well as more specific areas of study. Qualitative research questions are less directional, more flexible, and adaptable compared with their quantitative counterparts. Thus, studies based on these questions tend to focus on “discovering,” “explaining,” “elucidating,” and “exploring.”

Categories of qualitative research questions

Quantitative and qualitative research question examples.

Good and Bad Research Question Examples

Below are some good (and not-so-good) examples of research questions that researchers can use to guide them in crafting their own research questions.

Research Question Example 1

The first research question is too vague in both its independent and dependent variables. There is no specific information on what “exposure” means. Does this refer to comments, likes, engagement, or just how much time is spent on the social media platform?

Second, there is no useful information on what exactly “affected” means. Does the subject’s behavior change in some measurable way? Or does this term refer to another factor such as the user’s emotions?

Research Question Example 2

In this research question, the first example is too simple and not sufficiently complex, making it difficult to assess whether the study answered the question. The author could really only answer this question with a simple “yes” or “no.” Further, the presence of data would not help answer this question more deeply, which is a sure sign of a poorly constructed research topic.

The second research question is specific, complex, and empirically verifiable. One can measure program effectiveness based on metrics such as attendance or grades. Further, “bullying” is made into an empirical, quantitative measurement in the form of recorded disciplinary actions.

Steps for Writing a Research Question

Good research questions are relevant, focused, and meaningful. It can be difficult to come up with a good research question, but there are a few steps you can follow to make it a bit easier.

1. Start with an interesting and relevant topic

Choose a research topic that is interesting but also relevant and aligned with your own country’s culture or your university’s capabilities. Popular academic topics include healthcare and medical-related research. However, if you are attending an engineering school or humanities program, you should obviously choose a research question that pertains to your specific study and major.

Below is an embedded graph of the most popular research fields of study based on publication output according to region. As you can see, healthcare and the basic sciences receive the most funding and earn the highest number of publications. 

2. Do preliminary research  

You can begin doing preliminary research once you have chosen a research topic. Two objectives should be accomplished during this first phase of research. First, you should undertake a preliminary review of related literature to discover issues that scholars and peers are currently discussing. With this method, you show that you are informed about the latest developments in the field.

Secondly, identify knowledge gaps or limitations in your topic by conducting a preliminary literature review . It is possible to later use these gaps to focus your research question after a certain amount of fine-tuning.

3. Narrow your research to determine specific research questions

You can focus on a more specific area of study once you have a good handle on the topic you want to explore. Focusing on recent literature or knowledge gaps is one good option. 

By identifying study limitations in the literature and overlooked areas of study, an author can carve out a good research question. The same is true for choosing research questions that extend or complement existing literature.

4. Evaluate your research question

Make sure you evaluate the research question by asking the following questions:

Is my research question clear?

The resulting data and observations that your study produces should be clear. For quantitative studies, data must be empirical and measurable. For qualitative, the observations should be clearly delineable across categories.

Is my research question focused and specific?

A strong research question should be specific enough that your methodology or testing procedure produces an objective result, not one left to subjective interpretation. Open-ended research questions or those relating to general topics can create ambiguous connections between the results and the aims of the study. 

Is my research question sufficiently complex?

The result of your research should be consequential and substantial (and fall sufficiently within the context of your field) to warrant an academic study. Simply reinforcing or supporting a scientific consensus is superfluous and will likely not be well received by most journal editors.  

Editing Your Research Question

Your research question should be fully formulated well before you begin drafting your research paper. However, you can receive English paper editing and proofreading services at any point in the drafting process. Language editors with expertise in your academic field can assist you with the content and language in your Introduction section or other manuscript sections. And if you need further assistance or information regarding paper compositions, in the meantime, check out our academic resources , which provide dozens of articles and videos on a variety of academic writing and publication topics.

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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on July 15, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Table of contents

Questionnaires vs. surveys, questionnaire methods, open-ended vs. closed-ended questions, question wording, question order, step-by-step guide to design, other interesting articles, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyze data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives , placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method , administering questionnaires, data cleansing and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalize your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimizing these will help you avoid several types of research bias , including sampling bias , ascertainment bias , and undercoverage bias .

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Questionnaires can be self-administered or researcher-administered . Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

• cost-effective
• easy to administer for small and large groups
• anonymous and suitable for sensitive topics

But they may also be:

• unsuitable for people with limited literacy or verbal skills
• susceptible to a nonresponse bias (most people invited may not complete the questionnaire)
• biased towards people who volunteer because impersonal survey requests often go ignored.

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents.

Researcher-administered questionnaires can:

• help you ensure the respondents are representative of your target audience
• allow clarifications of ambiguous or unclear questions and answers
• have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

• costly and time-consuming to perform
• more difficult to analyze if you have qualitative responses
• likely to contain experimenter bias or demand characteristics
• likely to encourage social desirability bias in responses because of a lack of anonymity

Your questionnaire can include open-ended or closed-ended questions or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalizable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Likert scale questions collect ordinal data using rating scales with 5 or 7 points.

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale . Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio scales , you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer “multiracial” for the question on race rather than selecting from a restricted list.

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle for productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarizing responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorize answers, and you may also need to involve other researchers in data analysis for high reliability .

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way ( reliable ) and measure exactly what you’re interested in ( valid ).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Use a mix of both positive and negative frames to avoid research bias , and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counter argument within the question as well.

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

• The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favor flexible work-from-home policies even after it’s safe to return to offices?
• Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

Keep your questions focused

Ask about only one idea at a time and avoid double-barreled questions. Double-barreled questions ask about more than one item at a time, which can confuse respondents.

This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

Instead, you should ask two separate questions to gauge respondents’ opinions.

Strongly Agree Agree Undecided Disagree Strongly Disagree

Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

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You can organize the questions logically, with a clear progression from simple to complex. Alternatively, you can randomize the question order between respondents.

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioral or opinion questions, and ending with more complex, sensitive, or controversial questions.

The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

• How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
• Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
• Do you approve or disapprove of the way Joe Biden is handling his job as president?

It’s important to minimize order effects because they can be a source of systematic error or bias in your study.

Randomization

Randomization involves presenting individual respondents with the same questionnaire but with different question orders.

When you use randomization, order effects will be minimized in your dataset. But a randomized order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

Step 1: Define your goals and objectives

The first step of designing a questionnaire is determining your aims.

• What topics or experiences are you studying?
• What specifically do you want to find out?
• Is a self-report questionnaire an appropriate tool for investigating this topic?

Once you’ve specified your research aims, you can operationalize your variables of interest into questionnaire items. Operationalizing concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

Step 2: Use questions that are suitable for your sample

Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

• Are the respondents familiar with the language and terms used in your questions?
• Would any of the questions insult, confuse, or embarrass them?
• Do the response items for any closed-ended questions capture all possible answers?
• Are the response items mutually exclusive?
• Do the respondents have time to respond to open-ended questions?

Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

Step 3: Decide on your questionnaire length and question order

Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

If respondents are not being incentivized or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomize questions. Randomizing questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

Step 4: Pretest your questionnaire

When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection , and analysis. You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered .

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

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415 Research Question Examples Across 15 Disciplines

A research question is a clearly formulated query that delineates the scope and direction of an investigation. It serves as the guiding light for scholars, helping them to dissect, analyze, and comprehend complex phenomena. Beyond merely seeking answers, a well-crafted research question ensures that the exploration remains focused and goal-oriented.

The significance of framing a clear, concise, and researchable question cannot be overstated. A well-defined question not only clarifies the objective of the research but also determines the methodologies and tools a researcher will employ. A concise question ensures precision, eliminating the potential for ambiguity or misinterpretation. Furthermore, the question must be researchable—posing a question that is too broad, too subjective, or unanswerable can lead to inconclusive results or an endless loop of investigation. In essence, the foundation of any meaningful academic endeavor rests on the articulation of a compelling and achievable research question.

Research questions can be categorized based on their intent and the nature of the information they seek. Recognizing the different types is essential for crafting an effective inquiry and guiding the research process. Let's delve into the various categories:

• Descriptive Research Questions: These types of questions aim to outline and characterize specific phenomena or attributes. They seek to provide a clear picture of a situation or context without necessarily diving into causal relationships. For instance, a question like "What are the main symptoms of the flu?" is descriptive as it seeks to list the symptoms.
• Explanatory (or Causal) Research Questions: Explanatory questions delve deeper, trying to uncover the reasons or causes behind certain phenomena. They are particularly common in experimental research where researchers are attempting to establish cause-and-effect relationships. An example might be, "Does smoking increase the risk of lung cancer?"
• Exploratory Research Questions: As the name suggests, these questions are used when researchers are entering uncharted territories. They are designed to gather preliminary information on topics that haven't been studied extensively. A question like "How do emerging technologies impact remote tribal communities?" can be seen as exploratory if there's limited existing research on the topic.
• Comparative Research Questions: These questions are formulated when the objective is to compare two or more groups, conditions, or variables. Comparative questions might look like "How do test scores differ between students who study regularly and those who cram?"
• Predictive Research Questions: The goal here is to forecast or predict potential outcomes based on certain variables or conditions. Predictive research might pose questions such as "Based on current climate trends, how will average global temperatures change by 2050?"

Here are examples of research questions across various disciplines, shedding light on queries that stimulate intellectual curiosity and advancement. In this post, we will delve into disciplines ranging from the Natural Sciences, such as Physics and Biology, to the Social Sciences, including Sociology and Anthropology, as well as the Humanities, like Literature and Philosophy. We'll also explore questions from fields as varied as Health Sciences, Engineering, Business, Environmental Sciences, Mathematics, Education, Law, Agriculture, Arts, Computer Science, Architecture, and Languages. This comprehensive overview aims to illustrate the breadth and depth of inquiries that shape our world of knowledge.

Agriculture and forestry examples

Architecture and planning examples, arts and design examples, business and finance examples, computer science and informatics examples, education examples, engineering and technology examples, environmental sciences examples, health sciences examples, humanities examples, languages and linguistics examples, law examples, mathematics and statistics examples, natural sciences examples, social sciences examples.

• Descriptive: What are the primary factors that influence crop yield in temperate climates?
• Explanatory: Why do certain soil types yield higher grain production than others?
• Exploratory: How might new organic farming techniques influence soil health over a decade?
• Comparative: How do the growth rates differ between genetically modified and traditional corn crops?
• Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal science

• Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?
• Explanatory: Why do certain breeds of chickens have a higher egg production rate?
• Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?
• Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?
• Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

• Descriptive: What are the most commonly farmed fish species in Southeast Asia?
• Explanatory: Why do shrimp farms have a higher disease outbreak rate compared to fish farms?
• Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?
• Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?
• Predictive: What will be the impact of ocean acidification on mollusk farming over the next three decades?
• Descriptive: What tree species dominate the temperate rainforests of North America?
• Explanatory: Why are certain tree species more resistant to pest infestations?
• Exploratory: What are the potential benefits of integrating drone technology in forest health monitoring?
• Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?
• Predictive: Given increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

• Descriptive: What are the common characteristics of plants suitable for urban vertical farming?
• Explanatory: Why do roses require specific pH levels in the soil for optimal growth?
• Exploratory: What potential methods might promote year-round vegetable farming in colder regions?
• Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?
• Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil science

• Descriptive: What are the main components of loamy soil?
• Explanatory: Why does clay-rich soil retain more water compared to sandy soil?
• Exploratory: How might biochar applications transform nutrient availability in degraded soils?
• Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?
• Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over the next 30 years?

Architectural design

• Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?
• Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?
• Exploratory: How might sustainable materials revolutionize the future of architectural design?
• Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?
• Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

• Descriptive: What are the primary components of a successful urban park design?
• Explanatory: Why do certain types of vegetation promote greater biodiversity in urban settings?
• Exploratory: What innovative techniques can be employed to restore and integrate wetlands into urban landscapes?
• Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?
• Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban planning

• Descriptive: What are the main components of a pedestrian-friendly city center?
• Explanatory: Why do certain urban layouts promote more efficient traffic flow than others?
• Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?
• Comparative: How do the air quality levels differ between cities with green belts and those without?
• Predictive: Based on increasing telecommuting trends, how will urban planning strategies adjust to potentially reduced daily commutes in the future?

Graphic design

• Descriptive: What are the prevailing typography trends in modern branding?
• Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?
• Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?
• Comparative: How do print and digital designs differ in terms of elements and principles when targeting a young adult audience?
• Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial design

• Descriptive: What characterizes the ergonomic features of leading office chairs in the market?
• Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?
• Exploratory: How might bio-inspired design influence the future of transportation vehicles?
• Comparative: How does user satisfaction differ between traditional versus modular product designs?
• Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

• Descriptive: What techniques define the most popular virtual reality (VR) experiences currently available?
• Explanatory: Why do certain sound designs enhance immersion in video games more effectively than others?
• Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?
• Comparative: How do user engagement levels differ between 2D animations and 3D animations in educational platforms?
• Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing arts

• Descriptive: What styles of dance are currently predominant in global theater productions?
• Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?
• Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?
• Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?
• Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual arts

• Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?
• Explanatory: Why have mixed media installations gained prominence in the 21st-century art scene?
• Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?
• Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?
• Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Entrepreneurship

• Descriptive: What are the main challenges faced by startups in the tech industry?
• Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?
• Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?
• Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?
• Predictive: What sectors are predicted to see the most startup growth in the next decade?
• Descriptive: What are the primary sources of external funding for large corporations?
• Explanatory: Why did the stock market experience a significant drop in Q4 2022?
• Exploratory: How might blockchain technology revolutionize the future of banking?
• Comparative: How do the financial markets in developing countries compare to those in developed countries?
• Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human resources

• Descriptive: What are the most sought-after employee benefits in the tech industry?
• Explanatory: Why is there a high turnover rate in the retail sector?
• Exploratory: How might the rise of remote work affect HR practices in the next decade?
• Comparative: How do HR practices in multinational corporations differ from those in local companies?
• Predictive: What skills will be in highest demand in the workforce by 2030?
• Descriptive: What are the core responsibilities of middle management in large manufacturing firms?
• Explanatory: Why do some management strategies fail in diverse cultural environments?
• Exploratory: How are companies adapting their management structures in response to the gig economy?
• Comparative: How does management style in Eastern companies compare with Western businesses?
• Predictive: How might artificial intelligence reshape management practices in the next decade?
• Descriptive: What are the most effective digital marketing channels for e-commerce businesses?
• Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?
• Exploratory: How might virtual reality change the landscape of product advertising?
• Comparative: How do marketing strategies differ between B2B and B2C sectors?
• Predictive: What consumer behaviors are forecasted to dominate online shopping trends in the next five years?

Operations research

• Descriptive: What are the primary optimization techniques used in supply chain management?
• Explanatory: Why do certain optimization algorithms perform better in specific industries?
• Exploratory: How can quantum computing impact the future of operations research?
• Comparative: How does operations strategy differ between service and manufacturing industries?
• Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?

Artificial intelligence

• Descriptive: What are the primary algorithms used in deep learning?
• Explanatory: Why do certain neural network architectures outperform others in image recognition tasks?
• Exploratory: How might quantum computing influence the development of AI models?
• Comparative: How do reinforcement learning methods compare to supervised learning in game playing scenarios?
• Predictive: Based on current trends, how will AI impact the job market over the next decade?

Cybersecurity

• Descriptive: What are the most common types of cyberattacks reported in 2022?
• Explanatory: Why are certain industries more vulnerable to ransomware attacks?
• Exploratory: How might advances in quantum computing challenge existing encryption methods?
• Comparative: How do open-source software vulnerabilities compare to those in proprietary systems?
• Predictive: Given emerging technologies, what types of cyber threats will likely dominate in the next five years?

Data science

• Descriptive: What are the main tools used by data scientists in large-scale data analysis?
• Explanatory: Why does algorithm X yield more accurate predictions than algorithm Y for certain datasets?
• Exploratory: How can machine learning models improve real-time data processing in IoT devices?
• Comparative: How does the performance of traditional statistical models compare to machine learning models in predicting stock prices?
• Predictive: Based on current data trends, what industries will likely benefit the most from data analytics advancements in the coming decade?

Information systems

• Descriptive: What are the core components of a modern enterprise resource planning (ERP) system?
• Explanatory: Why have cloud-based information systems seen a rapid adoption rate in recent years?
• Exploratory: How might the integration of blockchain technology revolutionize supply chain information systems?
• Comparative: How do information system strategies differ between e-commerce and brick-and-mortar retailers?
• Predictive: Given the rise of remote work, how will information systems evolve to support decentralized teams in the future?

Software engineering

• Descriptive: What are the standard practices in agile software development?
• Explanatory: Why do some software projects face significant delays despite rigorous planning?
• Exploratory: How are emerging programming languages shaping the future of software development?
• Comparative: How does the software development lifecycle in startup environments compare to that in large corporations?
• Predictive: Based on current development trends, which software platforms are forecasted to dominate market share by 2030?

Adult education

• Descriptive: What are the primary motivations behind adults seeking further education later in life?
• Explanatory: Why do some adult education programs have a higher success rate compared to others?
• Exploratory: How might online learning platforms revolutionize adult education in the next decade?
• Comparative: How do adult education methodologies differ from traditional collegiate teaching techniques?
• Predictive: Given current trends, how will the demand for adult education courses change in the upcoming years?

Curriculum studies

• Descriptive: What are the core components of a modern high school curriculum in the United States?
• Explanatory: Why have certain subjects, like financial literacy, become more emphasized in recent curriculum updates?
• Exploratory: How can interdisciplinary studies be better incorporated into traditional curricula?
• Comparative: How does the math curriculum in the US compare to that in other developed countries?
• Predictive: Based on pedagogical research, what subjects are forecasted to gain prominence in curricula over the next decade?

Educational administration

• Descriptive: What are the main responsibilities of a school principal in large urban schools?
• Explanatory: Why do some schools consistently perform better in standardized testing than others, despite similar resources?
• Exploratory: How might emerging technologies shape the administrative tasks of educational institutions in the future?
• Comparative: How does school administration differ between private and public educational institutions?
• Predictive: Given the rise of online education, how will the role of educational administrators evolve in the coming years?

Educational psychology

• Descriptive: What cognitive strategies are commonly used by students to enhance memory retention during studies?
• Explanatory: Why do certain teaching methodologies resonate better with students having specific learning styles?
• Exploratory: How can insights from behavioral psychology improve student engagement in virtual classrooms?
• Comparative: How does the motivation level of students differ between self-paced versus instructor-led courses?
• Predictive: With the increasing integration of technology in education, how will student learning behaviors change in the next decade?

Special education

• Descriptive: What interventions are commonly used to support students with autism spectrum disorders in inclusive classrooms?
• Explanatory: Why do some special education programs yield better academic outcomes for students with specific learning disabilities?
• Exploratory: How can augmented reality technologies be utilized to enhance learning for students with visual impairments?
• Comparative: How does special education support differ between urban and rural school districts?
• Predictive: Based on advancements in assistive technologies, how will the landscape of special education transform in the near future?

Aerospace engineering

• Descriptive: What are the key materials and technologies utilized in modern spacecraft design?
• Explanatory: Why are certain alloys preferred in high-temperature aerospace applications?
• Exploratory: How might advances in propulsion technologies revolutionize space travel in the next decade?
• Comparative: How do commercial aircraft designs differ from military aircraft designs in terms of aerodynamics?
• Predictive: Given current research trends, how will the efficiency of jet engines change in the upcoming years?

Biomedical engineering

• Descriptive: What are the foundational principles behind the design of modern prosthetic limbs?
• Explanatory: Why have bio-compatible materials like titanium become crucial in implantable medical devices?
• Exploratory: How can nanotechnology be leveraged to improve drug delivery systems in the future?
• Comparative: How do MRI machines differ from CT scanners in terms of their underlying technology and application?
• Predictive: Based on emerging trends, how will wearable health monitors evolve in the next decade?

Chemical engineering

• Descriptive: What processes are involved in the large-scale production of ethylene?
• Explanatory: Why is distillation the most common separation method in the petroleum industry?
• Exploratory: How might green chemistry principles transform traditional chemical manufacturing processes?
• Comparative: How does the production of biofuels compare to traditional fossil fuels in terms of yield and environmental impact?
• Predictive: Given global sustainability goals, how will the chemical industry's reliance on fossil resources shift in the future?

Civil engineering

• Descriptive: What are the primary considerations in the structural design of skyscrapers in earthquake-prone regions?
• Explanatory: Why are steel-reinforced concrete beams commonly used in bridge construction?
• Exploratory: How can smart city concepts influence the infrastructure planning of urban centers in the future?
• Comparative: How do tunneling methods differ between soft soil and hard rock terrains?
• Predictive: With the increasing threat of climate change, how will coastal infrastructure design criteria change to account for rising sea levels?

Computer engineering

• Descriptive: What are the main components of a modern central processing unit (CPU) and their functions?
• Explanatory: Why is silicon predominantly used in semiconductor manufacturing?
• Exploratory: How might quantum computing redefine the landscape of traditional computing architectures?
• Comparative: How do solid-state drives (SSDs) compare to traditional hard disk drives (HDDs) in terms of performance and longevity?
• Predictive: Given advancements in chip miniaturization, how will the form factor of consumer electronics evolve in the coming years?

Electrical engineering

• Descriptive: What are the standard stages involved in the transmission and distribution of electrical power?
• Explanatory: Why are transformers essential in the power distribution network?
• Exploratory: How can emerging smart grid technologies improve the efficiency and reliability of electrical distribution systems?
• Comparative: How do AC and DC transmission methods differ in terms of efficiency and infrastructure requirements?
• Predictive: With the rise of renewable energy sources, how will power grid management complexities change in the next decade?

Mechanical engineering

• Descriptive: What are the fundamental principles behind the operation of a four-stroke internal combustion engine?
• Explanatory: Why are certain polymers used as vibration dampeners in machinery?
• Exploratory: How might advancements in materials science impact the design of future automotive systems?
• Comparative: How do hydraulic systems compare to pneumatic systems in terms of energy efficiency and application?
• Predictive: With the push towards sustainability, how will traditional manufacturing methods evolve to reduce their carbon footprint?

Climatology

• Descriptive: What are the primary factors that influence the El Niño and La Niña phenomena?
• Explanatory: Why have certain regions experienced more intense and frequent heatwaves in the past decade?
• Exploratory: How might changing atmospheric CO2 concentrations impact global wind patterns in the future?
• Comparative: How do urban areas differ from rural areas in terms of microclimate conditions?
• Predictive: Given current greenhouse gas emission trends, what will be the average global temperature increase by the end of the century?

Conservation science

• Descriptive: What are the primary threats faced by tropical rainforests around the world?
• Explanatory: Why are certain species more vulnerable to habitat fragmentation than others?
• Exploratory: How can community involvement enhance conservation efforts in protected areas?
• Comparative: How does the effectiveness of in-situ conservation compare to ex-situ conservation for endangered species?
• Predictive: If current deforestation rates continue, how many species are predicted to go extinct in the next 50 years?
• Descriptive: What are the dominant flora and fauna in a temperate deciduous forest biome?
• Explanatory: Why do certain ecosystems, like wetlands, have higher biodiversity than others?
• Exploratory: How might the spread of invasive species alter nutrient cycling in freshwater lakes?
• Comparative: How do the trophic dynamics of grassland ecosystems differ from those of desert ecosystems?
• Predictive: How will global ecosystems change if bee populations continue to decline at current rates?

Environmental health

• Descriptive: What are the major pollutants found in urban air?
• Explanatory: Why do certain pollutants cause respiratory diseases in humans?
• Exploratory: How might green building designs reduce the health risks associated with indoor air pollutants?
• Comparative: How do the health impacts of living near coal-fired power plants compare to living near nuclear power plants?
• Predictive: Given increasing urbanization trends, how will air quality in major cities change over the next two decades?

Marine biology

• Descriptive: What are the primary species that comprise a coral reef ecosystem?
• Explanatory: Why are coral reefs particularly sensitive to changes in sea temperature?
• Exploratory: How might deep-sea exploration reveal unknown marine species and their adaptations?
• Comparative: How do the feeding strategies of pelagic fish differ from benthic fish in oceanic ecosystems?
• Predictive: If ocean acidification trends continue, what will be the impact on shell-forming marine organisms in the next 30 years?
• Descriptive: What are the most common oral health issues faced by elderly individuals?
• Explanatory: Why do sugary foods lead to a higher prevalence of cavities?
• Exploratory: How might emerging technologies revolutionize dental procedures in the coming decade?
• Comparative: How do the effects of electric toothbrushes compare to manual ones in reducing plaque?
• Predictive: Given current trends, how might the prevalence of gum diseases change in populations with increased sugar consumption over the next decade?

Kinesiology

• Descriptive: What are the primary physiological changes that occur during aerobic exercise?
• Explanatory: Why do some athletes experience muscle cramps during extensive physical activity?
• Exploratory: How might different stretching routines impact athletic performance?
• Comparative: How do the biomechanics of running on a treadmill differ from running outdoors?
• Predictive: If sedentary lifestyles continue to rise, what could be the potential impact on musculoskeletal health in the next 20 years?
• Descriptive: What are the main symptoms associated with the early stages of Parkinson's disease?
• Explanatory: Why are some viruses, like the flu, more prevalent in colder months?
• Exploratory: How might genetic editing technologies, like CRISPR, be utilized to treat hereditary diseases in the future?
• Comparative: How does the efficacy of traditional chemotherapy compare to targeted therapy in treating certain cancers?
• Predictive: Given advances in telemedicine, how might patient-doctor interactions evolve over the next decade?
• Descriptive: What are the primary responsibilities of nurses in intensive care units?
• Explanatory: Why is there a higher burnout rate among nurses compared to other healthcare professionals?
• Exploratory: How can training programs be improved to better equip nurses for challenges in emergency situations?
• Comparative: How does the patient recovery rate differ when cared for by specialized nurses versus general ward nurses?
• Predictive: How will the role of nurses change with the integration of more AI-based diagnostic tools in hospitals?
• Descriptive: What are the main nutritional components of a Mediterranean diet?
• Explanatory: Why does a diet high in processed sugars lead to increased risks of type 2 diabetes?
• Exploratory: How might gut microbiota be influenced by various diets and what are the potential health implications?
• Comparative: How does the nutritional profile of plant-based proteins compare to animal-based proteins?
• Predictive: If global meat consumption trends continue, what could be the implications for population-wide nutritional health in 30 years?
• Descriptive: What are the primary active ingredients in over-the-counter pain relievers?
• Explanatory: Why do certain medications cause drowsiness as a side effect?
• Exploratory: How might nanoparticle-based drug delivery systems enhance the efficacy of certain treatments?
• Comparative: How do the effects of generic drugs compare to their brand-name counterparts?
• Predictive: Given the rise of antibiotic-resistant bacteria, how might pharmaceutical approaches to bacterial infections change in the future?

Public health

• Descriptive: What are the main factors contributing to public health disparities in urban vs rural areas?
• Explanatory: Why did certain regions have higher transmission rates during the COVID-19 pandemic?
• Exploratory: How can community engagement strategies be optimized for more effective health campaigns?
• Comparative: How do vaccination rates and outcomes differ between countries with public vs private healthcare systems?
• Predictive: Based on current trends, how will global public health challenges evolve over the next 50 years?

Art history

• Descriptive: What are the primary artistic styles observed in the Renaissance era?
• Explanatory: Why did the Baroque art movement emerge after the Renaissance?
• Exploratory: How might newly discovered ancient art pieces reshape our understanding of prehistoric artistic practices?
• Comparative: How does European Romantic art differ from Asian Romantic art of the same period?
• Predictive: Given current trends, how might digital art impact traditional art gallery setups in the next decade?
• Descriptive: What are the primary themes in Homer's "Odyssey"?
• Explanatory: Why did Greek tragedies place a strong emphasis on the concept of fate?
• Exploratory: Are there undiscovered works that might provide more insight into daily life in ancient Rome?
• Comparative: How do Roman epics compare to their Greek counterparts in terms of character development?
• Predictive: How will emerging technologies like virtual reality affect the study of ancient ruins?

Cultural studies

• Descriptive: How is the concept of family portrayed in contemporary American media?
• Explanatory: Why has the influence of Western culture grown in certain Eastern countries over the last century?
• Exploratory: What are the emerging subcultures in the digital age and how do they communicate?
• Comparative: How does the representation of masculinity vary between Eastern and Western films?
• Predictive: In what ways might globalization affect cultural identities in the next two decades?
• Descriptive: What events led to the fall of the Berlin Wall?
• Explanatory: Why did the Industrial Revolution begin in Britain?
• Exploratory: Are there undocumented civilizational interactions in ancient times that new archaeological findings might reveal?
• Comparative: How did the responses to the Black Plague differ between European and Asian nations?
• Predictive: Given historical patterns, how might major global powers react to dwindling natural resources in the future?
• Descriptive: What are the main narrative techniques used in James Joyce's "Ulysses"?
• Explanatory: Why did the Gothic novel become popular in 19th-century England?
• Exploratory: How might translations of ancient texts reveal different interpretations based on the translator's cultural background?
• Comparative: How does the portrayal of war differ between post-WWII American and French literature?
• Predictive: How might the rise of AI-authored literature change the publishing industry?
• Descriptive: What are the core principles of existentialism as described by Jean-Paul Sartre?
• Explanatory: Why did the philosophy of existentialism gain prominence post-WWII?
• Exploratory: How might ancient Eastern philosophies provide insights into modern ethical dilemmas surrounding technology?
• Comparative: How does Nietzsche's concept of the "Ubermensch" compare to Aristotle's "virtuous person"?
• Predictive: As AI becomes more prevalent, how might philosophical discussions around consciousness evolve?

Religious studies

• Descriptive: What are the Five Pillars of Islam?
• Explanatory: Why did Protestantism emerge within Christianity during the 16th century?
• Exploratory: Are there common motifs in creation myths across various religions?
• Comparative: How do concepts of the afterlife compare between Christianity, Buddhism, and Ancient Egyptian beliefs?
• Predictive: How might interfaith dialogue shape religious practices in multi-faith societies over the next decade?

Classic languages

• Descriptive: What are the primary grammatical structures in Ancient Greek?
• Explanatory: Why did Latin play a foundational role in the development of many modern European languages?
• Exploratory: Are there yet-to-be-deciphered scripts from ancient civilizations that might provide insight into lost languages?
• Comparative: How do the verb conjugation patterns in Latin compare to those in Sanskrit?
• Predictive: Given the ongoing research in classical studies, how might our understanding of certain ancient texts change in the next decade?

Comparative literature

• Descriptive: What are the main themes in Japanese Haiku and English Sonnets?
• Explanatory: Why do certain folklore tales appear with variations across different cultures?
• Exploratory: How might newly translated works from lesser-known languages reshape the world literature canon?
• Comparative: How does the role of the tragic hero in French literature differ from its portrayal in Russian literature?
• Predictive: As global communication becomes more interconnected, how might the study of world literature evolve in universities?

Modern languages

• Descriptive: What are the primary tonal patterns observed in Mandarin Chinese?
• Explanatory: Why has English become a dominant lingua franca in international business and diplomacy?
• Exploratory: Which lesser-studied languages might become more prominent due to socio-political changes in their regions?
• Comparative: How do the grammatical complexities of Russian compare to those of German?
• Predictive: Given current global trends, which languages are predicted to become more widely spoken in the next two decades?
• Descriptive: What are the primary articulatory features of plosive sounds?
• Explanatory: Why do certain accents develop specific pitch fluctuations and intonations?
• Exploratory: How do various environmental factors affect vocal cord vibrations and sound production?
• Comparative: How does the pronunciation of fricatives differ between Spanish and Portuguese speakers?
• Predictive: How might advancements in voice recognition technology influence phonetics research in the next decade?
• Descriptive: What are the primary signs and symbols used in American road signage?
• Explanatory: Why do red roses universally symbolize love or passion in many cultures?
• Exploratory: Are there emerging symbols in digital communication that could become universally recognized signs in the future?
• Comparative: How do the semiotic structures in print advertisements differ between Western and Eastern cultures?
• Predictive: As emoji usage becomes more widespread, how might they impact written language semantics in the coming years?
• Descriptive: What are the key statutes governing tenant rights in residential leases?
• Explanatory: Why do personal injury claims vary significantly in settlement amounts even under similar circumstances?
• Exploratory: How might alternative dispute resolution mechanisms evolve in civil law contexts over the next decade?
• Comparative: How do defamation laws differ between jurisdictions that adopt the British common law system versus the Napoleonic code?
• Predictive: How might the rise of online transactions affect the volume and nature of civil law cases related to contract disputes?

Constitutional law

• Descriptive: What are the main principles enshrined in the First Amendment of the U.S. Constitution?
• Explanatory: Why have some constitutional rights been subject to varying interpretations over time?
• Exploratory: Are there emerging debates around digital rights and freedoms that might reshape constitutional interpretations in the future?
• Comparative: How does the protection of freedom of speech differ between the U.S. Constitution and the German Basic Law?
• Predictive: Given global socio-political trends, how might constitutional democracies adjust their foundational texts in the next two decades?

Corporate law

• Descriptive: What are the primary duties and liabilities of a board of directors in a publicly traded company?
• Explanatory: Why do mergers and acquisitions often involve extensive due diligence processes?
• Exploratory: How might the rise of digital currencies impact the regulatory landscape for corporations in the finance sector?
• Comparative: How does the legal framework for shareholder rights in the U.S. compare to that of Japan?
• Predictive: How might changing global trade dynamics influence corporate structuring and international partnerships?

Criminal law

• Descriptive: What constitutes first-degree murder in the majority of jurisdictions?
• Explanatory: Why are certain offenses classified as misdemeanors while others are felonies?
• Exploratory: Are there emerging patterns in cybercrime that suggest new areas of legal vulnerability?
• Comparative: How does the treatment of juvenile offenders differ between Scandinavian countries and the U.S.?
• Predictive: Given advancements in technology, how might criminal law evolve to address potential misuses of artificial intelligence?

International law

• Descriptive: What are the foundational principles of the Geneva Conventions?
• Explanatory: Why have some nations refused to recognize or be bound by certain international treaties?
• Exploratory: How might global climate change reshape international agreements and treaties in the coming years?
• Comparative: How do regional trade agreements in Africa compare to those in Southeast Asia in terms of provisions and enforcement mechanisms?
• Predictive: How might geopolitical shifts influence the role and effectiveness of international courts in resolving state disputes?

Applied mathematics

• Descriptive: What are the primary mathematical models used to predict the spread of infectious diseases?
• Explanatory: Why does the Navier–Stokes equation play a pivotal role in fluid dynamics?
• Exploratory: How might new computational methods enhance the efficiency of existing algorithms in applied mathematics?
• Comparative: How do optimization techniques in operations research differ from those in machine learning applications?
• Predictive: Given the rapid growth of quantum computing, how might it reshape the landscape of applied mathematical problems in the next decade?

Applied statistics

• Descriptive: What are the standard procedures for handling missing data in a large-scale survey?
• Explanatory: Why do statisticians use bootstrapping techniques in hypothesis testing?
• Exploratory: How might emerging data sources, like wearables and IoT devices, introduce new challenges and opportunities in applied statistics?
• Comparative: How does the performance of Bayesian methods compare to frequentist methods in complex hierarchical models?
• Predictive: With the increasing availability of big data, how might the role of applied statisticians evolve in the next five years?

Pure mathematics

• Descriptive: What are the axioms underpinning Euclidean geometry?
• Explanatory: Why is Gödel's incompleteness theorem considered a foundational result in the philosophy of mathematics?
• Exploratory: Are there newly emerging areas of study within number theory due to advancements in computational mathematics?
• Comparative: How do algebraic structures differ between rings and fields?
• Predictive: Considering current research trends, what areas of pure mathematics are poised for significant breakthroughs in the next decade?

Theoretical statistics

• Descriptive: What foundational principles underlie the Central Limit Theorem?
• Explanatory: Why is the concept of sufficiency crucial in the design of statistical tests?
• Exploratory: How might advances in artificial intelligence influence theoretical developments in statistical inference?
• Comparative: How do likelihood-based inference methods compare to Bayesian methods in terms of theoretical underpinnings?
• Predictive: As data generation mechanisms evolve, how might the theoretical foundations of statistics need to adapt in the future?
• Descriptive: What are the key features and behaviors of black holes?
• Explanatory: Why does the expansion of the universe appear to be accelerating?
• Exploratory: What potential insights might the study of exoplanets provide about the conditions necessary for life?
• Comparative: How do the properties of spiral galaxies differ from those of elliptical galaxies?
• Predictive: Based on current data, what are the projected future behaviors of our sun as it ages?
• Descriptive: What are the primary functions and structures of ribosomes in a cell?
• Explanatory: Why does DNA replication occur semi-conservatively?
• Exploratory: How might emerging technologies like CRISPR redefine our understanding of genetic engineering?
• Comparative: How do the metabolic processes of prokaryotic cells differ from those of eukaryotic cells?
• Predictive: Given the current trajectory of climate change, how might the biodiversity in tropical rainforests be affected over the next century?
• Descriptive: What are the key properties and uses of the noble gases?
• Explanatory: Why do exothermic reactions release heat?
• Exploratory: How might advances in nanochemistry influence drug delivery systems?
• Comparative: How do ionic bonds differ in strength and characteristics from covalent bonds?
• Predictive: Considering the rise in antibiotic-resistant bacteria, how might the field of medicinal chemistry adapt to produce effective treatments in the future?

Earth science

• Descriptive: What are the primary layers of Earth's atmosphere and their respective characteristics?
• Explanatory: Why do certain regions experience more seismic activity than others?
• Exploratory: How might the study of ancient ice cores provide insights into past climate conditions?
• Comparative: How do the processes of weathering differ between arid and humid climates?
• Predictive: Given current data on deforestation, what could be its impact on global soil quality and erosion patterns over the next 50 years?
• Descriptive: What are the fundamental principles underlying quantum mechanics?
• Explanatory: Why does the speed of light in a vacuum remain constant regardless of the observer's frame of reference?
• Exploratory: How might studies in string theory reshape our understanding of the universe at the smallest scales?
• Comparative: How do the effects of general relativity contrast with predictions from Newtonian physics under extreme gravitational conditions?
• Predictive: With advancements in particle physics, what potential new particles or phenomena might be discovered in the next decade?

Anthropology

• Descriptive: What are the primary rituals and customs of the indigenous tribes of the Amazon?
• Explanatory: Why did the ancient Mayan civilization collapse?
• Exploratory: How might modern urbanization impact the preservation of ancient burial sites?
• Comparative: How do hunter-gatherer societies differ from agricultural societies in terms of social structures?
• Predictive: Given global trends, how might indigenous cultures evolve over the next century?

Communication

• Descriptive: What are the main modes of communication used by millennials compared to baby boomers?
• Explanatory: Why has the usage of social media platforms surged in the last two decades?
• Exploratory: How might advancements in virtual reality reshape interpersonal communication in the future?
• Comparative: How do written communication skills differ between those educated in traditional schools versus online schools?
• Predictive: How might the nature of journalism change with the rise of automated content generation?
• Descriptive: What are the primary components of a nation's gross domestic product (GDP)?
• Explanatory: Why did the economic recession of 2008 occur?
• Exploratory: How might the concept of universal basic income impact labor market dynamics?
• Comparative: How do free market economies differ from command economies in terms of resource allocation?
• Predictive: Based on current global economic trends, which industries are predicted to boom in the next decade?
• Descriptive: What are the geographical features of the Himalayan mountain range?
• Explanatory: Why do desert regions exist on the western coasts of continents, such as the Atacama in South America?
• Exploratory: How might rising sea levels reshape the world's coastlines over the next century?
• Comparative: How does urban planning in European cities differ from that in American cities?
• Predictive: Given current urbanization rates, which cities are poised to become megacities by 2050?

Political science

• Descriptive: What are the foundational principles of a parliamentary democracy?
• Explanatory: Why do certain nations adopt federal systems while others prefer unitary systems?
• Exploratory: How might the rise of populism influence global diplomatic relations in the 21st century?
• Comparative: How do the rights of citizens in liberal democracies differ from those in authoritarian regimes?
• Predictive: Based on current political trends, which nations might see significant shifts in governance models over the next two decades?
• Descriptive: What are the primary stages of cognitive development in children according to Piaget?
• Explanatory: Why do certain individuals develop phobias?
• Exploratory: How might emerging neuroscientific tools, like fMRI, alter our understanding of human emotions?
• Comparative: How do coping mechanisms differ between individuals with high resilience versus those with low resilience?
• Predictive: Given the rise in digital communication, how might human attention spans evolve in future generations?

Social work

• Descriptive: What are the core principles and practices in child protective services?
• Explanatory: Why do certain communities have higher rates of child neglect and abuse?
• Exploratory: How might the integration of artificial intelligence in social work affect decision-making in child welfare cases?
• Comparative: How do intervention strategies for substance abuse differ between urban and rural settings?
• Predictive: Based on current societal trends, what challenges might social workers face in the next decade?
• Descriptive: What are the defining characteristics of Generation Z as a social cohort?
• Explanatory: Why have nuclear families become less prevalent in Western societies?
• Exploratory: How might the widespread adoption of virtual realities impact social interactions and community structures in the future?
• Comparative: How do the roles and perceptions of elderly individuals differ between Eastern and Western societies?
• Predictive: Given the rise in remote work, how might urban and suburban living patterns change over the next three decades?

In synthesizing the vast range of research questions posed across diverse disciplines, it becomes clear that every academic field, from the humanities to the social sciences, offers unique perspectives and methodologies to uncover and understand various facets of our world. These questions, whether descriptive, explanatory, exploratory, comparative, or predictive, serve as guiding lights, driving scholarship and innovation. As academia continues to evolve and adapt, these inquiries not only define the boundaries of current knowledge but also pave the way for future discoveries and insights, emphasizing the invaluable role of continuous inquiry in the ever-evolving tapestry of human understanding.

Header image by Zetong Li .

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