example of research problem and research objectives

The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

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Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

Very interesting. Thank you. Please I need a PhD topic in climate change in relation to health.

Your posts have provided a clear, easy to understand, motivating literature, mainly when these topics tend to be considered “boring” in some careers.

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Research Objectives | Examples & How To Write Them

Introduction

Why are research objectives important, characteristics of research objectives, what is an example of a research objective, types of research objectives, formulating research objectives.

Research objectives are clear, concise statements that outline what a research project aims to achieve. They guide the direction of the study, ensuring that researchers stay focused and organized. Properly formulated objectives help in identifying the scope of the research and the methods to be used. This article will cover the importance of research objectives, their characteristics, examples, types, and how to write them. By understanding these elements, researchers can develop effective research aims that enhance the clarity and purpose of their studies. This straightforward approach will provide practical guidance for both novice and experienced researchers in crafting clear research objectives.

Research objectives are crucial because they provide a clear focus and direction for a study. A well-defined research aim can help researchers stay on track by outlining specific goals that need to be achieved. This clarity ensures that all aspects of the research are aligned with the intended outcomes.

Having well-defined objectives also facilitates effective planning and execution. Researchers can allocate resources more efficiently, select appropriate methodologies , and set realistic timelines. Moreover, clear objectives help in the assessment and evaluation of the research process and its outcomes, making it easier to determine whether the study has been successful.

Research objectives also enhance communication. They allow researchers to clearly convey the purpose and scope of their study to stakeholders, including funding bodies, academic peers, and participants. This transparency builds credibility and trust, which are essential for the integrity of the research process.

Research objectives possess several key characteristics that make them effective and useful for guiding a study. These characteristics ensure that the objectives are clear, achievable, and relevant to the research problem . Here are the essential characteristics to keep in mind when you develop research objectives for a successful research project.

• Specific : Research objectives focus on what the researcher intends to accomplish in a precise and unambiguous manner. Specific objectives break a research study into discrete and manageable tasks.
• Measurable : Objectives should include criteria for measuring progress and success. This characteristic allows researchers to track their progress and determine whether the objectives have been achieved.
• Achievable : Objectives need to be realistic and attainable within the constraints of the research project, including time, resources, and expertise. Setting achievable goals prevents frustration and ensures steady progress.
• Relevant : Objectives must be aligned with the research problem and the overall purpose of the study. They should contribute directly to addressing the study's research question or research hypothesis.
• Time-bound : Objectives should have a clear timeline for completion. This characteristic helps in planning and managing the research process, ensuring that objectives are met within a specified period.
• Clear and concise : Objectives should be articulated in a straightforward manner, avoiding complex language and jargon. Clarity helps in communicating the objectives to others involved in the research process.
• Focused : Each objective should target a specific aspect of the research problem. Having focused objectives prevents the study from becoming too broad and unmanageable.
• Logical : Objectives should follow a logical sequence that reflects the research process. This characteristic ensures that the objectives build on one another and collectively contribute to the overall research aim.
• Feasible : Consider the availability of resources, including data, equipment, and personnel, when formulating objectives. Feasibility ensures that the objectives can be realistically achieved with the available resources.
• Ethical : Objectives should respect ethical standards and guidelines . They should consider the well-being of participants, confidentiality , and integrity of the research process.

By adhering to these characteristics, researchers can develop objectives that are effective in guiding their study. Clear and well-defined objectives not only enhance the research process but also improve the quality and credibility of the research outcomes.

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To illustrate what a well-defined research objective might look like, consider a study focused on improving reading comprehension among elementary school students. The research aims to evaluate the effectiveness of a new instructional strategy designed to enhance students' reading skills. Here is an example of a research objective for this study:

"To assess the impact of the interactive reading program on the reading comprehension scores of third-grade students over a six-month period."

This objective is effective because it meets several key criteria:

• Specific : The objective clearly states what will be assessed (the impact of the interactive reading program) and the target group (third-grade students).
• Measurable : The impact will be measured by changes in reading comprehension scores, providing a clear metric for evaluation.
• Achievable : The objective is realistic and attainable within a six-month period, assuming the necessary resources and support are available.
• Relevant : The objective is directly related to the study's research questions , which is to improve reading comprehension among elementary school students.
• Time-bound : The objective specifies a six-month period for the assessment, ensuring that the study is conducted within a defined timeframe.

By formulating objectives in this manner, researchers can create a clear roadmap for their study from research design to research paper . This example demonstrates how to incorporate the essential characteristics of research objectives into a practical and actionable statement.

Well-defined objectives help in planning the study, selecting an appropriate research methodology , and evaluating the outcomes. They also facilitate effective communication among members of the research team and with stakeholders, ensuring that everyone involved understands the purpose and scope of the research.

Research objectives can be categorized into different types based on their purpose and focus. Understanding these types helps researchers design studies that effectively address their research questions . Here are three common types of research objectives:

Descriptive objectives

Descriptive objectives aim to describe the characteristics or functions of a particular phenomenon or group. These objectives are often used in exploratory studies to gather information and provide a detailed picture of the subject being investigated. For example, a descriptive objective might be, "To describe the dietary habits of teenagers in urban areas." This type of objective helps in understanding the current state or conditions of the research subject.

Exploratory objectives

Exploratory objectives seek to explore new areas of knowledge or investigate relationships between variables. These objectives are often used in the initial stages of research to identify patterns, generate propositions, or uncover insights that can lead to further studies. An example of an exploratory objective is, "To investigate the relationship between social media usage and academic performance among college students." This type of objective is useful for studies that aim to look into new or under-researched areas.

Explanatory objectives

Explanatory objectives aim to explain the causes or reasons behind a particular phenomenon. These objectives often involve verifying a theory or determining relationships among variables. For instance, an explanatory objective could be, "To determine the impact of a structured exercise program on the mental health of elderly individuals." This type of objective is essential for studies that seek to understand the underlying mechanisms or effects of specific interventions or conditions.

Writing research aims is a critical step in the research process . Well-defined objectives provide a roadmap for the study and help ensure that the research stays focused and relevant. Here are some steps to guide the formulation of research objectives:

• Identify the research problem : Start by clearly defining the research problem or question you aim to address. Understanding the core issue helps in developing objectives that are directly related to the research focus.
• Conduct a literature review : Review existing research related to your topic to identify gaps in knowledge and areas that need further investigation. This background information can help in shaping specific and relevant objectives.
• Define the scope : Determine the scope of your study by considering factors such as the population, setting, and time frame. This will help in setting realistic and achievable objectives.
• Use the SMART criteria : Ensure that your objectives are Specific, Measurable, Achievable, Relevant, and Time-bound. This framework helps in creating clear and focused objectives that can guide the research process effectively.
• Break down the main objective : If your research has a broad aim, break it down into smaller, more specific sub-objectives. This makes the research more manageable and allows for a systematic approach to addressing the main research problem.
• Phrase objectives clearly : Write your objectives in clear and concise language, avoiding jargon and complex terms. Each objective should be easy to understand and communicate to others involved in the research.
• Align with research questions : Ensure that each objective aligns with your research question(s). The objectives should directly contribute to answering the key questions posed by your study.
• Seek feedback : Discuss your research objectives with peers, advisors, or experts in the field. Feedback can help refine the objectives and ensure that they are realistic and relevant.

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45 Research Problem Examples & Inspiration

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A research problem is an issue of concern that is the catalyst for your research. It demonstrates why the research problem needs to take place in the first place.

Generally, you will write your research problem as a clear, concise, and focused statement that identifies an issue or gap in current knowledge that requires investigation.

The problem will likely also guide the direction and purpose of a study. Depending on the problem, you will identify a suitable methodology that will help address the problem and bring solutions to light.

Research Problem Examples

In the following examples, I’ll present some problems worth addressing, and some suggested theoretical frameworks and research methodologies that might fit with the study. Note, however, that these aren’t the only ways to approach the problems. Keep an open mind and consult with your dissertation supervisor!

Psychology Problems

1. Social Media and Self-Esteem: “How does prolonged exposure to social media platforms influence the self-esteem of adolescents?”

• Theoretical Framework : Social Comparison Theory
• Methodology : Longitudinal study tracking adolescents’ social media usage and self-esteem measures over time, combined with qualitative interviews.

2. Sleep and Cognitive Performance: “How does sleep quality and duration impact cognitive performance in adults?”

• Theoretical Framework : Cognitive Psychology
• Methodology : Experimental design with controlled sleep conditions, followed by cognitive tests. Participant sleep patterns can also be monitored using actigraphy.

3. Childhood Trauma and Adult Relationships: “How does unresolved childhood trauma influence attachment styles and relationship dynamics in adulthood?

• Theoretical Framework : Attachment Theory
• Methodology : Mixed methods, combining quantitative measures of attachment styles with qualitative in-depth interviews exploring past trauma and current relationship dynamics.

4. Mindfulness and Stress Reduction: “How effective is mindfulness meditation in reducing perceived stress and physiological markers of stress in working professionals?”

• Theoretical Framework : Humanist Psychology
• Methodology : Randomized controlled trial comparing a group practicing mindfulness meditation to a control group, measuring both self-reported stress and physiological markers (e.g., cortisol levels).

5. Implicit Bias and Decision Making: “To what extent do implicit biases influence decision-making processes in hiring practices?

• Theoretical Framework : Cognitive Dissonance Theory
• Methodology : Experimental design using Implicit Association Tests (IAT) to measure implicit biases, followed by simulated hiring tasks to observe decision-making behaviors.

6. Emotional Regulation and Academic Performance: “How does the ability to regulate emotions impact academic performance in college students?”

• Theoretical Framework : Cognitive Theory of Emotion
• Methodology : Quantitative surveys measuring emotional regulation strategies, combined with academic performance metrics (e.g., GPA).

7. Nature Exposure and Mental Well-being: “Does regular exposure to natural environments improve mental well-being and reduce symptoms of anxiety and depression?”

• Theoretical Framework : Biophilia Hypothesis
• Methodology : Longitudinal study comparing mental health measures of individuals with regular nature exposure to those without, possibly using ecological momentary assessment for real-time data collection.

8. Video Games and Cognitive Skills: “How do action video games influence cognitive skills such as attention, spatial reasoning, and problem-solving?”

• Theoretical Framework : Cognitive Load Theory
• Methodology : Experimental design with pre- and post-tests, comparing cognitive skills of participants before and after a period of action video game play.

9. Parenting Styles and Child Resilience: “How do different parenting styles influence the development of resilience in children facing adversities?”

• Theoretical Framework : Baumrind’s Parenting Styles Inventory
• Methodology : Mixed methods, combining quantitative measures of resilience and parenting styles with qualitative interviews exploring children’s experiences and perceptions.

10. Memory and Aging: “How does the aging process impact episodic memory , and what strategies can mitigate age-related memory decline?

• Theoretical Framework : Information Processing Theory
• Methodology : Cross-sectional study comparing episodic memory performance across different age groups, combined with interventions like memory training or mnemonic strategies to assess potential improvements.

Education Problems

11. Equity and Access : “How do socioeconomic factors influence students’ access to quality education, and what interventions can bridge the gap?

• Theoretical Framework : Critical Pedagogy
• Methodology : Mixed methods, combining quantitative data on student outcomes with qualitative interviews and focus groups with students, parents, and educators.

12. Digital Divide : How does the lack of access to technology and the internet affect remote learning outcomes, and how can this divide be addressed?

• Theoretical Framework : Social Construction of Technology Theory
• Methodology : Survey research to gather data on access to technology, followed by case studies in selected areas.

13. Teacher Efficacy : “What factors contribute to teacher self-efficacy, and how does it impact student achievement?”

• Theoretical Framework : Bandura’s Self-Efficacy Theory
• Methodology : Quantitative surveys to measure teacher self-efficacy, combined with qualitative interviews to explore factors affecting it.

14. Curriculum Relevance : “How can curricula be made more relevant to diverse student populations, incorporating cultural and local contexts?”

• Theoretical Framework : Sociocultural Theory
• Methodology : Content analysis of curricula, combined with focus groups with students and teachers.

15. Special Education : “What are the most effective instructional strategies for students with specific learning disabilities?

• Theoretical Framework : Social Learning Theory
• Methodology : Experimental design comparing different instructional strategies, with pre- and post-tests to measure student achievement.

16. Dropout Rates : “What factors contribute to high school dropout rates, and what interventions can help retain students?”

• Methodology : Longitudinal study tracking students over time, combined with interviews with dropouts.

17. Bilingual Education : “How does bilingual education impact cognitive development and academic achievement?

• Methodology : Comparative study of students in bilingual vs. monolingual programs, using standardized tests and qualitative interviews.

18. Classroom Management: “What reward strategies are most effective in managing diverse classrooms and promoting a positive learning environment?

• Theoretical Framework : Behaviorism (e.g., Skinner’s Operant Conditioning)
• Methodology : Observational research in classrooms , combined with teacher interviews.

19. Standardized Testing : “How do standardized tests affect student motivation, learning, and curriculum design?”

• Theoretical Framework : Critical Theory
• Methodology : Quantitative analysis of test scores and student outcomes, combined with qualitative interviews with educators and students.

20. STEM Education : “What methods can be employed to increase interest and proficiency in STEM (Science, Technology, Engineering, and Mathematics) fields among underrepresented student groups?”

• Theoretical Framework : Constructivist Learning Theory
• Methodology : Experimental design comparing different instructional methods, with pre- and post-tests.

21. Social-Emotional Learning : “How can social-emotional learning be effectively integrated into the curriculum, and what are its impacts on student well-being and academic outcomes?”

• Theoretical Framework : Goleman’s Emotional Intelligence Theory
• Methodology : Mixed methods, combining quantitative measures of student well-being with qualitative interviews.

22. Parental Involvement : “How does parental involvement influence student achievement, and what strategies can schools use to increase it?”

• Theoretical Framework : Reggio Emilia’s Model (Community Engagement Focus)
• Methodology : Survey research with parents and teachers, combined with case studies in selected schools.

23. Early Childhood Education : “What are the long-term impacts of quality early childhood education on academic and life outcomes?”

• Theoretical Framework : Erikson’s Stages of Psychosocial Development
• Methodology : Longitudinal study comparing students with and without early childhood education, combined with observational research.

24. Teacher Training and Professional Development : “How can teacher training programs be improved to address the evolving needs of the 21st-century classroom?”

• Theoretical Framework : Adult Learning Theory (Andragogy)
• Methodology : Pre- and post-assessments of teacher competencies, combined with focus groups.

25. Educational Technology : “How can technology be effectively integrated into the classroom to enhance learning, and what are the potential drawbacks or challenges?”

• Theoretical Framework : Technological Pedagogical Content Knowledge (TPACK)
• Methodology : Experimental design comparing classrooms with and without specific technologies, combined with teacher and student interviews.

Sociology Problems

26. Urbanization and Social Ties: “How does rapid urbanization impact the strength and nature of social ties in communities?”

• Theoretical Framework : Structural Functionalism
• Methodology : Mixed methods, combining quantitative surveys on social ties with qualitative interviews in urbanizing areas.

27. Gender Roles in Modern Families: “How have traditional gender roles evolved in families with dual-income households?”

• Theoretical Framework : Gender Schema Theory
• Methodology : Qualitative interviews with dual-income families, combined with historical data analysis.

28. Social Media and Collective Behavior: “How does social media influence collective behaviors and the formation of social movements?”

• Theoretical Framework : Emergent Norm Theory
• Methodology : Content analysis of social media platforms, combined with quantitative surveys on participation in social movements.

29. Education and Social Mobility: “To what extent does access to quality education influence social mobility in socioeconomically diverse settings?”

• Methodology : Longitudinal study tracking educational access and subsequent socioeconomic status, combined with qualitative interviews.

30. Religion and Social Cohesion: “How do religious beliefs and practices contribute to social cohesion in multicultural societies?”

• Methodology : Quantitative surveys on religious beliefs and perceptions of social cohesion, combined with ethnographic studies.

31. Consumer Culture and Identity Formation: “How does consumer culture influence individual identity formation and personal values?”

• Theoretical Framework : Social Identity Theory
• Methodology : Mixed methods, combining content analysis of advertising with qualitative interviews on identity and values.

32. Migration and Cultural Assimilation: “How do migrants negotiate cultural assimilation and preservation of their original cultural identities in their host countries?”

• Theoretical Framework : Post-Structuralism
• Methodology : Qualitative interviews with migrants, combined with observational studies in multicultural communities.

33. Social Networks and Mental Health: “How do social networks, both online and offline, impact mental health and well-being?”

• Theoretical Framework : Social Network Theory
• Methodology : Quantitative surveys assessing social network characteristics and mental health metrics, combined with qualitative interviews.

34. Crime, Deviance, and Social Control: “How do societal norms and values shape definitions of crime and deviance, and how are these definitions enforced?”

• Theoretical Framework : Labeling Theory
• Methodology : Content analysis of legal documents and media, combined with ethnographic studies in diverse communities.

35. Technology and Social Interaction: “How has the proliferation of digital technology influenced face-to-face social interactions and community building?”

• Theoretical Framework : Technological Determinism
• Methodology : Mixed methods, combining quantitative surveys on technology use with qualitative observations of social interactions in various settings.

Nursing Problems

36. Patient Communication and Recovery: “How does effective nurse-patient communication influence patient recovery rates and overall satisfaction with care?”

• Methodology : Quantitative surveys assessing patient satisfaction and recovery metrics, combined with observational studies on nurse-patient interactions.

37. Stress Management in Nursing: “What are the primary sources of occupational stress for nurses, and how can they be effectively managed to prevent burnout?”

• Methodology : Mixed methods, combining quantitative measures of stress and burnout with qualitative interviews exploring personal experiences and coping mechanisms.

38. Hand Hygiene Compliance: “How effective are different interventions in improving hand hygiene compliance among nursing staff, and what are the barriers to consistent hand hygiene?”

• Methodology : Experimental design comparing hand hygiene rates before and after specific interventions, combined with focus groups to understand barriers.

39. Nurse-Patient Ratios and Patient Outcomes: “How do nurse-patient ratios impact patient outcomes, including recovery rates, complications, and hospital readmissions?”

• Methodology : Quantitative study analyzing patient outcomes in relation to staffing levels, possibly using retrospective chart reviews.

40. Continuing Education and Clinical Competence: “How does regular continuing education influence clinical competence and confidence among nurses?”

• Methodology : Longitudinal study tracking nurses’ clinical skills and confidence over time as they engage in continuing education, combined with patient outcome measures to assess potential impacts on care quality.

Communication Studies Problems

41. Media Representation and Public Perception: “How does media representation of minority groups influence public perceptions and biases?”

• Theoretical Framework : Cultivation Theory
• Methodology : Content analysis of media representations combined with quantitative surveys assessing public perceptions and attitudes.

42. Digital Communication and Relationship Building: “How has the rise of digital communication platforms impacted the way individuals build and maintain personal relationships?”

• Theoretical Framework : Social Penetration Theory
• Methodology : Mixed methods, combining quantitative surveys on digital communication habits with qualitative interviews exploring personal relationship dynamics.

43. Crisis Communication Effectiveness: “What strategies are most effective in managing public relations during organizational crises, and how do they influence public trust?”

• Theoretical Framework : Situational Crisis Communication Theory (SCCT)
• Methodology : Case study analysis of past organizational crises, assessing communication strategies used and subsequent public trust metrics.

44. Nonverbal Cues in Virtual Communication: “How do nonverbal cues, such as facial expressions and gestures, influence message interpretation in virtual communication platforms?”

• Theoretical Framework : Social Semiotics
• Methodology : Experimental design using video conferencing tools, analyzing participants’ interpretations of messages with varying nonverbal cues.

45. Influence of Social Media on Political Engagement: “How does exposure to political content on social media platforms influence individuals’ political engagement and activism?”

• Theoretical Framework : Uses and Gratifications Theory
• Methodology : Quantitative surveys assessing social media habits and political engagement levels, combined with content analysis of political posts on popular platforms.

Before you Go: Tips and Tricks for Writing a Research Problem

This is an incredibly stressful time for research students. The research problem is going to lock you into a specific line of inquiry for the rest of your studies.

So, here’s what I tend to suggest to my students:

• Start with something you find intellectually stimulating – Too many students choose projects because they think it hasn’t been studies or they’ve found a research gap. Don’t over-estimate the importance of finding a research gap. There are gaps in every line of inquiry. For now, just find a topic you think you can really sink your teeth into and will enjoy learning about.
• Take 5 ideas to your supervisor – Approach your research supervisor, professor, lecturer, TA, our course leader with 5 research problem ideas and run each by them. The supervisor will have valuable insights that you didn’t consider that will help you narrow-down and refine your problem even more.
• Trust your supervisor – The supervisor-student relationship is often very strained and stressful. While of course this is your project, your supervisor knows the internal politics and conventions of academic research. The depth of knowledge about how to navigate academia and get you out the other end with your degree is invaluable. Don’t underestimate their advice.

I’ve got a full article on all my tips and tricks for doing research projects right here – I recommend reading it:

• 9 Tips on How to Choose a Dissertation Topic

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
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Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Handy Tips To Write A Clear Research Objectives With Examples

Introduction.

Research objectives play a crucial role in any research study. They provide a clear direction and purpose for the research, guiding the researcher in their investigation. Understanding research objectives is essential for conducting a successful study and achieving meaningful results.

In this comprehensive review, we will delve into the definition of research objectives, exploring their characteristics, types, and examples. We will also discuss the relationship between research objectives and research questions , as well as provide insights into how to write effective research objectives. Additionally, we will examine the role of research objectives in research methodology and highlight the importance of them in a study. By the end of this review, you will have a comprehensive understanding of research objectives and their significance in the research process.

Definition of Research Objectives: What Are They?

A research objective is defined as a clear and concise statement that outlines the specific goals and aims of a research study. These objectives are designed to be specific, measurable, achievable, relevant, and time-bound (SMART), ensuring they provide a structured pathway to accomplishing the intended outcomes of the project. Each objective serves as a foundational element that summarizes the purpose of your study, guiding the research activities and helping to measure progress toward the study’s goals. Additionally, research objectives are integral components of the research framework , establishing a clear direction that aligns with the overall research questions and hypotheses. This alignment helps to ensure that the study remains focused and relevant, facilitating the systematic collection, analysis, and interpretation of data.

Characteristics of Effective Research Objectives

Characteristics of research objectives include:

• Specific: Research objectives should be clear about the what, why, when, and how of the study.
• Measurable: Research objectives should identify the main variables of the study that can be measured or observed.
• Relevant: Research objectives should be relevant to the research topic and contribute to the overall understanding of the subject.
• Feasible: Research objectives should be achievable within the constraints of time, resources, and expertise available.
• Logical: Research objectives should follow a logical sequence and build upon each other to achieve the overall research goal.
• Observable: Research objectives should be observable or measurable in order to assess the progress and success of the research project.
• Unambiguous: Research objectives should be clear and unambiguous, leaving no room for interpretation or confusion.
• Measurable: Research objectives should be measurable, allowing for the collection of data and analysis of results.

By incorporating these characteristics into research objectives, researchers can ensure that their study is focused, achievable, and contributes to the body of knowledge in their field.

Types of Research Objectives

Research objective can be broadly classified into general and specific objectives. General objectives are broad statements that define the overall purpose of the research. They provide a broad direction for the study and help in setting the context. Specific objectives, on the other hand, are detailed objectives that describe what will be researched during the study. They are more focused and provide specific outcomes that the researcher aims to achieve. Specific objectives are derived from the general objectives and help in breaking down the research into smaller, manageable parts. The specific objectives should be clear, measurable, and achievable. They should be designed in a way that allows the researcher to answer the research questions and address the research problem.

In addition to general and specific objectives, research objective can also be categorized as descriptive or analytical objectives. Descriptive objectives focus on describing the characteristics or phenomena of a particular subject or population. They involve surveys, observations, and data collection to provide a detailed understanding of the subject. Analytical objectives, on the other hand, aim to analyze the relationships between variables or factors. They involve data analysis and interpretation to gain insights and draw conclusions.

Both descriptive and analytical objectives are important in research as they serve different purposes and contribute to a comprehensive understanding of the research topic.

Examples of Research Objectives

Here are some examples of research objectives in different fields:

1. Objective: To identify key characteristics and styles of Renaissance art.

This objective focuses on exploring the characteristics and styles of art during the Renaissance period. The research may involve analyzing various artworks, studying historical documents, and interviewing experts in the field.

2. Objective: To analyze modern art trends and their impact on society.

This objective aims to examine the current trends in modern art and understand how they influence society. The research may involve analyzing artworks, conducting surveys or interviews with artists and art enthusiasts, and studying the social and cultural implications of modern art.

3. Objective: To investigate the effects of exercise on mental health.

This objective focuses on studying the relationship between exercise and mental health. The research may involve conducting experiments or surveys to assess the impact of exercise on factors such as stress, anxiety, and depression.

4. Objective: To explore the factors influencing consumer purchasing decisions in the fashion industry.

This objective aims to understand the various factors that influence consumers’ purchasing decisions in the fashion industry. The research may involve conducting surveys, analyzing consumer behavior data, and studying the impact of marketing strategies on consumer choices.

5. Objective: To examine the effectiveness of a new drug in treating a specific medical condition.

This objective focuses on evaluating the effectiveness of a newly developed drug in treating a particular medical condition. The research may involve conducting clinical trials, analyzing patient data, and comparing the outcomes of the new drug with existing treatment options.

These examples demonstrate the diversity of research objectives across different disciplines. Each objective is specific, measurable, and achievable, providing a clear direction for the research study.

Aligning Research Objectives with Research Questions

Research objectives and research questions are essential components of a research project. Research objective describe what you intend your research project to accomplish. They summarize the approach and purpose of the project and provide a clear direction for the research. Research questions, on the other hand, are the starting point of any good research. They guide the overall direction of the research and help identify and focus on the research gaps .

The main difference between research questions and objectives is their form. Research questions are stated in a question form, while objectives are specific, measurable, and achievable goals that you aim to accomplish within a specified timeframe. Research questions are broad statements that provide a roadmap for the research, while objectives break down the research aim into smaller, actionable steps.

Research objectives and research questions work together to form the ‘golden thread’ of a research project. The research aim specifies what the study will answer, while the objectives and questions specify how the study will answer it. They provide a clear focus and scope for the research project, helping researchers stay on track and ensure that their study is meaningful and relevant.

When writing research objectives and questions, it is important to be clear, concise, and specific. Each objective or question should address a specific aspect of the research and contribute to the overall goal of the study. They should also be measurable, meaning that their achievement can be assessed and evaluated. Additionally, research objectives and questions should be achievable within the given timeframe and resources of the research project. By clearly defining the objectives and questions, researchers can effectively plan and execute their research, leading to valuable insights and contributions to the field.

Guidelines for Writing Clear Research Objectives

Writing research objective is a crucial step in any research project. The objectives provide a clear direction and purpose for the study, guiding the researcher in their data collection and analysis. Here are some tips on how to write effective research objective:

1. Be clear and specific

Research objective should be written in a clear and specific manner. Avoid vague or ambiguous language that can lead to confusion. Clearly state what you intend to achieve through your research.

2. Use action verbs

Start your research objective with action verbs that describe the desired outcome. Action verbs such as ‘investigate’, ‘analyze’, ‘compare’, ‘evaluate’, or ‘identify’ help to convey the purpose of the study.

3. Align with research questions or hypotheses

Ensure that your research objectives are aligned with your research questions or hypotheses. The objectives should address the main goals of your study and provide a framework for answering your research questions or testing your hypotheses.

4. Be realistic and achievable

Set research objectives that are realistic and achievable within the scope of your study. Consider the available resources, time constraints, and feasibility of your objectives. Unrealistic objectives can lead to frustration and hinder the progress of your research.

5. Consider the significance and relevance

Reflect on the significance and relevance of your research objectives. How will achieving these objectives contribute to the existing knowledge or address a gap in the literature? Ensure that your objectives have a clear purpose and value.

6. Seek feedback

It is beneficial to seek feedback on your research objectives from colleagues, mentors, or experts in your field. They can provide valuable insights and suggestions for improving the clarity and effectiveness of your objectives.

7. Revise and refine

Research objectives are not set in stone. As you progress in your research, you may need to revise and refine your objectives to align with new findings or changes in the research context. Regularly review and update your objectives to ensure they remain relevant and focused.

By following these tips, you can write research objectives that are clear, focused, and aligned with your research goals. Well-defined objectives will guide your research process and help you achieve meaningful outcomes.

The Role of Research Objectives in Research Methodology

Research objectives play a crucial role in the research methodology . In research methodology, research objectives are formulated based on the research questions or problem statement. These objectives help in defining the scope and focus of the study, ensuring that the research is conducted in a systematic and organized manner.

The research objectives in research methodology act as a roadmap for the research project. They help in identifying the key variables to be studied, determining the research design and methodology, and selecting the appropriate data collection methods .

Furthermore, research objectives in research methodology assist in evaluating the success of the study. By setting clear objectives, researchers can assess whether the desired outcomes have been achieved and determine the effectiveness of the research methods employed. It is important to note that research objectives in research methodology should be aligned with the overall research aim. They should address the specific aspects or components of the research aim and provide a framework for achieving the desired outcomes.

Understanding The Dynamic of Research Objectives in Your Study

The research objectives of a study play a crucial role in guiding the research process, ensuring that the study is focused, purposeful, and contributes to the advancement of knowledge in the field. It is important to note that the research objectives may evolve or change as the study progresses. As new information is gathered and analyzed, the researcher may need to revise the objectives to ensure that they remain relevant and achievable.

In summary, research objectives are essential components in writing an effective research paper . They provide a roadmap for the research process, guiding the researcher in their investigation and helping to ensure that the study is purposeful and meaningful. By understanding and effectively utilizing research objectives, researchers can enhance the quality and impact of their research endeavors.

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How to Write a Statement of the Problem in Research

Table of Contents

The problem statement is a foundation of academic research writing , providing a precise representation of an existing gap or issue in a particular field of study.

Crafting a sharp and focused problem statement lays the groundwork for your research project.

• It highlights the research's significance .
• Emphasizes its potential to influence the broader academic community.
• Represents the initial step for you to make a meaningful contribution to your discipline.

Therefore, in this article, we will discuss what is a statement of the problem in research and how to craft a compelling research problem statement.

What is a research problem statement?

A research problem statement is a concise, clear, and specific articulation of a gap in current knowledge that your research aims to bridge. It not only sets forth the scope and direction of your research but also establishes its relevance and significance.

Your problem statement in your research paper aims to:

• Define the gap : Clearly identify and articulate a specific gap or issue in the existing knowledge.
• Provide direction : Serve as a roadmap, guiding the course of your research and ensuring you remain focused.
• Establish relevance : Highlight the importance and significance of the problem in the context of your field or the broader world.
• Guide inquiry :  Formulate the research questions or hypotheses you'll explore.
• Communicate intent : Succinctly convey the core purpose of your research to stakeholders, peers, and any audience.
• Set boundaries : Clearly define the scope of your research to ensure it's focused and achievable.

When should you write a problem statement in research?

Initiate your research by crafting a clear problem statement. This should be done before any data collection or analysis, serving as a foundational anchor that clearly identifies the specific issue you aim to address.

By establishing this early on, you shape the direction of your research, ensuring it targets a genuine knowledge gap.

Furthermore, an effective and a concise statement of the problem in research attracts collaborators, funders, and supporters, resonating with its clarity and purpose. Remember, as your research unfolds, the statement might evolve, reflecting new insights and staying pertinent.

But how do you distinguish between a well-crafted problem statement and one that falls short?

Effective vs. ineffective research problem statements

Imagine a scenario where medical researchers aim to tackle a new strain of virus. Their effective problem statement wouldn't merely state the existence of the virus. Instead, it would delve into the specifics — the regions most affected, the demographics most vulnerable, and the current limitations in medical interventions.

Whereas an ineffective research problem statement is vague, overly broad, or ambiguous, failing to provide a clear direction for the research. It may not be rooted in existing literature, might lack clarity on its significance, or could be framed in a way that makes the research objectives unachievable or irrelevant.

To understand it better, let's consider the topic of “Remote work and employee productivity.”

Effective problem statement

“Over the past decade, there has been a 70% increase in organizations adopting remote work policies. While some studies suggest remote work enhances employee productivity, others indicate potential declines due to distractions at home.

However, there’s a lack of comprehensive research examining the specific factors in a remote environment that influence productivity. This study aims to identify and analyze these factors, providing organizations with actionable insights to optimize remote work policies.”

Why is this statement of a problem in research effective?

• Specificity : The statement provides a clear percentage to highlight the rise in remote work.
• Context : It acknowledges existing research and the conflicting findings.
• Clear gap identification : It points out the lack of comprehensive research on specific factors affecting productivity in remote work.
• Purpose : The statement concludes with a clear aim for the research.

Ineffective problem statement

"People are working from home a lot now, especially since there are so many internet tools. Some say it's good; others say it's not that great. This research will just look into the whole work-from-home thing and see what's up."

Why is this statement of a problem in research ineffective?

• Informal language : Phrases like "what's up" and "the whole work-from-home thing" are not suitable for academic writing.
• Vagueness : The statement doesn't provide any specific data or context about the rise of remote work.
• Lack of clear focus : It's unclear what aspect of remote work the research will address.
• Ambiguous purpose : The statement doesn't specify the research's objectives or expected outcomes.

After gaining an understanding of what an effective research problem statement looks like, let's dive deeper into how to write one.

How to write a problem statement in research?

Drafting your research problem statement at the onset of your research journey ensures that your research remains anchored. That means by defining and articulating the main issue or challenge you intend to address at the very beginning of your research process; you provide a clear focus and direction for the entire study.

Here's a detailed guide to how you can write an effective statement of the problem in research.

Identify the research area : Before addressing a specific problem, you need to know the broader domain or field of your study. This helps in contextualizing your research and ensuring it aligns with existing academic disciplines.

Example: If you're curious about the effects of digital technology on human behavior, your broader research area might be Digital Sociology or Media Studies.

Conduct preliminary literature review : Familiarize yourself with existing research related to your topic. This will help you understand what's already known and, more importantly, identify gaps or unresolved questions in the existing knowledge. This step also ensures you're advancing upon existing work rather than replicating it.

Example: Upon reviewing literature on digital technology and behavior, you find many studies on social media's impact on youth but fewer on its effects on the elderly.

Read how to conduct an effective literature review .

Define the specific problem : After thoroughly reviewing the literature, pinpoint a particular issue that your research will address. Ensure that this chosen issue is not only of substantial importance in its field but also realistically approachable given your resources and expertise. To define it precisely, you might consider:

• Highlighting discrepancies or contradictions in existing literature.
• Emphasizing the real-world implications of this gap.
• Assessing the feasibility of exploring this issue within your means and timeframe.

Example: You decide to investigate how digital technology, especially social media, affects the mental well-being of the elderly, given the limited research in this area.

Articulate clearly and concisely : Your problem statement should be straightforward and devoid of jargon. It needs to convey the essence of your research issue in a manner that's understandable to both experts and non-experts.

Example: " The impact of social media on the mental well-being of elderly individuals remains underexplored, despite the growing adoption of digital technology in this age group. "

Highlight the significance : Explain why your chosen research problem matters. This could be due to its real-world implications, its potential to fill a knowledge gap or its relevance to current events or trends.

Example: As the elderly population grows and becomes more digitally connected, understanding the psychological effects of social media on this demographic could inform digital literacy programs and mental health interventions.

Ensure feasibility : Your research problem should be something you can realistically study, given your resources, timeframe, and expertise. It's essential to ensure that you can gather data, conduct experiments, or access necessary materials or participants.

Example: You plan to survey elderly individuals in local community centers about their social media usage and perceived mental well-being, ensuring you have the means to reach this demographic.

Seek feedback : Discuss your preliminary problem statement with peers, mentors, or experts in the field. They can provide insights, point out potential pitfalls, or suggest refinements.

Example: After discussing with a gerontologist, you decide to also consider the role of digital training in moderating the effects of social media on the elderly.

Refine and Revise : Based on feedback and further reflection, revise and improve your problem statement. This iterative process ensures clarity, relevance, and precision.

Example: Your refined statement reads: Despite the increasing digital connectivity of the elderly, the effects of social media on their mental well-being, especially in the context of digital training, remain underexplored.

By following these detailed steps, you can craft a research problem statement that is both compelling and academically rigorous.

Having explored the details of crafting a research problem statement, it's crucial to distinguish it from another fundamental element in academic research: the thesis statement.

Difference between a thesis statement and a problem statement

While both terms are central to research, a thesis statement presents your primary claim or argument, whereas a problem statement describes the specific issue your research aims to address.

Think of the thesis statement as the conclusion you're driving towards, while the problem statement identifies a specific gap in current knowledge.

For instance, a problem statement might highlight the rising mental health issues among teenagers, while the thesis statement could propose that increased screen time is a significant contributor.

Refer to the comparison table between what is a thesis and a problem statement in the research below:

Common mistakes to avoid in writing statement of the problem in research

Mistakes in the research problem statement can lead to a domino effect, causing misalignment in research objectives, wasted resources, and even inconclusive or irrelevant results.

Recognizing and avoiding these pitfalls not only strengthens the foundation of your research but also ensures that your efforts concede impactful insights.

Here's a detailed exploration of frequent subjective, qualitative, quantitative and measurable mistakes and how you can sidestep them.

Being too broad or too narrow

A problem statement that's too broad can lack focus, making it challenging to derive specific research questions or objectives. Conversely, a statement that's too narrow might limit the scope of your research or make it too trivial.

Example of mistake: "Studying the effects of diet on health" is too broad, while "Studying the effects of eating green apples at 3 pm on heart health" is overly narrow.

You can refine the scope based on preliminary research. The correct way to write this problem statement will be "Studying the effects of a high-fiber diet on heart health in adults over 50." This statement is neither too broad nor too narrow, and it provides a clear direction for the research.

Using unnecessary jargon or technical language

While academic writing often involves academic terms, overloading your problem statement with jargon can alienate readers and obscure the actual problem.

Example of Mistake: "Examining the diurnal variations in macronutrient ingestion vis-à-vis metabolic homeostasis."

To ensure it’s not complicated, you can simplify and clarify. "Examining how daily changes in nutrient intake affect metabolic balance" conveys the same idea more accessible.

Not emphasizing the "Why" of the problem

It's not enough to state a problem; you must also convey its significance. Why does this problem matter? What are the implications of not addressing it?

Example of Mistake: "Many students are not engaging with online learning platforms."

You can proceed with the approach of highlighting the significance here. "Many students are not engaging with online learning platforms, leading to decreased academic performance and widening educational disparities."

Circular reasoning and lack of relevance

Your problem statement should be grounded in existing research or observed phenomena. Avoid statements that assume what they set out to prove or lack a clear basis in current knowledge.

Example of Mistake: "We need to study X because not enough research has been done on X."

Instead, try grounding your statement based on already-known facts. "While several studies have explored Y, the specific impact of X remains unclear, necessitating further research."

Being overly ambitious

While it's commendable to aim high, your problem statement should reflect a challenge that's achievable within your means, timeframe, and resources.

Example of Mistake: "This research will solve world hunger."

Here, you need to be realistic and focused. "This research aims to develop sustainable agricultural techniques to increase crop yields in arid regions."

By being mindful of these common mistakes, you can craft a problem statement that is clear, relevant and sets a solid foundation for your research.

Over-reliance on outdated data

Using data that is no longer relevant can mislead the direction of your research. It's essential to ensure that the statistics or findings you reference are current and pertinent to the present scenario.

Example of Mistake: "According to a 1995 study, only 5% of the population uses the internet for daily tasks."

You always cross-check the dates and relevance of the data you're using. For a contemporary study on internet usage, you'd want to reference more recent statistics.

Not specifying the sample size or demographic

A problem statement should be clear about the population or sample size being studied, especially when making generalizations or claims.

Example of Mistake: "People prefer online shopping to in-store shopping."

Here, you would benefit from specifying the demographic or sample size when presenting data to avoid overgeneralization. " In a survey of 1,000 urban residents aged 18-35, 70% expressed a preference for online shopping over in-store shopping. "

Ignoring conflicting data

Cherry-picking data that supports your hypothesis while ignoring conflicting data can lead to a biased problem statement.

Example of Mistake: "Research shows that all students benefit from online learning."

You’ve to ensure a balanced view by considering all relevant data, even if it contradicts your hypothesis. " While many studies highlight the advantages of online learning for students, some research points to challenges such as decreased motivation and lack of face-to-face interaction. "

Making unsubstantiated predictions

Projecting future trends without solid data can weaken the credibility of your problem statement.

Example of Mistake: "The demand for electric cars will increase by 500% in the next year."

Base your predictions on current trends and reliable data sources, avoiding hyperbolic or unsupported claims. " With the current growth rate and recent advancements in battery technology, there's potential for a significant rise in the demand for electric cars. "

Wrapping Up

A well-crafted problem statement ensures that your research is focused, relevant, and contributes meaningfully to the broader academic community.

However, the consequences of an incorrect or poorly constructed problem statement can be severe. It can lead to misdirected research efforts, wasted resources, compromised credibility, and even ethical concerns. Such pitfalls underscore the importance of dedicating time and effort to craft a precise and impactful problem statement.

So, as you start your research journey , remember that a well-defined problem statement is not just a starting point; it guides your entire research journey, ensuring clarity, relevance, and meaningful contributions to your field.

Frequently Asked Questions

A problem statement is a clear, concise and specific articulation of a gap in current knowledge that your research aims to bridge.

The Problem Statement should highlight existing gaps in current knowledge and also the significance of the research. It should also include the research question and purpose of the research.

Clear articulation of the problem and establishing relevance; Working thesis (methods to solve the problem); Purpose and scope of study — are the 3 parts of the problem statement.

While the statement of the problem articulates and delineates a particular research problem, Objectives designates the aims, purpose and strategies to address the particular problem.

Here’s an example — “The study aims to identify and analyze the specific factors that impact employee productivity, providing organizations with actionable insights to optimize remote work policies.”

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on 8 November 2022 by Shona McCombes and Tegan George.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism, run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organisation. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organisation faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organisation focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organisation requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

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

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

Research objectives describe what you intend your research project to accomplish.

They summarise the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

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What is a Problem Statement in Research? How to Write It with Examples

The question, “What is a research problem statement?” is usually followed by “Why should I care about problem statements, and how can it affect my research?” In this article, we will try to simplify the concept so that you not only grasp its meaning but internalize its importance and learn how to craft a problem statement.

To put it simply, a “problem statement” as the name implies is any statement that describes a problem in research. When you conduct a study, your aim as a researcher is to answer a query or resolve a problem. This learned information is then typically disseminated by writing a research paper that details the entire process for readers (both for experts and the general public). To better grasp this concept, we’ll try to explain what a research problem statement is from the viewpoint of a reader. For the purpose of clarity and brevity the topic is divided into subsections.

Table of Contents

What is a research problem?

A research problem is a clearly defined issue in a particular field of study that requires additional investigation and study to resolve. Once identified, the problem can be succinctly stated to highlight existing knowledge gaps, the importance of solving the research problem, and the difference between a current situation and an improved state.

But why is it important to have a research problem ready? Keep in mind that a good research problem helps you define the main concepts and terms of research that not only guide your study but help you add to or update existing literature. A research problem statement should ideally be clear, precise, and tangible enough to assist you in developing a framework for establishing the objectives, techniques, and analysis of the research project. Hence, any research project, if it is to be completed successfully,  must start with a well-defined research problem.

What is research problem statement?

A research problem statement in research writing is the most crucial component of any study, which the researcher must perfect for a variety of reasons, including to get funding and boost readership. We’ve already established that a research article’s “research problem” is a sentence that expresses the specific problem that the research is addressing. But first, let’s discuss the significance of the problem statement in research and how to formulate one, using a few examples.

Do you recall the thoughts that went through your head the last time you read a study article? Have you ever tried to quickly scan the introduction or background of the research article to get a sense of the context and the exact issue the authors were attempting to address through the study? Were you stuck attempting to pinpoint the key sentence(s) that encapsulates the background and context of the study, the motivation behind its initial conduct, and its goals? A research problem statement is the descriptive statement which conveys the issue a researcher is trying to address through the study with the aim of informing the reader the context and significance of performing the study at hand . The research problem statement is crucial for researchers to focus on a particular component of a vast field of study, and for readers to comprehend the significance of the research. A well-defined problem allows you to create a framework to develop research objectives or hypotheses.

Now that we are aware of the significance of a problem statement in research, we can concentrate on creating one that is compelling. Writing a problem statement is a fairly simple process; first, you select a broad topic or research area based on your expertise and the resources at your disposal. Then, you narrow it down to a specific research question or problem relevant to that area of research while keeping the gaps in existing knowledge in mind. To give you a step-by-step instruction on how to write a problem statement for research proposal we’ve broken the process down into sections discussing individual aspects.

When to write a problem statement?

The placement of the research problem in the research project is another crucial component when developing a problem statement. Since the research problem statement is fundamental to writing any research project, it is best to write it at the start of the research process, before experimental setup, data collection, and analysis. Without identifying a specific research problem, you don’t know what exactly you are trying to address through the research so it would not be possible for you to set up the right conditions and foundation for the research project.

It is important to describe the research problem statement at the beginning of the research process to guide the research design and methodology. Another benefit of having a clear and defined research problem early on is that it helps researchers stay on track and focus on the problem at hand without deviating into other trajectories. Writing down the research problem statement also ensures that the current study is relevant, fitting, and fills a knowledge gap. However, note that a research statement can be refined or modified as the research advances and new information becomes available. This could be anything from further deconstructing a specific query to posing a fresh query related to the selected topic area. In fact, it is common practice to revise the problem statement in research to maintain specificity and clarity and to allow room to reflect advancement in the research field.

Bonus point:

A well-defined research problem statement that is referenced in the proper position in the research proposal/article is crucial to effectively communicate the goal and significance of the study to all stakeholders concerned with the research. It piques the reader’s interest in the research area, which can advance the work in several ways and open up future partnerships and even employment opportunities for authors.

What does a research problem statement include?

If you have to create a problem statement from scratch, follow the steps/important aspects listed below to create a well-defined research problem statement.

• Describe the wide-ranging research topics

To put things in perspective, it is important to first describe the background of the research issue, which derives from a broad area of study or interest that the research project is concerned with.

• Talk about the research problem/issue

As mentioned earlier, it’s important to state the problem or issues that the research project seeks to address in a clear, succinct manner, preferably in a sentence or two to set the premise of the entire study.

• Emphasize the importance of the issue

After defining the problem your research will try to solve, explain why it’s significant in the larger context and how your study aims to close the knowledge gap between the current state of knowledge and the ideal scenario.

• Outline research questions to address the issue

Give a brief description of the list of research questions your study will use to solve the problem at hand and explain how these will address various components of the problem statement.

• Specify the key goals of the research project

Next, carefully define a set of specific and measurable research objectives that the research project aims to address.

• Describe the experimental setup

Be sure to include a description of the experimental design, including the intended sample (population/size), setting, or context in the problem statement.

• Discuss the theoretical framework

Mention the numerous theoretical ideas and precepts necessary to comprehend the study issue and guide the research activity in this section.

• Include the research methodology

To provide a clear and concise research framework, add a brief description of the research methodologies, including collection and analysis of data, which will be needed to address the research questions and objectives.

Characteristics of a research problem statement

It is essential for a research statement to be clear and concise so that it can guide the development of the research project. A good research statement also helps other stakeholders in comprehending the scope and relevance of the research, which could further lead to opportunities for collaboration or exploration. Here is a list of the key characteristics of a research problem that you should keep in mind when writing an effective research problem statement.

• The “need” to resolve the issue must be present.

It is not enough to choose a problem in your area of interest and expertise; the research problem should have larger implications for a population or a specific subset. Unless the significance of the research problem is elaborated in detail, the research is not deemed significant. Hence, mentioning the “need” to conduct the research in the context of the subject area and how it will create a difference is of utmost importance.

• The research problem needs to be presented rationally and clearly

The research statement must be written at the start and be simple enough for even researchers outside the subject area to understand. The two fundamental elements of a successful research problem statement are clarity and specificity. So, check and rewrite your research problem statement if your peers have trouble understanding it. Aim to write in a straightforward manner while addressing all relevant issues and coherent arguments.

• The research issue is supported by facts and evidence

Before you begin writing the problem statement, you must collect all relevant information available to gain a better understanding of the research topic and existing gaps. A thorough literature search will give you an idea about the current situation and the specific questions you need to ask to close any knowledge gaps. This will also prevent you from asking the questions or identifying issues that have already been addressed. Also, the problem statement should be based on facts and data and should not depend upon hypothetical events.

• The research problem should generate more research questions

Ideally, the research problem should be such that it helps advance research and encourage more questions. The new questions could be specific to the research that highlights different components or aspects of the problem. These questions must also aid in addressing the problem in a more comprehensive manner which provides a solid foundation for the research study.

• The research problem should be tangible

The research issue should be concrete, which means that the study project’s budget and time constraints should be met. The research problem should not call for any actions and experiments that are impractical or outside of your area of competence.

To summarize the main characteristics of a research problem statement, it must:

• Address the knowledge gap
• Be current and relevant
• Aids in advancing the field
• Support future research
• Be tangible and should suit researcher’s time and interest
• Be based on facts and data

  How to write a problem statement in research proposal

The format of a problem statement might vary based on the nature and subject of the research; there is no set format. It is typically written in clear, concise sentences and can range from a few sentences to a few pages. Three considerations must be made when formulating a problem statement for a research proposal:

• Context: The research problem statement needs to be created in the right setting with sufficient background information on the research topic. Context makes it easier to distinguish between the current state and the ideal one in which the issue would not exist. In this section, you can also include instances of any prior attempts and significant roadblocks to solving the problem.
• Relevance: The main goal of the researcher here is to highlight the relevance of the research study. Explain how the research problem affects society or the field of research and, if the study is conducted to mitigate the issue, what an ideal scenario would look like. Who your study will most affect if the issue is resolved and how it can impact future research are other arguments that might be made in this section.
• Strategy: Be sure to mention the goals and objectives of your research, and your approach to solve the problem. The purpose of this section is to lay out the research approach for tackling various parts of the research subject.

Examples of problem statement in research proposal

To put what we learned into practice, let’s look at an example of a problem statement in a research report. Suppose you decide to conduct a study on the topic of attention span of different generations. After a thorough literature search you concluded that the attention span of university students is reducing over generations compared to the previous one, even though there are many websites and apps to simplify tasks and make learning easy . This decrease in attention span is attributed to constant exposure to digital content and multiple screens.

In this scenario, the problem statement could be written as – “The problem this study addresses is the lack of regulative measures to control consumption of digital content by young university students, which negatively impacts their attention span”. The research’s goals and objectives, which may employ strategies to increase university students’ attention span by limiting their internet exposure, can then be described in more detail in subsequent paragraphs.

Frequently asked questions

What is a problem statement.

A problem statement is a succinct and unambiguous overview of the research issue that the study is trying to solve.

What is the difference between problem statement and thesis statement?

A problem statement is different from a thesis statement in that the former highlights the main points of a research paper while emphasizing the hypothesis, whilst the latter identifies the issue for which research is being done.

Why is a problem statement needed in a research proposal?

A problem statement identifies the specific problem that the researchers are trying to solve through their research. It is necessary to establish a framework for the project, focus the researcher’s attention, and inform stakeholders of the study’s importance.

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The Importance Of Research Objectives

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw…

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw up a pocket-friendly plan. Where do you begin? The first step is to do your research.

Before that, you make a mental list of your objectives—finding reasonably-priced hotels, traveling safely and finding ways of communicating with someone back home. These objectives help you focus sharply during your research and be aware of the finer details of your trip.

More often than not, research is a part of our daily lives. Whether it’s to pick a restaurant for your next birthday dinner or to prepare a presentation at work, good research is the foundation of effective learning. Read on to understand the meaning, importance and examples of research objectives.

Why Do We Need Research?

What are the objectives of research, what goes into a research plan.

Research is a careful and detailed study of a particular problem or concern, using scientific methods. An in-depth analysis of information creates space for generating new questions, concepts and understandings. The main objective of research is to explore the unknown and unlock new possibilities. It’s an essential component of success.

Over the years, businesses have started emphasizing the need for research. You’ve probably noticed organizations hiring research managers and analysts. The primary purpose of business research is to determine the goals and opportunities of an organization. It’s critical in making business decisions and appropriately allocating available resources.

Here are a few benefits of research that’ll explain why it is a vital aspect of our professional lives:

Expands Your Knowledge Base

One of the greatest benefits of research is to learn and gain a deeper understanding. The deeper you dig into a topic, the more well-versed you are. Furthermore, research has the power to help you build on any personal experience you have on the subject.

Keeps You Up To Date

Research encourages you to discover the most recent information available. Updated information prevents you from falling behind and helps you present accurate information. You’re better equipped to develop ideas or talk about a topic when you’re armed with the latest inputs.

Builds Your Credibility

Research provides you with a good foundation upon which you can develop your thoughts and ideas. People take you more seriously when your suggestions are backed by research. You can speak with greater confidence because you know that the information is accurate.

Sparks Connections

Take any leading nonprofit organization, you’ll see how they have a strong research arm supported by real-life stories. Research also becomes the base upon which real-life connections and impact can be made. It even helps you communicate better with others and conveys why you’re pursuing something.

Encourages Curiosity

As we’ve already established, research is mostly about using existing information to create new ideas and opinions. In the process, it sparks curiosity as you’re encouraged to explore and gain deeper insights into a subject. Curiosity leads to higher levels of positivity and lower levels of anxiety.

Well-defined objectives of research are an essential component of successful research engagement. If you want to drive all aspects of your research methodology such as data collection, design, analysis and recommendation, you need to lay down the objectives of research methodology. In other words, the objectives of research should address the underlying purpose of investigation and analysis. It should outline the steps you’d take to achieve desirable outcomes. Research objectives help you stay focused and adjust your expectations as you progress.

The objectives of research should be closely related to the problem statement, giving way to specific and achievable goals. Here are the four types of research objectives for you to explore:

General Objective

Also known as secondary objectives, general objectives provide a detailed view of the aim of a study. In other words, you get a general overview of what you want to achieve by the end of your study. For example, if you want to study an organization’s contribution to environmental sustainability, your general objective could be: a study of sustainable practices and the use of renewable energy by the organization.

Specific Objectives

Specific objectives define the primary aim of the study. Typically, general objectives provide the foundation for identifying specific objectives. In other words, when general objectives are broken down into smaller and logically connected objectives, they’re known as specific objectives. They help define the who, what, why, when and how aspects of your project. Once you identify the main objective of research, it’s easier to develop and pursue a plan of action.

Let’s take the example of ‘a study of an organization’s contribution to environmental sustainability’ again. The specific objectives will look like this:

To determine through history how the organization has changed its practices and adopted new solutions

To assess how the new practices, technology and strategies will contribute to the overall effectiveness

Once you’ve identified the objectives of research, it’s time to organize your thoughts and streamline your research goals. Here are a few effective tips to develop a powerful research plan and improve your business performance.

Set SMART Goals

Your research objectives should be SMART—Specific, Measurable, Achievable, Realistic and Time-constrained. When you focus on utilizing available resources and setting realistic timeframes and milestones, it’s easier to prioritize objectives. Continuously track your progress and check whether you need to revise your expectations or targets. This way, you’re in greater control over the process.

Create A Plan

Create a plan that’ll help you select appropriate methods to collect accurate information. A well-structured plan allows you to use logical and creative approaches towards problem-solving. The complexity of information and your skills are bound to influence your plan, which is why you need to make room for flexibility. The availability of resources will also play a big role in influencing your decisions.

Collect And Collate

After you’ve created a plan for the research process, make a list of the data you’re going to collect and the methods you’ll use. Not only will it help make sense of your insights but also keep track of your approach. The information you collect should be:

Logical, rigorous and objective

Can be reproduced by other people working on the same subject

Free of errors and highlighting necessary details

Current and updated

Includes everything required to support your argument/suggestions

Analyze And Keep Ready

Data analysis is the most crucial part of the process and there are many ways in which the information can be utilized. Four types of data analysis are often seen in a professional environment. While they may be divided into separate categories, they’re linked to each other.

Descriptive Analysis:

The most commonly used data analysis, descriptive analysis simply summarizes past data. For example, Key Performance Indicators (KPIs) use descriptive analysis. It establishes certain benchmarks after studying how someone has been performing in the past.

Diagnostic Analysis:

The next step is to identify why something happened. Diagnostic analysis uses the information gathered through descriptive analysis and helps find the underlying causes of an outcome. For example, if a marketing initiative was successful, you deep-dive into the strategies that worked.

Predictive Analysis:

It attempts to answer ‘what’s likely to happen’. Predictive analysis makes use of past data to predict future outcomes. However, the accuracy of predictions depends on the quality of the data provided. Risk assessment is an ideal example of using predictive analysis.

Prescriptive Analysis: 

The most sought-after type of data analysis, prescriptive analysis combines the insights of all of the previous analyses. It’s a huge organizational commitment as it requires plenty of effort and resources. A great example of prescriptive analysis is Artificial Intelligence (AI), which consumes large amounts of data. You need to be prepared to commit to this type of analysis.

Review And Interpret

Once you’ve collected and collated your data, it’s time to review it and draw accurate conclusions. Here are a few ways to improve the review process:

Identify the fundamental issues, opportunities and problems and make note of recurring trends if any

Make a list of your insights and check which is the most or the least common. In short, keep track of the frequency of each insight

Conduct a SWOT analysis and identify the strengths, weaknesses, opportunities and threats

Write down your conclusions and recommendations of the research

When we think about research, we often associate it with academicians and students. but the truth is research is for everybody who is willing to learn and enhance their knowledge. If you want to master the art of strategically upgrading your knowledge, Harappa Education’s Learning Expertly course has all the answers. Not only will it help you look at things from a fresh perspective but also show you how to acquire new information with greater efficiency. The Growth Mindset framework will teach you how to believe in your abilities to grow and improve. The Learning Transfer framework will help you apply your learnings from one context to another. Begin the journey of tactful learning and self-improvement today!

Explore Harappa Diaries to learn more about topics related to the THINK Habit such as  Learning From Experience ,  Critical Thinking  & What is  Brainstorming  to think clearly and rationally.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

Crafting an effective problem statement

Aug 31, 2024

Posted by: Regine Fe Arat

Crafting a clear and concise problem statement is an essential skill in project management. It’s a powerful tool that you can use for effective problem-solving as it guides teams toward innovative solutions and measurable outcomes.

Whether you’re a seasoned project manager or a newcomer to the field, being able to write problem statements can significantly enhance your ability to tackle complex challenges and drive meaningful change.

A problem statement concisely describes an issue or challenge that needs to be addressed. An effective problem statement frames the issue in a way that facilitates a deeper understanding and guides the problem-solving process.

At its core, a well-crafted problem statement should capture the essence of the challenge at hand, providing enough context for stakeholders to grasp the issue’s significance. It helps you find the most appropriate solution and ensures that all team members are aligned in their understanding of the problem’s scope and implications.

In this comprehensive guide, you’ll find out what problem statements are and what types you can use. You’ll also find practical examples and actionable tips to help you create impactful problem statements of your own.

What are the key components of a problem statement?

Here are the three key components of a problem statement:

The problem

You should clearly state the core issue or challenge to be addressed. This is the heart of your problem statement. Articulate it in a way that’s easy to understand and free from ambiguity.

The method used to solve the problem

While the problem statement itself shouldn’t propose specific solutions, it can outline the general approach or methodology you’ll use to address the issue. For example, you might mention the type of research, analysis or problem-solving techniques your team will employ.

The purpose, statement of objective and scope

This component outlines why addressing the problem is important and what the desired outcome looks like. It should clarify the goals of the problem-solving effort and define the boundaries of what you’ll address. This helps focus efforts and set clear expectations for what the project or initiative aims to achieve.

When to use a problem statement

A problem statement is a versatile tool that you can use across various scenarios in both professional and personal contexts. They are particularly valuable in the following cases:

• Initiating new projects: a problem statement helps define the project’s purpose and sets clear objectives from the outset.
• Addressing organizational challenges: it provides a structured approach to tackling complex issues within a company or team.
• Conducting research: researchers use problem statements to focus their investigations and define the scope of their studies.
• Presenting ideas to stakeholders: a well-formulated problem statement can effectively communicate the need for change or investment to decision-makers.
• Personal goal-setting: even in individual pursuits, problem statements can help clarify objectives and motivate action.

Types of problem statements

Understanding different types of problem statements can help you choose the best approach for your specific situation.

Let’s explore three common types:

The status quo problem statement

This type of problem statement focuses on the current state of affairs and highlights the gap between the existing situation and the desired outcome.

It’s particularly effective when you are addressing ongoing issues or systemic problems within an organization.

Example: “Our customer support team currently handles 150 tickets, on average, per day with a resolution time of 48 hours. This prolonged response time has led to a 15% decrease in customer satisfaction scores over the past quarter, potentially impacting our retention rates and brand reputation.”

Destination problem statement

A destination problem statement emphasizes the desired future state or goal.

It’s ideal for situations where you want to inspire change and motivate teams to work toward a specific vision.

Example: “We aim to create a seamless onboarding experience for new employees that reduces the time to full productivity from 12 to six weeks while increasing new hire satisfaction scores by 25% within the next fiscal year.”

The stakeholder problem statement

This type of problem statement focuses on the impact of an issue on specific individuals or groups.

It’s particularly useful when you need to highlight the human element of a problem and garner support for change.

Example: “Junior software developers in our organization report feeling overwhelmed and unsupported, with 60% expressing dissatisfaction with their professional growth opportunities. This has resulted in a 30% turnover rate among this group in the past year, leading to increased recruitment costs and knowledge loss.”

How to write a problem statement

Crafting an effective problem statement takes practice and attention to detail. Follow these steps to create impactful problem statements:

Understand the problem

Before putting pen to paper, invest time in thoroughly understanding the issue at hand. Gather data, conduct interviews with stakeholders and observe the problem in action if possible. This deep understanding will form the foundation of your problem statement.

Articulate the problem in simple, straightforward language. Avoid jargon or overly technical terms that might confuse readers. Your goal is to ensure that anyone reading the statement can quickly grasp the core issue.

Provide context

Include relevant background information that helps readers understand the problem’s significance. This might include historical data, industry benchmarks or organizational goals that the issue is affecting.

Identify the root cause

Dig deeper to uncover the underlying reasons for the problem. Avoid focusing on symptoms. Instead, strive to identify the fundamental issues that need to be addressed. Tools like the “5 whys” technique can be helpful in this process.

Be specific

Use concrete details and quantifiable metrics whenever possible. Instead of saying, “Customer satisfaction is low,” specify, “Customer satisfaction scores have dropped by 15% in the past quarter.” This precision helps create a clear picture of the problem’s scope and impact.

Use measurable criteria

Incorporate measurable elements that can be used to track progress and determine when the problem has been resolved. This might include specific metrics, timeframes or benchmarks.

Make it feasible

Ensure the problem statement describes an issue the organization can actually address. You’ll need to be realistic.

Consider your organization’s resources and constraints. While ambition is important, an overly broad or unattainable goal can be demotivating and unhelpful.

Avoid solution language

Resist the temptation to propose solutions in the problem statement. The goal is to clearly define the problem, not to prescribe how it should be solved. This approach encourages creative thinking and enables you and your team to consider a range of potential solutions.

Consider the audience

Tailor your problem statement to the intended audience. The level of detail and technical language may vary depending on whether you’re presenting to executives, team members or external stakeholders.

Seek feedback

Share your draft problem statement with colleagues or stakeholders to gather their input. Fresh perspectives can help identify blind spots or areas that need clarification.

Revise and refine

Refine your problem statement based on the feedback you receive. Don’t be afraid to go through multiple iterations to achieve the most clear and impactful statement possible.

Test for objectivity

Review your problem statement to ensure it remains objective and free from bias. Avoid language that assigns blame or makes assumptions about causes or solutions.

Challenges of writing a problem statement

While problem statements can be a powerful tool for problem-solving, you may face several common challenges when writing yours. Being aware of these pitfalls can help you avoid them and create more effective problem statements.

Making it too complicated and lacking detail

One of the most frequent issues in problem statement writing is finding the right balance between detail and clarity.

Oversimplifying the problem can lead to a statement that’s too vague to be actionable. On the other hand, including too much detail can obscure the core issue and make the statement difficult to understand.

To overcome this challenge, focus on the essential elements of the problem. Start with a clear, concise statement about the issue, then add only the most relevant contextual details. Use specific, measurable criteria to define the problem’s scope and impact, but avoid getting bogged down in excessive technical jargon or minute, unhelpful details.

Ignoring stakeholders’ perspectives

Another common pitfall is failing to consider the diverse perspectives of all the stakeholders the problem affects. This can result in a problem statement that doesn’t fully capture the issue’s complexity or fails to resonate with key decision-makers.

To address this challenge, make an effort to gather input from a wide range of stakeholders before finalizing your problem statement. This might include conducting interviews, surveys or focus groups with employees, customers, partners or other relevant parties.

Incorporate these diverse viewpoints into your problem statement to create a more comprehensive and compelling representation of the issue.

Misalignment with organizational goals

Sometimes, problem statements can be well-crafted but fail to align with broader organizational objectives. This misalignment can lead to wasted resources and efforts on issues that, while important, may not be critical to the company’s overall success.

To ensure your problem statement aligns with the organization’s goals, review your company’s mission statement, strategic plans and key performance indicators before you get started. Consider how the problem you’re addressing relates to these broader objectives.

If possible, explicitly link the problem and its potential resolution to specific goals or metrics in your statement.

Failing to review and revise

An effective problem statement often requires multiple iterations and refinements. Many project managers make the mistake of treating their first draft as the final version, missing opportunities to improve clarity, precision and impact.

To overcome this challenge:

• Build time for revision into your problem statement writing process.
• After crafting your initial draft, step away from it for a short period.
• Return with fresh eyes to critically evaluate and refine your statement.
• Share it with colleagues or mentors for feedback. Be open to making substantive changes based on their input.

The last card

Being able to write problem statements is a valuable skill that can significantly enhance your problem-solving capabilities and drive meaningful change within your organization. They enable you to set the stage for innovative solutions and improved processes – but to do this, you’ll need to clearly articulate challenges, provide context and focus on measurable outcomes.

A well-crafted problem statement is a powerful tool for aligning teams, securing resources and guiding decision-making. It’s the foundation for effective problem-solving strategies. As you get better at writing problem statements, you’ll find that complex challenges become more manageable and your ability to drive positive change increases.

At Pip Decks, we’re passionate about equipping professionals with the tools and knowledge they need to excel in their roles. Whether you’re looking to improve your problem-solving skills, enhance team collaboration or develop your leadership abilities, you’ll find the answers you need in our expert-written card decks.

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Developing Surveys on Questionable Research Practices: Four Challenging Design Problems

• Open access
• Published: 02 September 2024

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

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

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Introduction

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

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

the lack of conceptual consensus and precise measurements,

the problem of social desirability bias.

the difficulty of covering both quantitative and qualitative research fields.

the problem of controversiality and sensitivity.

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

Four Design Problems in the Study of Questionable Research Practices

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

The Problem of Conceptual Clarity and Measurement Precision

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

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

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

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

The Problem of Social Desirability

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

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

The Problem of Covering Both Quantitative and Qualitative Research

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

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

The Problem of Institutional Controversiality and Personal Sensitivity

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

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

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

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

The Survey Design Process – Repeated Testing and Prototyping

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

Step 1: Development of the Baseline Survey

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

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

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

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

Step 3: Focused Pre-Testing and Technical Assessment

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

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

Step 4: Cognitive Interviews by Survey and Measurement Experts

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

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

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

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

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

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

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

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

Survey Content

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

Sample and Response Rate

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

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

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

Revisiting the Four Problems. Partial Solutions and Remaining Issues

Managing the precision problem - the value of factor analyses.

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

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

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

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

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

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

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

Reducing Desirability Bias - the Challenge of Nonresponse

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

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

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

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

Measuring QRPs in Qualitative Research - the Limited Role of Pretests

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

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

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

One comment focused on other problems related to qualitative research:

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

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

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

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

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

Controversiality and Collegial sensitivity - the Challenge of Predicting Nonresponse

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Data Availability

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

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Acknowledgements

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

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

Open access funding provided by Linköping University.

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

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Correspondence to Solmaz Filiz Karabag .

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

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

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

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

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

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

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

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

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

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

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

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

How are election polls being conducted?

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

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

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

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

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

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

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

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

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

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

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

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

How accurate are polls?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Other important things to remember

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

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

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

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

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

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

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

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

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