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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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Transitive and Intransitive Verbs in the World of Research

Language and grammar rules for academic writing, you may also like, measuring academic success: definition & strategies for excellence, phd qualifying exam: tips for success , quillbot review: features, pricing, and free alternatives, what is an academic paper types and elements , 9 steps to publish a research paper, what are the different types of research papers, how to make translating academic papers less challenging, 6 tips for post-doc researchers to take their..., presenting research data effectively through tables and figures, ethics in science: importance, principles & guidelines .

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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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on 30 October 2022 by Shona McCombes . Revised on 12 December 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, research questions quiz, frequently asked 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.

Prevent plagiarism, run a free check.

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.

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

An essay isn’t just a loose collection of facts and ideas. Instead, it should be centered on an overarching argument (summarised in your thesis statement ) that every part of the essay relates to.

The way you structure your essay is crucial to presenting your argument coherently. A well-structured essay helps your reader follow the logic of your ideas and understand your overall point.

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.

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How to Develop a Good Research Question? — Types & Examples

Cecilia is living through a tough situation in her research life. Figuring out where to begin, how to start her research study, and how to pose the right question for her research quest, is driving her insane. Well, questions, if not asked correctly, have a tendency to spiral us!

Image Source: https://phdcomics.com/

Questions lead everyone to answers. Research is a quest to find answers. Not the vague questions that Cecilia means to answer, but definitely more focused questions that define your research. Therefore, asking appropriate question becomes an important matter of discussion.

A well begun research process requires a strong research question. It directs the research investigation and provides a clear goal to focus on. Understanding the characteristics of comprising a good research question will generate new ideas and help you discover new methods in research.

In this article, we are aiming to help researchers understand what is a research question and how to write one with examples.

Table of Contents

What Is a Research Question?

A good research question defines your study and helps you seek an answer to your research. Moreover, a clear research question guides the research paper or thesis to define exactly what you want to find out, giving your work its objective. Learning to write a research question is the beginning to any thesis, dissertation , or research paper. Furthermore, the question addresses issues or problems which is answered through analysis and interpretation of data.

Why Is a Research Question Important?

A strong research question guides the design of a study. Moreover, it helps determine the type of research and identify specific objectives. Research questions state the specific issue you are addressing and focus on outcomes of the research for individuals to learn. Therefore, it helps break up the study into easy steps to complete the objectives and answer the initial question.

Types of Research Questions

Research questions can be categorized into different types, depending on the type of research you want to undergo. Furthermore, knowing the type of research will help a researcher determine the best type of research question to use.

1. Qualitative Research Question

Qualitative questions concern broad areas or more specific areas of research. However, unlike quantitative questions, qualitative research questions are adaptable, non-directional and more flexible. Qualitative research question focus on discovering, explaining, elucidating, and exploring.

i. Exploratory Questions

This form of question looks to understand something without influencing the results. The objective of exploratory questions is to learn more about a topic without attributing bias or preconceived notions to it.

Research Question Example: Asking how a chemical is used or perceptions around a certain topic.

ii. Predictive Questions

Predictive research questions are defined as survey questions that automatically predict the best possible response options based on text of the question. Moreover, these questions seek to understand the intent or future outcome surrounding a topic.

Research Question Example: Asking why a consumer behaves in a certain way or chooses a certain option over other.

iii. Interpretive Questions

This type of research question allows the study of people in the natural setting. The questions help understand how a group makes sense of shared experiences with regards to various phenomena. These studies gather feedback on a group’s behavior without affecting the outcome.

Research Question Example: How do you feel about AI assisting publishing process in your research?

2. Quantitative Research Question

Quantitative questions prove or disprove a researcher’s hypothesis through descriptions, comparisons, and relationships. These questions are beneficial when choosing a research topic or when posing follow-up questions that garner more information.

i. Descriptive Questions

It is the most basic type of quantitative research question and it seeks to explain when, where, why, or how something occurred. Moreover, they use data and statistics to describe an event or phenomenon.

Research Question Example: How many generations of genes influence a future generation?

ii. Comparative Questions

Sometimes it’s beneficial to compare one occurrence with another. Therefore, comparative questions are helpful when studying groups with dependent variables.

Example: Do men and women have comparable metabolisms?

iii. Relationship-Based Questions

This type of research question answers influence of one variable on another. Therefore, experimental studies use this type of research questions are majorly.

Example: How is drought condition affect a region’s probability for wildfires.  

How to Write a Good Research Question?

1. Select a Topic

The first step towards writing a good research question is to choose a broad topic of research. You could choose a research topic that interests you, because the complete research will progress further from the research question. Therefore, make sure to choose a topic that you are passionate about, to make your research study more enjoyable.

2. Conduct Preliminary Research

After finalizing the topic, read and know about what research studies are conducted in the field so far. Furthermore, this will help you find articles that talk about the topics that are yet to be explored. You could explore the topics that the earlier research has not studied.

3. Consider Your Audience

The most important aspect of writing a good research question is to find out if there is audience interested to know the answer to the question you are proposing. Moreover, determining your audience will assist you in refining your research question, and focus on aspects that relate to defined groups.

4. Generate Potential Questions

The best way to generate potential questions is to ask open ended questions. Questioning broader topics will allow you to narrow down to specific questions. Identifying the gaps in literature could also give you topics to write the research question. Moreover, you could also challenge the existing assumptions or use personal experiences to redefine issues in research.

5. Review Your Questions

Once you have listed few of your questions, evaluate them to find out if they are effective research questions. Moreover while reviewing, go through the finer details of the question and its probable outcome, and find out if the question meets the research question criteria.

6. Construct Your Research Question

There are two frameworks to construct your research question. The first one being PICOT framework , which stands for:

• Population or problem
• Intervention or indicator being studied
• Comparison group
• Outcome of interest
• Time frame of the study.

The second framework is PEO , which stands for:

• Population being studied
• Exposure to preexisting conditions
• Outcome of interest.

Research Question Examples

• How might the discovery of a genetic basis for alcoholism impact triage processes in medical facilities?
• How do ecological systems respond to chronic anthropological disturbance?
• What are demographic consequences of ecological interactions?
• What roles do fungi play in wildfire recovery?
• How do feedbacks reinforce patterns of genetic divergence on the landscape?
• What educational strategies help encourage safe driving in young adults?
• What makes a grocery store easy for shoppers to navigate?
• What genetic factors predict if someone will develop hypothyroidism?
• Does contemporary evolution along the gradients of global change alter ecosystems function?

How did you write your first research question ? What were the steps you followed to create a strong research question? Do write to us or comment below.

Frequently Asked Questions

Research questions guide the focus and direction of a research study. Here are common types of research questions: 1. Qualitative research question: Qualitative questions concern broad areas or more specific areas of research. However, unlike quantitative questions, qualitative research questions are adaptable, non-directional and more flexible. Different types of qualitative research questions are: i. Exploratory questions ii. Predictive questions iii. Interpretive questions 2. Quantitative Research Question: Quantitative questions prove or disprove a researcher’s hypothesis through descriptions, comparisons, and relationships. These questions are beneficial when choosing a research topic or when posing follow-up questions that garner more information. Different types of quantitative research questions are: i. Descriptive questions ii. Comparative questions iii. Relationship-based questions

Qualitative research questions aim to explore the richness and depth of participants' experiences and perspectives. They should guide your research and allow for in-depth exploration of the phenomenon under investigation. After identifying the research topic and the purpose of your research: • Begin with Broad Inquiry: Start with a general research question that captures the main focus of your study. This question should be open-ended and allow for exploration. • Break Down the Main Question: Identify specific aspects or dimensions related to the main research question that you want to investigate. • Formulate Sub-questions: Create sub-questions that delve deeper into each specific aspect or dimension identified in the previous step. • Ensure Open-endedness: Make sure your research questions are open-ended and allow for varied responses and perspectives. Avoid questions that can be answered with a simple "yes" or "no." Encourage participants to share their experiences, opinions, and perceptions in their own words. • Refine and Review: Review your research questions to ensure they align with your research purpose, topic, and objectives. Seek feedback from your research advisor or peers to refine and improve your research questions.

Developing research questions requires careful consideration of the research topic, objectives, and the type of study you intend to conduct. Here are the steps to help you develop effective research questions: 1. Select a Topic 2. Conduct Preliminary Research 3. Consider Your Audience 4. Generate Potential Questions 5. Review Your Questions 6. Construct Your Research Question Based on PICOT or PEO Framework

There are two frameworks to construct your research question. The first one being PICOT framework, which stands for: • Population or problem • Intervention or indicator being studied • Comparison group • Outcome of interest • Time frame of the study The second framework is PEO, which stands for: • Population being studied • Exposure to preexisting conditions • Outcome of interest

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How to write qualitative research questions.

11 min read Here’s how to write effective qualitative research questions for your projects, and why getting it right matters so much.

What is qualitative research?

Qualitative research is a blanket term covering a wide range of research methods and theoretical framing approaches. The unifying factor in all these types of qualitative study is that they deal with data that cannot be counted. Typically this means things like people’s stories, feelings, opinions and emotions , and the meanings they ascribe to their experiences.

Qualitative study is one of two main categories of research, the other being quantitative research. Quantitative research deals with numerical data – that which can be counted and quantified, and which is mostly concerned with trends and patterns in large-scale datasets.

What are research questions?

Research questions are questions you are trying to answer with your research. To put it another way, your research question is the reason for your study, and the beginning point for your research design. There is normally only one research question per study, although if your project is very complex, you may have multiple research questions that are closely linked to one central question.

A good qualitative research question sums up your research objective. It’s a way of expressing the central question of your research, identifying your particular topic and the central issue you are examining.

Research questions are quite different from survey questions, questions used in focus groups or interview questions. A long list of questions is used in these types of study, as opposed to one central question. Additionally, interview or survey questions are asked of participants, whereas research questions are only for the researcher to maintain a clear understanding of the research design.

Research questions are used in both qualitative and quantitative research , although what makes a good research question might vary between the two.

In fact, the type of research questions you are asking can help you decide whether you need to take a quantitative or qualitative approach to your research project.

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

Writing research questions is very important in both qualitative and quantitative research, but the research questions that perform best in the two types of studies are quite different.

Quantitative research questions

Quantitative research questions usually relate to quantities, similarities and differences.

It might reflect the researchers’ interest in determining whether relationships between variables exist, and if so whether they are statistically significant. Or it may focus on establishing differences between things through comparison, and using statistical analysis to determine whether those differences are meaningful or due to chance.

• How much? This kind of research question is one of the simplest. It focuses on quantifying something. For example:

How many Yoruba speakers are there in the state of Maine?

• What is the connection?

This type of quantitative research question examines how one variable affects another.

For example:

How does a low level of sunlight affect the mood scores (1-10) of Antarctic explorers during winter?

• What is the difference? Quantitative research questions in this category identify two categories and measure the difference between them using numerical data.

Do white cats stay cooler than tabby cats in hot weather?

If your research question fits into one of the above categories, you’re probably going to be doing a quantitative study.

Qualitative research questions

Qualitative research questions focus on exploring phenomena, meanings and experiences.

Unlike quantitative research, qualitative research isn’t about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn’t the primary objective.

In fact, you as a qualitative researcher might end up studying a very similar topic to your colleague who is doing a quantitative study, but your areas of focus will be quite different. Your research methods will also be different – they might include focus groups, ethnography studies, and other kinds of qualitative study.

A few example qualitative research questions:

• What is it like being an Antarctic explorer during winter?
• What are the experiences of Yoruba speakers in the USA?
• How do white cat owners describe their pets?

Qualitative research question types

Marshall and Rossman (1989) identified 4 qualitative research question types, each with its own typical research strategy and methods.

• Exploratory questions

Exploratory questions are used when relatively little is known about the research topic. The process researchers follow when pursuing exploratory questions might involve interviewing participants, holding focus groups, or diving deep with a case study.

• Explanatory questions

With explanatory questions, the research topic is approached with a view to understanding the causes that lie behind phenomena. However, unlike a quantitative project, the focus of explanatory questions is on qualitative analysis of multiple interconnected factors that have influenced a particular group or area, rather than a provable causal link between dependent and independent variables.

• Descriptive questions

As the name suggests, descriptive questions aim to document and record what is happening. In answering descriptive questions , researchers might interact directly with participants with surveys or interviews, as well as using observational studies and ethnography studies that collect data on how participants interact with their wider environment.

• Predictive questions

Predictive questions start from the phenomena of interest and investigate what ramifications it might have in the future. Answering predictive questions may involve looking back as well as forward, with content analysis, questionnaires and studies of non-verbal communication (kinesics).

Why are good qualitative research questions important?

We know research questions are very important. But what makes them so essential? (And is that question a qualitative or quantitative one?)

Getting your qualitative research questions right has a number of benefits.

• It defines your qualitative research project Qualitative research questions definitively nail down the research population, the thing you’re examining, and what the nature of your answer will be.This means you can explain your research project to other people both inside and outside your business or organization. That could be critical when it comes to securing funding for your project, recruiting participants and members of your research team, and ultimately for publishing your results. It can also help you assess right the ethical considerations for your population of study.
• It maintains focus Good qualitative research questions help researchers to stick to the area of focus as they carry out their research. Keeping the research question in mind will help them steer away from tangents during their research or while they are carrying out qualitative research interviews. This holds true whatever the qualitative methods are, whether it’s a focus group, survey, thematic analysis or other type of inquiry.That doesn’t mean the research project can’t morph and change during its execution – sometimes this is acceptable and even welcome – but having a research question helps demarcate the starting point for the research. It can be referred back to if the scope and focus of the project does change.
• It helps make sure your outcomes are achievable

Because qualitative research questions help determine the kind of results you’re going to get, it helps make sure those results are achievable. By formulating good qualitative research questions in advance, you can make sure the things you want to know and the way you’re going to investigate them are grounded in practical reality. Otherwise, you may be at risk of taking on a research project that can’t be satisfactorily completed.

Developing good qualitative research questions

All researchers use research questions to define their parameters, keep their study on track and maintain focus on the research topic. This is especially important with qualitative questions, where there may be exploratory or inductive methods in use that introduce researchers to new and interesting areas of inquiry. Here are some tips for writing good qualitative research questions.

1. Keep it specific

Broader research questions are difficult to act on. They may also be open to interpretation, or leave some parameters undefined.

Strong example: How do Baby Boomers in the USA feel about their gender identity?

Weak example: Do people feel different about gender now?

2. Be original

Look for research questions that haven’t been widely addressed by others already.

Strong example: What are the effects of video calling on women’s experiences of work?

Weak example: Are women given less respect than men at work?

3. Make it research-worthy

Don’t ask a question that can be answered with a ‘yes’ or ‘no’, or with a quick Google search.

Strong example: What do people like and dislike about living in a highly multi-lingual country?

Weak example: What languages are spoken in India?

4. Focus your question

Don’t roll multiple topics or questions into one. Qualitative data may involve multiple topics, but your qualitative questions should be focused.

Strong example: What is the experience of disabled children and their families when using social services?

Weak example: How can we improve social services for children affected by poverty and disability?

4. Focus on your own discipline, not someone else’s

Avoid asking questions that are for the politicians, police or others to address.

Strong example: What does it feel like to be the victim of a hate crime?

Weak example: How can hate crimes be prevented?

5. Ask something researchable

Big questions, questions about hypothetical events or questions that would require vastly more resources than you have access to are not useful starting points for qualitative studies. Qualitative words or subjective ideas that lack definition are also not helpful.

Strong example: How do perceptions of physical beauty vary between today’s youth and their parents’ generation?

Weak example: Which country has the most beautiful people in it?

Related resources

Qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, request demo.

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What’s in a Qualitative Research Question?

Qualitative research questions are driven by the need for the study. Ideally, research questions are formulated as a result of the problem and purpose, which leads to the identification of the methodology. When a qualitative methodology is chosen, research questions should be exploratory and focused on the actual phenomenon under study.

From the Dissertation Center, Chapter 1: Research Question Overview , there are several considerations when forming a qualitative research question. Qualitative research questions should

Below is an example of a qualitative phenomenological design. Note the use of the term “lived experience” in the central research question. This aligns with phenomenological design.

RQ1: “ What are the lived experiences of followers of mid-level managers in the financial services sector regarding their well-being on the job?”

If the researcher wants to focus on aspects of the theory used to support the study or dive deeper into aspects of the central RQ, sub-questions might be used. The following sub-questions could be formulated to seek further insight:

RQ1a.   “How do followers perceive the quality and adequacy of the leader-follower exchanges between themselves and their novice leaders?”

RQ1b.  “Under what conditions do leader-member exchanges affect a follower’s own level of well-being?”

Qualitative research questions also display the desire to explore or describe phenomena. Qualitative research seeks the lived experience, the personal experiences, the understandings, the meanings, and the stories associated with the concepts present in our studies.

We want to ensure our research questions are answerable and that we are not making assumptions about our sample. View the questions below:

How do healthcare providers perceive income inequality when providing care to poor patients?

In Example A, we see that there is no specificity of location or geographic areas. This could lead to findings that are varied, and the researcher may not find a clear pattern. Additionally, the question implies the focus is on “income inequality” when the actual focus is on the provision of care. The term “poor patients” can also be offensive, and most providers will not want to seem insensitive and may perceive income inequality as a challenge (of course!).

How do primary care nurses in outreach clinics describe providing quality care to residents of low-income urban neighborhoods?

In Example B, we see that there is greater specificity in the type of care provider. There is also a shift in language so that the focus is on how the individuals describe what they think about, experience, and navigate providing quality care.

Other Qualitative Research Question Examples

Vague : What are the strategies used by healthcare personnel to assist injured patients?

Try this : What is the experience of emergency room personnel in treating patients with a self-inflicted household injury?

The first question is general and vague. While in the same topic area, the second question is more precise and gives the reader a specific target population and a focus on the phenomenon they would have experienced. This question could be in line with a phenomenological study as we are seeking their experience or a case study as the ER personnel are a bounded entity.

Unclear : How do students experience progressing to college?

Try this : How do first-generation community members describe the aspects of their culture that promote aspiration to postsecondary education?

The first question does not have a focus on what progress is or what students are the focus. The second question provides a specific target population and provides the description to be provided by the participants. This question could be in line with a descriptive study.

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20 Common Researcher Interview Questions and Answers

Common Researcher interview questions, how to answer them, and sample answers from a certified career coach.

You’ve been invited to interview for a research position—congratulations! You know you have the skills and experience, but now it’s time to prove it.

The key to success? Being prepared. To help make sure you shine in your upcoming interview, we’ve compiled some of the most common questions asked during research interviews. Read on, get familiar with them, and practice your answers so you can ace that job interview like a pro.

• What research methods do you use to collect data?
• How do you ensure the accuracy and validity of your research results?
• Describe a time when you had to analyze complex data sets and draw meaningful conclusions from them.
• Explain how you would go about designing an experiment or survey to answer a specific research question.
• Are you familiar with any statistical software programs? If so, which ones?
• What strategies do you use to stay organized while conducting research?
• How do you handle ethical considerations when conducting research?
• Have you ever encountered a situation where you had to adjust your research methodology due to unexpected circumstances?
• Describe a time when you had to present your research findings in a clear and concise manner.
• Do you have experience working with large datasets?
• What challenges have you faced when collecting primary data for a research project?
• How do you approach writing up a research paper or report?
• What techniques do you use to identify potential sources of bias in your research?
• How do you evaluate the quality of secondary sources used in your research?
• What strategies do you use to keep track of changes in the field of research you are studying?
• How do you decide which research questions to pursue?
• What is your experience with peer review processes?
• How do you manage competing demands on your time when conducting research?
• What strategies do you use to ensure that your research remains relevant and up-to-date?
• How do you ensure that your research meets the highest standards of academic integrity?

1. What research methods do you use to collect data?

Research methods are the core of any researcher’s job. You’ll need to be familiar with a variety of different methods, such as surveys, interviews, focus groups, and experiments, and be able to explain how you use each one in your work. This will help the interviewer understand your process and how you can contribute to their organization.

How to Answer:

You should be prepared to explain the research methods you have used in your past work. Talk about how you use surveys, interviews, focus groups, and experiments to collect data, as well as any other methods you may have experience with. If you’re just starting out, then talk through the steps you would take to select a method for each project. You can also mention any specialized methods or software that you are familiar with.

Example: “I use a variety of research methods to collect data, depending on the project. I often use surveys and interviews as primary sources of information, but I also have experience with focus groups, experiments, and software tools like Qualtrics for collecting quantitative data. I’m familiar with specialized methods such as content analysis and ethnography when appropriate. My goal is always to select the method that will provide the most accurate and reliable data for each project.”

2. How do you ensure the accuracy and validity of your research results?

Research requires a level of precision that goes beyond the normal workplace. Good researchers are able to identify what data is relevant and how to collect it in order to make reliable conclusions. Interviewers will want to know that you have the skills and knowledge to conduct research that is both accurate and valid. They’ll also want to know if you use any specific methods or tools to ensure accuracy and validity.

You should be prepared to explain what methods you use to ensure accuracy and validity of your research. This could include double-checking sources, using multiple data points, or triangulating information from different sources to verify results. You can also mention any specific tools or techniques you use, such as conducting surveys or interviews with experts in the field. Be sure to emphasize how important it is for you to make sure that your research is accurate and valid before drawing conclusions.

Example: “When I was working on a research project for ABC Corporation, I had to analyze the data from three different sources. My approach was to use statistical analysis techniques and software tools to cross-reference the data sets and identify any potential discrepancies or outliers. After analyzing the results, I identified a number of key trends that allowed us to draw meaningful conclusions about the company’s operations. The insights gained from this research ultimately led to improvements in the organization’s processes, resulting in increased efficiency and productivity.”

3. Describe a time when you had to analyze complex data sets and draw meaningful conclusions from them.

Research projects often involve a lot of data analysis and interpretation. Knowing how to take large amounts of data and make it into something meaningful is a valuable skill for any researcher. This question is a way for the interviewer to gauge your ability to work with data and draw meaningful conclusions from it.

You should be prepared to provide a specific example of when you had to analyze complex data sets and draw meaningful conclusions from them. Talk about the project, your approach to analyzing the data, and any insights or conclusions that you drew from it. Be sure to emphasize the impact of your findings on the project or organization as well.

Example: “I recently worked on a project for my previous employer in which I had to analyze a large and complex data set. My approach was to break down the data into smaller, more manageable chunks and then look for patterns or correlations between different variables. After doing this, I was able to identify a few key trends that were relevant to the project goals. This allowed us to make better decisions about how to allocate resources and focus our efforts, resulting in a successful outcome.”

4. Explain how you would go about designing an experiment or survey to answer a specific research question.

This question is designed to determine if you have the skills necessary to design and implement valid research experiments. The interviewer wants to know if you understand the fundamentals of research design, such as how to select a sample, how to develop a hypothesis, and how to determine the validity of a study. They also want to know if you can explain the process in a clear and concise manner.

Start by explaining the steps you would take to design an experiment or survey. You should include the following: defining the research question, selecting a sample, developing a hypothesis, creating a data collection plan, and determining how to analyze the results. Be sure to explain any specific techniques you might use in each step, such as random sampling or stratified sampling for your sample selection process. Finally, emphasize the importance of validating the results to ensure they are accurate and reliable.

Example: “When designing an experiment or survey, the first step is to define the research question. Once the research question has been identified, I would then select a sample that is representative of the population being studied. I would also develop a hypothesis based on my understanding of the research question and the available data. After that, I would create a data collection plan that outlines how the data will be collected, such as using surveys, interviews, or focus groups. Finally, I would determine the best method for analyzing the results in order to draw valid conclusions from the research. In all cases, it’s important to validate the results to ensure they are accurate and reliable.”

5. Are you familiar with any statistical software programs? If so, which ones?

Researchers often have to analyze data and present it in a meaningful way. This requires familiarity with statistical software programs like SPSS, SAS, or R. Knowing how to use these programs is a critical part of being a successful researcher, so this question is meant to gauge your level of expertise.

If you are familiar with any of the programs mentioned above, be sure to mention that and explain how you have used them in past research projects. If you are not familiar with these programs, it is still important to emphasize your ability to learn new software quickly. Explain how you approach learning new technologies and provide examples of times when you have successfully done so in the past.

Example: “I have used SPSS and SAS in my previous research projects. I am also comfortable with learning new statistical software programs, as I have done so on multiple occasions in the past. For example, when starting a new project at my last job, I was asked to learn R quickly in order to analyze data. Within two weeks, I had become proficient enough to use it for all of our research needs.”

6. What strategies do you use to stay organized while conducting research?

Research can be a long and complex process, with lots of data to sift through, organize, and analyze. It’s important to show the interviewer that you have a system in place to stay organized throughout the research process, from the initial research plan to the final report. This will demonstrate that you can effectively manage your time and resources, as well as prioritize tasks and remain focused on the task at hand.

You can answer this question by talking about the strategies you use to stay organized while conducting research. You could mention that you create detailed research plans, break down large tasks into smaller ones, and prioritize tasks based on importance and deadlines. Additionally, you could talk about how you utilize organizational tools such as spreadsheets and databases to store data, track progress, and easily access information when needed. Finally, you might also discuss how you take notes during your research process in order to keep track of important ideas or findings.

Example: “I use a variety of strategies to stay organized while conducting research. I always start by creating a detailed research plan that outlines the scope of my work and any deadlines associated with it. From there, I break down large tasks into smaller ones in order to tackle them more efficiently. Additionally, I prioritize tasks based on importance and deadlines in order to remain focused on the task at hand. To help store data, track progress, and access information quickly, I also utilize organizational tools such as spreadsheets and databases. Finally, I take notes during my research process in order to keep track of important ideas or findings.”

7. How do you handle ethical considerations when conducting research?

Research often involves collecting personal data, and it’s important that researchers understand how to approach these situations with respect and integrity. Interviewers want to know that you are aware of ethical considerations and that you are capable of adhering to them. This question is likely to be asked to all potential researchers, as it is an important part of the job.

Talk about the ethical considerations you take into account when conducting research. These can include obtaining informed consent from participants, ensuring confidentiality and anonymity of data, respecting privacy laws, protecting vulnerable populations, and considering potential biases that may arise in your research. You should also mention any processes or protocols you have implemented to ensure ethical compliance with research projects. Finally, emphasize how important it is for researchers to adhere to ethical standards and how seriously you take them.

Example: “I understand the importance of adhering to ethical standards when conducting research, and I take this responsibility very seriously. In my current position as a researcher at ABC University, I follow a strict protocol for obtaining informed consent from participants and ensuring that data is kept confidential and anonymous. I also make sure to consider any potential biases in our research before collecting data and am familiar with applicable privacy laws. Lastly, I always strive to protect vulnerable populations, such as children or those with disabilities, when conducting research.”

8. Have you ever encountered a situation where you had to adjust your research methodology due to unexpected circumstances?

Research is a dynamic process and researchers must be prepared to adjust their methods as needed. This question is designed to assess the flexibility of potential candidates and their ability to think on their feet. It also provides insight into how well a candidate understands the research process, including how to identify and address potential problems.

To answer this question, provide an example of a situation where you had to adjust your research methodology due to unexpected circumstances. Explain how you identified the problem and how you adjusted your methods in order to successfully complete the project. Be sure to emphasize any creative solutions you implemented and the positive outcome that resulted from your adjustment.

Example: “I recently encountered a situation where I had to adjust my research methodology due to unexpected circumstances. I was conducting a survey to analyze consumer behavior in relation to a new product launch. After collecting the first round of data, I noticed a discrepancy in the results that could not be explained. After further investigation, I realized that the sample size I was using was not large enough to accurately capture the data. I quickly adjusted my methodology by increasing the sample size and collecting more data, which ultimately allowed me to identify the discrepancy and provide an accurate analysis of consumer behavior.”

9. Describe a time when you had to present your research findings in a clear and concise manner.

Researchers often have to communicate their findings to colleagues, stakeholders, and the public. The ability to communicate complex research findings in an understandable way is a key skill for someone in this role. This question allows the interviewer to gauge your ability to explain complex concepts in a clear and concise manner.

You should come prepared with an example of a time when you had to present your research findings. Talk about the project, what the goal was, and how you went about presenting it. If possible, provide specific details such as the type of presentation (oral, written, etc.), who you presented to, and the feedback you received. You should also explain the strategies that you used to make sure that the audience understood your message. This could include using visual aids, breaking down complex concepts into simpler terms, or providing examples to illustrate your points.

Example: “My most recent research project focused on the long-term effects of climate change on agricultural production. I knew that it was important to make sure that the findings were presented in a way that was easy to understand and digest. I created a PowerPoint presentation that included visuals and graphs to illustrate my points, as well as a written report that provided a detailed breakdown of the findings. I then presented my findings to a group of stakeholders and received positive feedback. They appreciated my ability to take complex concepts and explain them in a way that was easy to understand.”

10. Do you have experience working with large datasets?

Many research roles require the ability to work with large datasets and analyze the information within them. This question helps employers understand how comfortable you are with such tasks, and it also serves as a way to gauge your technical skills. To answer this question, talk about how you’ve used various tools and techniques to analyze data and how you’ve been able to draw meaningful insights from it.

Start by talking about the types of datasets you’ve worked with, such as structured or unstructured data, and explain how you’ve gone about analyzing them. Then, provide a few examples of projects you’ve completed that involved working with large datasets. Finally, discuss any tools or techniques you’ve used to work with the data, such as statistical software, data visualization tools, machine learning algorithms, etc. Be sure to emphasize your ability to draw meaningful insights from the data and how those insights have helped inform decisions.

Example: “I have experience working with large datasets in both structured and unstructured formats. I have utilized various tools and techniques to analyze the data, such as statistical software and data visualization tools. I’ve also employed machine learning algorithms to uncover patterns and trends from the data. For example, in my most recent project I utilized a variety of data sources to identify potential new markets for our company. Through analyzing the data, I was able to identify key demographic, geographic, and psychographic trends that we could use to target our new customers. This analysis provided valuable insights that informed our marketing strategy and ultimately led to increased sales.”

11. What challenges have you faced when collecting primary data for a research project?

Research often involves gathering primary data from sources such as surveys, interviews, focus groups, and observations. It’s important to determine whether the candidate has the skills necessary to design and implement a research project in order to successfully collect data. This question helps the interviewer understand the candidate’s ability to handle the logistics and challenges of primary data collection.

When answering this question, it’s important to provide specific examples of challenges you have faced and how you overcame them. For example, you could talk about the challenge of finding participants for a survey or focus group, or the difficulty in scheduling interviews with busy professionals. You can also discuss any logistical issues that arose during data collection, such as having unreliable equipment or dealing with uncooperative participants. Be sure to emphasize your problem-solving skills and ability to think on your feet when facing unexpected obstacles.

Example: “I’ve encountered a few challenges when gathering primary data for research projects. For example, when I was working on a survey project for a university, it took me several weeks to find participants willing to answer the survey. I had to be creative in my approach and reach out to different groups, such as student organizations, to recruit participants. I also encountered a few logistical issues, such as having unreliable equipment or dealing with uncooperative participants. I was able to quickly come up with solutions to these issues, such as having backup equipment and developing strategies to engage the participants. Overall, I was able to successfully gather the data I needed and produce valuable research findings.”

12. How do you approach writing up a research paper or report?

Research is a process that requires both creativity and structure. As a researcher, you must be able to synthesize information from a variety of sources, develop strong arguments, and communicate those arguments clearly and concisely in written form. Being able to articulate your approach to researching and writing up a paper will demonstrate your ability to think critically and logically.

Your answer should include the steps you take when writing up a research paper or report. This could include outlining your topic, researching relevant sources, organizing and synthesizing data, developing an argument, drafting and revising the paper, and proofreading for accuracy. It is also important to emphasize how you use critical thinking skills to develop strong arguments and draw meaningful conclusions from your research. Finally, make sure to mention any specific techniques or strategies that you have used successfully in the past.

Example: “When writing up a research paper or report, I approach the task systematically. I begin by outlining my topic and any relevant research questions. I then conduct research to find relevant sources, both primary and secondary. I carefully review and analyze the information I find, and use it to develop my argument. After that, I draft and revise the paper, making sure to include evidence to support my points. Finally, I proofread for accuracy and clarity. Throughout the process, I strive to use critical thinking skills to ensure that my arguments are sound and my conclusions are meaningful.”

13. What techniques do you use to identify potential sources of bias in your research?

Researchers need to be able to identify potential sources of bias in their work, such as selection bias or confirmation bias, in order to ensure the accuracy of their data and the validity of their results. By asking this question, the interviewer is gauging your ability to identify potential sources of bias and how you handle them.

To answer this question, you should discuss the techniques you use to identify potential sources of bias in your research. This could include methods such as double-checking data for accuracy and completeness, using multiple sources of information, or conducting blind studies. Additionally, you can talk about how you handle any biases you may find, such as adjusting your research design or changing your methodology. Be sure to emphasize that accuracy and validity are important to you and that you take steps to ensure they remain a priority.

Example: “I understand the importance of accuracy and validity in research, so I always strive to identify and address any potential sources of bias. I use several techniques to identify bias, such as double-checking my data for accuracy and completeness, using multiple sources of information, and conducting blind studies. When I do identify a potential source of bias, I adjust my research design or change my methodology to address it. I also make sure to communicate any changes to my team and stakeholders to ensure that we’re all on the same page.”

14. How do you evaluate the quality of secondary sources used in your research?

One of the most important skills of a researcher is being able to evaluate the quality of sources used in research. This question allows the interviewer to get a better understanding of your research process and your ability to critically evaluate sources. It also allows them to gauge your level of experience in the field and your knowledge of the research landscape.

To answer this question, you should explain your process for evaluating secondary sources. You can talk about the criteria that you use to evaluate a source’s credibility such as its author or publisher, the date of publication, and any peer reviews that have been conducted on the source. Additionally, you can mention any methods you use to assess the accuracy of information in the source such as cross-referencing with other sources or conducting additional research on the topic. Finally, you should discuss how you use these evaluations to inform your own research.

Example: “When evaluating the quality of secondary sources I use in my research, I consider a few key factors. I always look at the author or publisher of the source, the date of publication, and any peer reviews that have been conducted. I also use a variety of methods to assess the accuracy of the information in the source, such as cross-referencing with other sources and conducting additional research. From there, I use my evaluations to inform my own research and determine how best to use the source. This helps me ensure that I’m using the most reliable and up-to-date sources in my research.”

15. What strategies do you use to keep track of changes in the field of research you are studying?

Research is an ever-evolving field and keeping up with changes in the field is essential to remain relevant and up to date. Interviewers want to know that you have the skills and strategies to stay on top of the latest research, trends, and developments in the field. They’ll be looking for evidence that you have the self-discipline and organizational skills to stay on top of your work and be able to provide timely, accurate research.

You should be prepared to discuss the strategies and tools you use to stay up-to-date on changes in your field. Talk about how you keep track of new research articles, publications, conferences, and other sources of information that are relevant to your work. You can also talk about how you use technology such as RSS feeds, social media, or email alerts to ensure that you’re aware of any news or updates related to your research. Additionally, mention any methods you have for organizing and cataloging the information you collect so it is easily accessible when needed.

Example: “To stay on top of changes in my field, I use a variety of strategies and tools. I subscribe to relevant RSS feeds and email alerts to ensure I’m aware of any new research articles or publications. I also use social media to follow industry leaders and experts in the field and get updates on their work. I also keep an organized library of research material that I have collected over the years. I use a combination of software tools and physical filing systems to keep track of all the information I need. This allows me to quickly access any information I need, when I need it.”

16. How do you decide which research questions to pursue?

Being a researcher requires the ability to prioritize and select the best questions to pursue in order to achieve the desired outcome. This question helps the interviewer get a sense of your process and how you approach problem solving. It also gives them an insight into your critical thinking skills, as well as your ability to analyze data and make meaningful conclusions.

The best way to answer this question is to provide a step-by-step approach of how you decide which research questions to pursue. Start by explaining the research process you go through, such as collecting data, analyzing it and forming hypotheses. Then explain how you prioritize certain questions based on their importance and relevance to the project at hand. Finally, discuss how you use your findings to make informed decisions about which questions are worth pursuing further.

Example: “When I’m deciding which research questions to pursue, I start by gathering all the available data related to the project. From there, I analyze the data to form hypotheses and then prioritize the questions based on their importance and relevance to the project. I also consider the impact each question could have on the overall outcome of the research. Once I have a list of the most important questions, I evaluate the data and use my findings to make informed decisions about which questions are worth pursuing further. Ultimately, my goal is to select the best questions that will yield the most meaningful results.”

17. What is your experience with peer review processes?

Peer review is a critical part of the research process. It requires that researchers review and critique each other’s work in order to ensure that the research is unbiased and credible. This question is a way for the interviewer to assess your knowledge of the research process and your ability to work with other researchers.

To answer this question, you should provide specific examples of your experience with peer review processes. Talk about how you have worked with other researchers to review and critique their work, as well as how you have incorporated feedback from peers into your own research. You can also discuss any challenges or successes you had during the process. Finally, emphasize your understanding of the importance of peer review in the research process and why it is necessary for producing high-quality results.

Example: “I have extensive experience with peer review processes, both as a reviewer and as an author. I have worked with other researchers to review their work and provide constructive feedback, as well as incorporating feedback from peers into my own research. I understand the importance of peer review in the research process and am committed to producing high-quality results. I have also had success in resolving disagreements between reviewers and authors when needed, and I have a strong track record of producing quality research that has been accepted for publication.”

18. How do you manage competing demands on your time when conducting research?

Research can be a demanding job, with a lot of deadlines, competing agendas, and complex data sets to analyze. The interviewer wants to make sure you can prioritize tasks, keep track of multiple projects, and adjust when needed. Your ability to manage competing demands on your time is a key indicator of how successful you will be at the job.

To answer this question, you should focus on how you prioritize tasks and manage deadlines. Talk about the strategies you use to stay organized, such as setting up a calendar or using task management tools. Also discuss any techniques you have for staying focused when there are multiple demands on your time. Finally, emphasize your ability to adjust your plans when needed, such as if an unexpected project comes in or a deadline needs to be moved up.

Example: “I have a few strategies for managing competing demands on my time when conducting research. I prioritize tasks by breaking them down into smaller, manageable chunks and then assigning deadlines to each one. I also use task management tools to keep track of what I need to do and stay organized. And I make sure to take regular breaks to stay focused and energized. When I need to adjust my plans due to unexpected events, I’m able to reassess and re-prioritize my tasks accordingly. I’m confident in my ability to manage competing demands on my time and stay organized when conducting research.”

19. What strategies do you use to ensure that your research remains relevant and up-to-date?

Research is a dynamic field, and the best researchers know that they need to stay informed of the latest developments and trends in order to remain relevant. This question allows your interviewer to assess your knowledge of the field and your commitment to keeping up with the latest research. It shows that you are aware of the need to stay ahead of the curve and that you have the skills to do so.

To answer this question, you should start by discussing the strategies that you use to stay informed. You can talk about how you read industry publications, attend conferences and seminars, or network with other researchers in your field. You should also mention any specific platforms or tools that you use to keep up-to-date on the latest research. Finally, you should explain why staying informed is important to you and how it helps you do better work.

Example: “I use a variety of strategies to ensure that my research remains relevant and up-to-date. I read industry publications, attend conferences and seminars, and network with other researchers to stay informed. I also use specific tools like Google Scholar and ResearchGate to keep track of new developments in my field. It’s important to me to stay ahead of the curve and make sure that my research is as current and relevant as possible. Doing so not only helps me do better work, but it also helps me to provide more value to my employer and contribute to the success of their projects.”

20. How do you ensure that your research meets the highest standards of academic integrity?

Research is the backbone of any organization, and it is crucial for a researcher to maintain the highest standards of academic integrity. Employers want to know that you understand the importance of being thorough and accurate, as well as ethical in your research. They may also want to know how you go about verifying the accuracy of your data and sources, and how you ensure that your research meets the standards expected in the field.

Start off by detailing the steps you take to ensure that your research meets academic integrity standards. For example, you can mention how you always double-check sources and data for accuracy and reliability, or how you use peer review processes to vet your work. Additionally, be sure to emphasize any specific techniques or methods you have used in the past to verify the validity of your findings. Finally, explain why it is important to you to maintain the highest level of academic integrity in your research.

Example: “I understand the importance of academic integrity and take it very seriously in my research. To ensure the highest standards of accuracy, I always double-check my sources and data, and use peer review processes to vet my work. Additionally, I frequently use replication studies to verify the validity of my findings. To me, it is essential to ensure that my research meets the highest standards of academic integrity, as it is the foundation of any successful research project.”

20 Interview Questions Every Data Center Engineer Must Be Able To Answer

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30 sociology research questions for your next project

Last updated

30 April 2024

Reviewed by

Being a human being and living in modern society can be confusing and complicated. A wide range of historical, behavioral, and structural factors impact our day-to-day experiences. People who study sociology aim to better understand how culture, social interactions, and relationships impact individual and collective well-being.

Sociology research helps provide answers that policymakers, entrepreneurs, and individuals can use to improve the lives of local and global communities. But they can only do this if they ask and address the right questions.

Use this article to kickstart your research. It will help you choose an in-demand sociology research question to explore for your next project or assignment.

• Why sociology research matters

Sociology is a diverse, complex, and essential area of study. It’s the study of life, social change, and human behaviors. Sociology research explores how societal structures and organizations impact everyday life and well-being.

Social communication and interaction are fundamental components of the human experience. Studying these topics in detail helps uncover flaws or biases within our societal structures that impact particular individuals or groups of people.

To properly address societal issues, we first need to identify and understand them. Conducting your own high-quality sociology research gives you the opportunity to explore a topic you are passionate about. You can contribute valuable information to improve our existing societal structures and systems.

• How to choose a great sociology research topic

Whether you’re choosing a topic for a school project or want to expand into a new niche with your existing research practices, choosing the right sociology research question is essential during the early stages of your work.

The topic and people you choose to study will greatly differ depending on what you have access to. So, to make your efforts worthwhile, we recommend considering the following points before you make your final decision:

Consider your bandwidth

It’s easy to get in over your head with a particularly ambitious research project.

To be able to produce the best work and ensure you can actually complete your project, consider the following questions before choosing your research question:

What is my research project timeline?

How much support do I have to complete my research?

What research tools and platforms do I have access to?

How much research experience do I have?

Factor in your target audience

Sociology is the study of human behavior, so your study participants will significantly impact the results.

Depending on the scope of your work, research questions that focus on harder-to-access groups may pose a challenge for students, novice researchers, or projects with little funding. Children, people living in rural areas, or people with particular health conditions are all examples of groups that are harder to access for research.

So, depending on your experience level, resources, and support, you should consider these limitations before choosing a research question that involves working with these groups.

Focus on your skills

Everyone has a set of skills they bring to the table. If you want to get the most bang for your buck with your research efforts, lean into your skills when choosing your research question.

Sociology is a diverse area of study that has plenty of room for both anecdotal and emotional research and statistical analysis. For this reason, you need to factor in your preferences and skillset when you decide which type of question to pursue.

If you love talking to people and collecting nuanced opinions, a qualitative -focused question will best suit your project. Or, you might prefer more numeric analysis. In this case, choosing a question that allows you to collect quantitative data about a specific population will be better suited to your skill set.

Catch up on the latest trends

Finally, one of the most important things to consider when choosing your sociology research question is the existing trends within your area of study.

Reading up on the latest research projects surrounding your topic is an absolute must. It’s a great way to stay involved in the research community and ensure you don’t accidentally copy or repeat existing research.

Additionally, your research will become more nuanced and impactful the more plugged into your topic you are. Getting to grips with existing research will provide inspiration and ideas, particularly about knowledge gaps or challenges, giving your project the best chance of success.

• Sociology research questions for college students

Sociology research is a common project or assignment for college students looking to learn more about human behavior and society. 

College students are often limited by time, resources, and funding. However, they can still explore plenty of incredibly interesting and important sociology research questions. And hey, you never know, maybe this first project will kickstart your career as a sociology researcher!

Consider these examples of trending sociology research topics for college students:

How prevalent is bullying in a particular age category, and what strategies can we use to tackle it?

How does student debt impact college student spending habits?

What impact does living on campus vs. living off campus have on student friendships in the first year of university?

What are the most commonly reported stressors reported by rural students moving to campus for university?

How do students respond to group projects vs. individual assignments, and which option is best suited for post-secondary education?

• Sociology research questions about cultural bias

Culture (the behaviors, teachings, and beliefs that a group of people shares) plays a significant role in modern society. It’s often attributed to a specific region or location and is created by groups of like-minded people sharing ideas, opinions, and values.

Culture significantly influences how people interact with the world around them, and studying this impact is a hot-button topic for sociologists.

Here are some examples of sociology research questions about culture and cultural bias:

To what extent does cultural bias impact female empowerment?

What are the predominant traits a person who describes themselves as a “patriot” would attribute to themselves?

To what extent does Westernized culture impact health and wellness?

How are people who belong to cultural minorities treated differently from the majority?

How has globalization and social media affected the concept of cultural heritage?

• Sociology research questions about religion

Around the world, religion is a powerful connecting force. Some of the most commonly known religions today have thousands of years of history and impact.

Because of its prevalence and influence, it’s no surprise that religion is a common topic for sociology researchers—especially as the global community becomes more connected and aware of different religious practices.

Examples of trending sociology research questions about religion include the following:

To what extent should schools teach students about religion?

How important is it for [a particular group of people] to follow the rules of their chosen religion? Why do they feel it’s important to do so?

How does spirituality differ from religious practice?

How has religion shaped the structures of modern Western society?

Is it important for people to participate in traditional religious ceremonies, and how do they feel when participating?

• Sociology research questions about race and society

The impact of race and ethnicity on a person’s well-being and worldview is always a worthwhile topic to explore. It’s one of the more prevalent themes in sociology research.

Despite the world being more connected than ever, many of our society’s foundational social structures place unnecessary barriers that block people from minority ethnic groups from accessing the same opportunities as the majority.

Sociology research exploring the role of race and ethnicity in society can help provide insights into why this happens. We can use these insights to combat social inequities.

Here are five examples of sociology research questions focused on race, ethnicity, and society:

How does a person’s ethnic background impact their dietary preferences?

To what extent does race impact annual income?

What are the healthcare barriers people from ethnic minorities most commonly report experiencing?

How does international travel impact a person’s understanding of race and ethnicity?

How likely are people from the [X] ethnic community to experience stress, and what are the effects of this?

• Sociology research questions about generational differences

Generational differences play a significant role in how a person communicates with, relates to, and understands the people and environment around them.

New generations are entering the workforce and older generations are heading toward retirement. This means you can collect a wealth of information about each group’s experiences, opinions, values, and concerns.

Things like technology, political opinions, and family values are hot-button topics that differ from generation to generation. Choosing a research question that focuses on generational differences will likely be a great choice if you find any of these topics interesting.

Here are some example questions to consider:

To what extent should younger generations be expected to learn from their elders?

What are the financial differences between baby boomers and millennials?

How do different generations feel about the future?

How are people born before 1965 adapting to new changes in technology?

What are the most common stressors reported by people from different generations, and how do they differ from each other?

• Controversial sociology research questions

“Controversial” research topics are a popular option for many researchers. This is because they are compelling, modern, and useful for shedding light on emotionally charged topics.

Depending on your personal worldview and opinions, these topics may not be controversial or contentious at all. But, as questions that cover topics that trigger a strong emotional response in certain groups of people, these research questions are worth exploring.

Here are some examples of “controversial” sociology research questions: 

To what extent has social media changed communication, and should it be better monitored or regulated?

How have anti-LGBTQ+ policies impacted the health and well-being of people in that community?

How has diet culture impacted how young women feel about their bodies?

To what extent does student debt impact a person’s ability to thrive after post-secondary education?

How does a person’s political views impact their core values?

• Sociology research is essential 

Sociology is a vibrant, unique, and important area of study. Thorough research in this area, regardless of the topic, is always a valuable endeavor. It helps you gain a better understanding of human life, behavior, and connection.

No matter the size and breadth of your next research project or assignment, choosing the right research question will help you uncover important information about society and its structures.

Help break down existing barriers and improve the quality of life for people around the world by conducting your own sociology research on a topic that resonates with your values and experiences. Any insights you collect are valuable and could play a key role in improving the human experience.

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• Professional learning

Teach. Learn. Grow.

Teach. learn. grow. the education blog.

Summer learning loss: What we know and what we’re learning

Concerns about students losing ground academically during summer break go back at least a century, with early evidence suggesting that summer contributed to large disparities in students’ outcomes. This narrative spurred expansion of a variety of summer programs and interventions aimed at stemming summer learning loss.

However, in the last five years, there has been a spirited debate about two long-standing questions about students’ summers: 1) the degree to which test scores actually drop during the summer and 2) the degree to which summer break contributes to educational inequities. A new layer to this conversation is the response to the learning disruptions caused by the COVID-19 pandemic. School leaders and policymakers have used the summer break as a potential time for academic recovery. Summer programs have emerged as one of the most popular recovery strategies offered by school districts, with an estimated $5.8 billion of ESSER funds expected to be spent on summer programs by September 2024.

With more focus on the impact of summer on students’ learning and the potential to extend the school year, it is essential for educators, policymakers, and families to have an up-to-date understanding of the impact of summer breaks on students’ learning patterns. In this post, we aim to highlight what is known about summer learning loss by quickly summarizing recent research and posing some questions that remain unanswered about the role of summers on students’ learning.

Students’ test scores flatten or drop during the summer

While our initial understanding of summer learning loss dates back to studies conducted in the 70s and 80s , a recent collection of studies in the last six years provides a fresh look at students’ learning across summers using four modern assessments ( ECLS-K direct cognitive tests , MAP® Growth™, Star, and i-Ready) with large national (though not typically nationally representative) samples. See “School’s out: The role of summers in understanding achievement disparities,” “When does inequality grow? School, summer, and achievement gaps,” “Evidence of ‘summer learning loss’ on the i-Ready diagnostic assessment,” “Findings on summer learning loss often fail to replicate, even in recent data,” and “Inequality in reading and math skills forms mainly before kindergarten: A replication, and partial correction, of ‘Are schools the great equalizer?’”

Figure 1 compares the test score patterns across four different studies. Three important patterns stand out:

• On average, test scores flatten or drop during the summer , with larger drops typically in math than reading.
• Studies using test scores from ECLS-K:2011 show that student learning slows down but does not drop over the summers after kindergarten and first grade. However, research using interim and diagnostic assessments ( MAP Growth , Star, and i-Ready ) has found far larger summer drops across a range of grade levels.
• Given the sizable differences in the magnitude of test score drops across tests, it remains uncertain whether summer slide should be considered a trivial issue or a serious educational challenge.

Figure 1. Comparison of summer slide estimates across datasets

Note: All estimates are reported as the total average summer test score change in standard deviation (SD) units relative to the prior spring test score. Whenever possible, we report the estimate that adjusted scores for time in school prior/after testing in the fall and spring. Sources: Author calculations based on data reported in ECLS-K:20210-11 , MAP Growth , i-Ready , and Star .  

Who is most likely to show summer learning loss.

While all three diagnostic assessments show some degree of summer slide in grades 3–8 on average, the research community lacks consensus about whether summers disproportionately impact certain students. Paul von Hippel and colleagues have pointed out that whether and how much summers contribute to educational inequalities (across students of different income levels, races, ethnicities, and genders) depends on the test used to study students’ learning patterns. Nonetheless, we can present a few key patterns from this line of research:

• Learning rates are more variable during the summer than during the school year. See “School’s out: The role of summers in understanding achievement disparities,”   “When does inequality grow? School, summer, and achievement gaps,”  and  “Inequality in reading and math skills forms mainly before kindergarten: A replication, and partial correction, of ‘Are schools the great equalizer?’”
• Gaps between students attending low- and high-poverty schools do not consistently widen during the summer. See “Is summer learning loss real, and does it widen test score gaps by family income?”  and  “Is summer learning loss real?”
• Test score differences between Black and white students hold steady or narrow during the summer. See “Do test score gaps grow before, during, or between the school years? Measurement artifacts and what we know in spite of them”  and  “When does inequality grow? School, summer, and achievement gaps,” though results can be sensitive to the metric and test used. See also  “Black-white summer learning gaps: Interpreting the variability of estimates across representations” and “Findings on summer learning loss often fail to replicate, even in recent data.”
• The field cannot really explain why differences in students’ summer learning occur. See “Rethinking summer slide: The more you gain, the more you lose”  and  “Inequality in reading and math skills forms mainly before kindergarten: A replication, and partial correction, of ‘Are schools the great equalizer?’”

Planning effective summer programming

It is clear across recent studies that summer is a particularly variable time for students. Summer break is also increasingly a time in which districts are offering a range of academic offerings.

During summer 2022, an estimated 90% of school districts offered summer programs with an academic focus. However, evidence on the effectiveness of academic summer programs during and after the COVID-19 pandemic is limited. One study of eight summer programs in summer 2022 found a small positive impact on math test scores (0.03 SD), but not on reading. The improvements in math were largely driven by elementary students compared to middle schoolers. However, the effectiveness of these programs remained consistent across student groups, including race/ethnicity, poverty, and English learner status.

It is crucial to recognize the challenges associated with scaling up summer programs. In the districts studied, only 13% of students participated in the summer programs , which only lasted for an average of three to four weeks. Prior research indicates that for summer programs to yield measurable academic benefits, they should run at least five weeks with at least three hours of instruction a day. Additionally, getting students to regularly attend summer programs remains a significant hurdle. To address this issue, districts should actively recruit families to participate and offer a mix of academic instruction and engaging extracurricular activities. By adopting these strategies, districts can maximize the effectiveness of their summer programs and better support student learning during the break.

If you’re interested in learning more about effective summer programs, we encourage you to read the following:

• “Effective summer programming: What educators and policymakers should know”
• “Investing in successful summer programs: A review of evidence under the Every Student Succeeds Act”
• “Analysis: Summer learning is more popular than ever. How to make sure your district’s program is effective”
• “The impact of summer learning programs on low-income children’s mathematics achievement: A meta-analysis”
• “The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8: A meta-analysis of classroom and home interventions”

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• Open access
• Published: 02 May 2024

Understanding the value of a doctorate for allied health professionals in practice in the UK: a survey

• Jo Watson 1 ,
• Steven Robertson 2 ,
• Tony Ryan 2 ,
• Emily Wood 3 ,
• Jo Cooke 2 ,
• Susan Hampshaw 4 &
• Hazel Roddam 5  

BMC Health Services Research volume  24 , Article number:  566 ( 2024 ) Cite this article

12 Accesses

Metrics details

The need to transform the United Kingdom’s (UK) delivery of health and care services to better meet population needs and expectations is well-established, as is the critical importance of research and innovation to drive those transformations. Allied health professionals (AHPs) represent a significant proportion of the healthcare workforce. Developing and expanding their skills and capabilities is fundamental to delivering new ways of working. However, career opportunities combining research and practice remain limited. This study explored the perceived utility and value of a doctorate to post-doctoral AHPs and how they experience bringing their research-related capabilities into practice environments.

With a broadly interpretivist design, a qualitatively oriented cross-sectional survey, with closed and open questions, was developed to enable frequency reporting while focusing on the significance and meaning participants attributed to the topic. Participants were recruited via professional networks and communities of practice. Descriptive statistics were used to analyse closed question responses, while combined framework and thematic analysis was applied to open question responses.

Responses were received from 71 post-doctoral AHPs located across all four UK nations. Findings are discussed under four primary themes of utilisation of the doctorate; value of the doctorate; impact on career, and impact on self and support. Reference is also made at appropriate points to descriptive statistics summarising closed question responses.

The findings clearly articulate variability of experiences amongst post-doctoral AHPs. Some were able to influence team and organisational research cultures, support the development of others and drive service improvement. The challenges, barriers and obstacles encountered by others reflect those that have been acknowledged for many years. Acknowledging them is important, but the conversation must move forward and generate positive action to ensure greater consistency in harnessing the benefits and value-added these practitioners bring. If system-wide transformation is the aim, it is inefficient to leave navigating challenges to individual creativity and tenacity or forward-thinking leaders and organisations. There is an urgent need for system-wide responses to more effectively, consistently and equitably enable career pathways combining research and practice for what is a substantial proportion of the UK healthcare workforce.

Peer Review reports

The imperative to transform the delivery of health and social care in the United Kingdom (UK) to better meet the changing needs and expectations of the population has been acknowledged for some time. Equally well recognised is the critical importance of research and innovation to drive those transformations, including advances in treatments and interventions [ 1 , 2 ]. As the third largest clinical workforce in the UK’s National Health Service (NHS), the allied health professions Footnote 1 (AHPs) are acknowledged as having an essential role in helping meet demand. In addition, their impact reaches beyond the NHS with significant contributions made across the health and care sector in roles within social care, housing, early years, schools, public health, the criminal justice system and in private, voluntary, community and social enterprise organisations [ 3 ].

With a fundamental need to identify new ways of working and delivering care, developing the skills and expanding the capabilities of the workforce, and creating meaningful career pathways to support retention of experienced staff, are paramount [ 1 , 2 , 4 , 5 , 6 ]. Building research capability and capacity to complement practice expertise is key to optimising the workforce and strengthening the evidence-base informing safe, clinically effective, cost efficient services [ 7 , 8 , 9 , 10 ]. A complex interplay between developing strong internal organisational infrastructure and supporting individual career planning and skills development has been identified [ 11 ]. Where this is successfully navigated, healthcare organisations that are research-active are noted to have improved performance and patient experience, and better staff recruitment and retention, compared to those with lower levels of research engagement [ 12 , 13 , 14 , 15 ].

Clinical academics are an important strand of the workforce who work concurrently in practice and academic environments and are research-active [ 9 ]. Newington et al’s [ 16 ] systematic review identified a wide range of positive impacts from non-medical clinical academics in the UK. These included benefits to patients, service provision and the workforce, including: recruitment and retention; the research profile, culture and capacity of organisations; knowledge exchange and the economy. Clinical academics themselves were also noted to benefit through, for example, increased job satisfaction, growing their networks and influence, developing leadership as well as research skills, and unlocking new career opportunities [ 16 ].

Acknowledging these potential benefits, Comer et al. [ 17 ] explored the perceived level of research capacity and culture within AHPs working in the NHS, using the Research Capacity and Culture (RCC) tool. Only 34% of respondents reported research-related activities being part of their roles, and of these, 79% had less than 25% of their time allocated for research-related activity. Further, only 18% reported that research engagement was routinely discussed at annual appraisals. Similarly, and using the same RCC tool, Cordrey et al. [ 18 ] found that 31% of responding AHPs from a single NHS department reported research-related activities as a component of their role, and of these 21% had dedicated time for research. Lack of time and opportunity are noted to curtail research engagement to a greater extent than limits in capability or ambition [ 17 , 18 ].

Despite faring better than their nursing and midwifery colleagues in securing funding via National Institute for Health and Care Research (NIHR) developmental pathways [ 19 ], opportunities for AHPs (amongst others) to develop clinical academic careers remain limited [ 20 , 21 ]. Further, key barriers to research engagement persist, despite having been highlighted over a number of years (see, for example [ 7 , 22 , 23 ]), . Even where funding has been secured, backfill to enable release from practice duties is a particular challenge [ 16 , 18 ]. A related obstacle is a lack of time for research [ 17 , 18 ]. This in turn is linked to the need to accommodate practice-facing and research components of roles [ 16 ], with practice roles frequently taking priority [ 17 , 18 ]. It has also been noted that feelings of personal guilt can direct AHPs’ own prioritisation towards clinical workload management at the expense of engaging in research activities and their own career development [ 18 ]. The scarcity of research-engaged organisational cultures and clinical academic roles to aspire to [ 16 ] remains a foundational issue that must be addressed.

In this context, the publication of the Allied Health Professions’ Research and Innovation Strategy for England [ 24 ] was driven by recognition of the urgent need for transformational change in the pace of growth, stability and sustainability of research engagement by AHPs. Understanding more about how AHPs who have undertaken doctoral studies experience bringing their research-related capabilities into practice environments provides helpful insights to inform the actions required to progress this agenda and realise the visions outlined in the HEE Research and Innovation Strategy.

To understand the perceived utility and value of a doctorate to post-doctoral Allied Health Professionals in practice in the UK.

Study design

The overall design of this study was broadly interpretivist in nature, an approach concerned with discovering the meaning people attach to experiences and how this influences their actions [ 25 ]. A cross-sectional survey, with closed and open questions, was developed to report the frequency of participant responses and to facilitate a focus on the significance participants attributed to the research topic. In this sense, the research project and survey tool were qualitative in their orientation [ 26 ]. Mixed surveys with a qualitative emphasis (and even qualitative questionnaires) are increasingly being used in health and social care research as they limit the number of constraining responses and allow participants to provide as much information as they choose in their own terms [ 26 ].

Ethics approval was obtained from the University of Sheffield ethics committee (Ref: 023667). The Standards for Reporting Qualitative Research (SRQR) checklist for cross-sectional studies was used to guide this study’s conduct and reporting [ 27 ].

Participants and recruitment

Nurses, midwives, healthcare scientists and AHPs currently undertaking or having completed doctorates were recruited via professional networks and via the Yorkshire and Humber Collaboration and Leadership for Allied Health and Care Research (CLAHRC) infrastructure. A Twitter© account was established for the purpose of the study and a link to an online survey was disseminated via this Twitter© feed. Within Twitter©, relevant communities of practice were targeted and encouraged to actively retweet the survey. Respondents were also asked to retweet and share the survey link within their networks to generate a diverse sample. Only those respondents based within the UK were included in the study.

Data collection

A bespoke, 23-item questionnaire containing closed and open questions, was developed to collect information about demographics and the self-reported benefits and impact of doctoral study. Closed questions included information about: motivation; mode, length and funding sources of doctoral study; prior research experience; perceived benefits, utility and value of the doctorate; and impact on career and self. The survey also included open questions for respondents to provide greater detail about their experiences and views (see Table  1 ). These closed and open questions were developed by drawing on issues raised within the literature [ 28 , 29 , 30 ]. As is recommended in questionnaire development [ 31 ], these questions were piloted with those sharing similar characteristics to the intended survey recipients, in this case members of the Addressing Organisational Capacity to do Research Network (ACORN) community of practice. ACORN was developed as part of a capacity building programme within the Yorkshire and Humber CLAHRC. The online survey, with associated information sheet, was open from 5th Feb 2019 to 15th March 2019. Participation was optional and anonymous.

For the purpose of this paper, data from AHPs was separated from the nurses/midwives and healthcare scientists. The intention is to focus on AHP experiences to complement previously published work from this dataset that focused on the reported experiences of nurses and midwives [ 21 ].

Analysis of AHP responses to closed questions, supported by IBM SPSS© software, was undertaken with descriptive statistics reported here. As noted by others [ 26 ], open question data in mixed surveys can be analysed in a thoroughly qualitative way. Open question responses from the AHPs were therefore analysed using a combined framework and thematic analysis. First stage analysis involved placing open response data into a three-theme framework developed following the earlier analysis of the data for the nurses and midwives [ 21 ]. This was conducted by SR and was cross-checked for accuracy by TR. Second stage analysis involved categorising and merging data within these themes into sub-themes. This was conducted by SR and was cross-checked for accuracy by TR. The final stage involved clustering the sub-themes into existing or new themes. This was conducted by SR, cross-checked for accuracy by TR and agreed by all research team members. The themes developed through and during the analysis differed little from the previous analysis of the data from nurses and midwives, although the sub-themes altered slightly and one previous theme was split into two themes, resulting in four themes with 13 sub-themes (see Table  2 ).

Apart from the context section below on ‘route to doctoral completion’, the closed and open question data analysis is integrated and presented under the four theme headings that were derived from the analysis of the open question data.

There were 214 respondents from across the UK. They included 47 nurses, 96 healthcare scientists and 71 AHPs. Representing the AHP disciplines, respondents were: physiotherapists (PT, n  = 26), speech and language therapists (SLT, n  = 15), occupational therapists (OT, n  = 9), radiographers (RAD, n  = 8), podiatrists (POD, n  = 4), dieticians (DIET, n  = 3), art therapists ( n  = 2), paramedics ( n  = 2), a music therapist ( n  = 1), and an orthoptist ( n  = 1). To aid anonymity, art therapists, paramedics, music therapists and orthoptists were all coded as MISC. Table  3 summarised the geographical spread of this self-selecting sample. As noted earlier, only data from the AHP survey respondents are reported here.

Route to doctoral completion

All AHP respondents had completed study at doctoral level; none were still undertaking their studies. One-third had studied full-time (30%, n  = 21). Well over half of AHP respondents (56%, n  = 40) had taken five years or fewer to complete, with 29 (41%) taking more than five years (2 missing). Just under half of AHP respondents had some form of research experience prior to commencing their doctorates (46%, n  = 33). These experiences were typically formed through internships (including NIHR opportunities), Master’s dissertation study, secondments and co-investigator roles.

Post-doctoral experiences

The following section outlines the findings from the analysis of the open question data which allowed respondents to reflect upon their current professional experiences in a post-doctoral context. However, at appropriate points references are also made to some of the descriptive statistics summarising responses to closed question items included within the questionnaire. These data are discussed under four theme headings (see Table  2 ) with illustrative data quotations.

Utilisation of the doctorate

Most participants who commented mentioned utilising the skills and knowledge gained through their doctoral studies. For some, this related directly to aspects of clinical care including shifting team culture and changing existing practices:

“I now influence my team’s way of thinking about what we do with patients. We are all more analytical and confident to question practices that have been historically used for many years.” [PT4] .

“My research and critical thinking contributes to redesigned pathways and patient outcome improvement” [RAD6] .

Important here was the recognition that developing, enhancing or extending the skill of critical thinking was a key aspect that helped drive these service improvements and change:

“I think my critical analysis skills and research skills have helped significantly in developing our service and providing effective evidence-based interventions for the patients seen by our team […] My doctorate has given me many transferable skills and has enabled me to deliver much better evidence-based care for my clients and our service.” [SLT14] .

As well directly linking to service improvement and change, the doctoral journey was noted to enhance a wider skill-base that is transferrable and applicable across roles, and across teams, particularly, but not solely, in relation to the research aspects of their practice:

“The doctorate allowed me to deepen my research and clinical expertise somewhat but the benefits in terms of ‘soft-skills’; e.g. time management, resilience, negotiation have been enormous.” [RAD3] .

“These skills have filtered through to my clinical work, helped me to facilitate service changes within the clinical team and helped to foster an ethos of research as core business within my immediate team, but also more widely in the hospital Trust and wider professional networks.” [SLT13] .

A further skill developed by some through the doctoral journey was an improved ability (and confidence) to train, educate, supervise and encourage others:

“I utilise these benefits on a daily basis […] I teach and train colleagues where I work to be able to critically appraise research and consider the applications for clinical practice. I also lecture and believe that my experience clinically and with research is an important combination.” [SLT12] .

While the majority of comments were positive, there was also suggestion that the ability to utilise the skills learnt through doctoral experiences was not always present. Almost one-third agreed or strongly agreed with the statement: “there are limited opportunities to use skills gained during my doctorate” (31%, n = 22). One-quarter of participants felt over-qualified for their role (24%, n = 18). There were some general comments in response to the question “To what extent are you able to utilise these benefits in your current role?” , such as; “Limited” [POD3] , “Limited ability” [DIET2] , and “To some degree” [DIET1] . Others, expanding on this, demonstrated some frustration with the opportunities available to utilise the skills and knowledge developed through their doctoral studies, particularly in clinical contexts:

“Research sits in an uncertain position in my organisation so doctorate level skills are difficult to show the benefit of.” [MISC7] .

“Now I’m in an academic role, completely […] In a university department I could not do my job without a PhD. In the NHS they didn’t quite know how to best exploit my new skills and knowledge (or what they were).” [SLT9] .

“The doctorate seems to open up more opportunities outside of the NHS as opposed to within it…” [MISC7] .

Importantly, some have worked hard to create opportunities to put the skills and knowledge learnt into practice, but this has taken a personal toll (see also later section “Impact on self”):

“My clinical caseload is heavier now than prior to my Doctorate and I have no protected research time. Implementing and sharing my skills has taken a monumental amount of work and perseverance.” [OT8] .

While almost all participants recognised the importance of the skills and knowledge gained from their doctoral studies, it was not always easy to put these into practice, particularly in clinical contexts. Some suggested this difficulty links to the extent to which doctorates (and research skills and knowledge generally) are valued within their organisations or profession.

Value of the doctorate

On completion of their doctorate, almost 60% ( n  = 43) of AHPs were confident that the qualification was valued by employers, with 11% ( n  = 8) certain that their doctorate was not valued and one quarter remaining unsure (26%, n  = 20). For many of those whose employers recognised the value doctoral learning and experience brought, this was often linked to either the importance of improved clinical expertise or to increased credibility and prestige for the individual, their team or their organisation:

“Adds to credibility in a national role and multi-professional arena. Clinical expertise and insight adds value to strategic NHS planning associated research and development activity.” [RAD1] .

“My PhD brought/brings credibility not only to me but to this specialist centre.” [SLT12] .

For those who did not think their employers valued doctoral learning and experience, or who were not sure if it was valued, comments indicate a disinterest in doctoral study – its associated skills and what they might bring - among colleagues and managers, particularly in clinical settings:

“My NHS role respect my clinical leadership but not my research leadership as much. It’s not considered core business.” [POD1] .

“Having a PhD was not valued in my previous job in the NHS because I was seen as developing skills in the wrong area - extremely disappointing for me.” [OT7] .

“My employer is oblivious to them [doctoral skills gained].[…] My NHS employer has no interest in my academic skills, experience or knowledge. No-one at work acknowledges my doctorate, uses Dr when addressing me or writing to me, or recognises in a positive sense the study I have undertaken.” [PT24] .

A more frequent response to the question about whether employers valued doctoral learning and experience was to present a nuanced view of how, and by whom, the doctoral skills and knowledge were valued. For those with combined academic and clinical roles, it was often stated that the doctorate was valued in the academic context but not in their clinical role:

“I have two roles - these skills are valued in my academic job. Maybe less so in my clinical job.” [MISC1] .

For those employed fully within a clinical setting, important differences were noted regarding who valued what doctoral study could bring and which elements were valued, although this wasn’t consistent:

“Locally (departmental) I think they are valued. On a hospital basis, I am not sure.” [RAD2] .

“I think they are respected by senior colleagues but I find my own departmental managers find little value in either higher education achievement or research, which they often consider to be a burden.” [PT3] .

Finally, participants noted whether their doctoral studies were valued in financial terms. Around one-third of AHPs regarded their current earnings to be misaligned with their post-doctoral expectations, with 27% ( n  = 19) agreeing or strongly agreeing with the statement: “My post-doctoral earnings are lower than I envisaged” . The following comments were a little contradictory but are suggestive of a negative, or at best static, influence on post-doctoral earning capacity:

“I feel it’s enhanced my career clinically and my national leadership and teaching profile, but not my earning potential.” [POD1] .

“PhD has been very rewarding intellectually and clinically. However, it’s offered less job security and absolutely no financial rewards, my grading remaining static since pre PhD.” [SLT7] .

“I wouldn’t have my lecturer job without it. Although ironically I now work at a lower clinical band so that I can maintain a clinical foothold.” [PT17] .  This issue of progression and financial value links to larger issues of the impact on careers that participants experienced following completion of their doctoral study.

Impact on career

A large proportion of respondents reported being in a clinical academic post at the time of the survey (41%, n  = 29), whilst just under one-third (28%, n  = 20) were in academic positions. A small proportion remained in clinical positions (11%, n  = 8), with the remainder being in what were described as managerial and leadership roles (20%, n  = 14).

Many participants noted the positive impact that completing doctoral studies had on their career. For several, completing a doctorate facilitated or cemented an academic career path:

“I started my PhD whilst in clinical practice and during my studies I took a role in academia. It was pivotal in being offered a position at a university where a doctorate, or working towards one, is required.” [OT2] .

Such positive comments were also made in relation to clinical and clinical academic career development:

“As a reporting radiographer, my role was a blend of image reporting and acquisition. My PhD facilitated growth and progression to a consultant clinical academic position.” [RAD6] .

“Career, research and collaborative opportunities arise much more for me post-PhD compared with pre-PhD. I seem to have greater credibility. I have freedom to choose what I do with my career now.” [PT11] .

However, not all participants experienced this positive career impact. Some felt that undertaking doctoral study had limited impact, or even represented a backward step, particularly in relation to clinical career components:

“After my PhD I had a phase of feeling it had derailed my career. I enjoyed my doctoral studies but never wanted an academic career. I felt as though I had stepped off the career ladder and struggled to get back on it.” [PT1] .

Others experienced frustration with the limited opportunities for on-going research and concomitant post-doctoral career development:

“I hoped that it would have enabled me to actively pursue further clinical research. However, opportunities were limited when I moved to [geographical area] […] I ended up falling into a management post and find it difficult now to downgrade / get opportunity to be involved directly in research.” [DIET2] .

“I had high hopes that this role would provide networking and research opportunities as it’s within a large teaching hospital. Despite trying to develop an AHP research culture, there is no dedicated time or support for this to happen. […] I am desperate to progress but can’t seem to navigate into that split clinical academic world that seems to be made for medics only.” [SLT11] .

For many participants it was not a clear-cut case of whether completing doctoral level study had or had not impacted their career. Rather, most described a somewhat crooked path of post-doctoral career development; a mixture of opportunities and barriers:

“I had diverse skills that didn’t necessarily follow a recognised path. Pleased to say that things are falling into place and after a couple of stepping stones I am finding roles that value diverse experience and are an appropriate grade/salary. The PhD helped me get here but it wasn’t a straightforward path.” [PT1] .

“I loved doing my PhD. However there is no career pathway for me to follow. I was lucky to be employed in a research management role and have been lucky to gain funding to continue with my research career.” [RAD2] .

“I feel my doctorate has given me a platform to carry out more research; however I wasn’t expecting on having to leave my senior leadership position in the NHS to do this.” [OT7] .

What becomes clear from the above is that it often took a significant amount of personal effort, resilience and flexibility to generate a positive post-doctoral career path. This can take its toll on individual AHPs and those around them.

Impact on self and support

There was overwhelming recognition that the doctoral experience led to changes in relation to skills and resourcefulness. Evidence amongst respondents suggested that the doctoral experience facilitated positive changes in relation to: critical thinking (100%, n  = 71), research skills (100%, n  = 71), specialist knowledge (97%, n  = 69), fresh perspective (90%, n  = 64), resilience and confidence (83%, n  = 59) and problem solving (93%, n  = 66).

Some participants provided positive personal accounts of undertaking doctoral study and the impact it had for them in terms of satisfaction, confidence and resilience:

“Deciding to undertake my doctorate part-time and remain part-time in clinical practice was the best decision I made.” [POD2] .

“I feel that my doctoral experience has changed the way I think about everything and I continue to be thirsty for further research. I love this feeling! […] I loved it, and would always recommend others to do so for their own benefit, even if it won’t benefit their career.” [RAD5] .

For a few, however, it represented a difficult journey having a negative impact through increased stress, the exertion of considerable effort for little gain, and disruptions to career and family:

“It’s [PhD] hard, requires perseverance and tenacity and no guarantees of anything at the end!” [MISC3] .

“Being a clinical academic is problematic when it comes to stability in posts, equality in promotion, etc. Pursuing this career has resulted in many challenges in gaining recognition, promotion, work-life balance, etc.” [PT7] .

“I would have liked to have had a clinical-research career, but there is no support for this, it’s something I would have to carve out myself, and due to other pressures (family, financial, etc.), I just haven’t felt able to do this.” [SLT1] .

The majority presented a mixed picture of the personal cost and impact, describing both the difficulties and the benefits doctoral study brought and the personal change it produced:

“I have enjoyed the journey immensely and feel it was the right pathway for me. That said, it is tough and maintaining a career in research as an AHP requires not only resilience and perseverance but a willingness and ability to take risks. Job security is still uncertain and as the main breadwinner that is a big concern.” [SLT2] .

“It was the hardest challenge of my life. I’m still recovering and re-orientating as I changed so much during my registration period. Colleagues in clinical settings often don’t appreciate the internal changes a PhD brings which can be frustrating. It’s also not great for work/life balance at all… tough on mental health at times too. I’d absolutely do it again though because of the value it has brought me personally.” [POD4] .

Support, including that from family, was clearly important in facilitating positive personal experiences of doctoral study and for positive post-doctoral experiences. Sources of financial support to undertake a doctorate were varied. The most cited form of support was self-funding (25%, n  = 18), typically alongside the use of smaller funds (such as regional HEE, charity and continuing professional development funds) used during study programmes. Employer support (17%, n  = 12) and charitable trusts were also highly cited (23%, n  = 17). NIHR funding (including that from Fellowships and CLAHRCs) supported 13 (18%) respondents and higher education in was also cited as a significant source of financial support (12%, n  = 9) (missing n  = 2). This reliance upon self-funding may have contributed to almost two-fifths of respondents agreeing or strongly agreeing with the statement: “My doctoral study was a financial risk” .

Beyond family and financial support, employer and colleague support in terms of allowing space and time for study, and in facilitating appropriate research and personal development opportunities, was key:

“My employer supports my development as a clinical academic by allowing me to build research into my new role, supported by ongoing application for research funding to pay for the research portion of my post.” [SLT13] .

“My manager has also initiated discussions about optimising the research (and training) skills I have in terms of a new role.” [MISC8] .

However, such support (as noted in earlier sections) was not always forthcoming in relation to on-going post-doctoral research opportunities, which could be very disheartening:

“I felt well-supported to complete the doctorate itself but I had zero post-PhD career support, including during my first post-doc position. I think this is a real gap for AHPs doing a PhD.” [MISC6] .

“There is a lot of help for clinicians who wants to get into research but there is not much for researchers who need support to return to the clinical practice.” [PT15] .

“I would like to be a clinical academic but this is not a role valued by my Trust or managers. I have had some support from previous managers to use my research skills within my current post, but research is to some degree viewed as a luxury and clinical risk and managerial issues always take priority.” [SLT14] .

These personal accounts of the impact of doctoral experiences on individual participants, and the potential ripple effects of that for departments and organisations, have rarely been explored in previous research. Findings here therefore comprise an original contribution to understanding the lived experience of AHP doctoral study and the pursuit of career pathways combining research and practice.

Organisational benefits

Some participants in this study clearly identified the organisational benefits derived from their completion of doctoral level study. Noteworthy is their articulation of ‘value added’ across all four pillars of practice (namely: professional practice; facilitation of learning; leadership; and evidence, research and development), not solely the research pillar. Echoing the findings of Newington et al. [ 16 ] and the reflections of Cooper at al [ 20 ], the findings of this study indicate the strong potential for post-doctoral practitioners to actively contribute to, and lead, service improvements, delivery of evidence-based interventions, local workforce development and the building of team and organisational cultures of research engagement. The findings also clearly illustrate the variable nature of departmental and organisational cultures related to research. It is evident that the extent to which research is embraced and embedded as fundamental to the core business of health and care providers has a strong bearing on the extent to which organisations are able to realise the benefits of, and value added by, post-doctoral practitioners.

Where research is valued, and where organisation, service leaders and managers are willing to make the sometimes initially challenging decisions to create time and space to enable research-active practitioners, there is evidence of value to people accessing services, services, departments and organisations themselves [ 7 , 12 , 13 , 14 , 15 , 16 , 32 ]. The findings from this study highlight that some organisations / departments do very well when it comes to supporting research capacity building and engagement amongst practitioners, and reaping the associated benefits. Some are on a positive journey towards developing and embedding research within practice. While it may be perceived that other organisations remain ambivalent, apparent inaction is possibly more likely associated with a determined focus on meeting the demands placed on pressurised services. This, coupled with prioritising non research-related key performance indicators linked to service commissioning, creates a challenging backdrop against which to find the time or a way to embed research engagement into service delivery.

National policy imperatives

Notwithstanding the genuine pressures felt by services and organisations, delays in building cultures of research engagement slow and hamper the collective progress required to respond to national policy imperatives. The CQC standards for Well Led Research in NHS Trusts, introduced in 2018, specifically require evidence that research is supported across the breadth of all services [ 33 ]. The NHS Long Term Plan [ 1 ] is a recent illustration, but is by no means the first to emphasise the role of, and need for, practice-based research engagement. Further, as our findings illustrate, organisational failure to enable practice-based research engagement becomes a contributing factor in the attrition of experienced and sometimes senior practitioners from service delivery. The findings from this sample exemplify decisions to move fully into academia, as it presents an environment where post-doctoral knowledge and skills are overtly valued. The apparent reluctance or regret expressed by some who have decided to do so is particularly telling.

The NHS People Plan [ 4 ] emphasises the need to make effective use of the full range of staff skills and experience to deliver the best possible care. It also contains a significant theme related to staff retention, identifying that ‘systems and employers must make greater efforts to design and offer more varied roles to retain our people’ (p46). Employers, line managers and supervisors are called on to ‘create the time and space for training and development … with a renewed emphasis on the importance of flexible skills and building capabilities rather than staying within traditionally-defined roles’ (p36). The findings presented here suggest that there is still some way to go to consistently implementing these approaches for those practitioners with post-doctoral careers.

This study’s findings also identify that it can be difficult for post-doctoral AHPs to find a viable pathway to return to practice, whether entirely or in clinical academic roles. Those who remain in practice, often experienced relentless barriers and obstacles to deploying their hard-won knowledge and skills. Many ended up settling for the status quo. This reflects a waste of resource for individuals and the organisations who backed them financially or with initial protected time, often resulting in disheartened practitioners and missed opportunities for organisations and the people and communities they serve. Wasted resource is also amplified by the lack of retention of those who do not accept the status quo. Such practitioners and their skills become lost to the organisation that initially supported them. Systems and structures are not consistently working in favour of enabling practitioners to become and remain research active. In some respects and in some, but certainly not all, instances, systems and cultures appears to be resistant to change.

The ongoing need for enabling infrastructure and systems

The findings of this study echo those of Cromer et al. 17] and Newington et al. [ 16 ] by providing personal insights into the lived experience of research activity being de-prioritised in favour of attending to service delivery pressures. As these pressures give no indication of abating in the near future, any thoughts of postponing or deferring action to enable research in practice until demands ease, seem ill-advised. Post-doctoral practitioners are essential to help identify and implement the changes required to reshape and reorient health and social care services to more effectively meet the changing needs of the population. With an aim of system-wide transformation, it is inefficient to leave the creation of viable roles and clinical academic career pathways to individual creativity and tenacity, or to the efforts of forward-thinking leaders and organisations.

Local, context-specific research capacity building programmes and strategies help to ensure congruence with local research priorities [ 18 ]. Close alignment of these strategies to wider organisational strategic objectives, business planning, quality strategies and audit activities effectively ‘hard wires’ research, and its supporting infrastructure, as core business [ 11 ]. However, the organisational and geographical variability in experience identified by the findings suggests that something more than broad national policy is required to drive consistent and comparable progress in local implementation.

As the findings exemplify, the absence of credible, sustainable, financially viable and equitably accessible career pathways combining research with practice is an ongoing issue for AHPs. It is a long-standing matter that requires urgent attention, not only for AHPs, but for all health and care professions beyond medicine [ 4 , 5 , 6 , 20 , 21 ]. A greater level of direction, new systems, structures and infrastructure, and more effective coordination and the sharing of good practice may help to accelerate and smooth out the rate of progress across the UK. Normalising access to clinical academic career pathways, and normalising an appropriate degree of research engagement for all practitioners, is fundamental to this. What is certain is that repeatedly spotlighting barriers and obstacles, yet failing to take action, will not resolve the issues.

Harnessing the value added by post-doctoral AHPs

The findings of this study illustrate the personal and professional development accruing from doctoral study for individual AHPs. Beyond the more obvious research-related skills, the value it brings includes the development of analytical and critical thinking skills, practice expertise, time management, resilience, negotiating skills, educational skills, job satisfaction, career development / progression, and enhanced professional standing / credibility. As previously indicated, these areas of growth span all four pillars of practice.

As the findings highlight, in a receptive environment, the value of this personal and professional development has the potential to ripple outwards and positively influence colleagues, services, departments, organisations and even professions – all for the ultimate benefit of the people who access services. Post-doctoral AHPs bring enormous value to organisations but, as we have heard from this study’s participants, they are frequently unrecognised and under-utilised. That in itself generates significant ripple effects, this time in the form of missed opportunities and the associated adverse consequences across the system.

Limitations

There are of course limitations to this study. The relatively small number of responses, uneven geographical spread across the UK, and the fact that not all AHP disciplines are represented, restricts the opportunity to generalise from the study. Similarly, the convenience and self-selecting nature of the sampling process raises questions about how representative the participants are among AHPs in the UK. However, given the qualitative orientation of this study and its analysis, the aim was to gain a deeper understanding of the significance of participants experiences rather than producing data that is representative and generalizable. It is for others to then assess whether the data presented here, and its interpretation, resonates and is applicable and useful within their own clinical context.

Despite being informed by previous research, the bespoke nature of the questionnaire and lack of formal validation, could mean that questions lacked sensitivity to the complex issues involved in understanding the value of a doctorate for AHPs. However, as noted earlier, the questionnaire was sense checked and adjusted prior to being used in order to minimise any lack of sensitivity. Whilst the qualitative open question approach did not permit clarification or probing of responses, this is true of any qualitative survey. Indeed, Braun et al. [ 34 ] demonstrate that online qualitative surveys can deliver rich and nuanced data by promoting a higher level of anonymity than other qualitative approaches and by allowing participants to generate thoughtful (rather than immediate) responses at a time convenient to them.

Conclusions

This study offers findings that clearly articulate the variability of experiences of post-doctoral AHPs. There are powerful exemplars that role model the optimising of benefits for the individual practitioner, the service, organisation and the community it serves. These provide valuable insights to inspire and inform organisations, services leaders and managers with less experience, helping them to move the research in practice agenda [ 1 , 2 , 3 , 4 , 24 ] forward in their own contexts.

The challenges, barriers and obstacles to post-doctoral research engagement described by participants reflect those that have been acknowledged for many years across a range of health systems and countries (see, for example, 7 , 12 , 17 , 18 , 22 , 23 ]. It is important to acknowledge them, but more important is the need to desist from circling around and revisiting them. Instead, the conversation must move forward and generate positive action.

The need to navigate and mitigate the challenges to realise the wide-reaching benefits is fundamental. Reframing perspectives to centre what is to be gained, how it will contribute to enhancing the experiences and outcomes of people accessing services, and what is possible, will help to focus attention on how it can be achieved, one incremental step at a time. There is existing evidence identifying approaches that are productive in this regard, once again including some that are well-established (see, for example, 7 , 8 , 11 , 18 , 22 , 23 , 35 ].

The findings based on the AHP data reported on here demonstrate significant commonalities with our previous findings from nursing and midwifery data [ 21 ]. Given the commonality of the broad systems within which these health and care professionals work, this is unsurprising. Notwithstanding the need to address nuanced differences on a more specific basis, it reinforces the need for urgent, system-wide responses to more effectively, consistently and equitably enable career pathways that combine research and practice for what is a very substantial proportion of the health and care workforce in the UK.

Data availability

The datasets generated and analysed during the current study are not publicly available due to the readily identifiable nature of some participants, given the demographic detail and relative scarcity of some roles mentioned (i.e. there are very few art therapists or music therapists or paramedics with a PhD). Combining this detail with geographical information and job titles could easily identify individuals. However, limited, sufficiently anonymised, data can be made available from the corresponding author on reasonable request.

The umbrella term ‘allied health professions’ encompasses 14 profession groups: art therapy, dietetics, dramatherapy, music therapy, occupational therapy, operating department practice, orthoptics, osteopathy, paramedicine, physiotherapy, podiatry, prosthetics and orthotics, diagnostic and therapeutic radiography and speech and language therapy.

Abbreviations

Addressing Organisational Capacity to do Research Network

Allied Health Profession/al

Collaboration for Leadership in Applied Health and Care Research

Dietician/s

Art therapist/s, Paramedic/s, Music Therapist/s and Orthoptist/s

National Health Service

National Institute for Health and Care Research

Occupational Therapist/s

Podiatrist/s

Physiotherapist/s

Radiographer/s

Speech and Language Therapist/s

United Kingdom

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This study was funded through the National Institute for Health Research (NIHR) Collaboration and Leadership for Allied Health and Care Research for Yorkshire and Humber and the specific AHP data analysis and writing by Health Education England.

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Contributions

JW – Contributed to data interpretation, led the preparation and revisions of the manuscript, specifically leading on background, discussion and conclusion. SR - Led on the qualitative data analysis, co-led on the interpretation, and contributed to the preparation and revision of the manuscript, specifically leading on method, results and limitations. TR - Led on the quantitative analysis, contributed to the qualitative analysis and interpretation, and contributed to revisions of the manuscript. EW - Completed initial data management and early analysis of the quantitative data and contributed to revisions of the manuscript. JC - Co-led the conception and design of the study, acquisition of data, interpretation of data, and contributed to revisions of the manuscript. SH - Co-led the conception and design of the study, acquisition of data, interpretation of data and contributed to revisions of the manuscript. HR – Contributed to data interpretation, brought together the writing team and contributed to planning and revisions of the manuscript.

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Watson, J., Robertson, S., Ryan, T. et al. Understanding the value of a doctorate for allied health professionals in practice in the UK: a survey. BMC Health Serv Res 24 , 566 (2024). https://doi.org/10.1186/s12913-024-11035-7

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Coming Soon: New R&D Value Added (and More) Statistics for the U.S. and States

How many research and development jobs are in your state? Which states have the largest R&D value added? How is industry and government research money spread across the states’ GDP? BEA is developing statistics that can help answer these and other questions about the role of R&D in the economy.

In the first milestone of this project, the Bureau of Economic Analysis will issue experimental U.S. and state data for a new Research and Development Satellite Account on May 9 . The statistics will complement BEA national data on investment in research and development and provide our first state-by-state numbers on R&D value added with corresponding employment and compensation statistics.  

By its nature, research and development looks to the future. Its ultimate value can’t yet be known, but we can measure the economic effects of the R&D work itself as it is performed, also known as R&D production.

The experimental statistics will combine R&D performance data from the National Science Foundation's National Center for Science and Engineering Statistics (NCSES) with the framework BEA uses to estimate gross domestic product or GDP . Expanded R&D data, consistent with our GDP statistics, will benefit economic development, government planning at all levels, business investment, and other decision-making.

BEA will measure R&D production by its contribution to GDP (known as R&D value added ), employment, and compensation. The initial data will include all three measures for the nation and for the 50 states and the District of Columbia, covering the years 2017 to 2021. In the state statistics, R&D production will be attributed to the state where it is performed.

The statistics will be broken into three R&D producing sectors—business, government, and nonprofit institutions serving households. Within the business sector, you’ll find data on each state’s R&D-intensive industries, such as computer and electronic product manufacturing or chemical manufacturing (which includes pharmaceuticals).

The value generated by R&D work is already included within estimates of GDP. At the national level, BEA currently publishes some measures of R&D production in the supply-use tables as well as measures of R&D investment. Our new work expands the R&D production measures at the national level and, for the first time, provides a state perspective, as R&D production is not separately identified in the GDP by state statistics.

Developing this satellite account allows us to complement and expand on BEA’s core statistics, as well as explore new methodologies that could improve the measurement of an important component of GDP.

The new R&D Satellite Account is supported by funding from NCSES as was work on a previous R&D satellite account, which culminated with the reclassification of R&D from an intermediate cost of production to investment as part of the 2013 comprehensive update of BEA’s GDP statistics.

BEA is seeking feedback to help refine the methodology and presentation of the R&D production statistics. Submit comments to  [email protected] .

• Category: AI

Tiny but mighty: The Phi-3 small language models with big potential

• Sally Beatty

Sometimes the best way to solve a complex problem is to take a page from a children’s book. That’s the lesson Microsoft researchers learned by figuring out how to pack more punch into a much smaller package.

Last year, after spending his workday thinking through potential solutions to machine learning riddles, Microsoft’s Ronen Eldan was reading bedtime stories to his daughter when he thought to himself, “how did she learn this word? How does she know how to connect these words?” 

That led the Microsoft Research machine learning expert to wonder how much an AI model could learn using only words a 4-year-old could understand – and ultimately to an innovative training approach that’s produced a new class of more capable small language models that promises to make AI more accessible to more people.

Large language models (LLMs) have created exciting new opportunities to be more productive and creative using AI.  But their size means they can require significant computing resources to operate. 

While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data.

The company announced today the Phi-3 family of open models , the most capable and cost-effective small language models available. Phi-3 models outperform models of the same size and next size up across a variety of benchmarks that evaluate language, coding and math capabilities, thanks to training innovations developed by Microsoft researchers.

Microsoft is now making the first in that family of more powerful small language models publicly available: Phi-3-mini , measuring 3.8 billion parameters, which performs better than models twice its size, the company said.

Starting today, it will be available in the Microsoft Azure AI Model Catalog and on Hugging Face , a platform for machine learning models, as well as Ollama , a lightweight framework for running models on a local machine. It will also be available as an NVIDIA NIM  microservice with a standard API interface that can be deployed anywhere. 

Microsoft also announced additional models to the Phi-3 family are coming soon to offer more choice across quality and cost. Phi-3-small (7 billion parameters) and Phi-3-medium (14 billion parameters) will be available in the Azure AI Model Catalog and other model gardens shortly. 

Small language models are designed to perform well for simpler tasks, are more accessible and easier to use for organizations with limited resources and they can be more easily fine-tuned to meet specific needs. 

“What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to make a decision on what is the best model for their scenario,” said Sonali Yadav, principal product manager for Generative AI at Microsoft.

“Some customers may only need small models, some will need big models and many are going to want to combine both in a variety of ways,” said Luis Vargas, vice president of AI at Microsoft.

Choosing the right language model depends on an organization’s specific needs, the complexity of the task and available resources. Small language models are well suited for organizations looking to build applications that can run locally on a device (as opposed to the cloud) and where a task doesn’t require extensive reasoning or a quick response is needed.

Large language models are more suited for applications that need orchestration of complex tasks involving advanced reasoning, data analysis and understanding of context.  

Small language models also offer potential solutions for regulated industries and sectors that encounter situations where they need high quality results but want to keep data on their own premises, said Yadav. 

Vargas and Yadav are particularly excited about the opportunities to place more capable SLMs on smartphones and other mobile devices that operate “at the edge,” not connected to the cloud. (Think of car computers, PCs without Wi-Fi, traffic systems, smart sensors on a factory floor, remote cameras or devices that monitor environmental compliance.) By keeping data within the device, users can “minimize latency and maximize privacy,” said Vargas. 

Latency refers to the delay that can occur when LLMs communicate with the cloud to retrieve information used to generate answers to users prompts. In some instances, high-quality answers are worth waiting for while in other scenarios speed is more important to user satisfaction.

Because SLMs can work offline, more people will be able to put AI to work in ways that haven’t previously been possible, Vargas said. 

For instance, SLMs could also be put to use in rural areas that lack cell service. Consider a farmer inspecting crops who finds signs of disease on a leaf or branch. Using a SLM with visual capability, the farmer could take a picture of the crop at issue and get immediate recommendations on how to treat pests or disease.  

“If you are in a part of the world that doesn’t have a good network,” said Vargas, “you are still going to be able to have AI experiences on your device.”    

The role of high-quality data  

Just as the name implies, compared to LLMs, SLMs are tiny, at least by AI standards. Phi-3-mini has “only” 3.8 billion parameters – a unit of measure that refers to the algorithmic knobs on a model that help determine its output. By contrast, the biggest large language models are many orders of magnitude larger.

The huge advances in generative AI ushered in by large language models were largely thought to be enabled by their sheer size. But the Microsoft team was able to develop small language models that can deliver outsized results in a tiny package. This breakthrough was enabled by a highly selective approach to training data – which is where children’s books come into play.

To date, the standard way to train large language models has been to use massive amounts of data from the internet. This was thought to be the only way to meet this type of model’s huge appetite for content, which it needs to “learn” to understand the nuances of language and generate intelligent answers to user prompts. But Microsoft researchers had a different idea.

“Instead of training on just raw web data, why don’t you look for data which is of extremely high quality?” asked Sebastien Bubeck, Microsoft vice president of generative AI research who has led the company’s efforts to develop more capable small language models. But where to focus?

Inspired by Eldan’s nightly reading ritual with his daughter, Microsoft researchers decided to create a discrete dataset starting with 3,000 words – including a roughly equal number of nouns, verbs and adjectives. Then they asked a large language model to create a children’s story using one noun, one verb and one adjective from the list – a prompt they repeated millions of times over several days, generating millions of tiny children’s stories.

They dubbed the resulting dataset “TinyStories” and used it to train very small language models of around 10 million parameters. To their surprise, when prompted to create its own stories, the small language model trained on TinyStories generated fluent narratives with perfect grammar.

Next, they took their experiment up a grade, so to speak. This time a bigger group of researchers used carefully selected publicly-available data that was filtered based on educational value and content quality to train Phi-1. After collecting publicly available information into an initial dataset, they used a prompting and seeding formula inspired by the one used for TinyStories, but took it one step further and made it more sophisticated, so that it would capture a wider scope of data. To ensure high quality, they repeatedly filtered the resulting content before feeding it back into a LLM for further synthesizing. In this way, over several weeks, they built up a corpus of data large enough to train a more capable SLM.

“A lot of care goes into producing these synthetic data,” Bubeck said, referring to data generated by AI, “looking over it, making sure it makes sense, filtering it out. We don’t take everything that we produce.” They dubbed this dataset “CodeTextbook.” 

The researchers further enhanced the dataset by approaching data selection like a teacher breaking down difficult concepts for a student. “Because it’s reading from textbook-like material, from quality documents that explain things very, very well,” said Bubeck, “you make the task of the language model to read and understand this material much easier.”

Distinguishing between high- and low-quality information isn’t difficult for a human, but sorting through more than a terabyte of data that Microsoft researchers determined they would need to train their SLM would be impossible without help from a LLM. 

“The power of the current generation of large language models is really an enabler that we didn’t have before in terms of synthetic data generation,” said Ece Kamar, a Microsoft vice president who leads the Microsoft Research AI Frontiers Lab, where the new training approach was developed. 

Starting with carefully selected data helps reduce the likelihood of models returning unwanted or inappropriate responses, but it’s not sufficient to guard against all potential safety challenges. As with all generative AI model releases, Microsoft’s product and responsible AI teams used a multi-layered approach to manage and mitigate risks in developing Phi-3 models.

For instance, after initial training they provided additional examples and feedback on how the models should ideally respond, which builds in an additional safety layer and helps the model generate high-quality results. Each model also undergoes assessment, testing and manual red-teaming, in which experts identify and address potential vulnerabilities.

Finally, developers using the Phi-3 model family can also take advantage of a suite of tools available in Azure AI  to help them build safer and more trustworthy applications.  

Choosing the right-size language model for the right task

But even small language models trained on high quality data have limitations. They are not designed for in-depth knowledge retrieval, where large language models excel due to their greater capacity and training using much larger data sets.

LLMs are better than SLMs at complex reasoning over large amounts of information due to their size and processing power. That’s a function that could be relevant for drug discovery, for example, by helping to pore through vast stores of scientific papers, analyze complex patterns and understand interactions between genes, proteins or chemicals. 

“Anything that involves things like planning where you have a task, and the task is complicated enough that you need to figure out how to partition that task into a set of sub tasks, and sometimes sub-sub tasks, and then execute through all of those to come with a final answer … are really going to be in the domain of large models for a while,” said Vargas.

Based on ongoing conversations with customers, Vargas and Yadav expect to see some companies “offloading” some tasks to small models if the task is not too complex. 

For instance, a business could use Phi-3 to summarize the main points of a long document or extract relevant insights and industry trends from market research reports. Another organization might use Phi-3 to generate copy, helping create content for marketing or sales teams such as product descriptions or social media posts. Or, a company might use Phi-3 to power a support chatbot to answer customers’ basic questions about their plan, or service upgrades.    

Internally, Microsoft is already using suites of models, where large language models play the role of router, to direct certain queries that require less computing power to small language models, while tackling other more complex requests itself.

“The claim here is not that SLMs are going to substitute or replace large language models,” said Kamar. Instead, SLMs “are uniquely positioned for computation on the edge, computation on the device, computations where you don’t need to go to the cloud to get things done. That’s why it is important for us to understand the strengths and weaknesses of this model portfolio.”

And size carries important advantages. There’s still a gap between small language models and the level of intelligence that you can get from the big models on the cloud, said Bubeck. “And maybe there will always be a gap because you know – the big models are going to keep making progress.”

Related links:

• Read more: Introducing Phi-3, redefining what’s possible with SLMs
• Learn more: Azure AI
• Read more: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Top image: Sebastien Bubeck, Microsoft vice president of Generative AI research who has led the company’s efforts to develop more capable small language models. (Photo by Dan DeLong for Microsoft)