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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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

Methodology

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

 Statistics

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

Research bias

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

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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How to Craft a Strong Research Question (With Research Question Examples)

A sound and effective research question is a key element that must be identified and pinned down before researchers can even begin their research study or work. A strong research question lays the foundation for your entire study, guiding your investigation and shaping your findings. Hence, it is critical that researchers spend considerable time assessing and refining the research question based on in-depth reading and comprehensive literature review. In this article, we will discuss how to write a strong research question and provide you with some good examples of research questions across various disciplines.

Table of Contents

The importance of a research question

A research question plays a crucial role in driving scientific inquiry, setting the direction and purpose of your study, and guiding your entire research process. By formulating a clear and focused research question, you lay the foundation for your investigation, ensuring that your research remains on track and aligned with your objectives so you can make meaningful contribution to the existing body of knowledge. A well-crafted research question also helps you define the scope of your study and identify the appropriate methodologies and data collection techniques to employ.

Key components of a strong research question

A good research question possesses several key components that contribute to the quality and impact of your study. Apart from providing a clear framework to generate meaningful results, a well-defined research question allows other researchers to understand the purpose and significance of your work. So, when working on your research question, incorporate the following elements:

• Specificity : A strong research question should be specific about the main focus of your study, enabling you to gather precise data and draw accurate conclusions. It clearly defines the variables, participants, and context involved, leaving no room for ambiguity.
• Clarity : A good research question is clear and easily understood, so articulate the purpose and intent of your study concisely without being generic or vague. Ensuring clarity in your research question helps both you and your readers grasp the research objective.
• Feasibility : While crafting a research question, consider the practicality of conducting the research and availability of necessary data or access to participants. Think whether your study is realistic and achievable within the constraints of time, resources, and ethical considerations.

How to craft a well-defined research question

A first step that will help save time and effort is knowing what your aims are and thinking about a few problem statements on the area or aspect one wants to study or do research on. Contemplating these statements as one undertakes more progressive reading can help the researcher in reassessing and fine-tuning the research question. This can be done over time as they read and learn more about the research topic, along with a broad literature review and parallel discussions with peer researchers and supervisors. In some cases, a researcher can have more than one research question if the research being undertaken is a PhD thesis or dissertation, but try not to cover multiple concerns on a topic.

A strong research question must be researchable, original, complex, and relevant. Here are five simple steps that can make the entire process easier.

• Identify a broad topic from your areas of interest, something that is relevant, and you are passionate about since you’ll be spending a lot of time conducting your research.
• Do a thorough literature review to weed out potential gaps in research and stay updated on what’s currently being done in your chosen topic and subject area.
• Shortlist possible research questions based on the research gaps or see how you can build on or refute previously published ideas and concepts.
• Assess your chosen research question using the FINER criteria that helps you evaluate whether the research is Feasible, Interesting, Novel, Ethical, and Relevant. 1
• Formulate the final research question, while ensuring it is clear, well-written, and addresses all the key elements of a strong research question.

Examples of research questions

Remember to adapt your research question to suit your purpose, whether it’s exploratory, descriptive, comparative, experimental, qualitative, or quantitative. Embrace the iterative nature of the research process, continually evaluating and refining your question as you progress. Here are some good examples of research questions across various disciplines.

Exploratory research question examples

• How does social media impact interpersonal relationships among teenagers?
• What are the potential benefits of incorporating mindfulness practices in the workplace?

Descriptive research question examples

• What factors influence customer loyalty in the e-commerce industry?
• Is there a relationship between socioeconomic status and academic performance among elementary school students?

Comparative research question examples

• How does the effectiveness of traditional teaching methods compare to online learning platforms in mathematics education?
• What is the impact of different healthcare policies on patient outcomes in various countries?

Experimental research question examples

• What are the effects of a new drug on reducing symptoms of a specific medical condition?
• Does a dietary intervention have an impact on weight loss among individuals with obesity?

Qualitative research question examples

• What are the lived experiences of immigrants adapting to a new culture?
• What factors influence job satisfaction among healthcare professionals?

Quantitative research question examples

• Is there a relationship between sleep duration and academic performance among college students?
• How effective is a specific intervention in reducing anxiety levels among individuals with phobias?

With these simple guidelines and inspiring examples of research questions, you are equipped to embark on your research journey with confidence and purpose. Here’s wishing you all the best for your future endeavors!

References:

• How to write a research question: Steps and examples. Indeed Career Guide. Available online at https://www.indeed.com/career-advice/career-development/how-to-write-research-questions

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 

• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Research Question 101 📖

Everything you need to know to write a high-quality research question

By: Derek Jansen (MBA) | Reviewed By: Dr. Eunice Rautenbach | October 2023

If you’ve landed on this page, you’re probably asking yourself, “ What is a research question? ”. Well, you’ve come to the right place. In this post, we’ll explain what a research question is , how it’s differen t from a research aim, and how to craft a high-quality research question that sets you up for success.

Research Question 101

What is a research question.

• Research questions vs research aims
• The 4 types of research questions
• How to write a research question
• Frequently asked questions
• Examples of research questions

As the name suggests, the research question is the core question (or set of questions) that your study will (attempt to) answer .

In many ways, a research question is akin to a target in archery . Without a clear target, you won’t know where to concentrate your efforts and focus. Essentially, your research question acts as the guiding light throughout your project and informs every choice you make along the way.

Let’s look at some examples:

What impact does social media usage have on the mental health of teenagers in New York?

How does the introduction of a minimum wage affect employment levels in small businesses in outer London?

How does the portrayal of women in 19th-century American literature reflect the societal attitudes of the time?

What are the long-term effects of intermittent fasting on heart health in adults?

As you can see in these examples, research questions are clear, specific questions that can be feasibly answered within a study. These are important attributes and we’ll discuss each of them in more detail a little later . If you’d like to see more examples of research questions, you can find our RQ mega-list here .

Research Questions vs Research Aims

At this point, you might be asking yourself, “ How is a research question different from a research aim? ”. Within any given study, the research aim and research question (or questions) are tightly intertwined , but they are separate things . Let’s unpack that a little.

A research aim is typically broader in nature and outlines what you hope to achieve with your research. It doesn’t ask a specific question but rather gives a summary of what you intend to explore.

The research question, on the other hand, is much more focused . It’s the specific query you’re setting out to answer. It narrows down the research aim into a detailed, researchable question that will guide your study’s methods and analysis.

Let’s look at an example:

Research Aim: To explore the effects of climate change on marine life in Southern Africa.

Research Question: How does ocean acidification caused by climate change affect the reproduction rates of coral reefs?

As you can see, the research aim gives you a general focus , while the research question details exactly what you want to find out.

Need a helping hand?

Types of research questions

Now that we’ve defined what a research question is, let’s look at the different types of research questions that you might come across. Broadly speaking, there are (at least) four different types of research questions – descriptive , comparative , relational , and explanatory . 

Descriptive questions ask what is happening. In other words, they seek to describe a phenomena or situation . An example of a descriptive research question could be something like “What types of exercise do high-performing UK executives engage in?”. This would likely be a bit too basic to form an interesting study, but as you can see, the research question is just focused on the what – in other words, it just describes the situation.

Comparative research questions , on the other hand, look to understand the way in which two or more things differ , or how they’re similar. An example of a comparative research question might be something like “How do exercise preferences vary between middle-aged men across three American cities?”. As you can see, this question seeks to compare the differences (or similarities) in behaviour between different groups.

Next up, we’ve got exploratory research questions , which ask why or how is something happening. While the other types of questions we looked at focused on the what, exploratory research questions are interested in the why and how . As an example, an exploratory research question might ask something like “Why have bee populations declined in Germany over the last 5 years?”. As you can, this question is aimed squarely at the why, rather than the what.

Last but not least, we have relational research questions . As the name suggests, these types of research questions seek to explore the relationships between variables . Here, an example could be something like “What is the relationship between X and Y” or “Does A have an impact on B”. As you can see, these types of research questions are interested in understanding how constructs or variables are connected , and perhaps, whether one thing causes another.

Of course, depending on how fine-grained you want to get, you can argue that there are many more types of research questions , but these four categories give you a broad idea of the different flavours that exist out there. It’s also worth pointing out that a research question doesn’t need to fit perfectly into one category – in many cases, a research question might overlap into more than just one category and that’s okay.

The key takeaway here is that research questions can take many different forms , and it’s useful to understand the nature of your research question so that you can align your research methodology accordingly.

How To Write A Research Question

As we alluded earlier, a well-crafted research question needs to possess very specific attributes, including focus , clarity and feasibility . But that’s not all – a rock-solid research question also needs to be rooted and aligned . Let’s look at each of these.

A strong research question typically has a single focus. So, don’t try to cram multiple questions into one research question; rather split them up into separate questions (or even subquestions), each with their own specific focus. As a rule of thumb, narrow beats broad when it comes to research questions.

Clear and specific

A good research question is clear and specific, not vague and broad. State clearly exactly what you want to find out so that any reader can quickly understand what you’re looking to achieve with your study. Along the same vein, try to avoid using bulky language and jargon – aim for clarity.

Unfortunately, even a super tantalising and thought-provoking research question has little value if you cannot feasibly answer it. So, think about the methodological implications of your research question while you’re crafting it. Most importantly, make sure that you know exactly what data you’ll need (primary or secondary) and how you’ll analyse that data.

A good research question (and a research topic, more broadly) should be rooted in a clear research gap and research problem . Without a well-defined research gap, you risk wasting your effort pursuing a question that’s already been adequately answered (and agreed upon) by the research community. A well-argued research gap lays at the heart of a valuable study, so make sure you have your gap clearly articulated and that your research question directly links to it.

As we mentioned earlier, your research aim and research question are (or at least, should be) tightly linked. So, make sure that your research question (or set of questions) aligns with your research aim . If not, you’ll need to revise one of the two to achieve this.

FAQ: Research Questions

Research question faqs, how many research questions should i have, what should i avoid when writing a research question, can a research question be a statement.

Typically, a research question is phrased as a question, not a statement. A question clearly indicates what you’re setting out to discover.

Can a research question be too broad or too narrow?

Yes. A question that’s too broad makes your research unfocused, while a question that’s too narrow limits the scope of your study.

Here’s an example of a research question that’s too broad:

“Why is mental health important?”

Conversely, here’s an example of a research question that’s likely too narrow:

“What is the impact of sleep deprivation on the exam scores of 19-year-old males in London studying maths at The Open University?”

Can I change my research question during the research process?

How do i know if my research question is good.

A good research question is focused, specific, practical, rooted in a research gap, and aligned with the research aim. If your question meets these criteria, it’s likely a strong question.

Is a research question similar to a hypothesis?

Not quite. A hypothesis is a testable statement that predicts an outcome, while a research question is a query that you’re trying to answer through your study. Naturally, there can be linkages between a study’s research questions and hypothesis, but they serve different functions.

How are research questions and research objectives related?

The research question is a focused and specific query that your study aims to answer. It’s the central issue you’re investigating. The research objective, on the other hand, outlines the steps you’ll take to answer your research question. Research objectives are often more action-oriented and can be broken down into smaller tasks that guide your research process. In a sense, they’re something of a roadmap that helps you answer your research question.

Need some inspiration?

If you’d like to see more examples of research questions, check out our research question mega list here .  Alternatively, if you’d like 1-on-1 help developing a high-quality research question, consider our private coaching service .

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Creating a Good Research Question

• Advice & Growth
• Process in Practice

Successful translation of research begins with a strong question. How do you get started? How do good research questions evolve? And where do you find inspiration to generate good questions in the first place?  It’s helpful to understand existing frameworks, guidelines, and standards, as well as hear from researchers who utilize these strategies in their own work.

In the fall and winter of 2020, Naomi Fisher, MD, conducted 10 interviews with clinical and translational researchers at Harvard University and affiliated academic healthcare centers, with the purpose of capturing their experiences developing good research questions. The researchers featured in this project represent various specialties, drawn from every stage of their careers. Below you will find clips from their interviews and additional resources that highlight how to get started, as well as helpful frameworks and factors to consider. Additionally, visit the Advice & Growth section to hear candid advice and explore the Process in Practice section to hear how researchers have applied these recommendations to their published research.

• Naomi Fisher, MD , is associate professor of medicine at Harvard Medical School (HMS), and clinical staff at Brigham and Women’s Hospital (BWH). Fisher is founder and director of Hypertension Services and the Hypertension Specialty Clinic at the BWH, where she is a renowned endocrinologist. She serves as a faculty director for communication-related Boundary-Crossing Skills for Research Careers webinar sessions and the Writing and Communication Center .
• Christopher Gibbons, MD , is associate professor of neurology at HMS, and clinical staff at Beth Israel Deaconess Medical Center (BIDMC) and Joslin Diabetes Center. Gibbons’ research focus is on peripheral and autonomic neuropathies.
• Clare Tempany-Afdhal, MD , is professor of radiology at HMS and the Ferenc Jolesz Chair of Research, Radiology at BWH. Her major areas of research are MR imaging of the pelvis and image- guided therapy.
• David Sykes, MD, PhD , is assistant professor of medicine at Massachusetts General Hospital (MGH), he is also principal investigator at the Sykes Lab at MGH. His special interest area is rare hematologic conditions.
• Elliot Israel, MD , is professor of medicine at HMS, director of the Respiratory Therapy Department, the director of clinical research in the Pulmonary and Critical Care Medical Division and associate physician at BWH. Israel’s research interests include therapeutic interventions to alter asthmatic airway hyperactivity and the role of arachidonic acid metabolites in airway narrowing.
• Jonathan Williams, MD, MMSc , is assistant professor of medicine at HMS, and associate physician at BWH. He focuses on endocrinology, specifically unravelling the intricate relationship between genetics and environment with respect to susceptibility to cardiometabolic disease.
• Junichi Tokuda, PhD , is associate professor of radiology at HMS, and is a research scientist at the Department of Radiology, BWH. Tokuda is particularly interested in technologies to support image-guided “closed-loop” interventions. He also serves as a principal investigator leading several projects funded by the National Institutes of Health and industry.
• Osama Rahma, MD , is assistant professor of medicine at HMS and clinical staff member in medical oncology at Dana-Farber Cancer Institute (DFCI). Rhama is currently a principal investigator at the Center for Immuno-Oncology and Gastroenterology Cancer Center at DFCI. His research focus is on drug development of combinational immune therapeutics.
• Sharmila Dorbala, MD, MPH , is professor of radiology at HMS and clinical staff at BWH in cardiovascular medicine and radiology. She is also the president of the American Society of Nuclear Medicine. Dorbala’s specialty is using nuclear medicine for cardiovascular discoveries.
• Subha Ramani, PhD, MBBS, MMed , is associate professor of medicine at HMS, as well as associate physician in the Division of General Internal Medicine and Primary Care at BWH. Ramani’s scholarly interests focus on innovative approaches to teaching, learning and assessment of clinical trainees, faculty development in teaching, and qualitative research methods in medical education.
• Ursula Kaiser, MD , is professor at HMS and chief of the Division of Endocrinology, Diabetes and Hypertension, and senior physician at BWH. Kaiser’s research focuses on understanding the molecular mechanisms by which pulsatile gonadotropin-releasing hormone regulates the expression of luteinizing hormone and follicle-stimulating hormone genes.

Insights on Creating a Good Research Question

Play Junichi Tokuda video

Play Ursula Kaiser video

Start Successfully: Build the Foundation of a Good Research Question

Start Successfully Resources

Ideation in Device Development: Finding Clinical Need Josh Tolkoff, MS A lecture explaining the critical importance of identifying a compelling clinical need before embarking on a research project. Play Ideation in Device Development video .

Radical Innovation Jeff Karp, PhD This ThinkResearch podcast episode focuses on one researcher’s approach using radical simplicity to break down big problems and questions. Play Radical Innovation .

Using Healthcare Data: How can Researchers Come up with Interesting Questions? Anupam Jena, MD, PhD Another ThinkResearch podcast episode addresses how to discover good research questions by using a backward design approach which involves analyzing big data and allowing the research question to unfold from findings. Play Using Healthcare Data .

Important Factors: Consider Feasibility and Novelty

Refining Your Research Question 

Play video of Clare Tempany-Afdhal

Play Elliott Israel video

Frameworks and Structure: Evaluate Research Questions Using Tools and Techniques

Frameworks and Structure Resources

Designing Clinical Research Hulley et al. A comprehensive and practical guide to clinical research, including the FINER framework for evaluating research questions. Learn more about the book .

Translational Medicine Library Guide Queens University Library An introduction to popular frameworks for research questions, including FINER and PICO. Review translational medicine guide .

Asking a Good T3/T4 Question  Niteesh K. Choudhry, MD, PhD This video explains the PICO framework in practice as participants in a workshop propose research questions that compare interventions. Play Asking a Good T3/T4 Question video

Introduction to Designing & Conducting Mixed Methods Research An online course that provides a deeper dive into mixed methods’ research questions and methodologies. Learn more about the course

Network and Support: Find the Collaborators and Stakeholders to Help Evaluate Research Questions

Network & Support Resource

Bench-to-bedside, Bedside-to-bench Christopher Gibbons, MD In this lecture, Gibbons shares his experience of bringing research from bench to bedside, and from bedside to bench. His talk highlights the formation and evolution of research questions based on clinical need. Play Bench-to-bedside. 

Dissertations & projects: Research questions

• Research questions
• The process of reviewing
• Project management
• Literature-based projects

Jump to content on these pages:

“The central question that you ask or hypothesis you frame drives your research: it defines your purpose.” Bryan Greetham, How to Write Your Undergraduate Dissertation

This page gives some help and guidance in developing a realistic research question. It also considers the role of sub-questions and how these can influence your methodological choices. 

Choosing your research topic

You may have been provided with a list of potential topics or even specific questions to choose from. It is more common for you to have to come up with your own ideas and then refine them with the help of your tutor. This is a crucial decision as you will be immersing yourself in it for a long time.

Some students struggle to find a topic that is sufficiently significant and yet researchable within the limitations of an undergraduate project. You may feel overwhelmed by the freedom to choose your own topic but you could get ideas by considering the following:

Choose a topic that you find interesting . This may seem obvious but a lot of students go for what they think will be easy over what they think will be interesting - and regret it when they realise nothing is particularly easy and they are bored by the work. Think back over your lectures or talks from visiting speakers - was there anything you really enjoyed? Was there anything that left you with questions?

Choose something distinct . Whilst at undergraduate level you do not have to find something completely unique, if you find something a bit different you have more opportunity to come to some interesting conclusions. Have you some unique experiences that you can bring: personal biography, placements, study abroad etc?

Don't make your topic too wide . If your topic is too wide, it will be harder to develop research questions that you can actually answer in the context of a small research project.

Don't make your work too narrow . If your topic is too narrow, you will not be able to expand on the ideas sufficiently and make useful conclusions. You may also struggle to find enough literature to support it.

Scope out the field before deciding your topic . This is especially important if you have a few different options and are not sure which to pick. Spend a little time researching each one to get a feel for the amount of literature that exists and any particular avenues that could be worth exploring.

Think about your future . Some topics may fit better than others with your future plans, be they for further study or employment. Becoming more expert in something that you may have to be interviewed about is never a bad thing!

Once you have an idea (or even a few), speak to your tutor. They will advise on whether it is the right sort of topic for a dissertation or independent study. They have a lot of experience and will know if it is too much to take on, has enough material to build on etc.

Developing a research question or hypothesis

Research question vs hypothesis.

First, it may be useful to explain the difference between a research question and a hypothesis. A research question is simply a question that your research will address and hopefully answer (or give an explanation of why you couldn't answer it). A hypothesis is a statement that suggests how you expect something to function or behave (and which you would test to see if it actually happens or not).

Research question examples

• How significant is league table position when students choose their university?
• What impact can a diagnosis of depression have on physical health?

Note that these are open questions - i.e. they cannot be answered with a simple 'yes' or 'no'. This is the best form of question.

Hypotheses examples

• Students primarily choose their university based on league table position.
• A diagnosis of depression can impact physical health.

Note that these are things that you can test to see if they are true or false. This makes them more definite then research questions - but you can still answer them more fully than 'no they don't' or 'yes it does'. For example, in the above examples you would look to see how relevant other factors were when choosing universities and in what ways physical health may be impacted.

For more examples of the same topic formulated as hypotheses, research questions and paper titles see those given at the bottom of this document from Oakland University: Formulation of Research Hypothesis

Which do you need?

Generally, research questions are more common in the humanities, social sciences and business, whereas hypotheses are more common in the sciences. This is not a hard rule though, talk things through with your supervisor to see which they are expecting or which they think fits best with your topic.

What makes a good research question or hypothesis?

Unless you are undertaking a systematic review as your research method, you will develop your research question  as a result of reviewing the literature on your broader topic. After all, it is only by seeing what research has already been done (or not) that you can justify the need for your question or your approach to answering it. At the end of that process, you should be able to come up with a question or hypothesis that is:

• Clear (easily understandable)
• Focused (specific not vague or huge)
• Answerable (the data is available and analysable in the time frame)
• Relevant (to your area of study)
• Significant (it is worth answering)

You can try a few out, using a table like this (yours would all be in the same discipline):

A similar, though different table is available from the University of California: What makes a good research topic?   The completed table has some supervisor comments which may also be helpful.

Ultimately, your final research question will be mutually agreed between yourself and your supervisor - but you should always bring your own ideas to the conversation.

The role of sub-questions

Your main research question will probably still be too big to answer easily. This is where sub-questions come in. They are specific, narrower questions that you can answer directly from your data.

So, looking at the question " How much do online users know and care about how their self-images can be used by Apple, Google, Microsoft and Facebook? " from the table above, the sub-questions could be:

• What rights do the terms and conditions of signing up for Apple, Google, Microsoft and Facebook accounts give those companies regarding the use of self-images?
• What proportion of users read the terms and conditions when creating accounts with these companies?
• How aware are users of the rights they are giving away regarding their self-images when creating accounts with these companies?
• How comfortable are users with giving away these rights?

Together, the answers to your sub-questions should enable you to answer the overarching research question.

How do you answer your sub-questions?

Depending on the type of dissertation/project your are undertaking, some (or all) the questions may be answered with information collected from the literature and some (or none) may be answered by analysing data directly collected as part of your primary empirical research .

In the above example, the first question would be answered by documentary analysis of the relevant terms and conditions, the second by a mixture of reviewing the literature and analysing survey responses from participants and the last two also by analysing survey responses. Different projects will require different approaches.

Some sub-questions could be answered by reviewing the literature and others from empirical study.

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For PhD students - how to formulate a research question

Different students enter the PhD program with different backgrounds. Some students take research-oriented modules (courses in US) at undergraduate level. Some other do a research masters before doing a PhD. However, the kind of research questions we address in a PhD are very new and requires a long period of deeper investigation. Therefore, it is important to know how to find a good question that gets you excited.

Direct encounter : Usually, a good question comes from an experience. In my case, I experienced how hard it is to derive the dynamics of a robot with high degrees of freedom (DoF). I actually tried to manually derive dynamics of a 4-DoF manipulator called Mark-II from Yasakawa Corporation, and then ran a Mathematica program to do a symbolic derivation for a 7-DoF robot manipulator called PA-10. I experienced how long the equations grew and thought how the brain might be dealing with a body of about 37 DoFs for model based predictive control. This direct encounter with the problem is very important, because it gives you a cause to work towards.

Look around : After finding a problem worthy of addressing, look around to see how others have approached to solve it. This is where you will see different schools of thought. Be careful. There are glaring band-waggons out there. It is so tempting to get in one of them. Don’t blindly follow them unless you have a good reason. Usually following is tiring. Think carefully trying out simple derivations and doing simulations or even doing simple physical experiments to see what kind of approaches get you excited. Some approaches appear very exciting, but direct usage will prove to be not so effective. At this point, it is very important to consult your supervisor. The supervisor may have a favorite approach. Most experienced supervisors are open for change and a good reasoned discussion will help you to benefit from their experience to polish up your research question and the method you want to address it. You should always check if there are quantifiable methods to address your research question. For instance, if you want to test whether there is a particular class of mechanisms available to minimise the size of collision force when a robot is dropped from a height, you should think about testing methods, candidate mechanisms, and the range of design paramaters to assess the scope of analysis. Sometimes, your laboratory may not have the full capacity to help you. This is where you can look for collaborations. Try to reach this level of planning logistics within the first 4-6 months in your PhD.

First experiment is important : Once you know your cause for the PhD and once the approach and collaborations are established, break your approach down to smaller steps. Don’t worry too much about how the last experiment will be done. Worry about your first experiment. Distill out a refined research question that needs a novel answer that you can reach in about 6 months. This is important to boost confidence. Temptations will be high to find the ultimate answer to bring your field to a conclusion, but even in that case, it is important to make a first firm step. In this first step, master the tools and techniques involved in your field. In my lab, students take this time to master robot design and fabrication skills, coding skills, data analysis skills, and cool math you can use to solve difficult problems. Develop the habit of reading at least one paper a week that empowers you with powerful tools to solve problems.

Documentation : It is important to develop the habit of keeping things in a well sorted file structure. Open a folder for each project. Have sub-folders for data, reports, codes, papers you read (using a repository like Madeley is also great), designs, and other resources. This is going to save time when you write a paper at some point. Now you have cloud resources like Box and Onedrive. Back up everything securely.

Writing the first paper : If everything works out, after about one year into the PhD, you will have some new results worthy of publishing. Sometimes, the first attempt doesn’t work out. But all failed attempts teach us lessons. Don’t get discouraged if the first experiment doesn’t work out. Develop the resilience to come back with a different approach or to formulate the question in a different way. Then when you write the first paper, you will have comparative results. The importance of reading papers at least one per week is that in 6 months, you would have read at least 25 papers. This is enough to write your first paper. Start writing why the question you addressed is new and important, and back it up with papers you read. Write down your methods very clearly keeping in mind that somebody should be able to read your paper and be able to replicate it for independent verification. Results and interpretations need to be as sharp and consistent as possible. Plan to go through several rounds of revisions with your supervisor and lab mates before any submission deadlines. I ask my PhD students to have the paper in a reasonable level for revision at least one month before the deadline. Have this as a ballpark period for revision in your first paper. This is the time where you develop the skills of articulating a concept clearly, present it to an audience, receive criticisms, and develop good habits of critical reflection.

Completing the cycle : You will of course get review feedback. Some suggestions I have  given in this note can be useful to go the rest of the journey. Once you get your first paper published, you will have your next research questions coming up easily. The advantage of taking an approach you are passionate about to serve the cause you selected is that it will naturally line up the next set of questions and methods you should be pursuing. My advise is to go through this full cycle of raising a question to publishing results at least 3 times during your PhD. It will give you a seasoned experience of the art of formulating good research questions.

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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.

How to write a research proposal

What is a research proposal.

A research proposal should present your idea or question and expected outcomes with clarity and definition – the what.

It should also make a case for why your question is significant and what value it will bring to your discipline – the why. 

What it shouldn't do is answer the question – that's what your research will do.

Why is it important?

Research proposals are significant because Another reason why it formally outlines your intended research. Which means you need to provide details on how you will go about your research, including:

• your approach and methodology
• timeline and feasibility
• all other considerations needed to progress your research, such as resources.

Think of it as a tool that will help you clarify your idea and make conducting your research easier.

How long should it be?

Usually no more than 2000 words, but check the requirements of your degree, and your supervisor or research coordinator.

Presenting your idea clearly and concisely demonstrates that you can write this way – an attribute of a potential research candidate that is valued by assessors.

What should it include?

Project title.

Your title should clearly indicate what your proposed research is about.

Research supervisor

State the name, department and faculty or school of the academic who has agreed to supervise you. Rest assured, your research supervisor will work with you to refine your research proposal ahead of submission to ensure it meets the needs of your discipline.

Proposed mode of research

Describe your proposed mode of research. Which may be closely linked to your discipline, and is where you will describe the style or format of your research, e.g. data, field research, composition, written work, social performance and mixed media etc. 

This is not required for research in the sciences, but your research supervisor will be able to guide you on discipline-specific requirements.

Aims and objectives

What are you trying to achieve with your research? What is the purpose? This section should reference why you're applying for a research degree. Are you addressing a gap in the current research? Do you want to look at a theory more closely and test it out? Is there something you're trying to prove or disprove? To help you clarify this, think about the potential outcome of your research if you were successful – that is your aim. Make sure that this is a focused statement.

Your objectives will be your aim broken down – the steps to achieving the intended outcome. They are the smaller proof points that will underpin your research's purpose. Be logical in the order of how you present these so that each succeeds the previous, i.e. if you need to achieve 'a' before 'b' before 'c', then make sure you order your objectives a, b, c.

A concise summary of what your research is about. It outlines the key aspects of what you will investigate as well as the expected outcomes. It briefly covers the what, why and how of your research. 

A good way to evaluate if you have written a strong synopsis, is to get somebody to read it without reading the rest of your research proposal. Would they know what your research is about?

Now that you have your question clarified, it is time to explain the why. Here, you need to demonstrate an understanding of the current research climate in your area of interest.

Providing context around your research topic through a literature review will show the assessor that you understand current dialogue around your research, and what is published.

Demonstrate you have a strong understanding of the key topics, significant studies and notable researchers in your area of research and how these have contributed to the current landscape.

Expected research contribution

In this section, you should consider the following:

• Why is your research question or hypothesis worth asking?
• How is the current research lacking or falling short?
• What impact will your research have on the discipline?
• Will you be extending an area of knowledge, applying it to new contexts, solving a problem, testing a theory, or challenging an existing one?
• Establish why your research is important by convincing your audience there is a gap.
• What will be the outcome of your research contribution?
• Demonstrate both your current level of knowledge and how the pursuit of your question or hypothesis will create a new understanding and generate new information.
• Show how your research is innovative and original.

Draw links between your research and the faculty or school you are applying at, and explain why you have chosen your supervisor, and what research have they or their school done to reinforce and support your own work. Cite these reasons to demonstrate how your research will benefit and contribute to the current body of knowledge.

Proposed methodology

Provide an overview of the methodology and techniques you will use to conduct your research. Cover what materials and equipment you will use, what theoretical frameworks will you draw on, and how will you collect data.

Highlight why you have chosen this particular methodology, but also why others may not have been as suitable. You need to demonstrate that you have put thought into your approach and why it's the most appropriate way to carry out your research. 

It should also highlight potential limitations you anticipate, feasibility within time and other constraints, ethical considerations and how you will address these, as well as general resources.

A work plan is a critical component of your research proposal because it indicates the feasibility of completion within the timeframe and supports you in achieving your objectives throughout your degree.

Consider the milestones you aim to achieve at each stage of your research. A PhD or master's degree by research can take two to four years of full-time study to complete. It might be helpful to offer year one in detail and the following years in broader terms. Ultimately you have to show that your research is likely to be both original and finished – and that you understand the time involved.

Provide details of the resources you will need to carry out your research project. Consider equipment, fieldwork expenses, travel and a proposed budget, to indicate how realistic your research proposal is in terms of financial requirements and whether any adjustments are needed.

Bibliography

Provide a list of references that you've made throughout your research proposal. 

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Examples of Research Questions

Phd in nursing science program, examples of broad clinical research questions include:.

• Does the administration of pain medication at time of surgical incision reduce the need for pain medication twenty-four hours after surgery?
• What maternal factors are associated with obesity in toddlers?
• What elements of a peer support intervention prevent suicide in high school females?
• What is the most accurate and comprehensive way to determine men’s experience of physical assault?
• Is yoga as effective as traditional physical therapy in reducing lymphedema in patients who have had head and neck cancer treatment?
• In the third stage of labor, what is the effect of cord cutting within the first three minutes on placenta separation?
• Do teenagers with Type 1 diabetes who receive phone tweet reminders maintain lower blood sugars than those who do not?
• Do the elderly diagnosed with dementia experience pain?
•  How can siblings’ risk of depression be predicted after the death of a child?
•  How can cachexia be prevented in cancer patients receiving aggressive protocols involving radiation and chemotherapy?

Examples of some general health services research questions are:

• Does the organization of renal transplant nurse coordinators’ responsibilities influence live donor rates?
• What activities of nurse managers are associated with nurse turnover?  30 day readmission rates?
• What effect does the Nurse Faculty Loan program have on the nurse researcher workforce?  What effect would a 20% decrease in funds have?
• How do psychiatric hospital unit designs influence the incidence of patients’ aggression?
• What are Native American patient preferences regarding the timing, location and costs for weight management counseling and how will meeting these preferences influence participation?
•  What predicts registered nurse retention in the US Army?
• How, if at all, are the timing and location of suicide prevention appointments linked to veterans‘ suicide rates?
• What predicts the sustainability of quality improvement programs in operating rooms?
• Do integrated computerized nursing records across points of care improve patient outcomes?
• How many nurse practitioners will the US need in 2020?

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Tips for developing PhD research questions

Time management skills and extensive reading can help students develop a sophisticated set of research questions. plus the latest higher education jobs and appointments.

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Researching a PhD for the first time is like a quest for which you have no map or overarching strategy to guide you, according to Victoria ­Perselli, associate professor in the School of Education at Kingston University .

Students are expected to lay down the theories that will underpin their research on their own, and many find this a struggle.

“Nobody can really pre-specify what that [theory] will be, or what sort of theory might fit best with what you want to know,” said Dr Perselli.

An important starting point to developing theory is reading, she added. “Right from the beginning, reading very widely and deeply is key because that helps you see exactly who else has researched that area already, which is something that you need to know,” she said. It also flags up the key thinkers in a particular area from the past and present day and shows how they describe what they know.

“That desk-based element of the research is very important because it broadens your own vocabulary and it enables you to think and to talk about theories that are already out there,” she said.

Armed with this information, doctoral students can then seek to understand what they could add to the field with the design of their own research questions. One way of getting a grip on this could be to look at people currently working in the field who have taken theory from the past and reframed it for the present, Dr Perselli suggested. This can help to further expand an individual’s lexicon, which in turn helps them to develop more sophisticated research questions.

Many students are caught out by exactly how much time and space is needed for this in-depth thinking. One of the most common pitfalls for PhD students is to underestimate the extent to which his or her life needs to be organised in order to provide necessary space and quality time to develop a theory.

Regularly discussing desk-based research with a supervisor will eventually lead to the development of a theory that fits your ideas and methodologies together. But a supervisor cannot tell you what to do or how to find solutions. Students must take ownership of the research and its development at this early stage, Dr Perselli said.

The theory is mapped on to an individual’s investigation, which is ongoing in an iterative process. The gathering and analysis of data during this stage should then be synthesised with time spent reading and writing along the way.

“You are thinking and discussing and writing all the way through…The thesis that you end up with will not be the sum of that work,” she said, adding that only a portion of it will make the final cut.

Students should be encouraged when their findings do not correlate with the theory, added Dr Perselli. “That is where it gets exciting,” she said. There would be nothing new to say if the findings had a direct and obvious relationship to the theory, she added.

It is the surprises “that take your breath away” that allow a researcher to take stock of what is known already and to look at how the findings relate to the theory. Using the new vocabulary gleaned from the exhaustive reading of the field can help figure out this enigma, she said.

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Appointments

Judith Squires has been appointed a council member for the Economic and Social Research Council by Greg Clark, the universities minister. Professor Squires is pro vice-chancellor for education and students at the University of Bristol .

The University of Huddersfield has made two new professorial appointments to the School of Art, Design and Architecture. Dilanthi Amaratunga and Richard Haigh are both experts in the built environment.

Donna Lee has joined the University of Bradford as dean of social and international studies. Professor Lee was previously at the University of Kent , where she was professor of international political economy and diplomacy.

The Bodleian Libraries at the University of Oxford have made Lucie Burgess associate director for digital libraries. Ms Burgess, who will join in November, is currently head of online services at the British Library.

A University of Manchester professor has been made the president of the European Association for Cancer Research. Richard Marais will hold the position from 2014 to 2016.

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PhD Interview Questions and Answers (13 Questions + Answers)

Most PhD applications include an interview. This allows your university (and perhaps even your prospective supervisor) to discuss the PhD with you in more detail.

This article lists some of the most common PhD interview questions along with their answers. The goal is to help you prepare for a PhD interview and pass with flying colors.

1) How did you develop this proposal?

When responding to this question, demonstrate your thought process, research skills, and the evolution of your ideas. Let's choose the subject of "Renewable Energy Integration in Urban Planning" as an example.

Sample answer:

"My proposal on 'Renewable Energy Integration in Urban Planning' originated from my undergraduate thesis on sustainable cities. Intrigued by the potential of renewable energy in urban environments, I conducted a literature review to identify gaps in current research. This review highlighted a lack of comprehensive strategies for integrating renewable technologies at a city-wide level. I then consulted with experts in urban planning and renewable energy, which provided practical insights into the challenges and opportunities in this field. I designed a methodology that combines spatial analysis with energy modeling to explore optimal renewable energy integration in urban landscapes. This proposal represents an amalgamation of academic research, expert consultation, and innovative methodology development."

This answer is effective because it mentions a literature review demonstrates the ability to conduct thorough research and identify gaps in existing knowledge.

2) Why do you wish to pursue a PhD?

For this question, it's important to articulate your passion for the subject, your long-term career goals, and how the PhD program aligns with these aspects.

Let's choose the subject of "Artificial Intelligence in Healthcare" for this example.

"I am passionate about leveraging technology to improve healthcare outcomes, and pursuing a PhD in Artificial Intelligence in Healthcare aligns perfectly with this passion. During my Master's, I was fascinated by the potential of AI to revolutionize diagnostic processes and personalized medicine. I believe a PhD will provide me with the deep technical knowledge and research skills necessary to contribute significantly to this field. My goal is to develop AI systems that enhance medical diagnostics, ultimately improving patient care and treatment efficiency. This PhD program, known for its pioneering research in AI and strong healthcare collaborations, is the ideal environment for me to develop these innovations and achieve my career aspirations in healthcare technology."

This is a great answer because you clearly state that the PhD will provide the necessary skills and knowledge, indicating a clear understanding of the purpose of the program.

3) Why do you think you are the right candidate for this PhD program?

Discuss how your research interests align with the program's strengths and the faculty's expertise. Explain how the program's resources, courses, and research opportunities can help you achieve your academic and career goals.

"I am deeply passionate about environmental science, particularly in the area of sustainable urban development. This passion was ignited during my master's program in Environmental Studies at XYZ University, where I completed a thesis on urban green spaces and their impact on city microclimates. This research not only honed my skills in data analysis and GIS mapping but also highlighted the importance of interdisciplinary approaches to environmental issues. I am drawn to your PhD program at ABC University because of its innovative research on sustainable urban planning and the renowned work of Professor Jane Smith in this field. Her research aligns with my interest in integrating green infrastructure into urban planning to mitigate climate change effects. My perseverance, attention to detail, and ability to synthesize complex data make me an ideal candidate for this challenging program. Pursuing this PhD is integral to my goal of becoming an environmental consultant, where I plan to develop strategies for cities to reduce their environmental footprint."

This response is effective because it mentions particular aspects of your experience and the program, avoiding generic statements. It also outlines how the PhD fits into your career path.

4) What do you plan to do after you have completed your PhD?

Be specific about the type of career you aspire to, whether it's in academia, industry, research, etc. Explain how the PhD will equip you with the skills and knowledge for your chosen career path.

"After completing my PhD in Computational Neuroscience, I plan to pursue a career in academia as a university professor. My doctoral research on neural network modeling will provide a strong foundation for teaching and conducting further research in this area. I aim to develop innovative courses that bridge computer science and neuroscience, addressing the growing demand for interdisciplinary knowledge in these fields. Additionally, I intend to continue my research on applying machine learning techniques to understand brain function, which has potential implications for developing new treatments for neurological disorders. This academic pathway allows me to contribute significantly to both education and research in Computational Neuroscience."

This is a great answer because it connects the PhD research directly to future career plans.

It also articulates how your work can impact both academia and the broader field of Computational Neuroscience.

5) Why have you chosen this specific PhD program?

Mention specific aspects of the program that attracted you, such as the curriculum, research facilities, faculty expertise, or reputation.

Explain how the program aligns with your research interests or academic background.

"I chose the PhD program in Artificial Intelligence at MIT because of its cutting-edge research and interdisciplinary approach, which perfectly aligns with my academic background in computer science and my passion for machine learning. The program's emphasis on both theoretical foundations and practical applications in AI is particularly appealing. Additionally, the opportunity to work under the guidance of Professor [Name], whose work in [specific area, e.g., neural networks or AI ethics] has deeply influenced my own research interests, is a significant draw. This program is an ideal fit for me to further develop my skills and contribute to the field of AI, ultimately aiming for a career in AI research and development in the tech industry."

This answer connects your background and goals to the program's offerings.

Including a specific professor's name shows detailed knowledge about the program and faculty.

6) What impact would you like your PhD project to have?

When answering this question, convey both the academic significance and the potential real-world applications of your research. Let's choose a project focused on developing eco-friendly battery technologies for electric vehicles for this example.

"My PhD project aims to develop new eco-friendly battery technologies for electric vehicles (EVs), addressing both the environmental impact of battery production and the efficiency of energy storage. I hope my research will contribute to the academic field by advancing our understanding of sustainable materials for energy storage, potentially leading to publications and patents. Beyond academia, I envision this project significantly impacting the EV industry by providing a more sustainable and efficient battery alternative. This innovation could play a crucial role in reducing the carbon footprint of transportation and supporting global efforts towards a greener future. Ultimately, I aspire for my work to not only advance scientific knowledge but also drive real-world changes in how we approach energy sustainability in transportation."

This is an excellent answer because it connects the project to larger environmental goals and societal benefits. It also reflects a forward-thinking approach, demonstrating your understanding of the project's potential long-term implications.

7) What difficulties would you expect to encounter during this project?

It's important to demonstrate awareness of potential challenges and convey a proactive mindset toward problem-solving. Let's choose a project focused on the development of a novel AI-driven diagnostic tool for early detection of neurological diseases for this example.

"In developing an AI-driven diagnostic tool for early detection of neurological diseases, I anticipate several challenges. Firstly, the accuracy and reliability of the tool depend heavily on the quality and diversity of the data used for training the AI algorithms. Obtaining a comprehensive dataset that adequately represents the population can be difficult due to privacy concerns and data availability. Secondly, ensuring the AI model's interpretability to be clinically useful while maintaining high performance is another challenge, given the complexity of neurological diseases. To address these, I plan to collaborate with interdisciplinary teams, including data privacy experts and neurologists, to source and utilize data ethically and effectively. I also intend to continuously refine the AI model, focusing on both its predictive accuracy and clinical applicability. These challenges, while significant, present valuable opportunities for innovation and interdisciplinary collaboration."

This response is effective because it clearly outlines realistic challenges specific to the AI diagnostic tool project. It also presents a proactive approach to overcoming these challenges, showing problem-solving skills.

8) How will you fund this project?

When answering this question, show that you've thought about the financial aspects of your research and are aware of funding sources that are available and applicable to your project. 

"I have identified multiple funding sources to support my renewable energy research project at Stanford University. Firstly, I plan to apply for the DOE Office of Science Graduate Student Research (SCGSR) Program, which offers substantial support for projects focusing on sustainable energy. My proposal for this grant is already in progress, highlighting how my project aligns with the DOE's priorities in advancing clean energy technologies. Additionally, I'm exploring departmental fellowships at Stanford, particularly those aimed at renewable energy research. I am also keen on establishing industry partnerships, given the project's relevance to current energy challenges and the potential for collaborative funding and technological exchange. Last but not least, I will seek conference grants to present my research findings, which can lead to further academic collaborations and additional funding opportunities."

Notice how this answer mentions funding sources that align with the renewable energy focus of the project and the resources available at Stanford University.

9) Tell us about a time you experienced a setback

Focus on a situation relevant to your academic or research experience. Let's use a real-world example where a research experiment failed due to unexpected variables.

"During my Master’s thesis on the effects of soil composition on plant growth, I faced a major setback. My initial experiments, which involved growing plants in different soil types, failed to produce consistent results due to unanticipated environmental variations in the greenhouse. This was disheartening, especially as the deadline approached. However, I responded by reassessing my experimental setup. I consulted with my supervisor and decided to control more variables, such as humidity and temperature. I also refined my data collection methods to include more frequent soil and plant measurements. These adjustments led to more reliable results, and I successfully completed my thesis. This experience taught me the importance of adaptability in research and reinforced the value of meticulous experimental design."

This is a great answer because it shows how you’ve encountered and overcame a specific problem, demonstrating resilience and adaptability.

10) What are your strengths and weaknesses?

When answering this question, it's important to present a balanced view of yourself, showing self-awareness and a commitment to personal development. Choose strengths that are relevant to a PhD program and weaknesses that you're actively working to improve.

"One of my key strengths is my analytical thinking, which I demonstrated during my Master's project where I developed a novel algorithm for data analysis. This required me to not only understand complex theories but also apply them creatively to solve real-world problems. As for weaknesses, I sometimes struggle with overcommitment, taking on too many projects at once. This occasionally led to stress during my undergraduate studies. However, I am actively working on this by improving my time management skills and learning to prioritize tasks more effectively. I've started using project management tools and setting clear boundaries, which has already shown improvements in my workflow and stress levels."

This answer maintains a good balance between strengths and weaknesses. It also shows self-awareness, demonstrating a proactive approach to personal development.

11) Why have you chosen to study for a PhD at this university?

Mention specific aspects of the PhD program that attracted you. Explain how your research interests align with the work being done at the university.

"I am drawn to the PhD program in Astrophysics at Caltech due to its outstanding reputation in space research and the unparalleled resources available at the Owens Valley Radio Observatory. My research interest lies in the study of exoplanets, and Caltech's active projects in this area, such as the Zwicky Transient Facility, align perfectly with my academic goals. The opportunity to work under the guidance of Professor [Name], known for pioneering work in exoplanetary atmospheres, is particularly exciting. Additionally, Caltech's collaborative environment and emphasis on interdisciplinary research are conducive to my professional growth, providing a platform to engage with experts from various fields in astrophysics."

This response directly connects your research interests with ongoing projects and facilities at Caltech. It also shows you’ve done your research on faculty members and their work.

12) What can you bring to this research group?

Focus on your unique skills, experiences, and perspectives that will contribute to the research group's success. Let's choose the field of Biomedical Engineering at Johns Hopkins University for this example.

"As a prospective member of the Biomedical Engineering research group at Johns Hopkins University, I bring a unique combination of skills and experiences. My expertise in microfluidics, honed during my Master’s research, aligns well with the group’s focus on developing lab-on-a-chip devices for medical diagnostics. I have also co-authored two papers in this field, demonstrating my ability to contribute to high-impact research. Additionally, my experience in a start-up environment, where I worked on developing portable diagnostic tools, has equipped me with a practical understanding of translating research into applications. I thrive in collaborative settings, often bringing interdisciplinary insights that foster innovative problem-solving. I am excited about the prospect of contributing to the group’s ongoing projects and introducing fresh perspectives to advance our understanding and application of biomedical technology."

This response shows your relevant expertise, ability to work in a team, and the unique perspectives you can offer, positioning you as a valuable addition to the research group.

13) Do you have any questions for us?

Asking good questions demonstrates your motivation. It also shows that you’ve given some genuine consideration to the project and/or program you’re applying to.

Some questions you can ask the interviewer include:

• What will the supervision arrangements be for the project?
• What kind of training and skills sessions are offered as part of the PhD program?
• How many other PhD students has this supervisor seen to completion?
• Are there any major developments or partnerships planned for the department?
• Are there likely to be any changes to the funding arrangements for the project?
• What opportunities will I have for presenting my research?

Remember: you’re a good student, with lots of potential. You’re considering at least three years of hard work with this university. You need to know that you’ll get on with your supervisor, that your work will be appreciated and that there are good prospects for your project.

What to wear to a PhD interview

Wear formal attire for a PhD interview. Your best bet is to wear a suit. A navy blue suit is the best and most versatile option. No matter your gender, a suit is always very professional.

For men, wear a suit with a tie, dress shirt, and dress shoes. For women, wear a suit (pantsuit or skirt suit) with a blouse, or conservative dress, and closed-toe shoes.

When in doubt, it’s better to be slightly overdressed than underdressed. The goal is to make a professional impression and feel confident, without your attire distracting from the conversation.

What to expect from a PhD interview

At its core, a PhD interview will consist of questions that allow your potential supervisors to get to know you better and have an understanding of what you’d like to study, why you’ve chosen your field of study, and whether you’d be a good fit for the PhD program.

You should expect general questions to help the interviewer get a sense of your likes and dislikes, and your overall personality.

Next, expect questions about your personal motivations for studying a PhD. Your interviewer will also be interested in any relevant experience you have to qualify you to study this PhD.

In the next section, expect questions about your PhD project. You should be prepared to discuss your project idea in detail and demonstrate to the interviewer that you are the ideal candidate.

Last but not least, the interviewer will discuss your future ambitions and give you an opportunity to ask questions. Remember that this interview goes both ways.

It’s important to ask the interviewer relevant questions to show your engagement and the serious consideration you are giving their program.

You are preparing to spend several years of your life at this school. Think about what is important to you and what would make or break your decision to attend this university.

Prepare a list of questions ahead of the interview.

Understanding the interviewer’s point of view

During a PhD interview, interviewers are typically looking for a range of traits that indicate whether you are well-suited for the rigors of a doctoral program and a research career.

These traits include:

Intellectual Curiosity and Passion: A strong enthusiasm for the subject area and a desire to contribute to and expand knowledge in the field.

Research Skills and Experience: Demonstrable skills in conducting research, including designing experiments, collecting and analyzing data, and interpreting results. Prior research experience relevant to the PhD topic is often a plus.

Resilience and Perseverance: The capacity to handle setbacks and challenges, which are common in research, and to persist in the face of difficulties.

Collaboration and Teamwork: Although PhD research can be quite independent, the ability to work well with others, including advisors, faculty, and other students, is crucial.

Self-Motivation and Independence: The drive to work independently, manage one's own project, and stay motivated over the long term.

Fit with the Program: Alignment of the candidate’s research interests and goals with the strengths and focus of the PhD program and faculty.

These traits not only indicate your readiness for a PhD program but also your potential to contribute meaningfully to their field of study and succeed in a research-oriented career.

Related posts:

• University Interview Questions (16 Questions + Answers)
• Project Manager Interview Questions (14 Specific Questions + Answers)
• Strength-Based Interview Questions (21 Questions + Answers)
• Engineering Interview Questions (15 Questions + Answers)
• Business Analyst Interview Questions (17 Questions + Answers)

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Most Asked PhD Viva-Voce Questions and Answers

Check this Mosted Asked 50 PhD Viva-Voce Questions with Answers

Defending a doctoral thesis and facing viva questions is a very critical part of every research scholar. After the submission of your thesis, you will be asked to defend your research work in the “ final viva voce “.

Defending your master’s thesis in front of domain experts, co-scholars, and students is quite an interesting experience. The difficult part of the viva for every researcher is facing unexpected questions.

In this article, ilovephd provides 50 possible PhD viva questions frequently asked during the thesis Viva voce.

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Mosed Asked PhD Thesis Defense Viva-Voce Questions and Answers

These are all the 50 Common Dissertation PhD Viva Questions and Sample Answers you can prepare to answer during the defense.

Thesis Title: “Understanding the Impact of Social Media Engagement on Consumer Behavior: A Mixed-Methods Approach”

1. Main Contribution:

What is the main contribution of your work.

My work offers a novel framework for analyzing social media data, enhancing understanding of online user behavior. For instance, by integrating machine learning algorithms with network analysis, we identified influential users in online communities.

2. Key Research Questions:

What are the key research questions you addressed in your dissertation.

In my dissertation , I addressed several key research questions, including how online interactions influence consumer purchasing decisions and whether social media campaigns effectively engage target audiences.

3. Study Design:

How did you design your study.

The study was designed employing a mixed-methods approach, combining surveys to gather qualitative insights with the analysis of social media data using advanced statistical techniques.

4. Data and Methods:

What data and methods did you use.

I utilized Twitter data collected through the Twitter API and analyzed public sentiment toward specific brands or products.

5. Main Findings:

What were your main findings and how do they relate to your hypothesis.

Our analysis revealed a strong correlation between user engagement with social media content and subsequent purchase behavior, supporting our hypothesis that social media plays a significant role in shaping consumer decisions.

6. Implications:

What implications does your work have for other researchers or practitioners in the field.

These findings have practical implications for marketers, suggesting the need for targeted social media strategies to effectively reach and engage potential customers.

7. Suggestions for Further Research:

What suggestions do you have for further research.

Sample Answer: Future research could explore the effectiveness of different types of social media content in driving consumer engagement and purchasing behavior.

8. Motivation:

Why did you choose this particular topic.

I chose this topic due to its relevance in today’s digital age and its potential to inform marketing strategies in a rapidly evolving online landscape.

9. Overcoming Challenges:

What motivated you during the project.

My motivation stemmed from the opportunity to contribute to an area with significant real-world implications and to advance understanding in the field of marketing research.

10. New Insights:

How did you overcome any challenges that emerged throughout the project.

Challenges were overcome through perseverance, collaboration with peers and mentors, and leveraging available resources effectively.

11. Adding Value:

What new knowledge, insights, or understanding has your research provided.

My research has provided new insights into the nuanced relationship between online engagement and consumer behavior, offering actionable insights for marketers.

12. Limitations:

How do you think your work adds value to the field.

By bridging the gap between social media data analysis and consumer psychology, my work adds significant value to the marketing field.

Also Read: How to Identify Research Gap ?

13. Fit with Existing Research:

What are the limitations of your work.

Limitations include the generalizability of findings due to sample biases inherent in social media data.

14. Utilization of Resources:

How do your results fit into the current body of research on the subject.

My results contribute to the current body of research by corroborating existing evidence and offering new perspectives.

15. Method Selection:

If you had more resources, what would you have done differently.

With more resources, I would have expanded the scope of data collection and employed more sophisticated analysis techniques to enhance the depth of insights generated.

Learn how to select a Research Method and how to Frame a Research Design: Check the following video.

16. Accuracy and Validity:

Why did you choose the particular methods you used.

The chosen methods were selected based on their appropriateness for capturing and analyzing large-scale social media data sets efficiently.

17. Applicability in Other Contexts:

How did you ensure accuracy and validity in your research.

Accuracy and validity were ensured through rigorous validation procedures, including cross-validation techniques and expert validation of sentiment analysis results.

18. Starting the Project Again:

Could your research be applied in other contexts.

Yes, my research could be applied in various contexts beyond the retail sector, such as hospitality, healthcare, or political campaigns, with potential implications for understanding online user behavior in different domains.

19. Ethical Considerations:

What would you do differently if you had to start the project again.

If starting the project again, I would prioritize establishing clearer ethical guidelines and procedures for data collection and analysis to ensure the responsible conduct of research.

20. Dissemination:

What ethical considerations did you take into account when designing your study.

Ethical considerations included ensuring user privacy, obtaining informed consent for data usage, and protecting participant confidentiality throughout the research process.

21. Unexpected Results:

How have your results been disseminated.

Results have been disseminated through academic conferences, peer-reviewed journals, and industry reports to reach diverse audiences.

22. Extension of Research:

Are there any unexpected results from your analysis, and why do you think they occurred.

Surprisingly, we found that user engagement on social media positively correlates with both online and offline purchase behavior, which may be attributed to the increasing integration of digital and traditional marketing channels in consumer decision-making processes.

23. Data Analysis Techniques:

Do you have any plans to extend or replicate your research.

Yes, plans include replicating the study across different demographic groups and geographical regions to validate the robustness and generalizability of the findings.

24. Reliability and Validity:

What techniques did you use to analyze your data.

Data analysis techniques included regression analysis, social network analysis, and sentiment analysis, chosen for their appropriateness to the nature of the data and research questions.

25. Lessons Learned:

How did you ensure the reliability and validity of your findings.

Reliability and validity were ensured through rigorous data validation procedures, including reliability checks, sensitivity analyses, and triangulation of multiple data sources.

26. Addressing Methodological Weaknesses:

What lessons can be drawn from your research.

Lessons drawn from my research include the importance of integrating quantitative and qualitative approaches to capture the complexity of online consumer behavior accurately.

27. Possibility of Contradiction:

How did you address any methodological weaknesses in your study.

Methodological weaknesses were addressed by iteratively refining the research design, improving data collection procedures, and conducting sensitivity analyses to assess the robustness of the findings.

28. Transferability of Findings:

Is there a possibility that future research may contradict your findings.

While possible, future research may challenge the findings, particularly if different data sources or analytical approaches are employed.

29. Implications for Policymakers:

How transferable are your findings.

Findings are transferable to similar contexts but should be interpreted with caution in different cultural or market settings due to potential variations in consumer behavior and social media usage patterns.

30. Recommendations:

What implications do your results have for policymakers.

Results have implications for policymakers in terms of informing regulations regarding online advertising practices, consumer protection measures, and data privacy laws.

31. Rigor and Integrity:

What recommendations would you make based on your research.

Based on my research, I recommend that policymakers consider implementing guidelines for transparent disclosure of sponsored content on social media platforms to enhance consumer trust and confidence in online advertising practices.

32. Unexplored Areas:

How did you maintain the rigor and integrity of your project.

Rigor and integrity were maintained through adherence to ethical guidelines, transparency in data collection and analysis procedures, and regular peer review of research findings.

33. Need for Further Research:

What areas remain unexplored in your field.

Unexplored areas in the field include the long-term effects of social media engagement on brand loyalty, customer retention, and the role of emotional content in driving user engagement and purchase behavior.

34. Validity of Results:

Do you think further research is needed in your area of study.

Yes, further research is needed to validate the findings across diverse populations, cultural contexts, and industry sectors to ensure the robustness and generalizability of the conclusions.

35. Time Constraints:

How valid do you think your results are.

I believe the results are valid, supported by rigorous data analysis procedures, triangulation of multiple data sources, and alignment with existing theoretical frameworks and empirical evidence.

36. Accurate Data Representation:

Would you have done anything differently if you had more time.

With more time, I might have conducted additional validation studies to strengthen the reliability and validity of the findings further, as well as explored alternative analytical approaches to corroborate the results.

37. Answering Research Questions:

How did you make sure you accurately represented the data collected.

Accurate data representation was ensured through careful data cleaning, validation procedures, and transparency in reporting the research findings to minimize biases and errors.

38. Applicability in Other Contexts:

How did you ensure the research question was answered.

The research question was answered through systematic data collection, rigorous analysis, and interpretation of the findings about the research objectives and hypotheses.

39. Possibility of Unendorsed Implications:

Are there any possible implications of your research that you don’t endorse.

While unlikely, possible implications that I don’t endorse include deterministic interpretations of causality between social media engagement and consumer behavior, as other factors may also influence purchase decisions.

40. Strengths and Weaknesses:

What do you think are the strengths and weaknesses of your research.

The strengths of my research include innovative methodology, robust data analysis techniques, and practical implications for marketers. Weaknesses include potential sample biases inherent in social media data and limitations in generalizability to offline consumer behavior.

41. Wider Community Implications:

What implications do your findings have for the wider community.

Findings offer valuable insights for marketers, policymakers, and researchers seeking to understand and leverage online consumer behavior effectively in various industries and societal contexts.

42. Useful Learnings:

What have you learned from your research that could be useful for others.

From my research, others can learn the importance of integrating user-generated content analysis into marketing strategies to enhance consumer engagement and inform decision-making processes.

43. Alignment with Literature:

How well do your findings match with the literature in the field.

My findings align well with existing literature on the influence of social media on consumer behavior, extending previous research by providing empirical evidence and theoretical insights into the mechanisms underlying online user engagement and purchase behavior.

44. Undiscussed Implications:

Are there any implications of your research that you haven’t discussed.

While extensively discussed, some implications, such as the role of emotional content in driving engagement and the potential impact of social media on brand loyalty, warrant further exploration in future studies.

45. Unresolved Issues:

Are there any unresolved issues that require further research.

Yes, unresolved issues include the long-term effects of social media engagement on brand loyalty, customer retention, and the effectiveness of different types of social media content in driving consumer engagement and purchase behavior.

46. Conclusions:

What can you conclude based on your research.

In conclusion, my research demonstrates the significant impact of social media engagement on consumer purchasing decisions, highlighting the need for targeted marketing strategies in the digital age to effectively engage and influence online audiences.

47. Contradictory Evidence:

Is there any evidence that contradicts your findings.

While limited, contradictory evidence may arise from studies employing different methodologies or focusing on distinct population groups, underscoring the need for further research to validate and contextualize the findings.

48. Resource Utilization:

What would you have done differently if you had more resources.

With more resources, I would have invested in longitudinal studies to track changes in consumer behavior over time and conducted experiments to test the effectiveness of different social media strategies in influencing user engagement and purchase behavior.

49. Applicability in Other Contexts:

How applicable are your findings to other contexts.

Findings are applicable beyond the retail sector, with potential implications for industries such as hospitality, healthcare, or political campaigns, where online user engagement and consumer behavior are also critical factors influencing decision-making processes.

50. Responsible Conduct:

How did you ensure that the research was conducted responsibly.

The research was conducted responsibly through adherence to ethical guidelines, transparency in data collection and analysis procedures, and regular peer review of research findings to ensure rigor and integrity in the research process.

I hope this article will help you to know the various PhD Viva-Voce Questions and sample Answers that are mostly asked during the final defense. All the best for your Thesis Defence. Happy Researching!

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Top 10 PhD Interview Questions

So, you’ve been invited for a PhD interview. Congratulations! This means that the admission committee thinks you are qualified and capable of doing a PhD at their university. The interview will allow the committee to determine if you’re a good fit, and you have the motivation and drive to complete a doctorate. While you cannot predict the exact questions you will be asked, certain topics are almost inevitable. Here are ten common PhD interview questions.

1. Tell us about yourself

This is a popular opener for just about any type of interview. It’s meant to be an easy icebreaker, but that doesn’t mean there isn’t a wrong answer. Make sure to your response is relevant to the context of a PhD interview. Talk about your academic background, motivation, and interests. You don’t have to get into the details at this point, just give an overview.

2. Why do you want to do a PhD?

This is another straightforward question that doesn’t have a straightforward answer. A PhD is a big undertaking and you’ll have to be driven to see it though. Your answer should address your motivation for doing a PhD in a way that conveys your passion and enthusiasm for the subject.

3. Why are you interested in this program?

What drew you to this program and this school? Does it have a unique feature or take a different approach than other programs? Are there certain professors you are interested in working with? Your answer to this questions shows you have done some research and are ready to engage in the department. It’s also an opportunity to demonstrate that you don’t just want a PhD, you want one from this school.

4. What experience makes you a good candidate?

Yes, the interviewer has read your CV, but this question allows you to draw their attention to specific qualifications or skills that might not be obvious from just your resume. Talk about courses you have taken that have taught you the necessary skills for graduate work or give examples of past research experience from your Bachelor’s or Master’s.

5. How did you develop this proposal?

There are no trick questions here. The interviewer wants to see that you are engaged with the field and spent some time preparing your proposal. Take them through your thought process and discuss the background reading and research you did. What other approaches did you consider before deciding on this one? What will your project contribute to the field?  

6. What difficulties would you expect to encounter during this project?

No matter how carefully you plan, no project goes off without a hitch. Be honest about where you see potential difficulties, but more importantly discuss how you plan to work through them.

7. What are your strengths and weaknesses?

Another classic interview question, and one you definitely don’t want to be answering off the top of your head. Pick a strength that is relevant to this position and then give a few examples of how you have used it well. When it comes to choosing a weakness, be truthful and then (using examples again) talk about how you have been working to overcome it.

8. Tell us about a time you experienced a setback

The next three to six years of your PhD won’t be smooth sailing. You are likely to hit many snags along the way. The interviewer wants to know you are resourceful and can handle these setback. Try to think of an academic challenge you have had to overcome rather than a personal one.

9. What are your future career plans?

This is another way to suss out your motivations for doing a PhD and see if you have given a thought to what comes after your doctorate. How will a PhD help you achieve your future goals? Someone with a clear goal in mind is likely to be more committed to doing a PhD. For many, the goal will be to pursue an academic career, in which case this is an opportunity to show you understand the academic career path.

10. Do you have any questions for us?

Remember that this interview goes both ways. It is important that you have some questions to ask the interviewer to show your engagement and the serious consideration you are giving their program. You are preparing to spend several years of your life at this school. Think about what is important to you and what would make or break your decision to attend this university. Prepare a list of questions ahead of the interview.

The interview is your time to shine, and being prepared will allow you to do just that.

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CTS Frequently Asked Questions

Am i eligible for admission into the cts program.

Eligible individuals are trained in the medical sciences, most commonly fully-trained physicians. Other with similar training are also considered: doctoral degrees in veterinary medicine, dental medicine, or pharmacy; predoctoral students enrolled in medical school (including MD/PhD candidates) or other PhD programs; and students with prior master’s degrees in epidemiology, biostatistics, clinical research, and related fields.

How do I apply for the CTS MS and Certificate Programs?

You must submit your application through the Graduate School of Biomedical Sciences (GSBS) online application system.  Information can be found in the Admissions section of this website. GSBS will process your application and transfer the materials to the CTS Program.

What is the application deadline?

There are three application deadlines for the CTS PhD, MS, and in-person Certificate Programs. 

• December 1 (Early Notification) 
• April 1 (Regular Deadline for Students that Require a Visa)
• May 1 (Regular Deadline for Students that Do Not Require a Visa)

Applications for the HEOR Program are due August 15.

Are GRE scores required?

Applicants to the CTS Program who have previously completed a PhD in a STEM field, or a MD, DO, DDS, DVM, or equivalent, are not required to complete the GRE general test. All other degree holders should take the GRE General Test.

Is the ECFMG certification required?

The ECFMG (Educational Commission for Foreign Medical Graduates) certification is not required for proof of medical and clinical work experience. The most competitive candidates have completed clinical training and work experience in US based medical or health services institutions. The ECFMG Certification is one way to waive the TOEFL/IELTS requirement for non-native English speakers.

Are fellowship and program application dates the same?

Yes. Candidates seeking fellowship support must include this information in their Personal Statements and outline their qualifications for fellowship consideration.

Can I apply before I have finished medical school?

Yes. If you are a medical student and interested in applying to the CTS Program before you complete your MD degree, please contact the CTS Program Director or Manager to discuss your options and your eligibility for a T32 Predoctoral Fellowship.

Can I submit a copy of my transcript?

Please upload a copy of your transcript from each college and/or university you have attended, regardless of whether you earned a degree. Additional information can be found  here .

Do applicants have to come to Boston for interview?

Candidates may be interviewed on site in Boston or via Zoom or telephone.  Candidate interviews generally take place from January to March.

Are there options that do not involve writing a thesis?

Yes our certificate programs do not require a thesis. 

The Clinical & Translational Certificate Program does not require a written thesis. However, a customized approved brief research project, initiated at the beginning of the final semester is a requirement as well as a poster presentation at the annual CTS Graduate Program Symposium which is held each May.

The online HEOR Certificate Program requires coursework only.

Do I have to write a thesis to obtain an MS?

Yes. In order to receive a MS in the CTS Program, all students must undertake independent clinical research and complete an approved final thesis.

How long is the MS program?

The MS degree program is structured as a two-year full-time commitment.  The first year of the program begins in July and continues with a Fall and Spring semester.  In Year Two, there are no summer courses, only Fall and Spring semester courses.

Can I participate in the CTS program without doing a fellowship?

Yes, all interested individuals may apply for admission to the CTS Program.  Students admitted into the Program without fellowship support do not receive stipends and are directly billed for tuition and fees by Tufts University.

Can I take courses without being admitted to the MS program?

Individuals who have not been admitted to a GSBS degree program may take core or elective courses, with approval from the course instructor as a Non-Degree Student.

Can I use NRSA or other student fellowship support as a CTS student?

Yes. Prospective students may apply for a limited number of fellowships with the Tufts Clinical and Translational Science Institute. These highly competitive fellowship include full tuition for study in the MS or PhD CTS Programs as well as annual stipends, and research and travel funds. These fellowships are NIH-funded and can only be held by US citizens or permanent US residents. 

Do I have to be working in a clinical setting to apply?

No. However, the most successful candidates intend to pursue careers as independent investigators in clinical and translational research and are able to demonstrate commitment to this goal in their application.

Do I qualify for a student visa if I am a nonmatriculated student?

No.  Student visa eligibility criteria include full-time enrollment in a degree program.

Are there postdoctoral fellowships in Clinical & Translational Sciences

Yes. Prospective students may apply for a limited number of fellowships with the Institute for Clinical Research and Health Policy Studies (ICRHPS). These highly competitive fellowships include full tuition for master-level study in the CTS Program, annual stipends, research and travel funds. Fellows are assigned ICRHPS office space.  ICRHPS postdoctoral fellowship opportunities vary by candidate eligibility, citizenship, required activities, and stipend amount, but all require full-time enrollment in the CTS Graduate Program.

Does the CTS Program offer online courses?

Some of the courses for the CTS Program have Distance Learning options. The program is not offered as an online only degree program. However, the HEOR Certificate Program is an online program.

If I can't devote two years of study, can I still be a part of the program?

The CTS Program has a Certificate Program that is designed to provide a basic foundation in clinical research for physicians and other doctorally-trained clinicians who are unable to devote two or more years of full-time study to obtain a MS or PhD degree.

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Speaker 1: Thanks for checking out this video. Today is going to be super short. I'm just going to be sharing with you five questions that you should be asking after PhD interviews. After this intro, I'll get into it. Welcome to Grad Life Grind. If you're new to this channel, thank you for checking it out. And if you're already a subscriber, thanks for being back again. My name is Arielle, and I'm a PhD student in clinical psychology. And in this channel, I bring you information about the mental and talk about my journey as a PhD student. So at PhD interviews, you may be spending the entire day at a campus or an entire day online during virtual interviews now. So during PhD interview days, you may have multiple opportunities to ask questions to either faculty or students. For example, if you have a one-on-one interview with a faculty member or a student, at the end of the interview, they'll probably give you the opportunity to ask any questions that you may have. And you definitely want to be prepared. I also had a PhD interview where I asked all the questions the entire time. So the faculty member who interviewed me did not ask me a single question. They just told me that I can ask any questions that I want. And that's basically what guided the interview. That caught me super off guard. So now I always know to go into an interview with questions. So here are some questions you can ask either in one-on-one interviews, in group interviews, or even during some down time that you may have with current graduate students at that program. A lot of PhD programs are designed in such a way that you will be signing on to work with one faculty member for the duration of your time at the program. And if that's the case, then you should have lots of questions for the faculty member you're interviewing with because this is the person who's going to be your advisor for the entire time that you're there. So some questions that you can ask faculty members are, what is your mentorship style? What are your expectations for your students at each year of the program? For example, for clinical psychology, they're usually five to seven year programs. And faculty will usually expect different things from their first years than they expect from their fourth or fifth year students. So that is a great question as well. You can also ask faculty, what is the culture of the lab? What are the interactions between lab members and between students and the mentor? Is the lab super collaborative or do students in the lab work more independently? Another question that I think is really important to ask faculty members, whether it's the individual advisor that you're applying to work with, or really any faculty or administrator, this question is, how does the university handle issues of cultural competence or diversity? This is a pretty timely question. And for those who are studying clinical psychology, like me, it's a good question to ask because clinicians or future therapists, future psychologists of the world really should be culturally competent. It is part of our ethics code and it's super important to know how to work with clients from diverse backgrounds. If the doctoral program that you're applying to is research heavy, then you should also ask faculty, what are the opportunities to present at conferences? What are the conferences that the lab attends on a regular basis or on an annual basis? And what are the opportunities for co-authorship? Now, the questions that you ask current graduate students are going to be pretty different and you should be careful about the questions you ask because basically anytime you are interacting with someone from the university or program during interview day, you are technically still being evaluated. So even if you're not in a one-on-one interview, maybe there's downtime and you're talking to students, you're still technically being evaluated or watched. They may report your questions or answers back to the admissions committee and some students are on the admissions committee. So some questions to ask students could be more on the practical side. For example, where do students usually live? Is there on-campus housing? How does off-campus housing work? What is the cost of living in this area? What do students out here do for fun? Things like that. It's also really common for me as someone who works for admissions at my program to be asked about the financial situation or financial assistance. So it's common to ask current graduate students, how do you afford this program? Do you have grants, scholarships, stipends, etc? If not, then how are you able to get by? And it may seem like a personal question and you can let the student that you're asking know, like feel free not to answer this if you don't feel comfortable, but this is a huge decision and I would really like to know what your financial situation is, how are you financing this program? And most of the time students are happy to share how they're able to afford the program, whether they have loans, etc. But it's important information for you to know because a PhD program or whether to commit to a PhD program is a huge decision and it's a pretty long chunk of your life, five to seven years. So ask what you feel you need to know to make an informed decision about this program. You are interviewing and evaluating the program as much as they're evaluating you. I also recommend asking current graduate students, what does the university or the program do to support students' well-being? So some schools may have different student organizations or social events or self-care related events or resources that help students. So if the program offers that or if the university at large offers that, it's important for you to know. And it's also common for me to receive this question from applicants. They usually ask me like, what is work-life balance like in your program? And you're going to hear a range of answers. Some PhD students are really on top of everything and they're, they feel like they're on top of the world and that's awesome. And some PhD students are, will be honest with you and say it's super hard. I work 12 hour days, etc. But it's important for you to ask that question and to know what does a typical day look like for you as a graduate student in this program? And I recommend asking that typical day question to multiple students because a typical day for a first year student might look really different than the typical day for a fourth or fifth year student. I also recommend asking current graduate students about networking opportunities. Are they able to go to conferences? Does the program provide funding for them to go to conferences? Are there student organizations or professional organizations that they are part of and able to find opportunities from? That's a great question as well. And lastly, a super common and honestly great question to ask current graduate students is what sold you about this program? Why did you pick this program over others? And that will tell you kind of what the selling points for those students are. And honestly, I recommend asking the questions that I just mentioned to multiple people. So not just for the question about what a typical day is like, but for all of these questions. And you can ask, you can pick and choose whether to ask some of these questions to faculty, whether to ask them to faculty and students, whether to ask multiple students. You're fielding the people that you're interacting with for impressions about the program. You want to know are students happy there? Are faculty collaborative? Are faculty hands-on? Basically, are the faculty and students representative of what you are looking for? And of course, you should be clear for yourself about what you're looking for in a mentor, in a program, and in an environment or in a school culture. So you should be asking these questions knowing what appeals to you and what you want as well. So if you already got an interview for a PhD program, congratulations. If you're still waiting, hang in there. I know it is anxiety-inducing, it is scary, but it's also really exciting and it's a huge feat just to submit applications because that is a process that is super stressful as well. So I wish you the best of luck. I hope this video was helpful to you and if it was, I hope that you will hit the like button. I hope that you will subscribe and I hope that you'll also keep in touch with me on other platforms such as Instagram and TikTok. Again, my name is Arielle. My goal is to inform you, to inspire you, to spark an interest in you, and hopefully also entertain you. So I hope you'll check back soon.

Developing Surveys on Questionable Research Practices: Four Challenging Design Problems

• Open access
• Published: 02 September 2024

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• Christian Berggren   ORCID: orcid.org/0000-0002-4233-5138 1 ,
• Bengt Gerdin   ORCID: orcid.org/0000-0001-8360-5387 2 &
• Solmaz Filiz Karabag   ORCID: orcid.org/0000-0002-3863-1073 1 , 3  

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The exposure of scientific scandals and the increase of dubious research practices have generated a stream of studies on Questionable Research Practices (QRPs), such as failure to acknowledge co-authors, selective presentation of findings, or removal of data not supporting desired outcomes. In contrast to high-profile fraud cases, QRPs can be investigated using quantitative, survey-based methods. However, several design issues remain to be solved. This paper starts with a review of four problems in the QRP research: the problem of precision and prevalence, the problem of social desirability bias, the problem of incomplete coverage, and the problem of controversiality, sensitivity and missing responses. Various ways to handle these problems are discussed based on a case study of the design of a large, cross-field QRP survey in the social and medical sciences in Sweden. The paper describes the key steps in the design process, including technical and cognitive testing and repeated test versions to arrive at reliable survey items on the prevalence of QRPs and hypothesized associated factors in the organizational and normative environments. Partial solutions to the four problems are assessed, unresolved issues are discussed, and tradeoffs that resist simple solutions are articulated. The paper ends with a call for systematic comparisons of survey designs and item quality to build a much-needed cumulative knowledge trajectory in the field of integrity studies.

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Introduction

The public revelations of research fraud and non-replicable findings (Berggren & Karabag, 2019 ; Levelt et al., 2012 ; Nosek et al., 2022 ) have created a lively interest in studying research integrity. Most studies in this field tend to focus on questionable research practices, QRPs, rather than blatant fraud, which is less common and hard to study with rigorous methods (Butler et al., 2017 ). Despite the significant contributions of this research about the incidence of QRPs in various countries and contexts, several issues still need to be addressed regarding the challenges of designing precise and valid survey instruments and achieving satisfactory response rates in this sensitive area. While studies in management (Hinkin, 1998 ; Lietz, 2010 ), behavioral sciences, psychology (Breakwell et al., 2020 ), sociology (Brenner, 2020 ), and education (Hill et al., 2022 ) have provided guidelines to design surveys, they rarely discuss how to develop, test, and use surveys targeting sensitive and controversial issues such as organizational or individual corruption (Lin & Yu, 2020 ), fraud (Lawlor et al., 2021 ), and misconduct. The aim of this study is to contribute to a systematic discussion of challenges facing survey designers in these areas and, by way of a detailed case study, highlight alternative ways to increase participation and reliability of surveys focusing on questionable research practices, scientific norms, and organizational climate.

The following section starts with a literature-based review of four important problems:

the lack of conceptual consensus and precise measurements,

the problem of social desirability bias.

the difficulty of covering both quantitative and qualitative research fields.

the problem of controversiality and sensitivity.

Section 3 presents an in-depth case study of developing and implementing a survey on QRPs in the social and medical sciences in Sweden 2018–2021, designed to target these problems. Its first results were presented in this journal (Karabag et al., 2024 ). The section also describes the development process and the survey content and highlights the general design challenges. Section 4 returns to the four problems by discussing partial solutions, difficult tradeoffs, and remaining issues.

Four Design Problems in the Study of Questionable Research Practices

Extant QRP studies have generated an impressive body of knowledge regarding the occurrence and complexities of questionable practices, their increasing trend in several academic fields, and the difficulty of mitigating them with conventional interventions such as ethics courses and espousal of integrity policies (Gopalakrishna et al., 2022 ; Karabag et al., 2024 ; Necker, 2014 ). However, investigations on the prevalence of QRPs have so far lacked systematic problem analysis. Below, four main problems are discussed.

The Problem of Conceptual Clarity and Measurement Precision

Studies of QRP prevalence in the literature exhibit high levels of questionable behaviors but also considerable variation in their estimates. This is illustrated in the examples below:

“42% hade collected more data after inspecting whether results were statistically significant… and 51% had reported an unexpected finding as though it had been hypothesized from the start (HARKing)”( Fraser et al., 2018 , p. 1) , “51 , 3% of respondents engaging frequently in at least one QRP” ( Gopalakrishna et al., 2022 , p. 1) , “…one third of the researchers stated that for the express purpose of supporting hypotheses with statistical significance they engaged in post hoc exclusion of data” ( Banks et al., 2016 , p. 10).

On a general level, QRPs constitute deviations from the responsible conduct of research, that are not severe enough to be defined as fraud and fabrication (Steneck, 2006 ). Within these borders, there is no conceptual consensus regarding specific forms of QRPs (Bruton et al., 2020 ; Xie et al., 2021 ). This has resulted in a considerable variation in prevalence estimates (Agnoli et al., 2017 ; Artino et al. Jr, 2019 ; Fiedler & Schwarz, 2016 ). Many studies emphasize the role of intentionality, implying a purpose to support a specific assertion with biased evidence (Banks et al., 2016 ). This tends to be backed by reports of malpractices in quantitative research, such as p-hacking or HARKing, where unexpected findings or results from an exploratory analysis are reported as having been predicted from the start (Andrade, 2021 ). Other QRP studies, however, build on another, often implicit conceptual definition and include practices that could instead be defined as sloppy or under-resourced research, e.g. insufficient attention to equipment, deficient supervision of junior co-workers, inadequate note-keeping of the research process, or use of inappropriate research designs (Gopalakrishna et al., 2022 ). Alternatively, those studies include behaviors such as “Fashion-determined choice of research topic”, “Instrumental and marketable approach”, and “Overselling methods, data or results” (Ravn & Sørensen, 2021 , p. 30; Vermeulen & Hartmann, 2015 ) which may be opportunistic or survivalist but not necessarily involve intentions to mislead.

To shed light on the prevalence of QRPs in different environments, the first step is to conceptualize and delimit the practices to be considered. The next step is to operationalize the conceptual approach into useful indicators and, if needed, to reformulate and reword the indicators into unambiguous, easily understood items (Hinkin, 1995 , 1998 ). The importance of careful item design has been demonstrated by Fiedler and Schwarz ( 2016 ). They show how the perceived QRP prevalence changes by adding specifications to well-known QRP items. Such specifications include: “ failing to report all dependent measures that are relevant for a finding ”, “ selectively reporting studies related to a specific finding that ‘’worked’ ” (Fiedler & Schwarz, 2016 , p. 46, italics in original ), or “collecting more data after seeing whether results were significant in order to render non-significant results significant ” (Fiedler & Schwarz, 2016 , p. 49, italics in original ). These specifications demonstrate the importance of precision in item design, the need for item tests before applications in a large-scale survey, and as the case study in Sect. 3 indicates, the value of statistically analyzing the selected items post-implementation.

The Problem of Social Desirability

Case studies of publicly exposed scientific misconduct have the advantage of explicitness and possible triangulation of sources (Berggren & Karabag, 2019 ; Huistra & Paul, 2022 ). Opinions may be contradictory, but researchers/investigators may often approach a variety of stakeholders and compare oral statements with documents and other sources (Berggren & Karabag, 2019 ). By contrast, quantitative studies of QRPs need to rely on non-public sources in the form of statements and appraisals of survey respondents for the dependent variables and for potentially associated factors such as publication pressure, job insecurity, or competitive climate.

Many QRP surveys use items that target the respondents’ personal attitudes and preferences regarding the dependent variables, indicating QRP prevalence, as well as the explanatory variables. This has the advantage that the respondents presumably know their own preferences and practices. A significant disadvantage, however, concerns social desirability, which in this context means the tendency of respondents to portray themselves, sometimes inadvertently, in more positive ways than justified by their behavior. The extent of this problem was indicated in a meta-study by Fanelli ( 2009 ), which demonstrated major differences between answers to sensitive survey questions that targeted the respondents’ own behavior and questions that focused on the behavior of their colleagues. In the case study below, the pros and cons of the latter indirect approaches are analyzed.

The Problem of Covering Both Quantitative and Qualitative Research

Studies of QRP prevalence are dominated by quantitative research approaches, where there exists a common understanding of the meaning of facts, proper procedures and scientific evidence. Several research fields, also in the social and medical sciences, include qualitative approaches — case studies, interpretive inquiries, or discourse analysis — where assessments of ‘truth’ and ‘evidence’ may be different or more complex to evaluate.

This does not mean that all qualitative endeavors are equal or that deceit—such as presenting fabricated interview quotes or referring to non-existent protocols —is accepted. However, while there are defined criteria for reporting qualitative research, such as the Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ) or the Standards for Reporting Qualitative Research (SRQR checklist) (O’Brien et al., 2014 ), the field of qualitative research encompasses a wide range of different approaches. This includes comparative case studies that offer detailed evidence to support their claims—such as the differences between British and Japanese factories (Dore, 1973 /2011)—as well as discourse analyses and interpretive studies, where the concept of ‘evidence’ is more fluid and hard to apply. The generative richness of the analysis is a key component of their quality (Flick, 2013 ). This intra-field variation makes it hard to pin down and agree upon general QRP items to capture such behaviors in qualitative research. Some researchers have tried to interpret and report qualitative research by means of quantified methods (Ravn & Sørensen, 2021 ), but so far, these attempts constitute a marginal phenomenon. Consequently, the challenges of measuring the prevalence of QRPs (or similar issues) in the variegated field of qualitative research remain largely unexplored.

The Problem of Institutional Controversiality and Personal Sensitivity

Science and academia depend on public trust for funding and executing research. This makes investigations of questionable behaviors a controversial issue for universities and may lead to institutional refusal/non-response. This resistance was experienced by the designers of a large-scale survey of norms and practices in the Dutch academia when several universities decided not to take part, referring to the potential danger of negative publicity (de Vrieze, 2021 ). A Flemish survey on academic careers encountered similar participation problems (Aubert Bonn & Pinxten, 2019 ). Another study on universities’ willingness to solicit whistleblowers for participation revealed that university officers, managers, and lawyers tend to feel obligated to protect their institution’s reputation (Byrn et al., 2016 ). Such institutional actors may resist participation to avoid the exposure of potentially negative information about their institutions and management practices, which might damage the university’s brand (Byrn et al., 2016 ; Downes, 2017 ).

QRP surveys involve sensitive and potentially intrusive questions also from a respondent’s personal perspective that can lead to a reluctance to participate and non-response behavior (Roberts & John, 2014 ; Tourangeau & Yan, 2007 ). Studies show that willingness to participate declines for surveys covering sensitive issues such as misconduct, crime, and corruption, compared to less sensitive ones like leisure activities (cf. Tourangeau et al., 2010 ). The method of survey administration—whether face-to-face, over the phone, via the web, or paper-based—can influence the perceived sensitivity and response rate (Siewert & Udani, 2016 ; Szolnoki & Hoffmann, 2013 ). In the case study below, the survey did not require any institutional support. Instead, the designers focused on minimizing the individual sensitivity problem by avoiding questions about the respondents’ personal practices. To manage this, they concentrated on their colleagues’ behaviors (see Sect. 4.2). Even if a respondent agrees to participate, they may not answer the QRP items due to insufficient knowledge about her colleagues’ practices or a lack of motivation to answer critical questions about their colleagues’ practices (Beatty & Herrmann, 2002 ; Yan & Curtin, 2010 ). Additionally, a significant time gap between observing specific QRPs in the respondent’s research environment and receiving the survey may make it difficult to recall and accurately respond to the questions. Such issues may also result in non-response problems.

Addressing the Problems: Case Study of a Cross-Field QRP Survey – Design Process, Survey Content, Design Challenges

This section presents a case study of the way these four problems were addressed in a cross-field survey intended to capture QRP prevalence and associated factors across the social and medical sciences in Sweden. The account is based on the authors’ intensive involvement in the design and analysis of the survey, including the technical and cognitive testing, and post-implementation analysis of item quality, missing responses, and open respondent comments. The theoretical background and the substantive results of the study are presented in a separate paper (Karabag et al., 2024 ). Method and language experts at Statistics Sweden, a government agency responsible for public statistics in Sweden, supported the testing procedures, the stratified respondent sampling and administered the survey roll-out.

The Survey Design Process – Repeated Testing and Prototyping

The design process included four steps of testing, revising, and prototyping, which allowed the researchers to iteratively improve the survey and plan the roll-out.

Step 1: Development of the Baseline Survey

This step involved searching the literature and creating a list of alternative constructs concerning the key concepts in the planned survey. Based on the study’s aim, the first and third authors compared these constructs and examined how they had been itemized in the literature. After two rounds of discussions, they agreed on construct formulations and relevant ways to measure them, rephrased items if deemed necessary, and designed new items in areas where the extant literature did not provide any guidance. In this way, Survey Version 1 was compiled.

Step 2: Pre-Testing by Means of a Large Convenience Sample

In the second step, this survey version was reviewed by two experts in organizational behavior at Linköping University. This review led to minor adjustments and the creation of Survey Version 2 , which was used for a major pretest. The aim was both to check the quality of individual items and to garner enough responses for a factor analysis that could be used to build a preliminary theoretical model. This dual aim required a larger sample than suggested in the literature on pretesting (Perneger et al., 2015 ). At the same time, it was essential to minimize the contamination of the planned target population in Sweden. To accomplish this, the authors used their access to a community of organization scholars to administer Survey Version 2 to 200 European management researchers.

This mass pre-testing yielded 163 responses. The data were used to form preliminary factor structures and test a structural equation model. Feedback from a few of the respondents highlighted conceptual issues and duplicated questions. Survey Version 3 was developed and prepared for detailed pretesting based on this feedback.

Step 3: Focused Pre-Testing and Technical Assessment

This step focused on the pre-testing and technical assessment. The participants in this step’s pretesting were ten researchers (six in the social sciences and four in the medical sciences) at five Swedish universities: Linköping, Uppsala, Gothenburg, Gävle, and Stockholm School of Economics. Five of those researchers mainly used qualitative research methods, two used both qualitative and quantitative methods, and three used quantitative methods. In addition, Statistics Sweden conducted a technical assessment of the survey items, focusing on wording, sequence, and response options. Footnote 1 Based on feedback from the ten pretest participants and the Statistics Sweden assessment, Survey Version 4 was developed, translated into Swedish, and reviewed by two researchers with expertise in research ethics and scientific misconduct.

It should be highlighted that Swedish academia is predominantly bilingual. While most researchers have Swedish as their mother tongue, many are more proficient in English, and a minority have limited or no knowledge of Swedish. During the design process, the two language versions were compared item by item and slightly adjusted by skilled bilingual researchers. This task was relatively straightforward since most items and concepts were derived from previously published literature in English. Notably, the Swedish versions of key terms and concepts have long been utilized within Swedish academia (see for example Berggren, 2016 ; Hasselberg, 2012 ). To secure translation quality, the language was controlled by a language expert at Statistics Sweden.

Step 4: Cognitive Interviews by Survey and Measurement Experts

Next, cognitive interviews (Willis, 2004 ) were organized with eight researchers from the social and medical sciences and conducted by an expert from Statistics Sweden (Wallenborg Likidis, 2019 ). The participants included four women and four men, ranging in age from 30 to 60. They were two doctoral students, two lecturers, and four professors, representing five different universities and colleges. Additionally, two participants had a non-Nordic background. To ensure confidentiality, no connections are provided between these characteristics and the individual participants.

An effort was made to achieve a distribution of gender, age, subject, employment, and institution. Four social science researchers primarily used qualitative research methods, while the remaining four employed qualitative and quantitative methods. Additionally, four respondents completed the Swedish version of the survey, and four completed the English version.

The respondents completed the survey in the presence of a methods expert from Statistics Sweden, who observed their entire response process. The expert noted spontaneous reactions and recorded instances where respondents hesitated or struggled to understand an item. After the survey, the expert conducted a structured interview with all eight participants, addressing details in each section of the survey, including the missive for recruiting respondents. Some respondents provided oral feedback while reading the cover letter and answering the questions, while others offered feedback during the subsequent interview.

During the cognitive interview process, the methods expert continuously communicated suggestions for improvements to the design team. A detailed test protocol confirmed that most items were sufficiently strong, although a few required minor modifications. The research team then finalized Survey Version 5 , which included both English and Swedish versions (for the complete survey, see Supplementary Material S1).

Although the test successfully captured a diverse range of participants, it would have been desirable to conduct additional tests of the English survey with more non-Nordic participants; as it stands, only one such test was conducted. Despite the participants’ different approaches to completing the survey, the estimated time to complete it was approximately 15–20 min. No significant time difference was observed between completing the survey in Swedish and English.

Design Challenges – the Dearth of an Item-Specific Public Quality Discussion

The design decision to employ survey items from the relevant literature as much as possible was motivated by a desire to increase comparability with previous studies of questionable research practices. However, this approach came with several challenges. Survey-based studies of QRPs rely on the respondents’ subjective assessments, with no possibility to compare the answers with other sources. Thus, an open discussion of survey problems would be highly valuable. However, although published studies usually present the items used in the surveys, there is seldom any analysis of the problems and tradeoffs involved when using a particular type of item or response format and meager information about item validity. Few studies, for example, contain any analysis that clarifies which items that measured the targeted variables with sufficient precision and which items that failed to do so.

Another challenge when using existing survey studies is the lack of information regarding the respondents’ free-text comments about the survey’s content and quality. This could be because the survey did not contain any open questions or because the authors of the report could not statistically analyze the answers. As seen below, however, open respondent feedback on a questionnaire involving sensitive or controversial aspects may provide important feedback regarding problems that did not surface during the pretest process, which by necessity targets much smaller samples.

Survey Content

The survey started with questions about the respondent’s current employment and research environment. It ended with background questions on the respondents’ positions and the extent of their research activity, plus space for open comments about the survey. The core content of the survey consisted of sections on the organizational climate (15 items), scientific norms (13 items), good and questionable research practices (16 items), perceptions of fairness in the academic system (4 items), motivation for conducting research (8 items), ethics training and policies (5 items); and questions on the quality of the research environment and the respondent’s perceived job security.

Sample and Response Rate

All researchers, teachers, and Ph.D. students employed at Swedish universities are registered by Statistics Sweden. To ensure balanced representation and perspectives from both large universities and smaller university colleges, the institutions were divided into three strata based on the number of researchers, teachers, and Ph.D. students: more than 1,000 individuals (7 universities and university colleges), 500–999 individuals (3 institutions), and fewer than 500 individuals (29 institutions). From these strata, Statistics Sweden randomly sampled 35%, 45%, and 50% of the relevant employees, resulting in a sample of 10,047 individuals. After coverage analysis and exclusion of wrongly included, 9,626 individuals remained.

The selected individuals received a personal postal letter with a missive in both English and Swedish informing them about the project and the survey and notifying them that they could respond on paper or online. The online version provided the option to answer in either English or Swedish. The paper version was available only in English to reduce the cost of production and posting. The missive provided the recipients with comprehensive information about the study and what their involvement would entail. It emphasized the voluntary character of participation and their right to withdraw from the survey at any time, adding: “If you do not want to answer the questions , we kindly ask you to contact us. Then you will not receive any reminders.” Sixty-three individuals used this decline option. In line with standard Statistics Sweden procedures, survey completion implied an agreement to participation and to the publication of anonymized results and indicated participants’ understanding of the terms provided (Duncan & Cheng, 2021 ). An email address was provided for respondents to request study outputs or for any other reason. The survey was open for data collection for two months, during which two reminders were sent to non-responders who had not opted out.

Once Statistics Sweden had collected the answers, they were anonymized and used to generate data files delivered to the authors. Statistics Sweden also provided anonymized information about age, gender, and type of employment of each respondent in the dataset delivered to the researchers. Of the targeted individuals, 3,295 responded, amounting to an overall response rate of 34.2%. An analysis of missing value patterns revealed that 290 of the respondents either lacked data for an entire factor or had too many missing values dispersed over several survey sections. After removing these 290 responses, we used SPSS algorithms (IBM-SPSS Statistics 27) to analyze the remaining missing values, which were randomly distributed and constituted less than 5% of the data. These values were replaced using the program’s imputation program (Madley-Dowd et al., 2019 ). The final dataset consisted of 3,005 individuals, evenly distributed between female and male respondents (53,5% vs. 46,5%) and medical and social scientists (51,3% vs. 48,5%). An overview of the sample and the response rate is provided in Table  1 , which can also be found in (Karabag et al., 2024 ). As shown in Table  1 , the proportion of male and female respondents, as well as the proportion of respondents from medical and social science, and the age distribution of the respondents compared well with the original selection frame from Statistics Sweden.

Revisiting the Four Problems. Partial Solutions and Remaining Issues

Managing the precision problem - the value of factor analyses.

As noted above, the lack of conceptual consensus and standard ways to measure QRPs has resulted in a huge variation in estimated prevalence. In the case studied here, the purpose was to investigate deviations from research integrity and not low-quality research in general. This conceptual focus implied that selected survey items regarding QRP should build on the core aspect of intention, as suggested by Banks et al. ( 2016 , p. 323): “design, analytic, or reporting practices that have been questioned because of the potential for the practice to be employed with the purpose of presenting biased evidence in favor of an assertion”. After scrutinizing the literature, five items were selected as general indicators of QRP, irrespective of the research approach (see Table  2 ).

An analysis of the survey responses indicated that the general QRP indicators worked well in terms of understandability and precision. Considering the sensitive nature of the items, features that typically yield very high rates of missing data (Fanelli, 2009 ; Tourangeau & Yan, 2007 ), our missing rates of 11–21% must be considered modest. In addition, there were a few critical comments on the item formulation in the open response section at the end of the survey (see below).

Regarding the explanatory (independent) variables, the survey was inspired by studies showing the importance of the organizational climate and the normative environment within academia (Anderson et al., 2010 ). Organizational climate can be measured in several ways; the studied survey focused on items related to a collegial versus a competitive climate. The analysis of the normative environment was inspired by the classical norms of science articulated by Robert Merton in his CUDOS framework: communism (communalism), universalism, disinterestedness, and organized skepticism (Merton, 1942 /1973). This framework has been extensively discussed and challenged but remains a key reference (Anderson et al., 2010 ; Chalmers & Glasziou, 2009 ; Kim & Kim, 2018 ; Macfarlane & Cheng, 2008 ). Moreover, we were inspired by the late work of Merton on the ambivalence and ambiguities of scientists (Merton, 1942 /1973), and the counter norms suggested by Mitroff ( 1974 ). Thus, the survey involved a composite set of items to capture the contradictory normative environment in academia: classical norms as well as their counter norms.

To reduce the problems of social desirability bias and personal sensitivity, the survey design avoided items about the respondent’s personal adherence to explicit ideals, which are common in many surveys (Gopalakrishna et al., 2022 ). Instead, the studied survey focused on the normative preferences and attitudes within the respondent’s environment. This necessitated the identification, selection, and refinement of 3–4 items for each potentially relevant norm/counter-norm. The selection process was used in previous studies of norm subscription in various research communities (Anderson et al., 2007 ; Braxton, 1993 ; Bray & von Storch, 2017 ). For the norm “skepticism”, we consulted studies in the accounting literature of the three key elements of professional skepticism: questioning mind, suspension of judgment and search for knowledge (Hurtt, 2010 ).

The first analytical step after receiving the completed survey set from Statistics Sweden was to conduct a set of factor analyses to assess the quality and validity of the survey items related to the normative environment and the organizational climate. These analyses suggested three clearly identifiable factors related to the normative environment: (1) a counter norm factor combining Mitroff’s particularism and dogmatism (‘Biasedness’ in the further analysis), and two Mertonian factors: (2) Skepticism and (3) Openness, a variant of Merton’s Communalism (see Table  3 ). A fourth Merton factor, Disinterestedness, could not be identified in our analysis.

The analytical process for organizational climate involved reducing the number of items from 15 to 11 (see Table 4 ). Here, the factor analysis suggested two clearly identifiable factors, one related to collegiality and the other related to competition (see Table  4 ). Overall, the factor analyses suggested that the design efforts had paid off in terms of high item quality, robust factor loadings, and a very limited need to remove any items.

In a parallel step, the open comments were assessed as an indication of how the study was perceived by the respondents (see Table  5 ). Of the 3005 respondents, 622 provided comprehensible comments, and many of them were extensive. 187 comments were related to the respondents’ own employment/role, 120 were related to the respondents’ working conditions and research environment, and 98 were related to the academic environment and atmosphere. Problems in knowing details of collegial practices were mentioned in 82 comments.

Reducing Desirability Bias - the Challenge of Nonresponse

It is well established that studies on topics where the respondent has anything embarrassing or sensitive to report suffer from more missing responses than studies on neutral subjects and that respondents may edit the information they provide on sensitive topics (Tourangeau & Yan, 2007 ). Such a social desirability bias is applicable for QRP studies which explicitly target the respondents’ personal attitudes and behaviors. To reduce this problem, the studied survey applied a non-self-format focusing on the behaviors and preferences of the respondents’ colleagues. Relevant survey items from published studies were rephrased from self-format designs to non-self-questions about practices in the respondent’s environment, using the format: “In my research environment, colleagues…” followed by a five-step incremental response format from “(1) never” to “(5) always”. In a similar way the survey avoided “should”-statements about ideal normative values: “Scientists and scholars should critically examine…”. Instead, the survey used items intended to indicate the revealed preferences in the respondent’s normative environment regarding universalism versus particularism or openness versus secrecy.

As indicated by Fanelli ( 2009 ), these redesign efforts probably reduced the social desirability bias significantly. At the same time, however, the redesign seemed to increase a problem not discussed by Fanelli ( 2009 ): an increased uncertainty problem related to the respondents’ difficulties of knowing the practices of their colleagues in questionable areas. This issue was indicated by the open comment at the end of the studied survey, where 13% of the 622 respondents pointed out that they lacked sufficient knowledge about the behavior of their colleagues to answer the QRP questions (see Table  5 ). One respondent wrote:

“It’s difficult to answer questions about ‘colleagues in my research area’ because I don’t have an insight into their research practices; I can only make informed guesses and generalizations. Therefore, I am forced to answer ‘don’t know’ to a lot of questions”.

Regarding the questions on general QRPs, the rate of missing responses varied between 11% and 21%. As for the questions targeting specific QRP practices in quantitative and qualitative research, the rate of missing responses ranged from 38 to 49%. Unfortunately, the non-response alternative to these questions (“Don’t know/not relevant”) combined the two issues: the lack of knowledge and the lack of relevance. Thus, we don’t know what part of the missing responses related to a non-presence of the specific research approach in the respondent’s environment and what part signaled a lack of knowledge about collegial practices in this environment.

Measuring QRPs in Qualitative Research - the Limited Role of Pretests

Studies of QRP prevalence focus on quantitative research approaches, where there exists a common understanding of the interpretation of scientific evidence, clearly recommended procedures, and established QRP items related to compliance with these procedures. In the heterogenous field of qualitative research, there are several established standards for reporting the research (O’Brien et al., 2014 ; Tong et al., 2007 ), but, as noted above, hardly any commonly accepted survey items that capture behaviors that fulfill the criteria for QRPs. As a result, the studied survey project designed such items from the start during the survey development process. After technical and cognitive tests, four items were selected. See Table  6 .

Despite the series of pretests, however, the first two of these items met severe criticism from a few respondents in the survey’s open commentary section. Here, qualitative researchers argued that the items were unduly influenced by the truth claims in quantitative studies, whereas their research dealt with interpretation and discourse analysis. Thus, they rejected the items regarding selective usage of respondents and of interview quotes as indicators of questionable practices:

“The alternative regarding using quotes is a bit misleading. Supporting your results by quotes is a way to strengthen credibility in a qualitative method….” “The question about dubious practices is off target for us, who work with interpretation rather than solid truths. You can present new interpretations, but normally that does not imply that previous ‘findings’ should be considered incorrect.” “The questions regarding qualitative research were somewhat irrelevant. Often this research is not guided by a given hypothesis, and researchers may use a convenient sample without this resulting in lower quality.”

One comment focused on other problems related to qualitative research:

“Several questions do not quite capture the ethical dilemmas we wrestle with. For example , is the issue of dishonesty and ‘inaccuracies’ a little misplaced for us who work with interpretation? …At the same time , we have a lot of ethical discussions , which , for example , deal with power relations between researchers and ‘researched’ , participant observation/informal contacts and informed consent (rather than patients participating in a study)”.

Unfortunately, the survey received these comments and criticism only after the full-scale rollout and not during the pretest rounds. Thus, we had no chance to replace the contested items with other formulations or contemplate a differentiation of the subsection to target specific types of qualitative research with appropriate questions. Instead, we had to limit the post-roll-out survey analysis to the last two items in Table  6 , although they captured devious behaviors rather than gray zone practices.

Why then was this criticism of QRP items related to qualitative research not exposed in the pretest phase? This is a relevant question, also for future survey designers. An intuitive answer could be that the research team only involved quantitative researchers. However, as highlighted above, the pretest participants varied in their research methods: some exclusively used qualitative methods, others employed mixed methods, and some utilized quantitative methods. This diversity suggests that the selection of test participants was appropriate. Moreover, all three members of the research team had experience of both quantitative and qualitative studies. However, as discussed above, the field of qualitative research involves several different types of research, with different goals and methods – from detailed case studies grounded in original empirical fieldwork to participant observations of complex organizational phenomena to discursive re-interpretations of previous studies. Of the 3,005 respondents who answered the survey in a satisfactory way, only 16 respondents, or 0,5%, had any critical comments about the QRP items related to qualitative research. A failure to capture the objections from such a small proportion in a pretest phase is hardly surprising. The general problem could be compared with the challenge of detecting negative side-effects in drug development. Although the pharmaceutical firms conduct large-scale tests of candidate drugs before government approval, doctors nevertheless detect new side-effects when the medicine is rolled out to significantly more people than the test populations – and report these less frequent problems in the additional drug information (Galeano et al., 2020 ; McNeil et al., 2010 ).

In the social sciences, the purpose of pre-testing is to identify problems related to ambiguities and bias in item formulation and survey format and initiate a search for relevant solutions. A pre-test on a small, selected subsample cannot guarantee that all respondent problems during the full-scale data collection will be detected. The pretest aims to reduce errors to acceptable levels and ensure that the respondents will understand the language and terminology chosen. Pretesting in survey development is also essential to help the researchers to assess the overall flow and structure of the survey, and to make necessary adjustments to enhance respondent engagement and data quality (Ikart, 2019 ; Presser & Blair, 1994 ).

In our view, more pretests would hardly solve the epistemological challenge of formulating generally acceptable QRP items for qualitative research. The open comments studied here suggest that there is no one-size-fits-all solution. If this is right, the problem should rather be reformulated to a question of identifying different strands of qualitative research with diverse views of integrity and evidence which need to be measured with different measures. To address this challenge in a comprehensive way, however, goes far beyond the current study.

Controversiality and Collegial sensitivity - the Challenge of Predicting Nonresponse

Studies of research integrity, questionable research practices, and misconduct in science tend to be organizationally controversial and personally sensitive. If university leaders are asked to support such studies, there is a considerable risk that the answer will be negative. In the case studied here, the survey roll-out was not dependent on any active organizational participation since Statistics Sweden possessed all relevant respondent information in-house. This, we assumed, would take the controversiality problem off the agenda. Our belief was supported by the non-existent complaints regarding a potential negativity bias from the pretest participants. Instead, the problem surfaced when the survey was rolled out, and all the respondents contemplated the survey. The open comment section at the end of the survey provided insights into this reception.

Many respondents provided positive feedback, reflected in 30 different comments such as:

“Thank you for doing this survey. I really hope it will lead to changes because it is needed”. “This is an important survey. However , there are conflicting norms , such as those you cite in the survey , /concerning/ for example , data protection. How are researchers supposed to be open when we cannot share data for re-analysis?” “I am glad that the problems with egoism and non-collegiality are addressed in this manner ”.

Several of them asked for more critical questions regarding power, self-interest, and leadership:

“What I lack in the survey were items regarding academic leadership. Otherwise, I am happy that someone is doing research on these issues”. “A good survey but needs to be complemented with questions regarding researchers who put their commercial interests above research and exploit academic grants for commercial purposes”.

A small minority criticized the survey for being overly negative towards academia:

“A major part of the survey feels very negative and /conveys/ the impression that you have a strong pre-understanding of academia as a horrible environments”. “Some of the questions are uncomfortable and downright suggestive. Why such a negative attitude towards research?” “The questions have a tendency to make us /the respondents/ informers. An unpleasant feeling when you are supposed to lay information against your university”. “Many questions are hard to answer, and I feel that they measure my degree of suspicion against my closest colleagues and their motivation … Several questions I did not want to answer since they contain a negative interpretation of behaviors which I don’t consider as automatically negative”.

A few of these respondents stated that they abstained from answering some of the ‘negative questions’, since they did not want to report on or slander their colleagues. The general impact is hard to assess. Only 20% of the respondents offered open survey comments, and only seven argued that questions were “negative”. The small number explains why the issue of negativity did not show up during the testing process. However, a perceived sense of negativity may have affected the willingness to answer among more respondents than those who provided free test comments.

Conclusion - The Needs for a Cumulative Knowledge Trajectory in Integrity Studies

In the broad field of research integrity studies, investigations of QRPs in different contexts and countries play an important role. The comparability of the results, however, depends on the conceptual focus of the survey design and the quality of the survey items. This paper starts with a discussion of four common problems in QRP research: the problems of precision, social desirability, incomplete coverage, and organizational controversiality and sensitivity. This is followed by a case study of how these problems were addressed in a detailed survey design process. An assessment of the solutions employed in the studied survey design reveals progress as well as unresolved issues.

Overall, the paper shows that the problem and challenges of precision could be effectively managed through explicit conceptual definitions and careful item design.

The problem of social desirability bias was probably reduced by means of a non-self-response format referring to preferences and behaviors among colleagues instead of personal behaviors. However, an investigation of open respondent comments indicated that the reduced risk of social bias came at the expense of higher uncertainty due to the respondents’ lack of insight in the concrete practices of their colleagues.

The problem of incomplete coverage of QRPs in qualitative research, the authors initially linked to “the lack of standard items” to capture QRPs in qualitative studies. Open comments at the end of the survey, however, suggested that the lack of such standards would not be easily managed by the design of new items. Rather, it seems to be an epistemological challenge related to the multifarious nature of the qualitative research field, where the understanding of ‘evidence’ is unproblematic in some qualitative sub-fields but contested in others. This conjecture and other possible explanations will hopefully be addressed in forthcoming epistemological and empirical studies.

Regarding the problem of controversiality and sensitivity, previous studies show that QRP research is a controversial and sensitive area for academic executives and university brand managers. The case study discussed here indicates that this is a sensitive subject also for rank-and-file researchers who may hesitate to answer, even when the questions do not target the respondents’ own practices but the practices and preferences of their colleagues. Future survey designers may need to engage in framing, presenting, and balancing sensitive items to reduce respondent suspicions and minimize the rate of missing responses. Reflections on the case indicate that this is doable but requires thoughtful design, as well as repeated tests, including feedback from a broad selection of prospective participants.

In conclusion, the paper suggests that more resources should be spent on the systematic evaluation of different survey designs and item formulations. In the long term, such investments in method development will yield a higher proportion of robust and comparable studies. This would mitigate the problems discussed here and contribute to the creation of a much-needed cumulative knowledge trajectory in research integrity studies.

An issue not covered here is that surveys, however finely developed, only give quantitative information about patterns, behaviors, and structures. An understanding of underlying thoughts and perspectives requires other procedures. Thus, methods that integrate and triangulate qualitative and quantitative data —known as mixed methods (Karabag & Berggren, 2016 ; Ordu & Yılmaz, 2024 ; Smajic et al., 2022 )— may give a deeper and more complete picture of the phenomenon of QRP.

Data Availability

The data supporting the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

We thank Jennica Wallenborg Likidis, Statistics Sweden, for providing expert support in the survey design. We are grateful to colleagues Ingrid Johansson Mignon, Cecilia Enberg, Anna Dreber Almenberg, Andrea Fried, Sara Liin, Mariano Salazar, Lars Bengtsson, Harriet Wallberg, Karl Wennberg, and Thomas Magnusson, who joined the pretest or cognitive tests. We also thank Ksenia Onufrey, Peter Hedström, Jan-Ingvar Jönsson, Richard Öhrvall, Kerstin Sahlin, and David Ludvigsson for constructive comments or suggestions.

Open access funding provided by Linköping University. Swedish Forte: Research Council for Health, Working Life and Welfare ( https://www.vr.se/swecris?#/project/2018-00321_Forte ) Grant No. 2018-00321.

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Conceptualization: CB. Survey Design: SFK, CB, Methodology: SFK, BG, CB. Visualization: SFK, BG. Funding acquisition: SFK. Project administration and management: SFK. Writing – original draft: CB. Writing – review & editing: CB, BG, SFK. Approval of the final manuscript: SFK, BG, CB.

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Berggren, C., Gerdin, B. & Karabag, S.F. Developing Surveys on Questionable Research Practices: Four Challenging Design Problems. J Acad Ethics (2024). https://doi.org/10.1007/s10805-024-09565-0

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Key things to know about U.S. election polling in 2024

Confidence in U.S. public opinion polling was shaken by errors in 2016 and 2020. In both years’ general elections, many polls underestimated the strength of Republican candidates, including Donald Trump. These errors laid bare some real limitations of polling.

In the midterms that followed those elections, polling performed better . But many Americans remain skeptical that it can paint an accurate portrait of the public’s political preferences.

Restoring people’s confidence in polling is an important goal, because robust and independent public polling has a critical role to play in a democratic society. It gathers and publishes information about the well-being of the public and about citizens’ views on major issues. And it provides an important counterweight to people in power, or those seeking power, when they make claims about “what the people want.”

The challenges facing polling are undeniable. In addition to the longstanding issues of rising nonresponse and cost, summer 2024 brought extraordinary events that transformed the presidential race . The good news is that people with deep knowledge of polling are working hard to fix the problems exposed in 2016 and 2020, experimenting with more data sources and interview approaches than ever before. Still, polls are more useful to the public if people have realistic expectations about what surveys can do well – and what they cannot.

With that in mind, here are some key points to know about polling heading into this year’s presidential election.

Probability sampling (or “random sampling”). This refers to a polling method in which survey participants are recruited using random sampling from a database or list that includes nearly everyone in the population. The pollster selects the sample. The survey is not open for anyone who wants to sign up.

Online opt-in polling (or “nonprobability sampling”). These polls are recruited using a variety of methods that are sometimes referred to as “convenience sampling.” Respondents come from a variety of online sources such as ads on social media or search engines, websites offering rewards in exchange for survey participation, or self-enrollment. Unlike surveys with probability samples, people can volunteer to participate in opt-in surveys.

Nonresponse and nonresponse bias. Nonresponse is when someone sampled for a survey does not participate. Nonresponse bias occurs when the pattern of nonresponse leads to error in a poll estimate. For example, college graduates are more likely than those without a degree to participate in surveys, leading to the potential that the share of college graduates in the resulting sample will be too high.

Mode of interview. This refers to the format in which respondents are presented with and respond to survey questions. The most common modes are online, live telephone, text message and paper. Some polls use more than one mode.

Weighting. This is a statistical procedure pollsters perform to make their survey align with the broader population on key characteristics like age, race, etc. For example, if a survey has too many college graduates compared with their share in the population, people without a college degree are “weighted up” to match the proper share.

How are election polls being conducted?

Pollsters are making changes in response to the problems in previous elections. As a result, polling is different today than in 2016. Most U.S. polling organizations that conducted and publicly released national surveys in both 2016 and 2022 (61%) used methods in 2022 that differed from what they used in 2016 . And change has continued since 2022.

One change is that the number of active polling organizations has grown significantly, indicating that there are fewer barriers to entry into the polling field. The number of organizations that conduct national election polls more than doubled between 2000 and 2022.

This growth has been driven largely by pollsters using inexpensive opt-in sampling methods. But previous Pew Research Center analyses have demonstrated how surveys that use nonprobability sampling may have errors twice as large , on average, as those that use probability sampling.

The second change is that many of the more prominent polling organizations that use probability sampling – including Pew Research Center – have shifted from conducting polls primarily by telephone to using online methods, or some combination of online, mail and telephone. The result is that polling methodologies are far more diverse now than in the past.

(For more about how public opinion polling works, including a chapter on election polls, read our short online course on public opinion polling basics .)

All good polling relies on statistical adjustment called “weighting,” which makes sure that the survey sample aligns with the broader population on key characteristics. Historically, public opinion researchers have adjusted their data using a core set of demographic variables to correct imbalances between the survey sample and the population.

But there is a growing realization among survey researchers that weighting a poll on just a few variables like age, race and gender is insufficient for getting accurate results. Some groups of people – such as older adults and college graduates – are more likely to take surveys, which can lead to errors that are too sizable for a simple three- or four-variable adjustment to work well. Adjusting on more variables produces more accurate results, according to Center studies in 2016 and 2018 .

A number of pollsters have taken this lesson to heart. For example, recent high-quality polls by Gallup and The New York Times/Siena College adjusted on eight and 12 variables, respectively. Our own polls typically adjust on 12 variables . In a perfect world, it wouldn’t be necessary to have that much intervention by the pollster. But the real world of survey research is not perfect.

Predicting who will vote is critical – and difficult. Preelection polls face one crucial challenge that routine opinion polls do not: determining who of the people surveyed will actually cast a ballot.

Roughly a third of eligible Americans do not vote in presidential elections , despite the enormous attention paid to these contests. Determining who will abstain is difficult because people can’t perfectly predict their future behavior – and because many people feel social pressure to say they’ll vote even if it’s unlikely.

No one knows the profile of voters ahead of Election Day. We can’t know for sure whether young people will turn out in greater numbers than usual, or whether key racial or ethnic groups will do so. This means pollsters are left to make educated guesses about turnout, often using a mix of historical data and current measures of voting enthusiasm. This is very different from routine opinion polls, which mostly do not ask about people’s future intentions.

When major news breaks, a poll’s timing can matter. Public opinion on most issues is remarkably stable, so you don’t necessarily need a recent poll about an issue to get a sense of what people think about it. But dramatic events can and do change public opinion , especially when people are first learning about a new topic. For example, polls this summer saw notable changes in voter attitudes following Joe Biden’s withdrawal from the presidential race. Polls taken immediately after a major event may pick up a shift in public opinion, but those shifts are sometimes short-lived. Polls fielded weeks or months later are what allow us to see whether an event has had a long-term impact on the public’s psyche.

How accurate are polls?

The answer to this question depends on what you want polls to do. Polls are used for all kinds of purposes in addition to showing who’s ahead and who’s behind in a campaign. Fair or not, however, the accuracy of election polling is usually judged by how closely the polls matched the outcome of the election.

By this standard, polling in 2016 and 2020 performed poorly. In both years, state polling was characterized by serious errors. National polling did reasonably well in 2016 but faltered in 2020.

In 2020, a post-election review of polling by the American Association for Public Opinion Research (AAPOR) found that “the 2020 polls featured polling error of an unusual magnitude: It was the highest in 40 years for the national popular vote and the highest in at least 20 years for state-level estimates of the vote in presidential, senatorial, and gubernatorial contests.”

How big were the errors? Polls conducted in the last two weeks before the election suggested that Biden’s margin over Trump was nearly twice as large as it ended up being in the final national vote tally.

Errors of this size make it difficult to be confident about who is leading if the election is closely contested, as many U.S. elections are .

Pollsters are rightly working to improve the accuracy of their polls. But even an error of 4 or 5 percentage points isn’t too concerning if the purpose of the poll is to describe whether the public has favorable or unfavorable opinions about candidates , or to show which issues matter to which voters. And on questions that gauge where people stand on issues, we usually want to know broadly where the public stands. We don’t necessarily need to know the precise share of Americans who say, for example, that climate change is mostly caused by human activity. Even judged by its performance in recent elections, polling can still provide a faithful picture of public sentiment on the important issues of the day.

The 2022 midterms saw generally accurate polling, despite a wave of partisan polls predicting a broad Republican victory. In fact, FiveThirtyEight found that “polls were more accurate in 2022 than in any cycle since at least 1998, with almost no bias toward either party.” Moreover, a handful of contrarian polls that predicted a 2022 “red wave” largely washed out when the votes were tallied. In sum, if we focus on polling in the most recent national election, there’s plenty of reason to be encouraged.

Compared with other elections in the past 20 years, polls have been less accurate when Donald Trump is on the ballot. Preelection surveys suffered from large errors – especially at the state level – in 2016 and 2020, when Trump was standing for election. But they performed reasonably well in the 2018 and 2022 midterms, when he was not.

During the 2016 campaign, observers speculated about the possibility that Trump supporters might be less willing to express their support to a pollster – a phenomenon sometimes described as the “shy Trump effect.” But a committee of polling experts evaluated five different tests of the “shy Trump” theory and turned up little to no evidence for each one . Later, Pew Research Center and, in a separate test, a researcher from Yale also found little to no evidence in support of the claim.

Instead, two other explanations are more likely. One is about the difficulty of estimating who will turn out to vote. Research has found that Trump is popular among people who tend to sit out midterms but turn out for him in presidential election years. Since pollsters often use past turnout to predict who will vote, it can be difficult to anticipate when irregular voters will actually show up.

The other explanation is that Republicans in the Trump era have become a little less likely than Democrats to participate in polls . Pollsters call this “partisan nonresponse bias.” Surprisingly, polls historically have not shown any particular pattern of favoring one side or the other. The errors that favored Democratic candidates in the past eight years may be a result of the growth of political polarization, along with declining trust among conservatives in news organizations and other institutions that conduct polls.

Whatever the cause, the fact that Trump is again the nominee of the Republican Party means that pollsters must be especially careful to make sure all segments of the population are properly represented in surveys.

The real margin of error is often about double the one reported. A typical election poll sample of about 1,000 people has a margin of sampling error that’s about plus or minus 3 percentage points. That number expresses the uncertainty that results from taking a sample of the population rather than interviewing everyone . Random samples are likely to differ a little from the population just by chance, in the same way that the quality of your hand in a card game varies from one deal to the next.

The problem is that sampling error is not the only kind of error that affects a poll. Those other kinds of error, in fact, can be as large or larger than sampling error. Consequently, the reported margin of error can lead people to think that polls are more accurate than they really are.

There are three other, equally important sources of error in polling: noncoverage error , where not all the target population has a chance of being sampled; nonresponse error, where certain groups of people may be less likely to participate; and measurement error, where people may not properly understand the questions or misreport their opinions. Not only does the margin of error fail to account for those other sources of potential error, putting a number only on sampling error implies to the public that other kinds of error do not exist.

Several recent studies show that the average total error in a poll estimate may be closer to twice as large as that implied by a typical margin of sampling error. This hidden error underscores the fact that polls may not be precise enough to call the winner in a close election.

Other important things to remember

Transparency in how a poll was conducted is associated with better accuracy . The polling industry has several platforms and initiatives aimed at promoting transparency in survey methodology. These include AAPOR’s transparency initiative and the Roper Center archive . Polling organizations that participate in these organizations have less error, on average, than those that don’t participate, an analysis by FiveThirtyEight found .

Participation in these transparency efforts does not guarantee that a poll is rigorous, but it is undoubtedly a positive signal. Transparency in polling means disclosing essential information, including the poll’s sponsor, the data collection firm, where and how participants were selected, modes of interview, field dates, sample size, question wording, and weighting procedures.

There is evidence that when the public is told that a candidate is extremely likely to win, some people may be less likely to vote . Following the 2016 election, many people wondered whether the pervasive forecasts that seemed to all but guarantee a Hillary Clinton victory – two modelers put her chances at 99% – led some would-be voters to conclude that the race was effectively over and that their vote would not make a difference. There is scientific research to back up that claim: A team of researchers found experimental evidence that when people have high confidence that one candidate will win, they are less likely to vote. This helps explain why some polling analysts say elections should be covered using traditional polling estimates and margins of error rather than speculative win probabilities (also known as “probabilistic forecasts”).

National polls tell us what the entire public thinks about the presidential candidates, but the outcome of the election is determined state by state in the Electoral College . The 2000 and 2016 presidential elections demonstrated a difficult truth: The candidate with the largest share of support among all voters in the United States sometimes loses the election. In those two elections, the national popular vote winners (Al Gore and Hillary Clinton) lost the election in the Electoral College (to George W. Bush and Donald Trump). In recent years, analysts have shown that Republican candidates do somewhat better in the Electoral College than in the popular vote because every state gets three electoral votes regardless of population – and many less-populated states are rural and more Republican.

For some, this raises the question: What is the use of national polls if they don’t tell us who is likely to win the presidency? In fact, national polls try to gauge the opinions of all Americans, regardless of whether they live in a battleground state like Pennsylvania, a reliably red state like Idaho or a reliably blue state like Rhode Island. In short, national polls tell us what the entire citizenry is thinking. Polls that focus only on the competitive states run the risk of giving too little attention to the needs and views of the vast majority of Americans who live in uncompetitive states – about 80%.

Fortunately, this is not how most pollsters view the world . As the noted political scientist Sidney Verba explained, “Surveys produce just what democracy is supposed to produce – equal representation of all citizens.”

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Scott Keeter is a senior survey advisor at Pew Research Center .

Courtney Kennedy is Vice President of Methods and Innovation at Pew Research Center .

How do people in the U.S. take Pew Research Center surveys, anyway?

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