examples of good research questions in science

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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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Formulating a good research question: Pearls and pitfalls

Wilson fandino.

Guys' and St Thomas' Hospital National Health Service Foundation Trust, London, United Kingdom

The process of formulating a good research question can be challenging and frustrating. While a comprehensive literature review is compulsory, the researcher usually encounters methodological difficulties in the conduct of the study, particularly if the primary study question has not been adequately selected in accordance with the clinical dilemma that needs to be addressed. Therefore, optimising time and resources before embarking in the design of a clinical protocol can make an impact on the final results of the research project. Researchers have developed effective ways to convey the message of how to build a good research question that can be easily recalled under the acronyms of PICOT (population, intervention, comparator, outcome, and time frame) and FINER (feasible, interesting, novel, ethical, and relevant). In line with these concepts, this article highlights the main issues faced by clinicians, when developing a research question.

INTRODUCTION

What is your research question? This is very often one of the first queries made by statisticians, when researchers come up with an interesting idea. In fact, the findings of a study may only acquire relevance if they provide an accurate and unbiased answer to a specific question,[ 1 , 2 ] and it has been suggested that up to one-third of the time spent in the whole process—from the conception of an idea to the publication of the manuscript—could be invested in finding the right primary study question.[ 3 ] Furthermore, selecting a good research question can be a time-consuming and challenging task: in one retrospective study, Mayo et al . reported that 3 out of 10 articles published would have needed a major rewording of the question.[ 1 ] This paper explores some recommendations to consider before starting any research project, and outlines the main difficulties faced by young and experienced clinicians, when it comes time to turn an exciting idea into a valuable and feasible research question.

OPTIMISATION OF TIME AND RESOURCES

Focusing on the primary research question.

The process of developing a new idea usually stems from a dilemma inherent to the clinical practice.[ 2 , 3 , 4 ] However, once the problem has been identified, it is tempting to formulate multiple research questions. Conducting a clinical trial with more than one primary study question would not be feasible. First, because each question may require a different research design, and second, because the necessary statistical power of the study would demand unaffordable sample sizes. It is the duty of editors and reviewers to make sure that authors clearly identify the primary research question, and as a consequence, studies approaching more than one primary research question may not be suitable for publication.

Working in the right environment

Teamwork is essential to find the appropriate research question. Working in the right environment will enable the investigator to interact with colleagues with different backgrounds, and create opportunities to exchange experiences in a collaborative way between clinicians and researchers. Likewise, it is of paramount importance to get involved colleagues with expertise in the field (lead clinicians, education supervisors, research mentors, department chairs, epidemiologists, biostatisticians, and ethical consultants, among others), and ask for their guidance.[ 5 , 6 , 7 , 8 ]

Evaluating the pertinence of the study

The researcher should wonder if, on the basis of the research question formulated, there is a need for a study to address the problem, as clinical research usually entails a large investment of resources and workforce involvement. Thus, if the answer to the posed clinical question seems to be evident before starting the study, investing in research to address the problem would become superfluous. For example, in a clinical trial, Herzog-Niescery et al . compared laryngeal masks with cuffed and uncuffed tracheal tubes, in the context of surgeons' exposure to sevoflurane, in infants undergoing adenoidectomy. However, it appears obvious that cuffed tracheal tubes are preferred to minimise surgeons' exposure to volatile gases, as authors concluded after recruiting 60 patients.[ 9 ]

Conducting a thorough literature review

Any research project requires the identification of at least one of three problems: the evidence is scarce, the existing literature yields conflicting results, or the results could be improved. Hence, a comprehensive review of the topic is imperative, as it allows the researcher to identify this gap in the literature, formulate a hypothesis and develop a research question.[ 2 ] To this end, it is crucial to be attentive to new ideas, keep the imagination roaming with reflective attitude, and remain sceptical to the new-gained information.[ 4 , 7 ]

Narrowing the research question

A broad research question may encompass an unaffordable extensive topic. For instance, do supraglottic devices provide similar conditions for the visualization of the glottis aperture in a German hospital? Such a general research question usually needs to be narrowed, not only by cutting away unnecessary components (a German hospital is irrelevant in this context), but also by defining a target population, a specific intervention, an alternative treatment or procedure to be compared with the intervention, a measurable primary outcome, and a time frame of the study. In contrast, an example of a good research question would be: among children younger than 1 year of age undergoing elective minor procedures, to what extent the insertion times are different, comparing the Supreme™ laryngeal mask airway (LMA) to Proseal™ LMA, when placed after reaching a BIS index <60?[ 10 ] In this example, the core ingredients of the research question can be easily identified as: children <1 year of age undergoing minor elective procedures, Supreme™ LMA, Proseal™ LMA and insertion times at anaesthetic induction when reaching a BIS index <60. These components are usually gathered in the literature under the acronym of PICOT (population, intervention, comparator, outcome and time frame, respectively).[ 1 , 3 , 5 ]

PICOT FRAMEWORK

Table 1 summarises the foremost questions likely to be addressed when working on PICOT frame.[ 1 , 6 , 8 ] These components are also applicable to observational studies, where the exposure takes place of the intervention.[ 1 , 11 ] Remarkably, if after browsing the title and the abstract of a paper, the reader is not able to clearly identify the PICOT parameters, and elucidate the question posed by the authors, there should be reasonable scepticism regarding the scientific rigor of the work.[ 12 , 13 ] All these elements are crucial in the design and methodology of a clinical trial, as they can affect the feasibility and reliability of results. Having formulated the primary study question in the context of the PICOT framework [ Table 1 ],[ 1 , 6 , 8 ] the researcher should be able to elucidate which design is most suitable for their work, determine what type of data needs to be collected, and write a structured introduction tailored to what they want to know, explicitly mentioning the primary study hypothesis, which should lead to formulate the main research question.[ 1 , 2 , 6 , 8 ]

Key questions to be answered when working with the PICOT framework (population, intervention, comparator, outcome, and time frame) in a clinical research design

Occasionally, the intended population of the study needs to be modified, in order to overcome any potential ethical issues, and/or for the sake of convenience and feasibility of the project. Yet, the researcher must be aware that the external validity of the results may be compromised. As an illustration, in a randomised clinical trial, authors compared the ease of tracheal tube insertion between C-MAC video laryngoscope and direct laryngoscopy, in patients presenting to the emergency department with an indication of rapid sequence intubation. However, owing to the existence of ethical concerns, a substantial amount of patients requiring emergency tracheal intubation, including patients with major maxillofacial trauma and ongoing cardiopulmonary resuscitation, had to be excluded from the trial.[ 14 ] In fact, the design of prospective studies to explore this subset of patients can be challenging, not only because of ethical considerations, but because of the low incidence of these cases. In another study, Metterlein et al . compared the glottis visualisation among five different supraglottic airway devices, using fibreroptic-guided tracheal intubation in an adult population. Despite that the study was aimed to explore the ease of intubation in patients with anticipated difficult airway (thus requiring fibreoptic tracheal intubation), authors decided to enrol patients undergoing elective laser treatment for genital condylomas, as a strategy to hasten the recruitment process and optimise resources.[ 15 ]

Intervention

Anaesthetic interventions can be classified into pharmacological (experimental treatment) and nonpharmacological. Among nonpharmacological interventions, the most common include anaesthetic techniques, monitoring instruments and airway devices. For example, it would be appropriate to examine the ease of insertion of Supreme™ LMA, when compared with ProSeal™ LMA. Notwithstanding, a common mistake is the tendency to be focused on the data aimed to be collected (the “stated” objective), rather than the question that needs to be answered (the “latent” objective).[ 1 , 4 ] In one clinical trial, authors stated: “we compared the Supreme™ and ProSeal™ LMAs in infants by measuring their performance characteristics, including insertion features, ventilation parameters, induced changes in haemodynamics, and rates of postoperative complications”.[ 10 ] Here, the research question has been centered on the measurements (insertion characteristics, haemodynamic variables, LMA insertion characteristics, ventilation parameters) rather than the clinical problem that needs to be addressed (is Supreme™ LMA easier to insert than ProSeal™ LMA?).

Comparators in clinical research can also be pharmacological (e.g., gold standard or placebo) or nonpharmacological. Typically, not more than two comparator groups are included in a clinical trial. Multiple comparisons should be generally avoided, unless there is enough statistical power to address the end points of interest, and statistical analyses have been adjusted for multiple testing. For instance, in the aforementioned study of Metterlein et al .,[ 15 ] authors compared five supraglottic airway devices by recruiting only 10--12 participants per group. In spite of the authors' recommendation of using two supraglottic devices based on the results of the study, there was no mention of statistical adjustments for multiple comparisons, and given the small sample size, larger clinical trials will undoubtedly be needed to confirm or refute these findings.[ 15 ]

A clear formulation of the primary outcome results of vital importance in clinical research, as the primary statistical analyses, including the sample size calculation (and therefore, the estimation of the effect size and statistical power), will be derived from the main outcome of interest. While it is clear that using more than one primary outcome would not be appropriate, it would be equally inadequate to include multiple point measurements of the same variable as the primary outcome (e.g., visual analogue scale for pain at 1, 2, 6, and 12 h postoperatively).

Composite outcomes, in which multiple primary endpoints are combined, may make it difficult to draw any conclusions based on the study findings. For example, in a clinical trial, 200 children undergoing ophthalmic surgery were recruited to explore the incidence of respiratory adverse events, when comparing desflurane with sevoflurane, following the removal of flexible LMA during the emergence of the anaesthesia. The primary outcome was the number of respiratory events, including breath holding, coughing, secretions requiring suction, laryngospasm, bronchospasm, and mild desaturation.[ 16 ] Should authors had claimed a significant difference between these anaesthetic volatiles, it would have been important to elucidate whether those differences were due to serious adverse events, like laryngospasm or bronchospasm, or the results were explained by any of the other events (e.g., secretions requiring suction). While it is true that clinical trials evaluating the occurrence of adverse events like laryngospasm/bronchospasm,[ 16 , 17 ] or life-threating complications following a tracheal intubation (e.g., inadvertent oesophageal placement, dental damage or injury of the larynx/pharynx)[ 14 ] are almost invariably underpowered, because the incidence of such events is expected to be low, subjective outcomes like coughing or secretions requiring suction should be avoided, as they are highly dependent on the examiner's criteria.[ 16 ]

Secondary outcomes are useful to document potential side effects (e.g., gastric insufflation after placing a supraglottic device), and evaluate the adherence (say, airway leak pressure) and safety of the intervention (for instance, occurrence, or laryngospasm/bronchospasm).[ 17 ] Nevertheless, the problem of addressing multiple secondary outcomes without the adequate statistical power is habitual in medical literature. A good illustration of this issue can be found in a study evaluating the performance of two supraglottic devices in 50 anaesthetised infants and neonates, whereby authors could not draw any conclusions in regard to potential differences in the occurrence of complications, because the sample size calculated made the study underpowered to explore those differences.[ 17 ]

Among PICOT components, the time frame is the most likely to be omitted or inappropriate.[ 1 , 12 ] There are two key aspects of the time component that need to be clearly specified in the research question: the time of measuring the outcome variables (e.g. visual analogue scale for pain at 1, 2, 6, and 12 h postoperatively), and the duration of each measurement (when indicated). The omission of these details in the study protocol might lead to substantial differences in the methodology used. For instance, if a study is designed to compare the insertion times of three different supraglottic devices, and researchers do not specify the exact moment of LMA insertion in the clinical trial protocol (i.e., at the anaesthetic induction after reaching a BIS index < 60), placing an LMA with insufficient depth of anaesthesia would have compromised the internal validity of the results, because inserting a supraglottic device in those patients would have resulted in failed attempts and longer insertion times.[ 10 ]

FINER CRITERIA

A well-elaborated research question may not necessarily be a good question. The proposed study also requires being achievable from both ethical and realistic perspectives, interesting and useful to the clinical practice, and capable to formulate new hypotheses, that may contribute to the generation of knowledge. Researchers have developed an effective way to convey the message of how to build a good research question, that is usually recalled under the acronym of FINER (feasible, interesting, novel, ethical and relevant).[ 5 , 6 , 7 ] Table 2 highlights the main characteristics of FINER criteria.[ 7 ]

Main features of FINER criteria (Feasibility, interest, novelty, ethics, and relevance) to formulate a good research question. Adapted from Cummings et al .[ 7 ]

Novelty and relevance

Although it is clear that any research project should commence with an accurate literature interpretation, in many instances it represents the start and the end of the research: the reader will soon realise that the answer to several questions can be easily found in the published literature.[ 5 ] When the question overcomes the test of a thorough literature review, the project may become novel (there is a gap in the knowledge, and therefore, there is a need for new evidence on the topic) and relevant (the paper may contribute to change the clinical practice). In this context, it is important to distinguish the difference between statistical significance and clinical relevance: in the aforementioned study of Oba et al .,[ 10 ] despite the means of insertion times were reported as significant for the Supreme™ LMA, as compared with ProSeal™ LMA, the difference found in the insertion times (528 vs. 486 sec, respectively), although reported as significant, had little or no clinical relevance.[ 10 ] Conversely, a statistically significant difference of 12 sec might be of clinical relevance in neonates weighing <5 kg.[ 17 ] Thus, statistical tests must be interpreted in the context of a clinically meaningful effect size, which should be previously defined by the researcher.

Feasibility and ethical aspects

Among FINER criteria, there are two potential barriers that may prevent the successful conduct of the project and publication of the manuscript: feasibility and ethical aspects. These obstacles are usually related to the target population, as discussed above. Feasibility refers not only to the budget but also to the complexity of the design, recruitment strategy, blinding, adequacy of the sample size, measurement of the outcome, time of follow-up of participants, and commitment of clinicians, among others.[ 3 , 7 ] Funding, as a component of feasibility, may also be implicated in the ethical principles of clinical research, because the choice of the primary study question may be markedly influenced by the specific criteria demanded in the interest of potential funders.

Discussing ethical issues with local committees is compulsory, as rules applied might vary among countries.[ 18 ] Potential risks and benefits need to be carefully weighed, based upon the four principles of respect for autonomy, beneficence, non-maleficence, and justice.[ 19 ] Although many of these issues may be related to the population target (e.g., conducting a clinical trial in patients with ongoing cardiopulmonary resuscitation would be inappropriate, as would be anaesthetising patients undergoing elective LASER treatment for condylomas, to examine the performance of supraglottic airway devices),[ 14 , 15 ] ethical conflicts may also arise from the intervention (particularly those involving the occurrence of side effects or complications, and their potential for reversibility), comparison (e.g., use of placebo or sham procedures),[ 19 ] outcome (surrogate outcomes should be considered in lieu of long term outcomes), or time frame (e.g., unnecessary longer exposition to an intervention). Thus, FINER criteria should not be conceived without a concomitant examination of the PICOT checklist, and consequently, PICOT framework and FINER criteria should not be seen as separated components, but rather complementary ingredients of a good research question.

Undoubtedly, no research project can be conducted if it is deemed unfeasible, and most institutional review boards would not be in a position to approve a work with major ethical problems. Nonetheless, whether or not the findings are interesting, is a subjective matter. Engaging the attention of readers also depends upon a number of factors, including the manner of presenting the problem, the background of the topic, the intended audience, and the reader's expectations. Furthermore, the interest is usually linked to the novelty and relevance of the topic, and it is worth nothing that editors and peer reviewers of high-impact medical journals are usually reluctant to accept any publication, if there is no novelty inherent to the research hypothesis, or there is a lack of relevance in the results.[ 11 ] Nevertheless, a considerable number of papers have been published without any novelty or relevance in the topic addressed. This is probably reflected in a recent survey, according to which only a third of respondents declared to have read thoroughly the most recent papers downloaded, and at least half of those manuscripts remained unread.[ 20 ] The same study reported that up to one-third of papers examined remained uncited after 5 years of publication, and only 20% of papers accounted for 80% of the citations.[ 20 ]

Formulating a good research question can be fascinating, albeit challenging, even for experienced investigators. While it is clear that clinical experience in combination with the accurate interpretation of literature and teamwork are essential to develop new ideas, the formulation of a clinical problem usually requires the compliance with PICOT framework in conjunction with FINER criteria, in order to translate a clinical dilemma into a researchable question. Working in the right environment with the adequate support of experienced researchers, will certainly make a difference in the generation of knowledge. By doing this, a lot of time will be saved in the search of the primary study question, and undoubtedly, there will be more chances to become a successful researcher.

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• Examples of good research questions

Last updated

Reviewed by

Tanya Williams

However, developing a good research question is often challenging. But, doing appropriate data analysis or drawing meaningful conclusions from your investigation with a well-defined question make it easier.

So, to get you on the right track, let’s start by defining a research question, what types of research questions are common, and the steps to drafting an excellent research question.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• What is a research question?

The definition of a research question might seem fairly obvious.

 At its simplest, a research question is a question you research to find the answer.

Researchers typically start with a problem or an issue and seek to understand why it has occurred, how it can be solved, or other aspects of its nature.

As you'll see, researchers typically start with a broad question that becomes narrower and more specific as the research stages are completed.

In some cases, a study may tackle more than one research question.

• Research question types

Research questions are typically divided into three broad categories: qualitative, quantitative, and mixed-method.

These categories reflect the research type necessary to answer the research question.

Qualitative research

When you conduct qualitative research, you're broadly exploring a subject to analyze its inherent qualities.

There are many types of qualitative research questions, which include:

Descriptive: describing and illuminating little-known or overlooked aspects of a subject

Emancipatory: uncovering data that can serve to emancipate a particular group of people, such as disadvantaged or marginalized communities

Evaluative:  assessing how well a particular research approach or method works

Explanatory: answering “how” or “why” a given phenomenon occurs 

Exploratory:  identifying reasons behind certain behaviors and exploring motivations (also known as generative research because it can generate solutions to problems)

Ideological: researching ideologies or beliefs, such as political affiliation

Interpretive: understanding group perceptions, decision-making, and behavior in a natural setting

Predictive: forecasting a likely outcome or scenario by examining past events 

While it's helpful to understand the differences between these qualitative research question types, writing a good question doesn't start with determining the precise type of research question you'll be asking.

It starts with determining what answers you're seeking.

Quantitative research

Unlike broad, flexible qualitative research questions, quantitative research questions are precise. They also directly link the research question and the proposed methodology.

So, in a quantitative research question, you'll usually find

The study method 

An independent variable (or variables)

A dependent variable

The study population 

Quantitative research questions can also fall into multiple categories, including:

Comparative research questions compare two or more groups according to specific criteria and analyze their similarities and differences.

Descriptive questions measure a population's response to one or more variables.

Relationship (or relationship-based) questions examine how two or more variables interact.

Mixed-methods research

As its name suggests, mixed-methods research questions involve qualitative and quantitative components.

These questions are ideal when the answers require an evaluation of a specific aspect of a phenomenon that you can quantify and a broader understanding of aspects that can't.

• How to write a research question

Writing a good research question can be challenging, even if you're passionate about the subject matter.

A good research question aims to solve a problem that still needs to be answered and can be solved empirically. 

The approach might involve quantitative or qualitative methodology, or a mixture of both. To write a well-developed research question, follow the four steps below:

1. Select a general topic

Start with a broad topic. You may already have one in mind or get one assigned to you. If you don't, think about one you're curious about. 

You can also use common brainstorming techniques , draw on discussions you've had with family and friends, take topics from the news, or use other similar sources of inspiration.

Also, consider a subject that has yet to be studied or addressed. If you're looking to tackle a topic that has already been thoroughly studied, you'll want to examine it from a new angle.

Still, the closer your question, approach, and outcomes are to existing literature, the less value your work will offer. It will also be less publishing-worthy (if that’s your goal).

2. Conduct preliminary research

Next, you'll want to conduct some initial research about your topic. You'll read coverage about your topic in academic journals, the news, and other credible sources at this stage.

You'll familiarize yourself with the terminology commonly used to describe your topic and the current take from subject matter experts and the general public. 

This preliminary review helps you in a few ways. First, you'll find many researchers will discuss challenges they found conducting their research in their "Limitations," "Results," and "Discussion" sections of research papers.

Assessing these sections also helps you avoid choosing the wrong methodological approach to answering your question. Initial research also enables you to avoid focusing on a topic that has already been covered. 

You can generate valuable research questions by tracking topics that have yet to be covered.

3. Consider your audience

Next, you'll want to give some thought to your audience. For example, what kinds of research material are they looking for, and what might they find valuable?

Reflect on why you’re conducting the research. 

What is your team looking to learn if your research is for a work assignment?

How does what they’re asking for from you connect to business goals?

Understanding what your audience is seeking can help you shape the direction of your research so that the final draft connects with your audience.

If you're writing for an academic journal, what types of research do they publish? What kinds of research approaches have they published? And what criteria do they expect submitted manuscripts to meet?

4. Generate potential questions

Take the insights you've gained from your preliminary research and your audience assessment to narrow your topic into a research question. 

Your question should be one that you can answer using the appropriate research methods. Unfortunately, some researchers start with questions they need more resources to answer and then produce studies whose outcomes are limited, limiting the study's value to the broader community. 

Make sure your question is one you can realistically answer.

• Examples of poor research questions

"How do electronics distract teen drivers?"

This question could be better from a researcher's perspective because it is overly broad. For instance, what is “electronics” in this context? Some electronics, like eye-monitoring systems in semi-autonomous vehicles, are designed to keep drivers focused on the road.

Also, how does the question define “teens”? Some states allow you to get a learner's permit as young as 14, while others require you to be 18 to drive. Therefore, conducting a study without further defining the participants' ages is not scientifically sound.

Here's another example of an ineffective research question:

"Why is the sky blue?"

This question has been researched thoroughly and answered. 

A simple online search will turn up hundreds, if not thousands, of pages of resources devoted to this very topic. 

Suppose you spend time conducting original research on a long-answered question; your research won’t be interesting, relevant, or valuable to your audience.

Alternatively, here's an example of a good research question:

"How does using a vehicle’s infotainment touch screen by drivers aged 16 to 18 in the U.S. affect driving habits?"

This question is far more specific than the first bad example. It notes the population of the study, as well as the independent and dependent variables.

And if you're still interested in the sky's color, a better example of a research question might be:

"What color is the sky on Proxima Centauri b, based on existing observations?"

A qualitative research study based on this question could extrapolate what visitors on Proxima Centauri b (a planet in the closest solar system to ours) might see as they look at the sky.

You could approach this by contextualizing our understanding of how the light scatters off the molecules of air resulting in a blue sky, and the likely composition of Proxima Centauri b's atmosphere from data NASA and others have gathered.

• Why the right research question is critical

As you can see from the examples, starting with a poorly-framed research question can make your study difficult or impossible to complete. 

Or it can lead you to duplicate research findings.

Ultimately, developing the right research question sets you up for success. It helps you define a realistic scope for your study, informs the best approach to answer the central question, and conveys its value to your audience. 

That's why you must take the time to get your research question right before you embark on any other part of your project.

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

What is a Research Question?

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

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

Research Question Writing Tips

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

A good research question should:

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

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

The research question should be specific and focused 

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

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

The research question should be based on the literature 

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

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

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

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

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

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

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

The research question should be in-depth

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

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

Research Question Types

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

Quantitative Research Questions

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

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

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

Categories of quantitative research questions

Qualitative research questions.

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

Categories of qualitative research questions

Quantitative and qualitative research question examples.

Good and Bad Research Question Examples

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

Research Question Example 1

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

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

Research Question Example 2

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

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

Steps for Writing a Research Question

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

1. Start with an interesting and relevant topic

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

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

2. Do preliminary research  

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

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

3. Narrow your research to determine specific research questions

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

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

4. Evaluate your research question

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

Is my research question clear?

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

Is my research question focused and specific?

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

Is my research question sufficiently complex?

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

Editing Your Research Question

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

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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Examples of Good and Bad Research Questions

#scribendiinc

Written by  Scribendi

So, you've got a research grant in your sights or you've been admitted to your school of choice, and you now have to write up a proposal for the work you want to perform. You know your topic, have done some reading, and you've got a nice quiet place where nobody will bother you while you try to decide where you'll go from here. The question looms:     

What Is a Research Question?

Your research question will be your focus, the sentence you refer to when you need to remember why you're researching. It will encapsulate what drives you and be something your field needs an answer for but doesn't have yet. 

Whether it seeks to describe a phenomenon, compare things, or show how one variable influences another, a research question always does the same thing: it guides research that will be judged based on how well it addresses the question.

So, what makes a research question good or bad? This article will provide examples of good and bad research questions and use them to illustrate both of their common characteristics so that you can evaluate your research question and improve it to suit your needs.

How to Choose a Research Question

At the start of your research paper, you might be wondering, "What is a good research question?"

A good research question focuses on one researchable problem relevant to your subject area.

To write a research paper , first make sure you have a strong, relevant topic. Then, conduct some preliminary research around that topic. It's important to complete these two initial steps because your research question will be formulated based on this research.

With this in mind, let's review the steps that help us write good research questions.

1. Select a Relevant Topic

When selecting a topic to form a good research question, it helps to start broad. What topics interest you most? It helps when you care about the topic you're researching!

Have you seen a movie recently that you enjoyed? How about a news story? If you can't think of anything, research different topics on Google to see which ones intrigue you the most and can apply to your assignment.

Also, before settling on a research topic, make sure it's relevant to your subject area or to society as a whole. This is an important aspect of developing your research question, because, in general, your research should add value to existing knowledge .

2. Thoroughly Research the Topic

Now that you've chosen a broad but relevant topic for your paper, research it thoroughly to see which avenues you might want to explore further.

For example, let's say you decide on the broad topic of search engines. During this research phase, try skimming through sources that are unbiased, current, and relevant, such as academic journals or sources in your university library.

Check out: 21 Legit Research Databases for Free Articles in 2022

Pay close attention to the subtopics that come up during research, such as the following: Which search engines are the most commonly used? Why do some search engines dominate specific regions? How do they really work or affect the research of scientists and scholars?

Be on the lookout for any gaps or limitations in the research. Identifying the groups or demographics that are most affected by your topic is also helpful, in case that's relevant to your work.

3. Narrow Your Topic to a Single Point

Now that you've spent some time researching your broad topic, it's time to narrow it down to one specific subject. A topic like search engines is much too broad to develop a research paper around. What specifically about search engines could you explore?

When refining your topic, be careful not to be either too narrow or too broad. You can ask yourself the following questions during this phase:

Can I cover this topic within the scope of my paper, or would it require longer, heavier research? (In this case, you'd need to be more specific.)

Conversely, is there not enough research about my topic to write a paper? (In this case, you'd need to be broader.)

Keep these things in mind as you narrow down your topic. You can always expand your topic later if you have the time and research materials.

4. Identify a Problem Related to Your Topic

When narrowing down your topic, it helps to identify a single issue or problem on which to base your research. Ask open-ended questions, such as why is this topic important to you or others? Essentially, have you identified the answer to "so what"?

For example, after asking these questions about our search engine topic, we might focus only on the issue of how search engines affect research in a specific field. Or, more specifically, how search engine algorithms manipulate search results and prevent us from finding the critical research we need.

Asking these "so what" questions will help us brainstorm examples of research questions we can ask in our field of study.

5. Turn Your Problem into a Question

Now that you have your main issue or problem, it's time to write your research question. Do this by reviewing your topic's big problem and formulating a question that your research will answer.

For example, ask, "so what?" about your search engine topic. You might realize that the bigger issue is that you, as a researcher, aren't getting the relevant information you need from search engines.

How can we use this information to develop a research question? We might phrase the research question as follows:

"What effect does the Google search engine algorithm have on online research conducted in the field of neuroscience?"

Note how specific we were with the type of search engine, the field of study, and the research method. It's also important to remember that your research question should not have an easy yes or no answer. It should be a question with a complex answer that can be discovered through research and analysis.

Perfect Your Paper

Hire an expert academic editor , or get a free sample, how to find good research topics for your research.

It can be fun to browse a myriad of research topics for your paper, but there are a few important things to keep in mind.

First, make sure you've understood your assignment. You don't want to pick a topic that's not relevant to the assignment goal. Your instructor can offer good topic suggestions as well, so if you get stuck, ask them!

Next, try to search for a broad topic that interests you. Starting broad gives you more options to work with. Some research topic examples include infectious diseases, European history, and smartphones .

Then, after some research, narrow your topic to something specific by extracting a single element from that subject. This could be a current issue on that topic, a major question circulating around that topic, or a specific region or group of people affected by that topic.

It's important that your research topic is focused. Focus lets you clearly demonstrate your understanding of the topic with enough details and examples to fit the scope of your project.

For example, if Jane Austen is your research topic, that might be too broad for a five-page paper! However, you could narrow it down to a single book by Austen or a specific perspective.

To keep your research topic focused, try creating a mind map. This is where you put your broad topic in a circle and create a few circles around it with similar ideas that you uncovered during your research. 

Mind maps can help you visualize the connections between topics and subtopics. This could help you simplify the process of eliminating broad or uninteresting topics or help you identify new relationships between topics that you didn't previously notice. 

Keeping your research topic focused will help you when it comes to writing your research question!

2. Researchable

A researchable question should have enough available sources to fill the scope of your project without being overwhelming. If you find that the research is never-ending, you're going to be very disappointed at the end of your paper—because you won't be able to fit everything in! If you are in this fix, your research question is still too broad.

Search for your research topic's keywords in trusted sources such as journals, research databases , or dissertations in your university library. Then, assess whether the research you're finding is feasible and realistic to use.

If there's too much material out there, narrow down your topic by industry, region, or demographic. Conversely, if you don't find enough research on your topic, you'll need to go broader. Try choosing two works by two different authors instead of one, or try choosing three poems by a single author instead of one.

3. Reasonable

Make sure that the topic for your research question is a reasonable one to pursue. This means it's something that can be completed within your timeframe and offers a new perspective on the research.

Research topics often end up being summaries of a topic, but that's not the goal. You're looking for a way to add something relevant and new to the topic you're exploring. To do so, here are two ways to uncover strong, reasonable research topics as you conduct your preliminary research:

Check the ends of journal articles for sections with questions for further discussion. These make great research topics because they haven't been explored!

Check the sources of articles in your research. What points are they bringing up? Is there anything new worth exploring? Sometimes, you can use sources to expand your research and more effectively narrow your topic.

4. Specific

For your research topic to stand on its own, it should be specific. This means that it shouldn't be easily mistaken for another topic that's already been written about.

If you are writing about a topic that has been written about, such as consumer trust, it should be distinct from everything that's been written about consumer trust so far.

There is already a lot of research done on consumer trust in specific products or services in the US. Your research topic could focus on consumer trust in products and services in a different region, such as a developing country.

If your research feels similar to existing articles, make sure to drive home the differences.

Whether it's developed for a thesis or another assignment, a good research topic question should be complex enough to let you expand on it within the scope of your paper.

For example, let's say you took our advice on researching a topic you were interested in, and that topic was a new Bridezilla reality show. But when you began to research it, you couldn't find enough information on it, or worse, you couldn't find anything scholarly.

In short, Bridezilla reality shows aren't complex enough to build your paper on. Instead of broadening the topic to all reality TV shows, which might be too overwhelming, you might consider choosing a topic about wedding reality TV shows specifically.

This would open you up to more research that could be complex enough to write a paper on without being too overwhelming or narrow.

6. Relevant

Because research papers aim to contribute to existing research that's already been explored, the relevance of your topic within your subject area can't be understated.

Your research topic should be relevant enough to advance understanding in a specific area of study and build on what's already been researched. It shouldn't duplicate research or try to add to it in an irrelevant way.

For example, you wouldn't choose a research topic like malaria transmission in Northern Siberia if the mosquito that transmits malaria lives in Africa. This research topic simply isn't relevant to the typical location where malaria is transmitted, and the research could be considered a waste of resources.

Do Research Questions Differ between the Humanities, Social Sciences, and Hard Sciences?

The art and science of asking questions is the source of all knowledge. 

–Thomas Berger

First, a bit of clarification: While there are constants among research questions, no matter what you're writing about, you will use different standards for the humanities and social sciences than for hard sciences, such as chemistry. The former depends on subjectivity and the perspective of the researcher, while the latter requires answers that must be empirically tested and replicable.

For instance, if you research Charles Dickens' writing influences, you will have to explain your stance and observations to the reader before supporting them with evidence. If you research improvements in superconductivity in room-temperature material, the reader will not only need to understand and believe you but also duplicate your work to confirm that you are correct.

Do Research Questions Differ between the Different Types of Research?

Research questions help you clarify the path your research will take. They are answered in your research paper and usually stated in the introduction.

There are two main types of research—qualitative and quantitative. 

If you're conducting quantitative research, it means you're collecting numerical, quantifiable data that can be measured, such as statistical information.

Qualitative research aims to understand experiences or phenomena, so you're collecting and analyzing non-numerical data, such as case studies or surveys.

The structure and content of your research question will change depending on the type of research you're doing. However, the definition and goal of a research question remains the same: a specific, relevant, and focused inquiry that your research answers.

Below, we'll explore research question examples for different types of research.

Comparative Research

Comparative research questions are designed to determine whether two or more groups differ based on a dependent variable. These questions allow researchers to uncover similarities and differences between the groups tested.

Because they compare two groups with a dependent variable, comparative research questions usually start with "What is the difference in…"

A strong comparative research question example might be the following:

"What is the difference in the daily caloric intake of American men and women?" ( Source .)

In the above example, the dependent variable is daily caloric intake and the two groups are American men and women.

A poor comparative research example might not aim to explore the differences between two groups or it could be too easily answered, as in the following example:

"Does daily caloric intake affect American men and women?"

Always ensure that your comparative research question is focused on a comparison between two groups based on a dependent variable.

Descriptive Research

Descriptive research questions help you gather data about measurable variables. Typically, researchers asking descriptive research questions aim to explain how, why, or what.

These research questions tend to start with the following:

What percentage?

How likely?

What proportion?

For example, a good descriptive research question might be as follows:

"What percentage of college students have felt depressed in the last year?" ( Source .)

A poor descriptive research question wouldn't be as precise. This might be something similar to the following:

"What percentage of teenagers felt sad in the last year?"

The above question is too vague, and the data would be overwhelming, given the number of teenagers in the world. Keep in mind that specificity is key when it comes to research questions!

Correlational Research

Correlational research measures the statistical relationship between two variables, with no influence from any other variable. The idea is to observe the way these variables interact with one another. If one changes, how is the other affected?

When it comes to writing a correlational research question, remember that it's all about relationships. Your research would encompass the relational effects of one variable on the other.

For example, having an education (variable one) might positively or negatively correlate with the rate of crime (variable two) in a specific city. An example research question for this might be written as follows:

"Is there a significant negative correlation between education level and crime rate in Los Angeles?"

A bad correlational research question might not use relationships at all. In fact, correlational research questions are often confused with causal research questions, which imply cause and effect. For example:

"How does the education level in Los Angeles influence the crime rate?"

The above question wouldn't be a good correlational research question because the relationship between Los Angeles and the crime rate is already inherent in the question—we are already assuming the education level in Los Angeles affects the crime rate in some way.

Be sure to use the right format if you're writing a correlational research question.

How to Avoid a Bad Question

Ask the right questions, and the answers will always reveal themselves. 

–Oprah Winfrey

If finding the right research question was easy, doing research would be much simpler. However, research does not provide useful information if the questions have easy answers (because the questions are too simple, narrow, or general) or answers that cannot be reached at all (because the questions have no possible answer, are too costly to answer, or are too broad in scope).

For a research question to meet scientific standards, its answer cannot consist solely of opinion (even if the opinion is popular or logically reasoned) and cannot simply be a description of known information.

However, an analysis of what currently exists can be valuable, provided that there is enough information to produce a useful analysis. If a scientific research question offers results that cannot be tested, measured, or duplicated, it is ineffective.

Bad Research Question Examples

Here are examples of bad research questions with brief explanations of what makes them ineffective for the purpose of research.

"What's red and bad for your teeth?"

This question has an easy, definitive answer (a brick), is too vague (What shade of red? How bad?), and isn't productive.

"Do violent video games cause players to act violently?"

This question also requires a definitive answer (yes or no), does not invite critical analysis, and allows opinion to influence or provide the answer.

"How many people were playing balalaikas while living in Moscow on July 8, 2019?"

This question cannot be answered without expending excessive amounts of time, money, and resources. It is also far too specific. Finally, it doesn't seek new insight or information, only a number that has no conceivable purpose.

How to Write a Research Question

The quality of a question is not judged by its complexity but by the complexity of thinking it provokes. 

–Joseph O'Connor

What makes a good research question? A good research question topic is clear and focused. If the reader has to waste time wondering what you mean, you haven't phrased it effectively.

It also needs to be interesting and relevant, encouraging the reader to come along with you as you explain how you reached an answer. 

Finally, once you explain your answer, there should be room for astute or interested readers to use your question as a basis to conduct their own research. If there is nothing for you to say in your conclusion beyond "that's the truth," then you're setting up your research to be challenged.

Good Research Question Examples

Here are some examples of good research questions. Take a look at the reasoning behind their effectiveness.

"What are the long-term effects of using activated charcoal in place of generic toothpaste for routine dental care?"

This question is specific enough to prevent digressions, invites measurable results, and concerns information that is both useful and interesting. Testing could be conducted in a reasonable time frame, without excessive cost, and would allow other researchers to follow up, regardless of the outcome.

"Why do North American parents feel that violent video game content has a negative influence on their children?"

While this does carry an assumption, backing up that assumption with observable proof will allow for analysis of the question, provide insight on a significant subject, and give readers something to build on in future research. 

It also discusses a topic that is recognizably relevant. (In 2022, at least. If you are reading this article in the future, there might already be an answer to this question that requires further analysis or testing!)

"To what extent has Alexey Arkhipovsky's 2013 album, Insomnia , influenced gender identification in Russian culture?"

While it's tightly focused, this question also presents an assumption (that the music influenced gender identification) and seeks to prove or disprove it. This allows for the possibilities that the music had no influence at all or had a demonstrable impact.

Answering the question will involve explaining the context and using many sources so that the reader can follow the logic and be convinced of the author's findings. The results (be they positive or negative) will also open the door to countless other studies.

How to Turn a Bad Research Question into a Good One

If something is wrong, fix it if you can. But train yourself not to worry. Worry never fixes anything.

–Ernest Hemingway

How do you turn something that won't help your research into something that will? Start by taking a step back and asking what you are expected to produce. While there are any number of fascinating subjects out there, a grant paying you to examine income disparity in Japan is not going to warrant an in-depth discussion of South American farming pollution. 

Use these expectations to frame your initial topic and the subject that your research should be about, and then conduct preliminary research into that subject. If you spot a knowledge gap while researching, make a note of it, and add it to your list of possible questions.

If you already have a question that is relevant to your topic but has flaws, identify the issues and see if they can be addressed. In addition, if your question is too broad, try to narrow it down enough to make your research feasible.

Especially in the sciences, if your research question will not produce results that can be replicated, determine how you can change it so a reader can look at what you've done and go about repeating your actions so they can see that you are right.

Moreover, if you would need 20 years to produce results, consider whether there is a way to tighten things up to produce more immediate results. This could justify future research that will eventually reach that lofty goal.

If all else fails, you can use the flawed question as a subtopic and try to find a better question that fits your goals and expectations.

Parting Advice

When you have your early work edited, don't be surprised if you are told that your research question requires revision. Quite often, results or the lack thereof can force a researcher to shift their focus and examine a less significant topic—or a different facet of a known issue—because testing did not produce the expected result. 

If that happens, take heart. You now have the tools to assess your question, find its flaws, and repair them so that you can complete your research with confidence and publish something you know your audience will read with fascination.

Of course, if you receive affirmation that your research question is strong or are polishing your work before submitting it to a publisher, you might just need a final proofread to ensure that your confidence is well placed. Then, you can start pursuing something new that the world does not yet know (but will know) once you have your research question down.

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How to Write a Science Research Question

Humans are a very curious species. We are always asking questions. But the way we formulate a question is very important when we think about science and research. Here we’ll lay out how to form a science research question and the concepts needed to formulate a good research question. Luckily, we’ve got some handy visuals to help you along.

In order to inquire about the world, produce new information, and solve a mystery of about the natural world, we always use the scientific process to inform research questions. So, we need to keep in mind the steps of the scientific process :

Observation

Data to be obtained

Ways to analyze data

Conclusions to obtain from the question

First, clearly define your population and your variables.

Now, what is a population ? Defined in ecologic terms, a population are all the individuals of one species in a given area (e.g. population of deer, leatherback turtles, spruce trees, mushrooms, etc.).

Now, what is a variable ? A variable is any factor, trait, or condition that can exist in differing amounts or types (e.g. length, quantity, temperature, speed, mass, distance, depth, etc.).

So, using different combinations of these two components, we can create three different types of research questions: descriptive, comparative, and correlative. These three types also match three of the modern research methodologies. 

Descriptive field investigations involve describing and/or quantifying parts of a natural system. Includes generally 1 population and one distinctive variable (figure 1). Examples of descriptive research questions:

How many pine trees are in the Mammoth Hot Springs area?

What is the wolf pack’s distribution range?

How frequently do humpback whales breed?    

Comparative field investigations involve collecting data on different populations/organisms, or under different conditions (e.g., times of year, locations), to make a comparison. Includes two or more populations and one distinctive variable (figure 2). Examples of comparative research questions:

Is there a difference in body length between male and female tortoises?

Is there a difference in diversity of fungi that live in the forest compared with non-forested areas?  

Correlative field investigations involve measuring or observing two variables and searching for a relationship between them for a distinctive population (figure 3). Examples of correlative research questions:

What is the relationship between length of the tail and age in humpback whales?

How does a spider’s reproduction rate change with a change in season?

To practice how to write a research question, we suggest the following steps:

Find a nice place where you can be alone and connected with nature. Bring nothing else but a journal and a pencil. Take a few moments to breath and observe everything that surrounds you. Use all of your senses to obtain information from your surroundings: smell the flowers around you, feel the leaves, hear the birds, and recognize all the life.

Choose a population that is around you and that interests you (flowers, trees, insects, rocks), and think about what would you like to know about that population. Write down what you want to study from that population (your variable). It is easier to choose the population first and the variables second. Think about a feasible and simple measurement. One easy measurement is counting, since it doesn’t require an instrument.

Write down your question using your population and variable. Remember to write a question that is going to be simple, measurable, attainable, relevant, and limited to a particular time and place. Avoid why questions.

Next, write a prediction that answers your question. This is your hypothesis .

Now that you have a defined population, measure your variable, and obtain data. Don’t forget to write it down in your journal.

Finally, compare your hypothesis with your actual data and write a conclusion about your findings.

These simple and fun steps will help you create great questions that will lead you to find interesting answers and discoveries. But remember, this process not only works for scientific questions but also for daily issues, such as why the car stopped working. You can use it to investigate local environmental problems and provide possible solutions for the benefit of your community and future generations.

You can find more information about this topic in: Ryken, A. E., Otto, P., Pritchard, K., & Owens, K. (2007). Field investigations: Using outdoor environments to foster student learning of scientific processes . Pacific Education Institute. 

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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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415 Research Question Examples Across 15 Disciplines

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

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

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

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

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

Agriculture and forestry examples

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

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

Animal science

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

Aquaculture

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

Horticulture

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

Soil science

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

Architectural design

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

Landscape architecture

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

Urban planning

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

Graphic design

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

Industrial design

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

Multimedia arts

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

Performing arts

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

Visual arts

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

Entrepreneurship

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

Human resources

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

Operations research

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

Artificial intelligence

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

Cybersecurity

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

Data science

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

Information systems

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

Software engineering

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

Adult education

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

Curriculum studies

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

Educational administration

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

Educational psychology

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

Special education

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

Aerospace engineering

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

Biomedical engineering

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

Chemical engineering

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

Civil engineering

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

Computer engineering

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

Electrical engineering

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

Mechanical engineering

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

Climatology

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

Conservation science

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

Environmental health

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

Marine biology

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

Kinesiology

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

Public health

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

Art history

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

Cultural studies

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

Religious studies

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

Classic languages

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

Comparative literature

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

Modern languages

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

Constitutional law

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

Corporate law

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

Criminal law

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

International law

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

Applied mathematics

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

Applied statistics

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

Pure mathematics

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

Theoretical statistics

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

Earth science

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

Anthropology

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

Communication

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

Political science

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

Social work

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

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

Header image by Zetong Li .

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9 9. Writing your research question

Chapter outline.

• Empirical vs. ethical questions (4 minute read)
• Characteristics of a good research question (4 minute read)
• Quantitative research questions (7 minute read)
• Qualitative research questions (3 minute read)
• Evaluating and updating your research questions (4 minute read)

Content warning: examples in this chapter include references to sexual violence, sexism, substance use disorders, homelessness, domestic violence, the child welfare system, cissexism and heterosexism, and truancy and school discipline.

9.1 Empirical vs. ethical questions

Learning objectives.

Learners will be able to…

• Define empirical questions and provide an example
• Define ethical questions and provide an example

Writing a good research question is an art and a science. It is a science because you have to make sure it is clear, concise, and well-developed. It is an art because often your language needs “wordsmithing” to perfect and clarify the meaning. This is an exciting part of the research process; however, it can also be one of the most stressful.

Creating a good research question begins by identifying a topic you are interested in studying. At this point, you already have a working question. You’ve been applying it to the exercises in each chapter, and after reading more about your topic in the scholarly literature, you’ve probably gone back and revised your working question a few times. We’re going to continue that process in more detail in this chapter. Keep in mind that writing research questions is an iterative process, with revisions happening week after week until you are ready to start your project.

Empirical vs. ethical questions

When it comes to research questions, social science is best equipped to answer empirical   questions —those that can be answered by real experience in the real world—as opposed to  ethical   questions —questions about which people have moral opinions and that may not be answerable in reference to the real world. While social workers have explicit ethical obligations (e.g., service, social justice), research projects ask empirical questions to help actualize and support the work of upholding those ethical principles.

In order to help you better understand the difference between ethical and empirical questions, let’s consider a topic about which people have moral opinions. How about SpongeBob SquarePants? [1] In early 2005, members of the conservative Christian group Focus on the Family (2005) [2] denounced this seemingly innocuous cartoon character as “morally offensive” because they perceived his character to be one that promotes a “pro-gay agenda.” Focus on the Family supported their claim that SpongeBob is immoral by citing his appearance in a children’s video designed to promote tolerance of all family forms (BBC News, 2005). [3] They also cited SpongeBob’s regular hand-holding with his male sidekick Patrick as further evidence of his immorality.

So, can we now conclude that SpongeBob SquarePants is immoral? Not so fast. While your mother or a newspaper or television reporter may provide an answer, a social science researcher cannot. Questions of morality are ethical, not empirical. Of course, this doesn’t mean that social science researchers cannot study opinions about or social meanings surrounding SpongeBob SquarePants (Carter, 2010). [4] We study humans after all, and as you will discover in the following chapters of this textbook, we are trained to utilize a variety of scientific data-collection techniques to understand patterns of human beliefs and behaviors. Using these techniques, we could find out how many people in the United States find SpongeBob morally reprehensible, but we could never learn, empirically, whether SpongeBob is in fact morally reprehensible.

Let’s consider an example from a recent MSW research class I taught. A student group wanted to research the penalties for sexual assault. Their original research question was: “How can prison sentences for sexual assault be so much lower than the penalty for drug possession?” Outside of the research context, that is a darn good question! It speaks to how the War on Drugs and the patriarchy have distorted the criminal justice system towards policing of drug crimes over gender-based violence.

Unfortunately, it is an ethical question, not an empirical one. To answer that question, you would have to draw on philosophy and morality, answering what it is about human nature and society that allows such unjust outcomes. However, you could not answer that question by gathering data about people in the real world. If I asked people that question, they would likely give me their opinions about drugs, gender-based violence, and the criminal justice system. But I wouldn’t get the real answer about why our society tolerates such an imbalance in punishment.

As the students worked on the project through the semester, they continued to focus on the topic of sexual assault in the criminal justice system. Their research question became more empirical because they read more empirical articles about their topic. One option that they considered was to evaluate intervention programs for perpetrators of sexual assault to see if they reduced the likelihood of committing sexual assault again. Another option they considered was seeing if counties or states with higher than average jail sentences for sexual assault perpetrators had lower rates of re-offense for sexual assault. These projects addressed the ethical question of punishing perpetrators of sexual violence but did so in a way that gathered and analyzed empirical real-world data. Our job as social work researchers is to gather social facts about social work issues, not to judge or determine morality.

Key Takeaways

• Empirical questions are distinct from ethical questions.
• There are usually a number of ethical questions and a number of empirical questions that could be asked about any single topic.
• While social workers may research topics about which people have moral opinions, a researcher’s job is to gather and analyze empirical data.
• Take a look at your working question. Make sure you have an empirical question, not an ethical one. To perform this check, describe how you could find an answer to your question by conducting a study, like a survey or focus group, with real people.

9.2 Characteristics of a good research question

• Identify and explain the key features of a good research question
• Explain why it is important for social workers to be focused and clear with the language they use in their research questions

Now that you’ve made sure your working question is empirical, you need to revise that working question into a formal research question. So, what makes a good research question? First, it is generally written in the form of a question. To say that your research question is “the opioid epidemic” or “animal assisted therapy” or “oppression” would not be correct. You need to frame your topic as a question, not a statement. A good research question is also one that is well-focused. A well-focused question helps you tune out irrelevant information and not try to answer everything about the world all at once. You could be the most eloquent writer in your class, or even in the world, but if the research question about which you are writing is unclear, your work will ultimately lack direction.

In addition to being written in the form of a question and being well-focused, a good research question is one that cannot be answered with a simple yes or no. For example, if your interest is in gender norms, you could ask, “Does gender affect a person’s performance of household tasks?” but you will have nothing left to say once you discover your yes or no answer. Instead, why not ask, about the relationship between gender and household tasks. Alternatively, maybe we are interested in how or to what extent gender affects a person’s contributions to housework in a marriage? By tweaking your question in this small way, you suddenly have a much more fascinating question and more to say as you attempt to answer it.

A good research question should also have more than one plausible answer. In the example above, the student who studied the relationship between gender and household tasks had a specific interest in the impact of gender, but she also knew that preferences might be impacted by other factors. For example, she knew from her own experience that her more traditional and socially conservative friends were more likely to see household tasks as part of the female domain, and were less likely to expect their male partners to contribute to those tasks. Thinking through the possible relationships between gender, culture, and household tasks led that student to realize that there were many plausible answers to her questions about how  gender affects a person’s contribution to household tasks. Because gender doesn’t exist in a vacuum, she wisely felt that she needed to consider other characteristics that work together with gender to shape people’s behaviors, likes, and dislikes. By doing this, the student considered the third feature of a good research question–she thought about relationships between several concepts. While she began with an interest in a single concept—household tasks—by asking herself what other concepts (such as gender or political orientation) might be related to her original interest, she was able to form a question that considered the relationships  among  those concepts.

This student had one final component to consider. Social work research questions must contain a target population. Her study would be very different if she were to conduct it on older adults or immigrants who just arrived in a new country. The target population is the group of people whose needs your study addresses. Maybe the student noticed issues with household tasks as part of her social work practice with first-generation immigrants, and so she made it her target population. Maybe she wants to address the needs of another community. Whatever the case, the target population should be chosen while keeping in mind social work’s responsibility to work on behalf of marginalized and oppressed groups.

In sum, a good research question generally has the following features:

• It is written in the form of a question
• It is clearly written
• It cannot be answered with “yes” or “no”
• It has more than one plausible answer
• It considers relationships among multiple variables
• It is specific and clear about the concepts it addresses
• It includes a target population
• A poorly focused research question can lead to the demise of an otherwise well-executed study.
• Research questions should be clearly worded, consider relationships between multiple variables, have more than one plausible answer, and address the needs of a target population.

Okay, it’s time to write out your first draft of a research question.

• Once you’ve done so, take a look at the checklist in this chapter and see if your research question meets the criteria to be a good one.

Brainstorm whether your research question might be better suited to quantitative or qualitative methods.

• Describe why your question fits better with quantitative or qualitative methods.
• Provide an alternative research question that fits with the other type of research method.

9.3 Quantitative research questions

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about student debt load, or they may include multiple variables. Because these are descriptive questions, our purpose is not to investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? If you recall from Chapter 2 , explanatory research tries to build nomothetic causal relationships. They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relationship between these variables. The standard format I was taught in graduate school for an explanatory quantitative research question is: “What is the relationship between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising that standard format to match what you really want to know about your topic.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 9.1 does just that. While reading the table, keep in mind that I have only noted what I view to be the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table. Each of these questions is drawn from student projects in my research methods classes and reflects the work of many students on their research question over many weeks.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A student investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the student’s definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researcher is clear on what they mean by “household tasks.” Clarifying these distinctions is important as we look ahead to specifying how your variables will be measured in Chapter 11 .

Table 9.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging to be this specific in social work research, particularly when you are just starting out your project and still reading the literature. If you’ve only read one or two articles on your topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project as working questions. However, moving from working questions to research questions in your research proposal requires that you examine the literature on the topic and refine your question over time to be more specific and clear. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing, that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ+ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 9.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Remember that exploratory research is appropriate only when the researcher does not know much about topic because there is very little scholarly research. In our examples above, there is extensive literature on the outcomes in family reunification programs and risk factors for child removal in child welfare. Make sure you’ve done a thorough literature review to ensure there is little relevant research to guide you towards a more explanatory question.

• Descriptive quantitative research questions are helpful for community scans but cannot investigate causal relationships between variables.
• Explanatory quantitative research questions must include an independent and dependent variable.
• Exploratory quantitative research questions should only be considered when there is very little previous research on your topic.
• Identify the type of research you are engaged in (descriptive, explanatory, or exploratory).
• Create a quantitative research question for your project that matches with the type of research you are engaged in.

Preferably, you should be creating an explanatory research question for quantitative research.

9.4 Qualitative research questions

• List the key terms associated with qualitative research questions
• Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Because qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, they are often more general and openly worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences ,  understandings , and  meanings that people have about the concepts in our research question. These keywords often make an appearance in qualitative research questions.

Let’s work through an example from our last section. In Table 9.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ+ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ+ status causes changes in homelessness.

However, what if the student were less interested in  predicting  homelessness based on LGBTQ+ status and more interested in  understanding  the stories of foster care youth who identify as LGBTQ+ and may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation . The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Because qualitative questions usually center on idiographic causal relationships, they look different than quantitative questions. Table 9.3 below takes the final research questions from Table 9.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions.

• Qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories.
• Qualitative research questions may be more general and less specific.
• Qualitative research questions may also contain only one variable, rather than asking about relationships between multiple variables.

Qualitative research questions have one final feature that distinguishes them from quantitative research questions: they can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or the lack of job opportunities in the area. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on information gleaned from participants.

However, this reflexivity and openness unacceptable in quantitative research for good reasons. Researchers using quantitative methods are testing a hypothesis, and if they could revise that hypothesis to match what they found, they could never be wrong! Indeed, an important component of open science and reproducability is the preregistration of a researcher’s hypotheses and data analysis plan in a central repository that can be verified and replicated by reviewers and other researchers. This interactive graphic from 538 shows how an unscrupulous research could come up with a hypothesis and theoretical explanation  after collecting data by hunting for a combination of factors that results in a statistically significant relationship. This is an excellent example of how the positivist assumptions behind quantitative research and intepretivist assumptions behind qualitative research result in different approaches to social science.

• Qualitative research questions often contain words or phrases like “lived experience,” “personal experience,” “understanding,” “meaning,” and “stories.”
• Qualitative research questions can change and evolve over the course of the study.
• Using the guidance in this chapter, write a qualitative research question. You may want to use some of the keywords mentioned above.

9.5 Evaluating and updating your research questions

• Evaluate the feasibility and importance of your research questions
• Begin to match your research questions to specific designs that determine what the participants in your study will do

Feasibility and importance

As you are getting ready to finalize your research question and move into designing your research study, it is important to check whether your research question is feasible for you to answer and what importance your results will have in the community, among your participants, and in the scientific literature

Key questions to consider when evaluating your question’s feasibility include:

• Do you have access to the data you need?
• Will you be able to get consent from stakeholders, gatekeepers, and others?
• Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality? (see Chapter 6 for more ethical considerations)
• Are you competent enough to complete the study?
• Do you have the resources and time needed to carry out the project?

Key questions to consider when evaluating the importance of your question include:

• Can we answer your research question simply by looking at the literature on your topic?
• How does your question add something new to the scholarly literature? (raises a new issue, addresses a controversy, studies a new population, etc.)
• How will your target population benefit, once you answer your research question?
• How will the community, social work practice, and the broader social world benefit, once you answer your research question?
• Using the questions above, check whether you think your project is feasible for you to complete, given the constrains that student projects face.
• Realistically, explore the potential impact of your project on the community and in the scientific literature. Make sure your question cannot be answered by simply reading more about your topic.

Matching your research question and study design

This chapter described how to create a good quantitative and qualitative research question. In Parts 3 and 4 of this textbook, we will detail some of the basic designs like surveys and interviews that social scientists use to answer their research questions. But which design should you choose?

As with most things, it all depends on your research question. If your research question involves, for example, testing a new intervention, you will likely want to use an experimental design. On the other hand, if you want to know the lived experience of people in a public housing building, you probably want to use an interview or focus group design.

We will learn more about each one of these designs in the remainder of this textbook. We will also learn about using data that already exists, studying an individual client inside clinical practice, and evaluating programs, which are other examples of designs. Below is a list of designs we will cover in this textbook:

• Surveys: online, phone, mail, in-person
• Experiments: classic, pre-experiments, quasi-experiments
• Interviews: in-person or via phone or videoconference
• Focus groups: in-person or via videoconference
• Content analysis of existing data
• Secondary data analysis of another researcher’s data
• Program evaluation

The design of your research study determines what you and your participants will do. In an experiment, for example, the researcher will introduce a stimulus or treatment to participants and measure their responses. In contrast, a content analysis may not have participants at all, and the researcher may simply read the marketing materials for a corporation or look at a politician’s speeches to conduct the data analysis for the study.

I imagine that a content analysis probably seems easier to accomplish than an experiment. However, as a researcher, you have to choose a research design that makes sense for your question and that is feasible to complete with the resources you have. All research projects require some resources to accomplish. Make sure your design is one you can carry out with the resources (time, money, staff, etc.) that you have.

There are so many different designs that exist in the social science literature that it would be impossible to include them all in this textbook. The purpose of the subsequent chapters is to help you understand the basic designs upon which these more advanced designs are built. As you learn more about research design, you will likely find yourself revising your research question to make sure it fits with the design. At the same time, your research question as it exists now should influence the design you end up choosing. There is no set order in which these should happen. Instead, your research project should be guided by whether you can feasibly carry it out and contribute new and important knowledge to the world.

• Research questions must be feasible and important.
• Research questions must match study design.
• Based on what you know about designs like surveys, experiments, and interviews, describe how you might use one of them to answer your research question.
• You may want to refer back to Chapter 2 which discusses how to get raw data about your topic and the common designs used in student research projects.
• Not familiar with SpongeBob SquarePants? You can learn more about him on Nickelodeon’s site dedicated to all things SpongeBob:  http://www.nick.com/spongebob-squarepants/ ↵
• Focus on the Family. (2005, January 26). Focus on SpongeBob.  Christianity Today . Retrieved from  http://www.christianitytoday.com/ct/2005/januaryweb-only/34.0c.html ↵
• BBC News. (2005, January 20). US right attacks SpongeBob video. Retrieved from:  http://news.bbc.co.uk/2/hi/americas/4190699.stm ↵
• In fact, an MA thesis examines representations of gender and relationships in the cartoon: Carter, A. C. (2010).  Constructing gender and   relationships in “SpongeBob SquarePants”: Who lives in a pineapple under the sea . MA thesis, Department of Communication, University of South Alabama, Mobile, AL. ↵

research questions that can be answered by systematically observing the real world

unsuitable research questions which are not answerable by systematic observation of the real world but instead rely on moral or philosophical opinions

the group of people whose needs your study addresses

attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

whether you can practically and ethically complete the research project you propose

the impact your study will have on participants, communities, scientific knowledge, and social justice

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Masters in Environmental Science

• Examples of Good and Bad Research Questions

Every research writing requires research questions. Your research questions are what guide you to keep researching. Note that the questions have to be tailored to your topic. In the beginning, you’ll require a problem statement about the topic; it’s the topic’s loophole.

Once you have the problem statement, develop research questions to help you write on the topic. Your research question is what drives your research and something within the field that must be provided.

So, when drafting research questions, ensure it guides your research. Because choosing the wrong research questions ruins the outcome of your work. This article covers everything about good and bad research topics.

How to choose research questions

Before choosing research questions, make sure you have a solid topic. There has to be a loophole in your area of research, which is what your research question helps you find solutions to. Here are steps to choosing a good research question:

• Choose a relevant topic

Your choice of topic has to be on point. When choosing a topic, ask yourself, what topic interests you? Which areas in this topic require more research? Also, make sure that the context you’ve chosen to base your topic on is of interest to people.

• Research the topic

During the research process, you begin to learn more about your topic. The more you learn, the more you realize angles that need to be addressed. Through research, you find a research problem.

• Narrow your topic down

After researching your topic, you’ve already started seeing a possible angle your research will take. Narrow your topic down to focus on a particular area.

• Identify the problem

Now that you’ve narrowed your topic down, you focus attention on an area and fish out issues within this research area. This is usually when the research problem comes up.

• Turn the problem into question

After finding your research problem, to dissect the problem, you’ll need to break it down into questions. For example, your research should be the “so what? now what?” question.

Characteristics of a good research question

Research questions can either be good or bad. A bad research question ruins the outcome of your research. So, you must be careful of your questions. Here are some of the features of a good research question:

• A good research question is focused and straight to the point
• A good research question targets and provides a solid answer to the problem
• Good research questions provide more depth into a topic
• Good research sets the context of the research
• It is grounded in current theoretical and empirical knowledge.

Research topic vs research question

Research topics are different from research questions. A research topic is a general area your research focuses on. It’s the context upon which you’ve based your research interest. On the other hand, research questions are formulated to solve the research problem.

Good research questions examples

Here are good sample research questions to consider:

• How can government regulations help to provide free healthcare services for low-income earners?
• What factors cause the increase in death rate during the first wave of Covid?
• What strategies can be implemented to prevent drug abuse amongst teenagers
• What effect does drug abuse have on underage young girls?
• What measures can the government implement to mitigate sex trafficking in district x?

Examples of bad research questions

While there are good research questions, there are also bad ones. Bad research questions affect the outcome and quality of your research. Below are some bad research questions:

• Why do low-income earners lack healthcare?
• How did Covid kill many people?
• Is drug abuse bad for teenagers?
• Does drug abuse affect girls?
• Is it possible to stop trafficking?

Why are these questions bad? They are bad because they are ineffective. What is ineffective about this research question? The questions are generalized; they are not specific and cannot be used to address any problem. The essence of a good research topic is that it points the arrow toward a core problem that needs to be addressed.

Bad research questions weaken the outcome of your research. Since the purpose of research is to draw attention to an area. The question has to be compelling enough to attract the attention of readers.

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Research Question

Ai generator.

A research question serves as the foundation of any academic study, driving the investigation and framing the scope of inquiry. It focuses the research efforts, ensuring that the study addresses pertinent issues systematically. Crafting a strong research question is essential as it directs the methodology, data collection, and analysis, ultimately shaping the study’s conclusions and contributions to the field.

What is a Research Question?

A research question is the central query that guides a study, focusing on a specific problem or issue. It defines the purpose and direction of the research, influencing the methodology and analysis. A well-crafted research question ensures the study remains relevant, systematic, and contributes valuable insights to the field.

Types of Research Questions

Research questions are a crucial part of any research project. They guide the direction and focus of the study. Here are the main types of research questions:

1. Descriptive Research Questions

These questions aim to describe the characteristics or functions of a specific phenomenon or group. They often begin with “what,” “who,” “where,” “when,” or “how.”

• What are the common symptoms of depression in teenagers?

2. Comparative Research Questions

These questions compare two or more groups or variables to identify differences or similarities.

• How do the academic performances of students in private schools compare to those in public schools?

3. Correlational Research Questions

These questions seek to identify the relationships between two or more variables. They often use terms like “relationship,” “association,” or “correlation.”

• Is there a relationship between social media usage and self-esteem among adolescents?

4. Causal Research Questions

These questions aim to determine whether one variable causes or influences another. They are often used in experimental research.

• Does a new teaching method improve student engagement in the classroom?

5. Exploratory Research Questions

These questions are used when the researcher is exploring a new area or seeking to understand a complex phenomenon. They are often open-ended.

• What factors contribute to the success of start-up companies in the tech industry?

6. Predictive Research Questions

These questions aim to predict future occurrences based on current or past data. They often use terms like “predict,” “forecast,” or “expect.”

• Can high school GPA predict college success?

7. Evaluative Research Questions

These questions assess the effectiveness or impact of a program, intervention, or policy .

• How effective is the new community outreach program in reducing homelessness?

8. Ethnographic Research Questions

These questions are used in qualitative research to understand cultural phenomena from the perspective of the participants.

• How do cultural beliefs influence healthcare practices in rural communities?

9. Case Study Research Questions

These questions focus on an in-depth analysis of a specific case, event, or instance.

• What were the critical factors that led to the failure of Company X?

10. Phenomenological Research Questions

These questions explore the lived experiences of individuals to understand a particular phenomenon.

• What is the experience of living with chronic pain?

Research Question Format

A well-formulated research question is essential for guiding your study effectively. Follow this format to ensure clarity and precision:

• Begin with a broad subject area.
• Example: “Education technology”
• Define a specific aspect or variable.
• Example: “Impact of digital tools”
• Decide if you are describing, comparing, or investigating relationships.
• Example: “Effectiveness”
• Identify who or what is being studied.
• Example: “High school students”
• Formulate the complete question.
• Example: “How effective are digital tools in enhancing the learning experience of high school students?”

Sample Format: “How [specific aspect] affects [target population] in [context]?” Example: “How does the use of digital tools affect the academic performance of high school students in urban areas?”

Research Question Examples

Research questions in business.

• “What are the primary factors influencing customer loyalty in the retail industry?”
• “How does employee satisfaction differ between remote work and in-office work environments in tech companies?”
• “What is the relationship between social media marketing and brand awareness among small businesses?”
• “How does implementing a four-day workweek impact productivity in consulting firms?”
• “What are the emerging trends in consumer behavior post-COVID-19 in the e-commerce sector?”
• “Why do some startups succeed in attracting venture capital while others do not?”
• “How effective is corporate social responsibility in enhancing brand reputation for multinational companies?”
• “How do decision-making processes in family-owned businesses differ from those in publicly traded companies?”
• “What strategies do successful entrepreneurs use to scale their businesses in competitive markets?”
• “How does supply chain management affect the operational efficiency of manufacturing firms?”

Research Questions in Education

• “What are the most common challenges faced by first-year teachers in urban schools?”
• “How do student achievement levels differ between traditional classrooms and blended learning environments?”
• “What is the relationship between parental involvement and student academic performance in elementary schools?”
• “How does the implementation of project-based learning affect critical thinking skills in middle school students?”
• “What are the emerging trends in the use of artificial intelligence in education?”
• “Why do some students perform better in standardized tests than others despite similar instructional methods?”
• “How effective is the flipped classroom model in improving student engagement and learning outcomes in high school science classes?”
• “How do teachers’ professional development programs impact teaching practices and student outcomes in rural schools?”
• “What strategies can be employed to reduce the dropout rate among high school students in low-income areas?”
• “How does classroom size affect the quality of teaching and learning in elementary schools?”

Research Questions in Health Care

• “What are the most common barriers to accessing mental health services in rural areas?”
• “How does patient satisfaction differ between telemedicine and in-person consultations in primary care?”
• “What is the relationship between diet and the incidence of type 2 diabetes in adults?”
• “How does regular physical activity influence the recovery rate of patients with cardiovascular diseases?”
• “What are the emerging trends in the use of wearable technology for health monitoring?”
• “Why do some patients adhere to their medication regimen while others do not despite similar health conditions?”
• “How effective are community-based health interventions in reducing obesity rates among children?”
• “How do interdisciplinary team meetings impact patient care in hospitals?”
• “What strategies can be implemented to reduce the spread of infectious diseases in healthcare settings?”
• “How does nurse staffing level affect patient outcomes in intensive care units?”

Research Questions in Computer Science

• “What are the key features of successful machine learning algorithms used in natural language processing?”
• “How does the performance of quantum computing compare to classical computing in solving complex optimization problems?”
• “What is the relationship between software development methodologies and project success rates in large enterprises?”
• “How does the implementation of cybersecurity protocols impact the frequency of data breaches in financial institutions?”
• “What are the emerging trends in blockchain technology applications beyond cryptocurrency?”
• “Why do certain neural network architectures outperform others in image recognition tasks?”
• “How effective are different code review practices in reducing bugs in open-source software projects?”
• “How do agile development practices influence team productivity and product quality in software startups?”
• “What strategies can improve the scalability of distributed systems in cloud computing environments?”
• “How does the choice of programming language affect the performance and maintainability of enterprise-level software applications?”

Research Questions in Psychology

• “What are the most common symptoms of anxiety disorders among adolescents?”
• “How does the level of job satisfaction differ between remote workers and in-office workers?”
• “What is the relationship between social media use and self-esteem in teenagers?”
• “How does cognitive-behavioral therapy (CBT) affect the severity of depression symptoms in adults?”
• “What are the emerging trends in the treatment of post-traumatic stress disorder (PTSD)?”
• “Why do some individuals develop resilience in the face of adversity while others do not?”
• “How effective are mindfulness-based interventions in reducing stress levels among college students?”
• “How does group therapy influence the social skills development of children with autism spectrum disorder?”
• “What strategies can improve the early diagnosis of bipolar disorder in young adults?”
• “How do sleep patterns affect cognitive functioning and academic performance in high school students?”

More Research Question Examples

Research question examples for students.

• “What are the primary study habits of high-achieving college students?”
• “How do academic performances differ between students who participate in extracurricular activities and those who do not?”
• “What is the relationship between time management skills and academic success in high school students?”
• “How does the use of technology in the classroom affect students’ engagement and learning outcomes?”
• “What are the emerging trends in online learning platforms for high school students?”
• “Why do some students excel in standardized tests while others struggle despite similar study efforts?”
• “How effective are peer tutoring programs in improving students’ understanding of complex subjects?”
• “How do different teaching methods impact the learning process of students with learning disabilities?”
• “What strategies can help reduce test anxiety among middle school students?”
• “How does participation in group projects affect the development of collaboration skills in university students?”

Research Question Examples for College Students

• “What are the most common stressors faced by college students during final exams?”
• “How does academic performance differ between students who live on campus and those who commute?”
• “What is the relationship between part-time employment and GPA among college students?”
• “How does participation in study abroad programs impact cultural awareness and academic performance?”
• “What are the emerging trends in college students’ use of social media for academic purposes?”
• “Why do some college students engage in academic dishonesty despite awareness of the consequences?”
• “How effective are university mental health services in addressing students’ mental health issues?”
• “How do different learning styles affect the academic success of college students in online courses?”
• “What strategies can be employed to improve retention rates among first-year college students?”
• “How does participation in extracurricular activities influence leadership skills development in college students?”

Research Question Examples in Statistics

• “What are the most common statistical methods used in medical research?”
• “How does the accuracy of machine learning models compare to traditional statistical methods in predicting housing prices?”
• “What is the relationship between sample size and the power of a statistical test in clinical trials?”
• “How does the use of random sampling affect the validity of survey results in social science research?”
• “What are the emerging trends in the application of Bayesian statistics in data science?”
• “Why do some datasets require transformation before applying linear regression models?”
• “How effective are bootstrapping techniques in estimating the confidence intervals of small sample data?”
• “How do different imputation methods impact the results of analyses with missing data?”
• “What strategies can improve the interpretation of interaction effects in multiple regression analysis?”
• “How does the choice of statistical software affect the efficiency of data analysis in academic research?”

Research Question Examples in Socialogy

• “What are the primary social factors contributing to urban poverty in major cities?”
• “How does the level of social integration differ between immigrants and native-born citizens in urban areas?”
• “What is the relationship between educational attainment and social mobility in different socioeconomic classes?”
• “How does exposure to social media influence political participation among young adults?”
• “What are the emerging trends in family structures and their impact on child development?”
• “Why do certain communities exhibit higher levels of civic engagement than others?”
• “How effective are community policing strategies in reducing crime rates in diverse neighborhoods?”
• “How do socialization processes differ in single-parent households compared to two-parent households?”
• “What strategies can be implemented to reduce racial disparities in higher education enrollment?”
• “How does the implementation of public housing policies affect the quality of life for low-income families?”

Research Question Examples in Biology

• “What are the primary characteristics of the various stages of mitosis in eukaryotic cells?”
• “How do the reproductive strategies of amphibians compare to those of reptiles?”
• “What is the relationship between genetic diversity and the resilience of plant species to climate change?”
• “How does the presence of pollutants in freshwater ecosystems impact the growth and development of aquatic organisms?”
• “What are the emerging trends in the use of CRISPR technology for gene editing in agricultural crops?”
• “Why do certain bacteria develop antibiotic resistance more rapidly than others?”
• “How effective are different conservation strategies in protecting endangered species?”
• “How do various environmental factors influence the process of photosynthesis in marine algae?”
• “What strategies can enhance the effectiveness of reforestation programs in tropical rainforests?”
• “How does the method of seed dispersal affect the spatial distribution and genetic diversity of plant populations?”

Research Question Examples in History

• “What were the key social and economic factors that led to the Industrial Revolution in Britain?”
• “How did the political systems of ancient Athens and ancient Sparta differ in terms of governance and citizen participation?”
• “What is the relationship between the Renaissance and the subsequent scientific revolution in Europe?”
• “How did the Treaty of Versailles contribute to the rise of Adolf Hitler and the onset of World War II?”
• “What are the emerging perspectives on the causes and impacts of the American Civil Rights Movement?”
• “Why did the Roman Empire decline and eventually fall despite its extensive power and reach?”
• “How effective were the New Deal programs in alleviating the effects of the Great Depression in the United States?”
• “How did the processes of colonization and decolonization affect the political landscape of Africa in the 20th century?”
• “What strategies did the suffragette movement use to secure voting rights for women in the early 20th century?”
• “How did the logistics and strategies of the D-Day invasion contribute to the Allied victory in World War II?”

Importance of Research Questions

Research questions are fundamental to the success and integrity of any study. Their importance can be highlighted through several key aspects:

• Research questions provide a clear focus and direction for the study, ensuring that the researcher remains on track.
• Example: “How does online learning impact student engagement in higher education?”
• They establish the boundaries of the research, determining what will be included or excluded.
• Example: “What are the effects of air pollution on respiratory health in urban areas?”
• Research questions dictate the choice of research design, methodology, and data collection techniques.
• Example: “What is the relationship between physical activity and mental health in adolescents?”
• They make the objectives of the research explicit, providing clarity and precision to the study’s goals.
• Example: “Why do some startups succeed in securing venture capital while others fail?”
• Well-crafted research questions emphasize the significance and relevance of the study, justifying its importance.
• Example: “How effective are public health campaigns in increasing vaccination rates among young adults?”
• They enable a systematic approach to inquiry, ensuring that the study is coherent and logically structured.
• Example: “What are the social and economic impacts of remote work on urban communities?”
• Research questions offer a framework for analyzing and interpreting data, guiding the researcher in making sense of the findings.
• Example: “How does social media usage affect self-esteem among teenagers?”
• By addressing specific gaps or exploring new areas, research questions ensure that the study contributes meaningfully to the existing body of knowledge.
• Example: “What are the emerging trends in the use of artificial intelligence in healthcare?”
• Clear and precise research questions increase the credibility and reliability of the research by providing a focused approach.
• Example: “How do educational interventions impact literacy rates in low-income communities?”
• They help in clearly communicating the purpose and findings of the research to others, including stakeholders, peers, and the broader academic community.
• Example: “What strategies are most effective in reducing youth unemployment in developing countries?”

Research Question vs. Hypothesis

Chracteristics of research questions.

Research questions are fundamental to the research process as they guide the direction and focus of a study. Here are the key characteristics of effective research questions:

1. Clear and Specific

• The question should be clearly articulated and specific enough to be understood without ambiguity.
• Example: “What are the effects of social media on teenagers’ mental health?” rather than “How does social media affect people?”

2. Focused and Researchable

• The question should be narrow enough to be answerable through research and data collection.
• Example: “How does participation in extracurricular activities impact academic performance in high school students?” rather than “How do activities affect school performance?”

3. Complex and Analytical

• The question should require more than a simple yes or no answer and should invite analysis and discussion.
• Example: “What factors contribute to the success of renewable energy initiatives in urban areas?” rather than “Is renewable energy successful?”

4. Relevant and Significant

• The question should address an important issue or problem in the field of study and contribute to knowledge or practice.
• Example: “How does climate change affect agricultural productivity in developing countries?” rather than “What is climate change?”

5. Feasible and Practical

• The question should be feasible to answer within the constraints of time, resources, and access to information.
• Example: “What are the challenges faced by remote workers in the tech industry during the COVID-19 pandemic?” rather than “What are the challenges of remote work?”

6. Original and Novel

• The question should offer a new perspective or explore an area that has not been extensively studied.
• Example: “How do virtual reality technologies influence empathy in healthcare training?” rather than “What is virtual reality?”
• The question should be framed in a way that ensures the research can be conducted ethically.
• Example: “What are the impacts of privacy laws on consumer data protection in the digital age?” rather than “How can we collect personal data more effectively?”

8. Open-Ended

• The question should encourage detailed responses and exploration, rather than limiting answers to a simple yes or no.
• Example: “In what ways do cultural differences affect communication styles in multinational companies?” rather than “Do cultural differences affect communication?”

9. Aligned with Research Goals

• The question should align with the overall objectives of the research project or study.
• Example: “How do early childhood education programs influence long-term academic achievement?” if the goal is to understand educational impacts.

10. Based on Prior Research

• The question should build on existing literature and research, identifying gaps or new angles to explore.
• Example: “What strategies have proven effective in reducing urban air pollution in European cities?” after reviewing current studies on air pollution strategies.

Benefits of Research Question

Research questions are fundamental to the research process and offer numerous benefits, which include the following:

1. Guides the Research Process

A well-defined research question provides a clear focus and direction for your study. It helps in determining what data to collect, how to collect it, and how to analyze it.

Benefit: Ensures that the research stays on track and addresses the specific issue at hand.

2. Clarifies the Purpose of the Study

Research questions help to articulate the purpose and objectives of the study. They make it clear what the researcher intends to explore, describe, compare, or test.

Benefit: Helps in communicating the goals and significance of the research to others, including stakeholders and funding bodies.

3. Determines the Research Design

The type of research question informs the research design, including the choice of methodology, data collection methods, and analysis techniques.

Benefit: Ensures that the chosen research design is appropriate for answering the specific research question, enhancing the validity and reliability of the results.

4. Enhances Literature Review

A well-crafted research question provides a framework for conducting a thorough literature review. It helps in identifying relevant studies, theories, and gaps in existing knowledge.

Benefit: Facilitates a comprehensive understanding of the topic and ensures that the research is grounded in existing literature.

5. Focuses Data Collection

Research questions help in identifying the specific data needed to answer them. This focus prevents the collection of unnecessary data and ensures that all collected data is relevant to the study.

Benefit: Increases the efficiency of data collection and analysis, saving time and resources.

6. Improves Data Analysis

Having a clear research question aids in the selection of appropriate data analysis methods. It helps in determining how the data will be analyzed to draw meaningful conclusions.

Benefit: Enhances the accuracy and relevance of the findings, making them more impactful.

7. Facilitates Hypothesis Formation

In quantitative research, research questions often lead to the development of hypotheses that can be tested statistically.

Benefit: Provides a basis for hypothesis testing, which is essential for establishing cause-and-effect relationships.

8. Supports Result Interpretation

Research questions provide a lens through which the results of the study can be interpreted. They help in understanding what the findings mean in the context of the research objectives.

Benefit: Ensures that the conclusions drawn from the research are aligned with the original aims and objectives.

9. Enhances Reporting and Presentation

A clear research question makes it easier to organize and present the research findings. It helps in structuring the research report or presentation logically.

Benefit: Improves the clarity and coherence of the research report, making it more accessible and understandable to the audience.

10. Encourages Critical Thinking

Formulating research questions requires critical thinking and a deep understanding of the subject matter. It encourages researchers to think deeply about what they want to investigate and why.

Benefit: Promotes a more thoughtful and analytical approach to research, leading to more robust and meaningful findings.

How to Write a Research Question

Crafting a strong research question is crucial for guiding your study effectively. Follow these steps to write a clear and focused research question:

Identify a Broad Topic:

Start with a general area of interest that you are passionate about or that is relevant to your field. Example: “Climate change”

Conduct Preliminary Research:

Explore existing literature and studies to understand the current state of knowledge and identify gaps. Example: “Impact of climate change on agriculture”

Narrow Down the Topic:

Focus on a specific aspect or issue within the broad topic to make the research question more manageable. Example: “Effect of climate change on crop yields”

Consider the Scope:

Ensure the question is neither too broad nor too narrow. It should be specific enough to be answerable but broad enough to allow for thorough exploration. Example: “How does climate change affect corn crop yields in the Midwest United States?”

Determine the Research Type:

Decide whether your research will be descriptive, comparative, relational, or causal, as this will shape your question. Example: “How does climate change affect corn crop yields in the Midwest United States over the past decade?”

Formulate the Question:

Write a clear, concise question that specifies the variables, population, and context. Example: “What is the impact of increasing temperatures and changing precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

Ensure Feasibility:

Make sure the question can be answered within the constraints of your resources, time, and data availability. Example: “How have corn crop yields in the Midwest United States been affected by climate change-related temperature increases and precipitation changes between 2010 and 2020?”

Review and Refine:

Evaluate the question for clarity, focus, and relevance. Revise as necessary to ensure it is well-defined and researchable. Example: “What are the specific impacts of temperature increases and changes in precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

What is a research question?

A research question is a specific query guiding a study’s focus and objectives, shaping its methodology and analysis.

Why is a research question important?

It provides direction, defines scope, ensures relevance, and guides the methodology of the research.

How do you formulate a research question?

Identify a topic, narrow it down, conduct preliminary research, and ensure it is clear, focused, and researchable.

What makes a good research question?

Clarity, specificity, feasibility, relevance, and the ability to guide the research effectively.

Can a research question change?

Yes, it can evolve based on initial findings, further literature review, and the research process.

What is the difference between a research question and a hypothesis?

A research question guides the study; a hypothesis is a testable prediction about the relationship between variables.

How specific should a research question be?

It should be specific enough to provide clear direction but broad enough to allow for comprehensive investigation.

What are examples of good research questions?

Examples include: “How does social media affect academic performance?” and “What are the impacts of climate change on agriculture?”

Can a research question be too broad?

Yes, a too broad question can make the research unfocused and challenging to address comprehensively.

What role does a research question play in literature reviews?

It helps identify relevant studies, guides the search for literature, and frames the review’s focus.

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Fats and Cholesterol

When it comes to dietary fat, what matters most is the type of fat you eat. Contrary to past dietary advice promoting low-fat diets , newer research shows that healthy fats are necessary and beneficial for health.

• When food manufacturers reduce fat, they often replace it with carbohydrates from sugar, refined grains, or other starches. Our bodies digest these refined carbohydrates and starches very quickly, affecting blood sugar and insulin levels and possibly resulting in weight gain and disease. ( 1-3 )
• Findings from the Nurses’ Health Study ( 4 ) and the Health Professionals Follow-up Study ( 5 ) show that no link between the overall percentage of calories from fat and any important health outcome, including cancer, heart disease, and weight gain.

Rather than adopting a low-fat diet, it’s more important to focus on eating beneficial “good” fats and avoiding harmful “bad” fats. Fat is an important part of a healthy diet. Choose foods with “good” unsaturated fats, limit foods high in saturated fat, and avoid “bad” trans fat.

• “Good” unsaturated fats — Monounsaturated and polyunsaturated fats — lower disease risk. Foods high in good fats include vegetable oils (such as olive, canola, sunflower, soy, and corn), nuts, seeds, and fish.
• “Bad” fats — trans fats — increase disease risk, even when eaten in small quantities. Foods containing trans fats are primarily in processed foods made with trans fat from partially hydrogenated oil. Fortunately, trans fats have been eliminated from many of these foods.
• Saturated fats , while not as harmful as trans fats, by comparison with unsaturated fats negatively impact health and are best consumed in moderation. Foods containing large amounts of saturated fat include red meat, butter, cheese, and ice cream. Some plant-based fats like coconut oil and palm oil are also rich in saturated fat.
• When you cut back on foods like red meat and butter, replace them with fish, beans, nuts, and healthy oils instead of refined carbohydrates.

Read more about healthy fats in this “Ask the Expert” with HSPH’s Dr. Walter Willett and Amy Myrdal Miller, M.S., R.D., formerly of The Culinary Institute of America

1. Siri-Tarino, P.W., et al., Saturated fatty acids and risk of coronary heart disease: modulation by replacement nutrients. Curr Atheroscler Rep, 2010. 12(6): p. 384-90.

2. Hu, F.B., Are refined carbohydrates worse than saturated fat? Am J Clin Nutr, 2010. 91(6): p. 1541-2.

3. Jakobsen, M.U., et al., Intake of carbohydrates compared with intake of saturated fatty acids and risk of myocardial infarction: importance of the glycemic index. Am J Clin Nutr, 2010. 91(6): p. 1764-8.

4. Hu, F.B., et al., Dietary fat intake and the risk of coronary heart disease in women. N Engl J Med, 1997. 337(21): p. 1491-9.

5. Ascherio, A., et al., Dietary fat and risk of coronary heart disease in men: cohort follow up study in the United States. BMJ, 1996. 313(7049): p. 84-90.

6. Hu, F.B., J.E. Manson, and W.C. Willett, Types of dietary fat and risk of coronary heart disease: a critical review. J Am Coll Nutr, 2001. 20(1): p. 5-19.

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Latanya Sweeney is exploring the clashes between technology and society

Sweeney, head of the Public Interest Technology Lab, is helping to better understand how technology can cause social harms as well as serve the common good.

By Nora Delaney Photos by M. Scott Brauer Summer 2022

“POLICY MOVES in a function of years,” Latanya Sweeney explains. “Technology moves in a function of months. There is an incredible mismatch in the rate of change.”

This mismatch has driven Sweeney’s work for more than two decades as she has explored what she calls “clashes” between technology and society, leading her to major discoveries in the fields of privacy and algorithmic fairness.

A computer scientist and the Daniel Paul Professor of the Practice of Government and Technology at Harvard Kennedy School, Sweeney is committed to public interest technology, a field built on the understanding that technology can cause social harms as well as serve the common good.

Sweeney established and leads the Public Interest Technology Lab , which is housed in the School’s Shorenstein Center on Media, Politics and Public Policy . The lab enables students and scholars to conduct their own experiments to address collisions between technology and society, which range from the bias baked into algorithms to vulnerabilities in voting technology to the immense power that tech platforms wield. “We go where the problem is,” she says.

Going Where the Problem Is

This mindset developed from Sweeney’s experiences in graduate school. She was achieving her lifelong dream of becoming a computer scientist at the Massachusetts Institute of Technology, when a colleague told her that computers are evil. Sweeney disagreed, but as she learned about how data was being shared through technology in ways that put people’s privacy at risk, she realized that technology and society could, indeed, be at odds. So, she started investigating and experimenting, eventually writing her dissertation on data privacy protection. In 2001, she became the first African American woman to receive a PhD in computer science from MIT.

Continuing her graduate school focus on data privacy, Sweeney became a pioneer in the field, with her discoveries leading to changes in law and policy. For example, she conducted experiments that showed vulnerabilities in supposedly anonymized health data. She revealed that using just a few pieces of publicly available information—date of birth, gender, and ZIP code—87% of the U.S. population could be matched to health records. In response, HIPAA—the Health Insurance Portability and Accountability Act, a major federal health privacy law—was revised to better protect individuals’ privacy. 

Sweeney had another breakthrough when she discovered just how biased computer algorithms used by ad providers can be. She learned from a journalist that an ad for arrest records popped up when her name was searched online. Sweeney was alarmed. She had never been arrested. She decided to figure out what was going on. Her ensuing research, published in a 2013 paper, revealed racial discrimination built into algorithms. Ads for arrest records popped up more frequently when someone searched a stereotypical Black name rather than a stereotypically white one—“Latanya” rather than “Tanya,” for example—regardless of whether the person searched for really had an arrest record. “I showed how Google’s ad network was delivering ads that were actually in violation of the Civil Rights Act,” Sweeney says.

A Laboratory at a Policy School

Although Sweeney has made significant contributions to the public good in her research on technology, a large part of her work as an academic has been teaching students and building networks of scholars.

While she was a professor at Carnegie Mellon University, Sweeney started the Data Privacy Lab (which is now part of the Public Interest Technology Lab) and brought it with her to Harvard in 2011 when she joined the University’s Department of Government. After a leave to serve as chief technology officer at the U.S. Federal Trade Commission, she returned to Harvard and eventually took a joint position at the Kennedy School. (Since 2016, she has also been a faculty dean at Currier House, where she lives with her family among 300 Harvard undergraduates and enjoys mentoring and interacting with them.)

In 2021, Sweeney launched the Public Interest Technology Lab with three primary goals: developing public interest technologies, conducting research, and connecting students and scholars to share knowledge across universities. She has drawn faculty affiliates to the lab from the Kennedy School and across Harvard.

The idea, she says, “was to take all these experiments that have worked”—the tinkering and exploring that have led Sweeney to discover privacy vulnerabilities and algorithmic bias, for example— “and put them on steroids.” Although the Tech Lab is fairly new, it traces the arc of Sweeney’s evolving interests in an evolving field and aims to address the growing conflicts between technology and society. 

To deal with these increasingly frequent clashes, Sweeney realized, she needed to reach more people. So, she designed the lab as a hub for students and scholars to learn, experiment, and connect—to train others in order to extend the work far beyond Harvard. Sweeney, who holds a faculty position at both the Kennedy School and the Faculty of Arts and Sciences, teaches what she calls “save the world” classes that embody the ethos of the lab. 

“I teach students how to spot unforeseen consequences and how to do these simple experiments,” she says. She emphasizes the simplicity: it is “not rocket science.” But these modest investigations into technology can make a difference and unearth threats to privacy, to fairness, and to democracy. “The issue is: what do we do about these clashes and how do we make the public aware?” As her own work has revealed, asking the right questions and investigating can change laws and business practices at large tech companies.

“I teach students how to spot unforeseen consequences and how to do these simple experiments.”

Latanya sweeney.

Sweeney “is going to save the world from technology run amok,” says Joan Donovan , research director of the Shorenstein Center and an adjunct lecturer affiliated with the Tech Lab. “She not only teaches ethics and values in tech but illustrates for students how to build technology that has social significance beyond monetization.” Donovan, who is an expert in media manipulation and disinformation campaigns, explains, “I have been able to sit in on several of her classes, where the students debate the real stakes of technological design, not just from a critical perspective. They have to prototype how to make things work without ignoring the inconvenient truth. Technology is both process and product, helpful and harmful, and most of all, intentional design can mitigate risks.”

Kathy Pham , a senior fellow at the Shorenstein Center and an affiliate of the Public Interest Technology Lab, also emphasizes how uniquely placed Sweeney is to lead the lab, with her background as a computer scientist and former chief technologist at the Federal Trade Commission. “When I think of Dr. Sweeney, I think of the embodiment of public interest tech,” Pham says. As a computer scientist herself—who was named deputy chief technology officer of the Federal Trade Commission in 2021—Pham is inspired by Sweeney, who, she says, has been a pioneer in the field for many years, “rethinking how technology affects democracy.” Pham explains, “she has done all these roles. She brings a depth of experience that is unique.”

Drawing on her deep and varied background and knowledge, Sweeney has positioned the lab as a hub to connect with others doing public interest technology projects. For example, it is part of the Public Interest Technology University Network, which consists of roughly 50 universities where students and faculty members do this type of work. The lab also sponsors the Technology Science Research Network, which is made up of scholars from across the country who study technology-society issues, host events, train students, and publish research. The network’s scholars contribute to The Journal of Technology Science , of which Sweeney is the founding editor in chief. 

Through the Public Interest Technology Lab, students, staff, and faculty have developed a range of tools and platforms that solve public problems—from enhancing voting security to helping people without internet access schedule vaccinations to understanding how large social media companies shape conversations.

Credit Monitoring for Elections

One tool that Sweeney is excited about is VoteFlare. The idea emerged in 2016 in one of her “save the world” classes when students asked how they could empower voters across the political spectrum. Sweeney, along with Harvard PhD student Jinyan Zang and Data Privacy Lab researcher Ji Su Yoo, discovered how easy it was for voters’ registration information to be changed by fraudsters, potentially disrupting elections. In a Technology Science paper , they shared their findings: In 2016, websites for 35 states and Washington, D.C., were vulnerable to voter identity theft whereby imposters could change voter registration information. All that they needed were a few key pieces of information, most of which are publicly searchable or obtainable through data brokers or darknet markets. 

Sweeney and her students wondered how they could fix that problem. The answer they developed was VoteFlare, a tool that Sweeney compares to credit monitoring for voting information. People who sign up for the VoteFlare app are notified by a “flare” in the form of a text, a phone call, or an email if their information is changed online, so they can take corrective action if warranted. 

VoteFlare was piloted in 2020 during the Georgia runoff election successfully and has also been used in the Texas 2022 primary. The team plans to roll it out across the country before the general election, particularly in areas where voter suppression is a risk. Josh Visnaw, the project manager for VoteFlare, explains that the tool “allows individuals to fix issues before it is too late, ensuring that they’re able to participate in the democratic process.” He says, “We envision this technology complementing the good-faith efforts of election officials and civil society groups across the country, as we approach the 2022 midterm elections.”

Inside Facebook’s Decision-Making

Another project Sweeney is excited about is fbarchive.org, a new online archive of 20,000 internal Facebook documents that the whistleblower Frances Haugen, a former Facebook employee, released in 2021. Sweeney and her team received the documents, verified their authenticity, and are digitizing and curating them so that they will be available to researchers, journalists, and to some extent the public. For this project, the lab is collaborating with the leadership and core team of the Shorenstein Center, as well as the center’s Technology and Social Change Project .

Sweeney explains two basic challenges to creating the archive: one is privacy, and the other is usability. “Names and identities have to be scrubbed,” she says, because the documents—which are photographs of internal Facebook employee messages—include not only Facebook’s leaders but also ordinary employees, who might be harmed by exposure, and Facebook users, whose conversations are sometimes included in internal employee discussions. 

As for usability, Sweeney says, “What Frances Haugen did was call up a document on a screen and she would take a picture with her mobile phone. So, you end up with 20,000 images from a phone. Sometimes she was not the same distance from the screen. The orientation’s different. The lighting’s different.” All those factors pose difficulties. But the usability issue goes beyond the challenges of working with inconsistent photographs. The researchers also need to decipher the content. “There’s a huge amount of inside-Facebook talk,” Sweeney says. “So, we had to unpack acronyms and so forth and put a glossary together.”

The fbarchive.org platform, Sweeney says, will provide insight into not only what goes on at Facebook, but also the power and sway of the company’s decisions. Using a machine learning algorithm, her team is organizing the documents into clusters of topics to make them easier to explore and curate. “The documents are fascinating,” Sweeney says. “They cover almost every contemporary issue in society around the world.” No matter the topic, “somehow Facebook is engaged in it, involved in it, magnifying it, causing it.” She notes, too, that the documents can reveal Facebook’s behind-the-scenes policy decisions: “What are the knobs they could use to change things, and do they tend to use them or not?” 

A Dizzying Industrial Revolution

We are, Sweeney says, in the middle of a revolution. Unlike earlier technological revolutions—for example, the mass development and adoption of cars—digital tools are changing so rapidly that society and policy struggle to keep up. “Technology today doesn’t require slowing down,” she says. “It’s immediate. You have an idea: Let’s put up a website. People are mesmerized by the shiny new thing they can do.” The Public Interest Technology Lab, however, is there to respond and to look out for those unforeseen consequences of fast-moving technology. 

“The number of challenges—the number of clashes—dwarf what one person or small group could ever do in response,” she says. But Sweeney is hopeful that the Public Interest Technology Lab—through its experiments, teaching, and far-reaching networks with other scholars and universities—can help policy catch up and create avenues for technology to help, rather than harm, society.

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Claire henly mpp 2021 is working on creative environmental solutions, suparna gupta mc/mpa 2013 helps to protect the most vulnerable, sandra susan smith aims to eradicate disparities in criminal courts.

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• Open access
• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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• Human behaviour
• Information technology

An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Correspondence to Fabian Braesemann .

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The original online version of this Article was revised: The Data Availability section in the original version of this Article was incomplete, the link to the GitHub repository was omitted. Full information regarding the corrections made can be found in the correction for this Article.

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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Epigenetic Clocks: Using DNA to Estimate the Age of Dolphins

You probably know your age and you might be good at estimating the ages of other people. However, have you ever tried to guess the age of an animal, for example a dolphin? Estimating the age of dolphins is really difficult, because their appearance does not change once they are fully grown. Fortunately, the shape of their DNA changes over time. This allowed us to develop a tool, called an epigenetic clock, to determine the age of dolphins based on their DNA. Using this clock, we can estimate the age of a dolphin with an accuracy of 2 years. Although it is not exact, this still helps us answer questions about the lives of dolphins, such as when they start their own families. Knowing dolphin ages can also tell us if a group of dolphins contains a healthy mix of older and younger individuals. Epigenetic clocks are now being developed for many other species, offering exciting new research opportunities.

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When studying animals, one of the most important pieces of information to know about individual animals is their age. Scientists must know how old an animal is to understand many parts of its life history and, for example, to estimate if a population of animals can survive. But estimating an animal’s age is not easy when its date of birth is unknown. Across all animal species, there are no general signs for the aging process, such as the wrinkles or gray hair seen in humans. Because there are no visible signs, it is difficult to know the age of fully grown animals, especially if they live for a very long time. For example, how would you be able to tell if a dolphin is 20 or 30 years old?

Whales and dolphins are a particular challenge because they move around a lot and are difficult or forbidden to capture [ 1 ]. A good way to estimate a dolphin’s age is to pull out a tooth and look at the growth layers of its dentine , like you would count the rings in the trunk of a tree. This way, teeth can help scientists find a dolphin’s age quite accurately. However, this method is not pleasant for the dolphins and therefore difficult to do with live animals. For some species, such as Indo-Pacific bottlenose dolphins ( Tursiops aduncus ), a pattern of spots on their skin can tell us about their age, because they gain more spots as they grow older ( Figure 1 ). However, individuals tend to have different amounts of spots in general, and it is often quite difficult to see enough of a dolphin’s body to clearly see and count the spots. So, trying to age an animal based on its spotting pattern will only give us a rough estimate.

• Figure 1 - Indo-pacific bottlenose dolphin with speckling pattern.
• Photo by Simon Allen.

Because of these difficulties, most studies rely on data collected over a long time, in studies where animals have been regularly observed for many years since their birth, and thus their ages are known. For species that can live for many years, it can be difficult to gather enough data in a human’s lifetime.

Shark Bay Dolphins

One of the places where dolphins have been studied for many years is Shark Bay, Western Australia. There, scientists have observed a population of Indo-Pacific bottlenose dolphins for more than 30 years [ 2 ], using techniques called photo-identification and biopsy sampling . For photo-identification, researchers take photos of the dolphins’ dorsal fins . The shape of the fin is like a human fingerprint, and by comparing the fins of different animals, researchers can recognize individuals. Biopsy sampling means collecting a small piece of dolphin skin that can be analyzed in the lab to get the animals’ DNA. Because we have been studying this dolphin population for a long time and we recognize the individual dolphins, we also know when one is born and, therefore, can calculate its age. Thanks to biopsy sampling we have small skin samples from all the dolphins we study.

What is Epigenetics?

Our dolphin DNA samples have opened doors to new methods of age estimation for our dolphin species, using epigenetics . Epigenetics is the study of how the environment and an individual’s behaviors can affect the way their genes work. Epigenetic changes modify the shape of the DNA, which alters which genes are switched on or turned off. This shape change can happen by adding or removing small molecules to the DNA—a process called DNA methylation . As animals age, different sets of genes are switched on or “expressed”, meaning that the DNA takes on different shapes throughout an animal’s life ( Figure 2 ).

• Figure 2 - DNA (blue spiral) loses methylation as individuals age.
• A baby has the most methyl groups attached to its DNA, while adults have less than babies. Older people have the lowest number of methyl groups attached to their DNA. The same can be seen in dolphins and many other animals. These age-related differences in DNA methylation led us to develop our epigenetic clock to determine the ages of dolphins from the DNA found in tiny skin samples.

How Does the Epigenetic Clock Work?

Aging changes the levels of DNA methylation. Newborn dolphin calves have the highest levels of DNA methylation, while old individuals have the lowest. This change in methylation is also referred to as the epigenetic clock . Using DNA from skin samples, we measured the levels of DNA methylation in dolphins of a known age, using a new tool called the HorvathMammalMethylChip40 [ 3 ], which can measure DNA methylation on up to 37,492 sites on the DNA at once. We then built an epigenetic clock using machine learning , which we used to estimate the age of other dolphins of the same species based on their methylation levels ( Figure 3 ). Our clock had an average accuracy of 2.1 years, meaning if we estimated an animal to be 25 years old, it might actually be anywhere between 23–27 years old [ 4 ]. This is quite close and therefore a really good result!

• Figure 3 - There are 4 basic steps in building/using an epigenetic clock.
• (A) Take genetic samples (for example skin) of animals for which you know the age (Age = !). (B) Analyze the DNA methylation of these samples. (C) Build the clock by connecting the age of the dolphins to the amount of DNA methylation in each sample. (D) Using the clock: now you can analyze a sample from a dolphin for which you do not know the age (Age = ?). Based on the amount of DNA methylation, the clock will estimate how old the dolphin is (Age = !).

We also tested how well a different clock, developed using skin samples from a related species (common bottlenose dolphins [ 5 ]), worked for our samples. Common bottlenose dolphins look almost the same as Indo-Pacific dolphins but are often a bit bigger. We found that the common bottlenose dolphin clock worked almost as well for our Indo-pacific bottlenose dolphins as for the species it was made for.

Why is the Epigenetic Clock Useful?

Dolphins can live for more than 30 years, and in the past it has been very difficult to gather information from many animals with known ages. By using the epigenetic clock, we can now estimate the age of individual dolphins just based on their DNA, which means we only need a small skin sample from each dolphin, and we are good to go! This makes our clock extremely useful for scientists that study Indo-Pacific bottlenose dolphins but who do not have access to long-term data. The fact that the common bottlenose dolphin clock also worked really well for our species means that even researchers studying other closely related species can use our clock. Because it is expensive to build an epigenetic clock, sharing a clock can save researchers thousands of dollars, which they can then use for other interesting research.

What Are the Next Steps?

We now know the age for many dolphins in the Shark Bay population. With this knowledge, we can ask further questions, such as at what time in their lives they produce the most calves or what influences their aging. We can also study whether males and females age differently. As so often in science, an answer leads to many more questions! This is an exciting time for us, and we cannot wait to get deeper into this work!

Dentine : ↑ Hard, dense, bony tissue forming most of a tooth, beneath the outer coating of protective enamel.

Photo-Identification : ↑ A method of identifying individual dolphins using photos of their dorsal fins. The scars and marks on their fins make them unique, like a fingerprint in humans.

Biopsy Sampling : ↑ A data collection technique where we take a small skin sample from a live dolphin using a little dart with a sharp hollow end.

Dorsal Fin : ↑ The fin on the back of a whale or dolphin.

Epigenetics : ↑ The study of how our genes are affected by our behaviors and the environment we are exposed to.

DNA Methylation : ↑ Small molecules are added to the DNA to help tell the cells in our body which parts of the DNA to read and use, like how bookmarks help us find the right page in a book. These tags can turn genes on or off, which helps control how our bodies grow and work.

Epigenetic Clock : ↑ A test that looks at small chemical markers on our DNA to figure out our age. It is based on changes of these markers, called methyl groups, with age.

Machine Learning : ↑ A type of artificial intelligence that learns from data.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author of original paper Steve Horvath is a founder of the non-profit Epigenetic Clock Development Foundation, which plans to license several of his patents from his employer UC Regents.

Acknowledgments

KP was supported by a Postdoc Grant from the University of Zurich. LG was supported by an Early Postdoc Mobility Grant from the Swiss National Science Foundation (P2ZHP3_200011). The A.H. Schultz Foundation and the Claraz Foundation contributed to the sequencing costs.

Original Source Article

↑ Peters, K. J., Gerber, L., Scheu, L., Cicciarella, R., Zoller, J. A., Fei, Z., et al. 2023. An epigenetic DNA methylation clock for age estimates in Indo-Pacific bottlenose dolphins ( Tursiops aduncus ). Evolut. Applic . 16:126-33. doi: 10.1111/eva.13516

[1] ↑ Read, F. L., Hohn, A. A., and Lockyer, C. H. 2018. A review of age estimation methods in marine mammals with special reference to monodontids. NAMMCO Sci. Publ. 10:1–67. doi: 10.7557/3.4474

[2] ↑ Connor, R. C., and Krützen, M. 2015. Male dolphin alliances in Shark Bay: changing perspectives in a 30-year study. Anim. Behav. 103:223–235. doi: 10.1016/j.anbehav.2015.02.019

[3] ↑ Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M., Kwon, S. B., Vu, H., et al. 2022. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat. Commun. 13:1–13. doi: 10.1038/s41467-022-28355-z

[4] ↑ Peters, K. J., Gerber, L., Scheu, L., Cicciarella, R., Zoller, J. A., Fei, Z., et al. 2023. An epigenetic DNA methylation clock for age estimates in Indo-Pacific bottlenose dolphins ( Tursiops aduncus ). Evolut. Applic. 16:126–33. doi: 10.1111/eva.13516

[5] ↑ Robeck, T. R., Fei, Z., Haghani, A., Zoller, J. A., Li, C. Z., Steinman, K. J., et al. 2021. Multi-tissue methylation clocks for age and sex estimation in the common bottlenose dolphin. Front. Mar. Sci. 8:713373. doi: 10.3389/fmars.2021.713373