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Identifying a Research Problem: A Step-by-Step Guide

Identifying a Research Problem: A Step-by-Step Guide

Identifying a research problem is a crucial first step in the research process, serving as the foundation for all subsequent research activities. This guide provides a comprehensive overview of the steps involved in identifying a research problem, from understanding its essence to employing advanced strategies for refinement.

Key Takeaways

  • Understanding the definition and importance of a research problem is essential for academic success.
  • Exploring diverse sources such as literature reviews and consultations can help in formulating a solid research problem.
  • A clear problem statement, aligned research objectives, and well-defined questions are crucial for a focused study.
  • Evaluating the feasibility and potential impact of a research problem ensures its relevance and scope.
  • Advanced strategies, including interdisciplinary approaches and technology utilization, can enhance the identification and refinement of research problems.

Understanding the Essence of Identifying a Research Problem

Defining the research problem.

A research problem is the focal point of any academic inquiry. It is a concise and well-defined statement that outlines the specific issue or question that the research aims to address. This research problem usually sets the tone for the entire study and provides you, the researcher, with a clear purpose and a clear direction on how to go about conducting your research.

Importance in Academic Research

It also demonstrates the significance of your research and its potential to contribute new knowledge to the existing body of literature in the world. A compelling research problem not only captivates the attention of your peers but also lays the foundation for impactful and meaningful research outcomes.

Initial Steps to Identification

To identify a research problem, you need a systematic approach and a deep understanding of the subject area. Below are some steps to guide you in this process:

  • Conduct a thorough literature review to understand what has been studied before.
  • Identify gaps in the existing research that could form the basis of your study.
  • Consult with academic mentors to refine your ideas and approach.

Exploring Sources for Research Problem Identification

Literature review.

When you embark on the journey of identifying a research problem, a thorough literature review is indispensable. This process involves scrutinizing existing research to find literature gaps and unexplored areas that could form the basis of your research. It's crucial to analyze recent studies, seminal works, and review articles to ensure a comprehensive understanding of the topic.

Existing Theories and Frameworks

The exploration of existing theories and frameworks provides a solid foundation for developing a research problem. By understanding the established models and theories, you can identify inconsistencies or areas lacking in depth which might offer fruitful avenues for research.

Consultation with Academic Mentors

Engaging with academic mentors is vital in shaping a well-defined research problem. Their expertise can guide you through the complexities of your field, offering insights into feasible research questions and helping you refine your focus. This interaction often leads to the identification of unique and significant research opportunities that align with current academic and industry trends.

Formulating the Research Problem

Crafting a clear problem statement.

To effectively address your research problem, start by crafting a clear problem statement . This involves succinctly describing who is affected by the problem, why it is important, and how your research will contribute to solving it. Ensure your problem statement is concise and specific to guide the entire research process.

Setting Research Objectives

Setting clear research objectives is crucial for maintaining focus throughout your study. These objectives should directly align with the problem statement and guide your research activities. Consider using a bulleted list to outline your main objectives:

  • Understand the underlying factors contributing to the problem
  • Explore potential solutions
  • Evaluate the effectiveness of proposed solutions

Determining Research Questions

The formulation of precise research questions is a pivotal step in defining the scope and direction of your study. These questions should be directly derived from your research objectives and designed to be answerable through your chosen research methods. Crafting well-defined research questions will help you maintain a clear focus and avoid common pitfalls in the research process.

Evaluating the Scope and Relevance of the Research Problem

Feasibility assessment.

Before you finalize a research problem, it is crucial to assess its feasibility. Consider the availability of resources, time, and expertise required to conduct the research. Evaluate potential constraints and determine if the research problem can be realistically tackled within the given limitations.

Significance to the Field

Ensure that your research problem has a clear and direct impact on the field. It should aim to contribute to existing knowledge and address a real-world issue that is relevant to your academic discipline.

Potential Impact on Existing Knowledge

The potential impact of your research problem on existing knowledge cannot be understated. It should challenge, extend, or refine current understanding in a meaningful way. Consider how your research can add value to the existing body of work and potentially lead to significant advancements in your field.

Techniques for Refining the Research Problem

Narrowing down the focus.

To effectively refine your research problem, start by narrowing down the focus . This involves pinpointing the specific aspects of your topic that are most significant and ensuring that your research problem is not too broad. This targeted approach helps in identifying knowledge gaps and formulating more precise research questions.

Incorporating Feedback

Feedback is crucial in the refinement process. Engage with academic mentors, peers, and experts in your field to gather insights and suggestions. This collaborative feedback can lead to significant improvements in your research problem, making it more robust and relevant.

Iterative Refinement Process

Refinement should be seen as an iterative process, where you continuously refine and revise your research problem based on new information and feedback. This approach ensures that your research problem remains aligned with current trends and academic standards, ultimately enhancing its feasibility and relevance.

Challenges in Identifying a Research Problem

Common pitfalls and how to avoid them.

Identifying a research problem can be fraught with common pitfalls such as selecting a topic that is too broad or too narrow. To avoid these, you should conduct a thorough literature review and seek feedback from peers and mentors. This proactive approach ensures that your research question is both relevant and manageable.

Dealing with Ambiguity

Ambiguity in defining the research problem can lead to significant challenges down the line. Ensure clarity by operationalizing variables and explicitly stating the research objectives. This clarity will guide your entire research process, making it more structured and focused.

Balancing Novelty and Practicality

While it's important to address a novel issue in your research, practicality should not be overlooked. A research problem should not only contribute new knowledge but also be feasible and have clear implications. Balancing these aspects often requires iterative refinement and consultation with academic mentors to align your research with real-world applications.

Advanced Strategies for Identifying a Research Problem

Interdisciplinary approaches.

Embrace the power of interdisciplinary approaches to uncover unique and comprehensive research problems. By integrating knowledge from various disciplines, you can address complex issues that single-field studies might overlook. This method not only broadens the scope of your research but also enhances its applicability and depth.

Utilizing Technology and Data Analytics

Leverage technology and data analytics to refine and identify research problems with precision. Advanced tools like machine learning and big data analysis can reveal patterns and insights that traditional methods might miss. This approach is particularly useful in fields where large datasets are involved, or where real-time data integration can lead to more dynamic research outcomes.

Engaging with Industry and Community Needs

Focus on the needs of industry and community to ensure your research is not only academically sound but also practically relevant. Engaging with real-world problems can provide a rich source of research questions that are directly applicable and beneficial to society. This strategy not only enhances the relevance of your research but also increases its potential for impact.

Dive into the world of academic success with our 'Advanced Strategies for Identifying a Research Problem' at Research Rebels. Our expertly crafted guides and action plans are designed to simplify your thesis journey, transforming complex academic challenges into manageable tasks. Don't wait to take control of your academic future. Visit our website now to learn more and claim your special offer!

In conclusion, identifying a research problem is a foundational step in the academic research process that requires careful consideration and systematic approach. This guide has outlined the essential steps involved, from understanding the context and reviewing existing literature to formulating clear research questions. By adhering to these guidelines, researchers can ensure that their studies are grounded in a well-defined problem, enhancing the relevance and impact of their findings. It is crucial for scholars to approach this task with rigor and critical thinking to contribute meaningfully to the body of knowledge in their respective fields.

Frequently Asked Questions

What is a research problem.

A research problem is a specific issue, inconsistency, or gap in knowledge that needs to be addressed through scientific inquiry. It forms the foundation of a research study, guiding the research questions, methodology, and analysis.

Why is identifying a research problem important?

Identifying a research problem is crucial as it determines the direction and scope of the study. It helps researchers focus their inquiry, formulate hypotheses, and contribute to the existing body of knowledge.

How do I identify a suitable research problem?

To identify a suitable research problem, start with a thorough literature review to understand existing research and identify gaps. Consult with academic mentors, and consider relevance, feasibility, and your own interests.

What are some common pitfalls in identifying a research problem?

Common pitfalls include choosing a problem that is too broad or too narrow, not aligning with existing literature, lack of originality, and failing to consider the practical implications and feasibility of the study.

Can technology help in identifying a research problem?

Yes, technology and data analytics can aid in identifying research problems by providing access to a vast amount of data, revealing patterns and trends that might not be visible otherwise. Tools like digital libraries and research databases are particularly useful.

How can I refine my research problem?

Refine your research problem by narrowing its focus, seeking feedback from peers and mentors, and continually reviewing and adjusting the problem statement based on new information and insights gained during preliminary research.

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  • Statement of the Problem

Q: How do I identify a research problem and properly state it?

I have started writing my PhD thesis, but along the line it seems that my problem statement is not in order.

Asked on 07 Oct, 2019

I understand that you need help with either or both of the following:

  • Identifying the research problem
  • Stating the research problem, that is, writing the problem statement

In case of the former, you will be able to identify a research problem based on a thorough examination of the research area. A good starting point for this is doing an extensive literature search, which will uncover potential gaps and directions. Here’s a detailed article on how to do a comprehensive literature search:  Tips for effective literature searching and keeping up with new publications

In case of the latter, again, a comprehensive examination of the research area and the literature in the area will help you arrive at the problem statement. The problem statement is a crystallization – a focused expression – of the research problem. A good problem statement will do the following:

  • Describe the problem(s) succinctly
  • Include a vision (solution)
  • Suggest a method to solve the problem(s)
  • Provide a hypothesis

Again, here is an excellent detailed article, with multiple examples and a downloadable template, on how to approach writing a problem statement: The basics of writing a statement of the problem for your research proposal [If you would like to go through the same content in a video format, you may also check out this link: 3 Easy steps to write an effective statement of problem ]

Finally, if you wish to find out what other PhD folk are doing or have done – professionally and personally – check out our Researchers and Their Stories section, which has many engaging stories that you may connect with.

Good luck with the problem statement – and the rest of the thesis!

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Answered by Editage Insights on 11 Oct, 2019

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how to identify the problem in a research article

Pls can I get related research proposal on electronic and blended learning. The role of the teacher, limitations and solutions

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Answered by Joshua Quayson on 01 Aug, 2021

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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 .

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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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how to identify the problem in a research article

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.

The relationship between the research problem and research gap

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 .

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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 .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. & George, T. (2022, November 08). How to Define a Research Problem | Ideas & Examples. Scribbr. Retrieved 7 June 2024, from https://www.scribbr.co.uk/the-research-process/define-research-problem/

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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

  • Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
  • Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
  • Place the topic into a particular context that defines the parameters of what is to be investigated.
  • Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

  • Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
  • Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
  • Identification of what would be studied, while avoiding the use of value-laden words and terms,
  • Identification of an overarching question or small set of questions accompanied by key factors or variables,
  • Identification of key concepts and terms,
  • Articulation of the study's conceptual boundaries or parameters or limitations,
  • Some generalizability in regards to applicability and bringing results into general use,
  • Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
  • Does not have unnecessary jargon or overly complex sentence constructions; and,
  • Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

  • Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
  • Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
  • Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
  • Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

  • A lead-in that helps ensure the reader will maintain interest over the study,
  • A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
  • An indication of the central focus of the study [establishing the boundaries of analysis], and
  • An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE:   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

  • Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
  • Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
  • Provokes meaningful thought or discussion;
  • Raises the visibility of the key ideas or concepts that may be understudied or hidden;
  • Suggests the need for complex analysis or argument rather than a basic description or summary; and,
  • Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

  • The need is for a hospital
  • The objective is to create a hospital
  • The method is to plan for building a hospital, and
  • The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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How to identify and resolve research problems

Updated July 12, 2023

In this article, we’re going to take you through one of the most pertinent parts of conducting research: a research problem (also known as a research problem statement).

When trying to formulate a good research statement, and understand how to solve it for complex projects, it can be difficult to know where to start.

Not only are there multiple perspectives (from stakeholders to project marketers who want answers), you have to consider the particular context of the research topic: is it timely, is it relevant and most importantly of all, is it valuable?

In other words: are you looking at a research worthy problem?

The fact is, a well-defined, precise, and goal-centric research problem will keep your researchers, stakeholders, and business-focused and your results actionable.

And when it works well, it's a powerful tool to identify practical solutions that can drive change and secure buy-in from your workforce.

Free eBook: The ultimate guide to market research

What is a research problem?

In social research methodology and behavioral sciences , a research problem establishes the direction of research, often relating to a specific topic or opportunity for discussion.

For example: climate change and sustainability, analyzing moral dilemmas or wage disparity amongst classes could all be areas that the research problem focuses on.

As well as outlining the topic and/or opportunity, a research problem will explain:

  • why the area/issue needs to be addressed,
  • why the area/issue is of importance,
  • the parameters of the research study
  • the research objective
  • the reporting framework for the results and
  • what the overall benefit of doing so will provide (whether to society as a whole or other researchers and projects).

Having identified the main topic or opportunity for discussion, you can then narrow it down into one or several specific questions that can be scrutinized and answered through the research process.

What are research questions?

Generating research questions underpinning your study usually starts with problems that require further research and understanding while fulfilling the objectives of the study.

A good problem statement begins by asking deeper questions to gain insights about a specific topic.

For example, using the problems above, our questions could be:

"How will climate change policies influence sustainability standards across specific geographies?"

"What measures can be taken to address wage disparity without increasing inflation?"

Developing a research worthy problem is the first step - and one of the most important - in any kind of research.

It’s also a task that will come up again and again because any business research process is cyclical. New questions arise as you iterate and progress through discovering, refining, and improving your products and processes. A research question can also be referred to as a "problem statement".

Note: good research supports multiple perspectives through empirical data. It’s focused on key concepts rather than a broad area, providing readily actionable insight and areas for further research.

Research question or research problem?

As we've highlighted, the terms “research question” and “research problem” are often used interchangeably, becoming a vague or broad proposition for many.

The term "problem statement" is far more representative, but finds little use among academics.

Instead, some researchers think in terms of a single research problem and several research questions that arise from it.

As mentioned above, the questions are lines of inquiry to explore in trying to solve the overarching research problem.

Ultimately, this provides a more meaningful understanding of a topic area.

It may be useful to think of questions and problems as coming out of your business data – that’s the O-data (otherwise known as operational data) like sales figures and website metrics.

What's an example of a research problem?

Your overall research problem could be: "How do we improve sales across EMEA and reduce lost deals?"

This research problem then has a subset of questions, such as:

"Why do sales peak at certain times of the day?"

"Why are customers abandoning their online carts at the point of sale?"

As well as helping you to solve business problems, research problems (and associated questions) help you to think critically about topics and/or issues (business or otherwise). You can also use your old research to aid future research -- a good example is laying the foundation for comparative trend reports or a complex research project.

(Also, if you want to see the bigger picture when it comes to research problems, why not check out our ultimate guide to market research? In it you'll find out: what effective market research looks like, the use cases for market research, carrying out a research study, and how to examine and action research findings).

The research process: why are research problems important?

A research problem has two essential roles in setting your research project on a course for success.

1. They set the scope

The research problem defines what problem or opportunity you’re looking at and what your research goals are. It stops you from getting side-tracked or allowing the scope of research to creep off-course .

Without a strong research problem or problem statement, your team could end up spending resources unnecessarily, or coming up with results that aren’t actionable - or worse, harmful to your business - because the field of study is too broad.

2. They tie your work to business goals and actions

To formulate a research problem in terms of business decisions means you always have clarity on what’s needed to make those decisions. You can show the effects of what you’ve studied using real outcomes.

Then, by focusing your research problem statement on a series of questions tied to business objectives, you can reduce the risk of the research being unactionable or inaccurate.

It's also worth examining research or other scholarly literature (you’ll find plenty of similar, pertinent research online) to see how others have explored specific topics and noting implications that could have for your research.

Four steps to defining your research problem

Defining a research problem

Image credit: http://myfreeschooltanzania.blogspot.com/2014/11/defining-research-problem.html

1. Observe and identify

Businesses today have so much data that it can be difficult to know which problems to address first. Researchers also have business stakeholders who come to them with problems they would like to have explored. A researcher’s job is to sift through these inputs and discover exactly what higher-level trends and key concepts are worth investing in.

This often means asking questions and doing some initial investigation to decide which avenues to pursue. This could mean gathering interdisciplinary perspectives identifying additional expertise and contextual information.

Sometimes, a small-scale preliminary study might be worth doing to help get a more comprehensive understanding of the business context and needs, and to make sure your research problem addresses the most critical questions.

This could take the form of qualitative research using a few in-depth interviews , an environmental scan, or reviewing relevant literature.

The sales manager of a sportswear company has a problem: sales of trail running shoes are down year-on-year and she isn’t sure why. She approaches the company’s research team for input and they begin asking questions within the company and reviewing their knowledge of the wider market.

2. Review the key factors involved

As a marketing researcher, you must work closely with your team of researchers to define and test the influencing factors and the wider context involved in your study. These might include demographic and economic trends or the business environment affecting the question at hand. This is referred to as a relational research problem.

To do this, you have to identify the factors that will affect the research and begin formulating different methods to control them.

You also need to consider the relationships between factors and the degree of control you have over them. For example, you may be able to control the loading speed of your website but you can’t control the fluctuations of the stock market.

Doing this will help you determine whether the findings of your project will produce enough information to be worth the cost.

You need to determine:

  • which factors affect the solution to the research proposal.
  • which ones can be controlled and used for the purposes of the company, and to what extent.
  • the functional relationships between the factors.
  • which ones are critical to the solution of the research study.

The research team at the running shoe company is hard at work. They explore the factors involved and the context of why YoY sales are down for trail shoes, including things like what the company’s competitors are doing, what the weather has been like – affecting outdoor exercise – and the relative spend on marketing for the brand from year to year.

The final factor is within the company’s control, although the first two are not. They check the figures and determine marketing spend has a significant impact on the company.

3. Prioritize

Once you and your research team have a few observations, prioritize them based on their business impact and importance. It may be that you can answer more than one question with a single study, but don’t do it at the risk of losing focus on your overarching research problem.

Questions to ask:

  • Who? Who are the people with the problem? Are they end-users, stakeholders, teams within your business? Have you validated the information to see what the scale of the problem is?
  • What? What is its nature and what is the supporting evidence?
  • Why? What is the business case for solving the problem? How will it help?
  • Where? How does the problem manifest and where is it observed?

To help you understand all dimensions, you might want to consider focus groups or preliminary interviews with external (including consumers and existing customers) and internal (salespeople, managers, and other stakeholders) parties to provide what is sometimes much-needed insight into a particular set of questions or problems.

After observing and investigating, the running shoe researchers come up with a few candidate questions, including:

  • What is the relationship between US average temperatures and sales of our products year on year?
  • At present, how does our customer base rank Competitor X and Competitor Y’s trail running shoe compared to our brand?
  • What is the relationship between marketing spend and trail shoe product sales over the last 12 months?

They opt for the final question, because the variables involved are fully within the company’s control, and based on their initial research and stakeholder input, seem the most likely cause of the dive in sales. The research question is specific enough to keep the work on course towards an actionable result, but it allows for a few different avenues to be explored, such as the different budget allocations of offline and online marketing and the kinds of messaging used.

Get feedback from the key teams within your business to make sure everyone is aligned and has the same understanding of the research problem and questions, and the actions you hope to take based on the results. Now is also a good time to demonstrate the ROI of your research and lay out its potential benefits to your stakeholders.

Different groups may have different goals and perspectives on the issue. This step is vital for getting the necessary buy-in and pushing the project forward.

The running shoe company researchers now have everything they need to begin. They call a meeting with the sales manager and consult with the product team, marketing team, and C-suite to make sure everyone is aligned and has bought into the direction of the research topic. They identify and agree that the likely course of action will be a rethink of how marketing resources are allocated, and potentially testing out some new channels and messaging strategies .

Can you explore a broad area and is it practical to do so?

A broader research problem or report can be a great way to bring attention to prevalent issues, societal or otherwise, but are often undertaken by those with the resources to do so.

Take a typical government cybersecurity breach survey, for example. Most of these reports raise awareness of cybercrime, from the day-to-day threats businesses face to what security measures some organizations are taking. What these reports don't do, however, is provide actionable advice - mostly because every organization is different.

The point here is that while some researchers will explore a very complex issue in detail, others will provide only a snapshot to maintain interest and encourage further investigation. The "value" of the data is wholly determined by the recipients of it - and what information you choose to include.

To summarize, it can be practical to undertake a broader research problem, certainly, but it may not be possible to cover everything or provide the detail your audience needs. Likewise, a more systematic investigation of an issue or topic will be more valuable, but you may also find that you cover far less ground.

It's important to think about your research objectives and expected findings before going ahead.

Ensuring your research project is a success

A complex research project can be made significantly easier with clear research objectives, a descriptive research problem, and a central focus. All of which we've outlined in this article.

If you have previous research, even better. Use it as a benchmark

Remember: what separates a good research paper from an average one is actually very simple: valuable, empirical data that explores a prevalent societal or business issue and provides actionable insights.

And we can help.

Sophisticated research made simple with Qualtrics

Trusted by the world's best brands, our platform enables researchers from academic to corporate to tackle the hardest challenges and deliver the results that matter.

Our CoreXM platform supports the methods that define superior research and delivers insights in real-time. It's easy to use (thanks to drag-and-drop functionality) and requires no coding, meaning you'll be capturing data and gleaning insights in no time.

Satisfaction New York vs Massachusetts

It also excels in flexibility; you can track consumer behavior across segments , benchmark your company versus competitors , carry out complex academic research, and do much more, all from one system.

It's one platform with endless applications, so no matter your research problem, we've got the tools to help you solve it. And if you don't have a team of research experts in-house, our market research team has the practical knowledge and tools to help design the surveys and find the respondents you need.

Of course, you may want to know where to begin with your own market research . If you're struggling, make sure to download our ultimate guide using the link below.

It's got everything you need and there’s always information in our research methods knowledge base.

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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How to Identify an Appropriate Research Problem

Students working on a scholastic problem

By Mansureh Kebritchi, Ph.D.

A research problem is the heart of the study. It is a clear, definite statement of the area of concern or investigation and is backed by evidence (Bryman, 2007).  It drives the research questions and processes and provides the framework for understanding the research findings. To begin, you will need to know where to look for your research problem and how to evaluate when a research problem for success.

Where to Find a Research Problem

Ideas for research problems tend to come from two sources: real life and the scholarly arena.  First, identifying a research problem can be as simple as observing the complications and issues in your local workplace. You may encounter ongoing issues on a daily basis in your workplace or observe your colleagues struggle with major issues or questions in your field. These ongoing obstacles and issues in the workplace can be the catalyst for developing a research problem.  

Alternatively, research problems can be identified by reviewing recent literature, reports, or databases in your field. Often the section on “recommendations for future studies” provided at the end of journal articles or doctoral dissertations suggests potential research problems. In addition, major reports and databases in the field may reveal findings or data-based facts that call for additional investigation or suggest potential issues to be addressed. Looking at what theories need to be tested is another opportunity to develop a research problem.

How to Evaluate a Research Problem 

Once you find your potential research problem, you will need to evaluate the problem and ensure that it is appropriate for research. A research problem is deemed appropriate when it is supported by the literature and considered significant, timely, novel, specific, and researchable.  Stronger research problems are more likely to succeed in publication, presentation, and application.

Supported by the Literature

Your research problem should be relevant to the field and supported by a number of recent peer-reviewed studies in the field. Even if you identify the problem based on the recommendation of one journal article or dissertation, you will still need to conduct a literature search and ensure that other researchers support the problem and the need for conducting research to further address the problem.

Significant

Your research problem should have a positive impact on the field. The impact can be practical, in the form of direct application of the results in the field, or conceptual, where the work advances the field by filling a knowledge gap.  

Your research problem should be related to the current needs in the field and well-suited for the present status of the issues in your field. Explore what topics are being covered in current journals in the field. Look at calls from relevant disciplinary organizations. Review your research center agenda and focused topics. For example, the topics of the Research Labs at the Center for Educational and Instructional Technology Research including critical thinking, social media and cultural competency, diversity, and Science, Technology, Engineering, and Mathematics (STEM) in higher education are representative of the current timely topics in the field of education.  Identifying a current question in the field and supporting the problem with recent literature can justify the problem's timeliness.

Your research problem should be original and unique. It should seek to address a gap in our knowledge or application. An exhaustive review of the literature can help you identify whether the problem has already been addressed with your particular sample and/or context. Talking to experts in the research area can illuminate a problem.  Replication of an existing study warrants a discussion of value elsewhere, but the novelty can be found in determining if an already-resolved problem holds in a new sample and/or context.

Specific and Clear

Your research problem should be specific enough to set the direction of the study, raise research question(s), and determine an appropriate research method and design. Vague research problems may not be useful to specify the direction of the study or develop research questions.  

Researchable

Research problems are solved through the scientific method. This means researchability, or feasibility of the problem, is more important than all of the above characteristics. You as the researcher should be able to solve the problem with your abilities and available research methods, designs, research sites, resources, and timeframe. If a research problem retains all of the aforementioned characteristics but it is not researchable, it may not be an appropriate research problem.

References and More Information

Bryman, Alan. “The Research Question in Social Research: What is its Role?”  International Journal of Social Research Methodology  10 (2007): 5-20.

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  • Research Process

What is a Problem Statement? [with examples]

  • 5 minute read

Table of Contents

The statement of the problem is one of the first things that a colleague or potential client will read. With the vastness of the information available at one’s fingertips in the online9 world, your work may have just a few seconds to draw in a reader to take a deeper look at your proposal before moving on to the next option. It explains quickly to the reader, the problem at hand, the need for research, and how you intend to do it.

A strong, clear description of the problem that drew you to your research has to be straightforward, easy to read and, most important, relevant. Why do you care about this problem? How can solving this problem impact the world? The problem statement is your opportunity to explain why you care and what you propose to do in the way of researching the problem.

A problem statement is an explanation in research that describes the issue that is in need of study . What problem is the research attempting to address? Having a Problem Statement allows the reader to quickly understand the purpose and intent of the research. The importance of writing your research proposal cannot be stressed enough. Check for more information on Writing a Scientific Research Project Proposal .

It is expected to be brief and concise , and should not include the findings of the research or detailed data . The average length of a research statement is generally about one page . It is going to define the problem, which can be thought of as a gap in the information base. There may be several solutions to this gap or lack of information, but that is not the concern of the problem statement. Its purpose is to summarize the current information and where a lack of knowledge may be presenting a problem that needs to be investigated .

The purpose of the problem statement is to identify the issue that is a concern and focus it in a way that allows it to be studied in a systematic way . It defines the problem and proposes a way to research a solution, or demonstrates why further information is needed in order for a solution to become possible.

What is Included in a Problem Statement?

Besides identifying the gap of understanding or the weakness of necessary data, it is important to explain the significance of this lack.

-How will your research contribute to the existing knowledge base in your field of study?

-How is it significant?

-Why does it matter?

Not all problems have only one solution so demonstrating the need for additional research can also be included in your problem statement. Once you identify the problem and the need for a solution, or for further study, then you can show how you intend to collect the needed data and present it.

How to Write a Statement of Problem in Research Proposal

It is helpful to begin with your goal. What do you see as the achievable goal if the problem you outline is solved? How will the proposed research theoretically change anything? What are the potential outcomes?

Then you can discuss how the problem prevents the ability to reach your realistic and achievable solution. It is what stands in the way of changing an issue for the better. Talk about the present state of affairs and how the problem impacts a person’s life, for example.

It’s helpful at this point to generally layout the present knowledge and understanding of the subject at hand, before then describing the gaps of knowledge that are currently in need of study. Your problem statement is a proposed solution to address one of these gaps.

A good problem statement will also layout the repercussions of leaving the problem as it currently stands. What is the significance of not addressing this problem? What are the possible future outcomes?

Example of Problem Statement in Research Proposal

If, for example , you intended to research the effect of vitamin D supplementation on the immune system , you would begin with a review of the current knowledge of vitamin D’s known function in relation to the immune system and how a deficiency of it impacts a person’s defenses.

You would describe the ideal environment in the body when there is a sufficient level of vitamin D. Then, begin to identify the problems associated with vitamin D deficiency and the difficulty of raising the level through supplementation, along with the consequences of that deficiency. Here you are beginning to identify the problem of a common deficiency and the current difficulty of increasing the level of vitamin D in the blood.

At this stage, you may begin to identify the problem and narrow it down in a way that is practical to a research project. Perhaps you are proposing a novel way of introducing Vitamin D in a way that allows for better absorption by the gut, or in a combination with another product that increases its level in the blood.

Describe the way your research in this area will contribute to the knowledge base on how to increase levels of vitamin D in a specific group of subjects, perhaps menopausal women with breast cancer. The research proposal is then described in practical terms.

How to write a problem statement in research?

Problem statements differ depending on the type and topic of research and vary between a few sentences to a few paragraphs.

However, the problem statement should not drag on needlessly. Despite the absence of a fixed format, a good research problem statement usually consists of three main parts:

Context: This section explains the background for your research. It identifies the problem and describes an ideal scenario that could exist in the absence of the problem. It also includes any past attempts and shortcomings at solving the problem.

Significance: This section defines how the problem prevents the ideal scenario from being achieved, including its negative impacts on the society or field of research. It should include who will be the most affected by a solution to the problem, the relevance of the study that you are proposing, and how it can contribute to the existing body of research.

Solution: This section describes the aim and objectives of your research, and your solution to overcome the problem. Finally, it need not focus on the perfect solution, but rather on addressing a realistic goal to move closer to the ideal scenario.

Here is a cheat sheet to help you with formulating a good problem statement.

1. Begin with a clear indication that the problem statement is going to be discussed next. You can start with a generic sentence like, “The problem that this study addresses…” This will inform your readers of what to expect next.

2. Next, mention the consequences of not solving the problem . You can touch upon who is or will be affected if the problem continues, and how.

3. Conclude with indicating the type of research /information that is needed to solve the problem. Be sure to reference authors who may have suggested the necessity of such research.

This will then directly lead to your proposed research objective and workplan and how that is expected to solve the problem i.e., close the research gap.

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What is a Research Problem? Characteristics, Types, and Examples

What is a Research Problem? Characteristics, Types, and Examples

A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets the problem into a particular context, and defines the relevant parameters, providing the framework for reporting the findings. Therein lies the importance of research problem s.  

The formulation of well-defined research questions is central to addressing a research problem . A research question is a statement made in a question form to provide focus, clarity, and structure to the research endeavor. This helps the researcher design methodologies, collect data, and analyze results in a systematic and coherent manner. A study may have one or more research questions depending on the nature of the study.   

how to identify the problem in a research article

Identifying and addressing a research problem is very important. By starting with a pertinent problem , a scholar can contribute to the accumulation of evidence-based insights, solutions, and scientific progress, thereby advancing the frontier of research. Moreover, the process of formulating research problems and posing pertinent research questions cultivates critical thinking and hones problem-solving skills.   

Table of Contents

What is a Research Problem ?  

Before you conceive of your project, you need to ask yourself “ What is a research problem ?” A research problem definition can be broadly put forward as the primary statement of a knowledge gap or a fundamental challenge in a field, which forms the foundation for research. Conversely, the findings from a research investigation provide solutions to the problem .  

A research problem guides the selection of approaches and methodologies, data collection, and interpretation of results to find answers or solutions. A well-defined problem determines the generation of valuable insights and contributions to the broader intellectual discourse.  

Characteristics of a Research Problem  

Knowing the characteristics of a research problem is instrumental in formulating a research inquiry; take a look at the five key characteristics below:  

Novel : An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.   

Significant : A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.  

Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem . These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.  

Clear and specific : A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.  

Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.

how to identify the problem in a research article

Types of Research Problems  

Across fields and disciplines, there are different types of research problems . We can broadly categorize them into three types.  

  • Theoretical research problems

Theoretical research problems deal with conceptual and intellectual inquiries that may not involve empirical data collection but instead seek to advance our understanding of complex concepts, theories, and phenomena within their respective disciplines. For example, in the social sciences, research problem s may be casuist (relating to the determination of right and wrong in questions of conduct or conscience), difference (comparing or contrasting two or more phenomena), descriptive (aims to describe a situation or state), or relational (investigating characteristics that are related in some way).  

Here are some theoretical research problem examples :   

  • Ethical frameworks that can provide coherent justifications for artificial intelligence and machine learning algorithms, especially in contexts involving autonomous decision-making and moral agency.  
  • Determining how mathematical models can elucidate the gradual development of complex traits, such as intricate anatomical structures or elaborate behaviors, through successive generations.  
  • Applied research problems

Applied or practical research problems focus on addressing real-world challenges and generating practical solutions to improve various aspects of society, technology, health, and the environment.  

Here are some applied research problem examples :   

  • Studying the use of precision agriculture techniques to optimize crop yield and minimize resource waste.  
  • Designing a more energy-efficient and sustainable transportation system for a city to reduce carbon emissions.  
  • Action research problems

Action research problems aim to create positive change within specific contexts by involving stakeholders, implementing interventions, and evaluating outcomes in a collaborative manner.  

Here are some action research problem examples :   

  • Partnering with healthcare professionals to identify barriers to patient adherence to medication regimens and devising interventions to address them.  
  • Collaborating with a nonprofit organization to evaluate the effectiveness of their programs aimed at providing job training for underserved populations.  

These different types of research problems may give you some ideas when you plan on developing your own.  

How to Define a Research Problem  

You might now ask “ How to define a research problem ?” These are the general steps to follow:   

  • Look for a broad problem area: Identify under-explored aspects or areas of concern, or a controversy in your topic of interest. Evaluate the significance of addressing the problem in terms of its potential contribution to the field, practical applications, or theoretical insights.
  • Learn more about the problem: Read the literature, starting from historical aspects to the current status and latest updates. Rely on reputable evidence and data. Be sure to consult researchers who work in the relevant field, mentors, and peers. Do not ignore the gray literature on the subject.
  • Identify the relevant variables and how they are related: Consider which variables are most important to the study and will help answer the research question. Once this is done, you will need to determine the relationships between these variables and how these relationships affect the research problem . 
  • Think of practical aspects : Deliberate on ways that your study can be practical and feasible in terms of time and resources. Discuss practical aspects with researchers in the field and be open to revising the problem based on feedback. Refine the scope of the research problem to make it manageable and specific; consider the resources available, time constraints, and feasibility.
  • Formulate the problem statement: Craft a concise problem statement that outlines the specific issue, its relevance, and why it needs further investigation.
  • Stick to plans, but be flexible: When defining the problem , plan ahead but adhere to your budget and timeline. At the same time, consider all possibilities and ensure that the problem and question can be modified if needed.

Researcher Life

Key Takeaways  

  • A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.   
  • The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.  
  • Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.  
  • Different types of research problems include theoretical, applied, and action research problems , and these depend on the discipline and nature of the study.  
  • An ideal problem is original, important, feasible, specific, and based on evidence.  

Frequently Asked Questions  

Why is it important to define a research problem?  

Identifying potential issues and gaps as research problems is important for choosing a relevant topic and for determining a well-defined course of one’s research. Pinpointing a problem and formulating research questions can help researchers build their critical thinking, curiosity, and problem-solving abilities.   

How do I identify a research problem?  

Identifying a research problem involves recognizing gaps in existing knowledge, exploring areas of uncertainty, and assessing the significance of addressing these gaps within a specific field of study. This process often involves thorough literature review, discussions with experts, and considering practical implications.  

Can a research problem change during the research process?  

Yes, a research problem can change during the research process. During the course of an investigation a researcher might discover new perspectives, complexities, or insights that prompt a reevaluation of the initial problem. The scope of the problem, unforeseen or unexpected issues, or other limitations might prompt some tweaks. You should be able to adjust the problem to ensure that the study remains relevant and aligned with the evolving understanding of the subject matter.

How does a research problem relate to research questions or hypotheses?  

A research problem sets the stage for the study. Next, research questions refine the direction of investigation by breaking down the broader research problem into manageable components. Research questions are formulated based on the problem , guiding the investigation’s scope and objectives. The hypothesis provides a testable statement to validate or refute within the research process. All three elements are interconnected and work together to guide the research.  

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

  • EXAMPLE 1. Descriptive research question (quantitative research)
  • - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
  • “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
  • RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
  • EXAMPLE 2. Relationship research question (quantitative research)
  • - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
  • “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
  • Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
  • EXAMPLE 3. Comparative research question (quantitative research)
  • - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
  • “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
  • RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
  • STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
  • EXAMPLE 4. Exploratory research question (qualitative research)
  • - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
  • “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
  • EXAMPLE 5. Relationship research question (quantitative research)
  • - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
  • “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

  • EXAMPLE 1. Working hypothesis (quantitative research)
  • - A hypothesis that is initially accepted for further research to produce a feasible theory
  • “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
  • “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
  • EXAMPLE 2. Exploratory hypothesis (qualitative research)
  • - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
  • “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
  • “Conclusion
  • Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
  • EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
  • “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
  • Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
  • EXAMPLE 4. Statistical hypothesis (quantitative research)
  • - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
  • “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
  • “Statistical Analysis
  • ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

  • EXAMPLE 1. Background, hypotheses, and aims are provided
  • “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
  • “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
  • “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
  • EXAMPLE 2. Background, hypotheses, and aims are provided
  • “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
  • “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
  • EXAMPLE 3. Background, aim, and hypothesis are provided
  • “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
  • “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
  • “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Barroga E, Matanguihan GJ.
  • Methodology: Barroga E, Matanguihan GJ.
  • Writing - original draft: Barroga E, Matanguihan GJ.
  • Writing - review & editing: Barroga E, Matanguihan GJ.

Identifying the Research Problem

  • First Online: 13 April 2022

Cite this chapter

how to identify the problem in a research article

  • Yanmei Li 3 &
  • Sumei Zhang 4  

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The first step of conducting research is identifying a researchable problem. This chapter explains the procedures to identify the research problem. The procedure of research involves systematically investigating a subject matter to reveal facts or reach new conclusions. Research can be theory driven or problem driven. Most of the studies in urban and regional planning attempt to investigate issues or problems either to: (1) identity and define the problem, and/or (2) probe further to answer research questions after identifying, defining, and operationalizing the problem. This chapter introduces methods, such as factual data mining, back of envelope analysis, quick research, and issues unique to a sub-discipline of planning, to identify the research problem.

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Research: Establishing the Problem Space

  • Establishing the Problem Space
  • Finding Qualitative Research
  • Finding Quantitative Research
  • What is Emperical Research?
  • What is Seminal Research?

What is the Problem Space?

A gap is a space between two objects or a break in continuity.  A research gap is a break or missing part of the existing research when you define the research gap or the problem space you are defining what is known and what is missing in the existing research.  The "problem space" of a study is a definition of the topic, the problem statements or research gaps mentioned by other researchers, and the steps other researchers took to answer the research question. The problem space is a way to identify and establish boundaries for your research, it helps to guide what should be included or excluded from your research.  The problem statement expresses how your study will answer or fill the research gap.

The problem space is thus comprised of identifying what is known about a topic, understanding how it has come to be known (the theories, designs, methods, instruments), and then figuring out what is not yet known (or perspective not explored) .   Problem spaces are built by taking note of the limitations and recommendations discussed in the empirical research articles you gather as you build your literature review.

  • Don't know where to start? 6 Tips on identifying research gaps
  • What are Gap Statements? From the Middlebury University 'Write Like a Scientist" guide.
  • Farooq, R. (2017). A framework for identifying research gap in social sciences: Evidence from the past. IUP Journal of Management Research, 16(4), 66-75. Retrieved from https://uscupstate.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/
  • Robinson KA, Akinyede O, Dutta T, et al. Framework for Determining Research Gaps During Systematic Review: Evaluation [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb. Introduction. Available from: https://www.ncbi.nlm

Examples From Empirical Articles

When looking to find discussions of research that has yet to be done (AKA research gap) in existing articles there are a few keywords to look out for such as limitations identified, further research needed, needs clarification, not been reported (studied, reported, or elucidated), suggestions for further research, questions remains, poorly understood, and/or lack of studies

Below are two examples of types of passages to look for.

Example of a Limitations Section

From the article:

Spanhove, V., De Wandele, I., Kjær, B. H., Malfait, F., Vanderstukken, F., & Cools, A. (2020). The effect of five isometric exercises on glenohumeral translations in healthy subjects and patients with the hypermobility type of the ehlers-danlos syndrome (heds) or hypermobility spectrum disorder (hsd) with multidirectional shoulder instability: an observational study.  Physiotherapy ,  107 , 11–18. https://doi.org/10.1016/j.physio.2019.06.010

From this passage, an argument could be made for performing a similar study, but with 3D analysis.

Example of a Recommendation for Further Research

Some articles will go beyond discussing their limitations and describe further research that should be done. 

For example, this article:

Carey, J., Pathak, A., & Johnson, S. C. (2020). Use, Perceptions, and Awareness of LibGuides among Undergraduate and Graduate Health Professions Students.  Evidence Based Library and Information Practice ,  15 (3), 157-172. https://doi.org/10.18438/eblip29653

Suggests several different avenues of further research:

How to Use Review Articles

Review articles can help formulate a gap, or at least point out a direction to look for one. Since they provide an overview of the published literature, they can give you a head start on what kinds of research are lacking.

How to Locate Review Articles: Systematic Reviews, Literature Reviews, and Meta-Analyses

  • handwashing or hand washing or hand hygiene or hand sanitation
  • systematic review or meta-analysis or literature review or scoping review
  • Adjust dates to be within 2 years. 
  • For instance the above search was used to locate this article:

Seo, H.-J., Sohng, K.-Y., Chang, S. O., Chaung, S. K., Won, J. S., & Choi, M.-J. (2019). Interventions to improve hand hygiene compliance in emergency departments: a systematic review. The Journal of Hospital Infection , 102(4), 394–406. https://doi.org/10.1016/j.jhin.2019.03.013

  • (hand antisepsis or handwash* or hand wash* or hand disinfection or hand hygiene or surgical scrub*)
  • With terms that should be included when searching on this topic.
  • "Further well-designed controlled studies are necessary to examine the true effects and identify which intervention modalities are more effective than others for HHC improvement in EDs."
  • Reviewing the articles this article studied would then provide support for this gap.

Pursuing a health care topic? Search Cochrane Reviews or Joanna Biggs EBP as well as the more general databases.

Example of a Review Article With a Discussion of Areas Needing Research

Example of a Review Article

Review articles can clarify where a lack of research exists. To then establish the problem space fully, you will need to track down the articles cited in the review.

For instance, consider the following passage from this review article:

Martin, A. (2019). An acquired or heritable connective tissue disorder? A review of hypermobile Ehlers Danlos Syndrome. European Journal of Medical Genetics, 62(7), 103672. https://doi.org/10.1016/j.ejmg.2019.103672

This is indicating a need for longitudinal studies for this condition to better understand the relationship between muscle strength and muscle waste. Further examining the cited articles would establish this avenue for a study.

Problem Formulation

  • Trochim, William M.K. “Problem Formulation.” Research Methods Knowledge Base, Conjoint.ly, https://conjointly.com/kb/problem-formulation/.
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Research Gap – Types, Examples and How to Identify

Table of Contents

Research Gap

Research Gap

Definition:

Research gap refers to an area or topic within a field of study that has not yet been extensively researched or is yet to be explored. It is a question, problem or issue that has not been addressed or resolved by previous research.

How to Identify Research Gap

Identifying a research gap is an essential step in conducting research that adds value and contributes to the existing body of knowledge. Research gap requires critical thinking, creativity, and a thorough understanding of the existing literature . It is an iterative process that may require revisiting and refining your research questions and ideas multiple times.

Here are some steps that can help you identify a research gap:

  • Review existing literature: Conduct a thorough review of the existing literature in your research area. This will help you identify what has already been studied and what gaps still exist.
  • Identify a research problem: Identify a specific research problem or question that you want to address.
  • Analyze existing research: Analyze the existing research related to your research problem. This will help you identify areas that have not been studied, inconsistencies in the findings, or limitations of the previous research.
  • Brainstorm potential research ideas : Based on your analysis, brainstorm potential research ideas that address the identified gaps.
  • Consult with experts: Consult with experts in your research area to get their opinions on potential research ideas and to identify any additional gaps that you may have missed.
  • Refine research questions: Refine your research questions and hypotheses based on the identified gaps and potential research ideas.
  • Develop a research proposal: Develop a research proposal that outlines your research questions, objectives, and methods to address the identified research gap.

Types of Research Gap

There are different types of research gaps that can be identified, and each type is associated with a specific situation or problem. Here are the main types of research gaps and their explanations:

Theoretical Gap

This type of research gap refers to a lack of theoretical understanding or knowledge in a particular area. It can occur when there is a discrepancy between existing theories and empirical evidence or when there is no theory that can explain a particular phenomenon. Identifying theoretical gaps can lead to the development of new theories or the refinement of existing ones.

Empirical Gap

An empirical gap occurs when there is a lack of empirical evidence or data in a particular area. It can happen when there is a lack of research on a specific topic or when existing research is inadequate or inconclusive. Identifying empirical gaps can lead to the development of new research studies to collect data or the refinement of existing research methods to improve the quality of data collected.

Methodological Gap

This type of research gap refers to a lack of appropriate research methods or techniques to answer a research question. It can occur when existing methods are inadequate, outdated, or inappropriate for the research question. Identifying methodological gaps can lead to the development of new research methods or the modification of existing ones to better address the research question.

Practical Gap

A practical gap occurs when there is a lack of practical applications or implementation of research findings. It can occur when research findings are not implemented due to financial, political, or social constraints. Identifying practical gaps can lead to the development of strategies for the effective implementation of research findings in practice.

Knowledge Gap

This type of research gap occurs when there is a lack of knowledge or information on a particular topic. It can happen when a new area of research is emerging, or when research is conducted in a different context or population. Identifying knowledge gaps can lead to the development of new research studies or the extension of existing research to fill the gap.

Examples of Research Gap

Here are some examples of research gaps that researchers might identify:

  • Theoretical Gap Example : In the field of psychology, there might be a theoretical gap related to the lack of understanding of the relationship between social media use and mental health. Although there is existing research on the topic, there might be a lack of consensus on the mechanisms that link social media use to mental health outcomes.
  • Empirical Gap Example : In the field of environmental science, there might be an empirical gap related to the lack of data on the long-term effects of climate change on biodiversity in specific regions. Although there might be some studies on the topic, there might be a lack of data on the long-term effects of climate change on specific species or ecosystems.
  • Methodological Gap Example : In the field of education, there might be a methodological gap related to the lack of appropriate research methods to assess the impact of online learning on student outcomes. Although there might be some studies on the topic, existing research methods might not be appropriate to assess the complex relationships between online learning and student outcomes.
  • Practical Gap Example: In the field of healthcare, there might be a practical gap related to the lack of effective strategies to implement evidence-based practices in clinical settings. Although there might be existing research on the effectiveness of certain practices, they might not be implemented in practice due to various barriers, such as financial constraints or lack of resources.
  • Knowledge Gap Example: In the field of anthropology, there might be a knowledge gap related to the lack of understanding of the cultural practices of indigenous communities in certain regions. Although there might be some research on the topic, there might be a lack of knowledge about specific cultural practices or beliefs that are unique to those communities.

Examples of Research Gap In Literature Review, Thesis, and Research Paper might be:

  • Literature review : A literature review on the topic of machine learning and healthcare might identify a research gap in the lack of studies that investigate the use of machine learning for early detection of rare diseases.
  • Thesis : A thesis on the topic of cybersecurity might identify a research gap in the lack of studies that investigate the effectiveness of artificial intelligence in detecting and preventing cyber attacks.
  • Research paper : A research paper on the topic of natural language processing might identify a research gap in the lack of studies that investigate the use of natural language processing techniques for sentiment analysis in non-English languages.

How to Write Research Gap

By following these steps, you can effectively write about research gaps in your paper and clearly articulate the contribution that your study will make to the existing body of knowledge.

Here are some steps to follow when writing about research gaps in your paper:

  • Identify the research question : Before writing about research gaps, you need to identify your research question or problem. This will help you to understand the scope of your research and identify areas where additional research is needed.
  • Review the literature: Conduct a thorough review of the literature related to your research question. This will help you to identify the current state of knowledge in the field and the gaps that exist.
  • Identify the research gap: Based on your review of the literature, identify the specific research gap that your study will address. This could be a theoretical, empirical, methodological, practical, or knowledge gap.
  • Provide evidence: Provide evidence to support your claim that the research gap exists. This could include a summary of the existing literature, a discussion of the limitations of previous studies, or an analysis of the current state of knowledge in the field.
  • Explain the importance: Explain why it is important to fill the research gap. This could include a discussion of the potential implications of filling the gap, the significance of the research for the field, or the potential benefits to society.
  • State your research objectives: State your research objectives, which should be aligned with the research gap you have identified. This will help you to clearly articulate the purpose of your study and how it will address the research gap.

Importance of Research Gap

The importance of research gaps can be summarized as follows:

  • Advancing knowledge: Identifying research gaps is crucial for advancing knowledge in a particular field. By identifying areas where additional research is needed, researchers can fill gaps in the existing body of knowledge and contribute to the development of new theories and practices.
  • Guiding research: Research gaps can guide researchers in designing studies that fill those gaps. By identifying research gaps, researchers can develop research questions and objectives that are aligned with the needs of the field and contribute to the development of new knowledge.
  • Enhancing research quality: By identifying research gaps, researchers can avoid duplicating previous research and instead focus on developing innovative research that fills gaps in the existing body of knowledge. This can lead to more impactful research and higher-quality research outputs.
  • Informing policy and practice: Research gaps can inform policy and practice by highlighting areas where additional research is needed to inform decision-making. By filling research gaps, researchers can provide evidence-based recommendations that have the potential to improve policy and practice in a particular field.

Applications of Research Gap

Here are some potential applications of research gap:

  • Informing research priorities: Research gaps can help guide research funding agencies and researchers to prioritize research areas that require more attention and resources.
  • Identifying practical implications: Identifying gaps in knowledge can help identify practical applications of research that are still unexplored or underdeveloped.
  • Stimulating innovation: Research gaps can encourage innovation and the development of new approaches or methodologies to address unexplored areas.
  • Improving policy-making: Research gaps can inform policy-making decisions by highlighting areas where more research is needed to make informed policy decisions.
  • Enhancing academic discourse: Research gaps can lead to new and constructive debates and discussions within academic communities, leading to more robust and comprehensive research.

Advantages of Research Gap

Here are some of the advantages of research gap:

  • Identifies new research opportunities: Identifying research gaps can help researchers identify areas that require further exploration, which can lead to new research opportunities.
  • Improves the quality of research: By identifying gaps in current research, researchers can focus their efforts on addressing unanswered questions, which can improve the overall quality of research.
  • Enhances the relevance of research: Research that addresses existing gaps can have significant implications for the development of theories, policies, and practices, and can therefore increase the relevance and impact of research.
  • Helps avoid duplication of effort: Identifying existing research can help researchers avoid duplicating efforts, saving time and resources.
  • Helps to refine research questions: Research gaps can help researchers refine their research questions, making them more focused and relevant to the needs of the field.
  • Promotes collaboration: By identifying areas of research that require further investigation, researchers can collaborate with others to conduct research that addresses these gaps, which can lead to more comprehensive and impactful research outcomes.

Disadvantages of Research Gap

While research gaps can be advantageous, there are also some potential disadvantages that should be considered:

  • Difficulty in identifying gaps: Identifying gaps in existing research can be challenging, particularly in fields where there is a large volume of research or where research findings are scattered across different disciplines.
  • Lack of funding: Addressing research gaps may require significant resources, and researchers may struggle to secure funding for their work if it is perceived as too risky or uncertain.
  • Time-consuming: Conducting research to address gaps can be time-consuming, particularly if the research involves collecting new data or developing new methods.
  • Risk of oversimplification: Addressing research gaps may require researchers to simplify complex problems, which can lead to oversimplification and a failure to capture the complexity of the issues.
  • Bias : Identifying research gaps can be influenced by researchers’ personal biases or perspectives, which can lead to a skewed understanding of the field.
  • Potential for disagreement: Identifying research gaps can be subjective, and different researchers may have different views on what constitutes a gap in the field, leading to disagreements and debate.

About the author

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New database features 250 AI tools that can enhance social science research

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Assistant Research Professor at the Social Science Research Center, Mississippi State University

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AI – or artificial intelligence – is often used as a way to summarize data and improve writing. But AI tools also represent a powerful and efficient way to analyze large amounts of text to search for patterns. In addition, AI tools can assist with developing research products that can be shared widely.

It’s with that in mind that we , as researchers in social science , developed a new database of AI tools for the field . In the database, we compiled information about each tool and documented whether it was useful for literature reviews, data collection and analyses, or research dissemination. We also provided information on the costs, logins and plug-in extensions available for each tool.

When asked about their perceptions of AI, many social scientists express caution or apprehension. In a sample of faculty and students from over 600 institutions, only 22% of university faculty reported that they regularly used AI tools .

From combing through lengthy transcripts or text-based data to writing literature reviews and sharing results, we believe AI can help social science researchers – such as those in psychology, sociology and communication – as well as others get the most out of their data and present it to a wider audience.

Analyze text using AI

Qualitative research often involves poring over transcripts or written language to identify themes and patterns. While this kind of research is powerful, it is also labor-intensive. The power of AI platforms to sift through large datasets not only saves researchers time, but it can also help them analyze data that couldn’t have been analyzed previously because of the size of the dataset.

Specifically, AI can assist social scientists by identifying potential themes or common topics in large, text-based data that scientists can interrogate using qualitative research methods. For example, AI can analyze 15 million social media posts to identify themes in how people coped with COVID-19. These themes can then give researchers insight into larger trends in the data, allowing us to refine criteria for a more in-depth, qualitative analysis.

AI tools can also be used to adapt language and scientists’ word choice in research designs. In particular, AI can reduce bias by improving the wording of questions in surveys or refining keywords used in social media data collection.

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Another key task in research is to scan the field for previous work to identify gaps in knowledge. AI applications are built on systems that can synthesize text . This makes literature reviews – the section of a research paper that summarizes other research on the same topic – and writing processes more efficient.

Research shows that human feedback to AI, such as providing examples of simple logic, can significantly improve the tools’ ability to perform complex reasoning . With this in mind, we can continually revise our instructions to AI and refine its ability to pull relevant literature.

However, social scientists must be wary of fake sources – a big concern with generative AI . It is essential to verify any sources AI tools provide to ensure they come from peer-reviewed journals.

Share research findings

AI tools can quickly summarize research findings in a reader-friendly way by assisting with writing blogs, creating infographics and producing presentation slides and even images.

Our database contains AI tools that can also help scientists present their findings on social media. One tool worth highlighting is BlogTweet . This free AI tool allows users to copy and paste text from an article like this one to generate tweet threads and start conversations.

Be aware of the cost of AI tools

Two-thirds of the tools in the database cost money. While our primary objective was to identify the most useful tools for social scientists, we also sought to identify open-source tools and curated a list of 85 free tools that can support literature reviews, writing, data collection, analysis and visualization efforts.

In our analysis of the cost of AI tools, we also found that many offer “freemium” access to tools. This means you can explore a free version of the product. More advanced versions of the tool are available through the purchase of tokens or subscription plans.

For some tools, costs can be somewhat hidden or unexpected. For instance, a tool that seems open source on the surface may actually have rate limits, and users may find that they’ve run out of free questions to ask the AI.

The future of the database

Since the release of the Artificial Intelligence Applications for Social Science Research Database on Oct. 5, 2023, it has been downloaded over 400 times across 49 countries. In the database, we found 131 AI tools useful for literature reviews, summaries or writing. As many as 146 AI tools are useful for data collection or analysis, and 108 are useful for research dissemination.

We continue to update the database and hope that it can aid academic communities in their exploration of AI and generate new conversations. The more that social scientists use the database, the more they can work toward consensus of adopting ethical approaches to using AI in research and analysis.

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  • Open access
  • Published: 03 June 2024

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

  • Weiwei Hu 1 ,
  • Shiyu Chen 1 ,
  • Jiaxin Cai 1 ,
  • Yuhui Yang 1 ,
  • Hong Yan 1 &
  • Fangyao Chen 1 , 2  

BMC Medical Research Methodology volume  24 , Article number:  125 ( 2024 ) Cite this article

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Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation.

In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing.

Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene.

Conclusions

The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Peer Review reports

The analysis of the mediating effect was first proposed by Baron and Kenny (1986) [ 1 ] and was broadly applied in many scientific fields, such as psychological, sociological, and biomedical studies [ 2 , 3 , 4 ]. Mediation analysis has become a powerful tool to investigate the underlying mechanism of environmental exposures on health outcomes and identify the factors mediating the effect of exposures on outcomes [ 5 ]. Currently, analytical methods including the single mediator model [ 6 , 7 ], multiple-mediators model [ 8 ], and high-dimensional mediation model [ 9 ] are proposed and available for researchers in many scientific fields.

With the development of advanced data collection techniques, high-dimensional data has become common in biomedical research. For example, in the epigenetic study, the Illumina Infinium HumanMethylation450 BeadChip array platform allows to measure the DNA methylation levels of roughly 480 K probes [ 10 ] and generates high dimensional data. Focusing on practical research, smoking affects lung function, and some DNA methylation sites may mediate the effect of smoking on lung function [ 11 , 12 ]. To identify the significant mediators (CpG sites) between smoking and lung function, we can conduct mediation analysis in the collected high-dimensional data [ 9 , 13 , 14 ]. Obviously, this method can be used to identify the methylation sites mediating the association between environmental factors other than smoking and other health outcomes including some physical signs and diseases.

However, there are also some issues in high dimensional mediation analysis (HDMA), such as the curse of dimensionality, the false positive rate inflation caused by multiplicity and the confounding existing in observational research. To overcome these issues, scholars have proposed a series of statistical methods. Zhang et al. [ 9 ]. proposed the HIMA model consisting of variable screening based on sure independence screening (SIS), variable selection techniques based on minimax concave penalty (MCP) estimation and joint significance test. HIMA extends the multiple mediator framework to the high-dimensional setting by incorporating variable screening and variable selection techniques into multiple mediation analysis. The following high-dimensional mediation analysis methods also employ the generic procedure [ 13 , 14 , 15 , 16 ], which reduces dimensionality from high to moderate or low scale and then conducts multiple mediation test. For example, the HIMA2 procedure proposed by Perera et al. [ 17 ], which employs the SIS method based on the indirect effect of every single mediator and conducts debiased Lasso to obtain more accurate estimates, then utilizes the multiple-testing procedure proposed by James et al. [ 18 ] to control the false discovery rate. Moreover, to adjust the confounders of observational epigenetic studies, researchers tried to integrate propensity score (PS) into the high-dimensional mediation model by weighting or considering it as a covariate [ 14 , 16 ], except for the classic regression adjustment.

Although many works have been made to tackle these problems, there are still some issues remaining in the dimensionality reduction and adjustment for confounders. For high dimensional mediation analysis, the previous studies don’t take confounders into account, just consider them as covariates [ 15 , 19 ], such as HDMA, HIMA, and HIMA2 [ 5 , 9 , 17 ]. As is known to all, the multivariable model cannot adequately account for confounding effects in the presence of a large number of confounders [ 20 ]. If we only control confounding during the mediation test, but not in the dimension reduction stage, then a biased variable selection result may be obtained [ 14 ]. Thus, it is necessary to adjust confounders to improve the performance of variable selection.

To address this issue, researchers have adopted the PS-based method including PS regression adjustment (termed PSR) and classical PS weighting (also called inverse probability weighting, IPW) to adjust confounding during both stages [ 14 ]. However, the adjustment for confounders using the IPW based on PS still faces the issue of extreme weights caused by extreme PS distribution [ 21 , 22 ]. To address the issue of extreme PS distribution, Li et al. [ 23 ]. proposed the overlap weighting (OW) method, which emphasizes individuals with the most overlap in their observed characteristics and is beneficial to provide a consistent estimator of the effect of exposure on outcome in the presence of extreme PS tails. OW belongs to the weighting confounding adjustment method based on PS and is gaining more popularity because of excellent statistical properties [ 24 , 25 ]. However, the above OW method is only applied to traditional epidemic analysis, which needs to be extended to mediation analysis and high-dimensional data setting. Besides, most of the existing methods all hold the independent assumption between potential mediators, which is hard to ensure in high dimensional epigenetic data analysis [ 5 , 9 , 13 , 14 , 15 , 18 ].

In this article, we incorporated the OW method into HIMA [ 9 ] and HIMA2 [ 17 ] models, respectively. In order to develop the accuracy of the screening of potential mediators, we modified the framework of variable screening in the original HIMA2 procedure. Eventually, we proposed the OW-based modified HIMA2 (mHIMA2) procedure for HDMA. We evaluated the performance of the proposed procedure and the existing models through simulation studies. All the above evaluations are based on the simulation study and real data application.

The rest of the article is structured as follows. In the next section, we introduced the notions, assumptions, models, and the procedure of adjustment for confounders in the high-dimensional mediation analysis model. Then, we conducted the Monte Carlo simulation study to evaluate the performance of various methods of confounding adjustment and two different mediation test approaches. Additionally, we applied the proposed method to the dataset GSE117859 in the Gene Expression Omnibus (GEO) databases and identified some DNA methylation markers that mediate the effect of smoking on the estimated natural killer (NK) cell level. Finally, we concluded the advantages and limitations of this study.

Model definitions

Our high-dimensional mediation model is shown in Fig. 1 . Let \(X\) be the exposure variable, where \(X=1\) represents the exposed group and \(X=0\) represents the controlled group. Denote the outcome as Y , here we mainly focus on continuous outcome. Let \(M={\left({M}_{1},{M}_{2},\cdots ,{M}_{P}\right)}^{T}\) be the set of the \(p\) -dimensional potential mediators, where \(p\gg n\) , \(n\) is the sample size. Let \(C={\left({C}_{1},{C}_{2},\cdots ,{C}_{q}\right)}^{T}\) be the \(q\) -dimension baseline confounders which influence the relation of exposure-mediator, mediator-outcome, and exposure-outcome. For individual \(i\) , \(i=\text{1,2},\cdots ,n\) , we have the high-dimensional mediation models as follows:

figure 1

Causal diagram. High-dimensional mediation model with confounders between exposure, mediator and outcome

where \(\alpha ={\left({\alpha }_{1},{\alpha }_{2},\cdots ,{\alpha }_{p}\right)}^{T}\) is the coefficient vector relating the exposure to the mediators, \(\beta ={\left({\beta }_{1},{\beta }_{2},\cdots ,{\beta }_{p}\right)}^{T}\) represents the effect of the mediators on the outcome, \({\alpha }_{k}{\beta }_{k}\) corresponds to the mediation effect of \({M}_{k}\) according to the definition of coefficients product method, and \(\left[p\right]\) denotes the set of \(\left\{\text{1,2},\cdots ,p\right\}\) . One can consider whether \({M}_{k}\) is the statistically significant mediator or not by testing the null hypothesis \({H}_{0}:{\alpha }_{k}{\beta }_{k}=0\) . \({\phi }_{k}\) and \(\eta\) are the effect of \(C\) on \(M\) and \(C\) on \(Y\) , respectively. \({a}_{k}\) and \(a\) are the intercept term in the Eqs. 1 and 2 , respectively. The same as above, \({e}_{k}\) and \(\epsilon\) are each the corresponding error term. We will compare the different variable selection strategies and methods of adjusting confounders.

Assumptions

To ensure the identification of path-specific mediating effects, some assumptions need to be held as below. These assumptions were proposed referring to necessary condition required for high-dimensional mediation analysis suggested in published studies [ 8 , 15 , 17 , 19 , 26 , 27 ]:

A1: There is no causal association between mediators. This means the proposed model contains only parallel mediators.

A2: Sequential ignorability. That consists of four assumptions listed below:

(A2.1) There are no unmeasured confounders between the exposure and the outcome;

(A2.2) There are no unmeasured confounders between the mediators and the outcome;

(A2.3) There are no unmeasured confounders between the exposure and the mediators;

(A2.4) There is no exposure-induced confounding between the mediators and the outcome.

A3: Stable unit treatment value assumption (SUTVA) [ 28 , 29 ] for both the mediators and the outcome. That is to say, there is no interference between individuals.

A4: Consistency for the mediators and the outcome. That is to say, there are no measurement errors in the mediators.

A5: Positivity assumption [ 30 ]. Every individual has some positive probability of being exposed to the factor of interest.

Proposed Procedure

We improved the HIMA procedure proposed by Zhang et al. (2016) [ 9 ] and the HIMA2 procedure proposed by Perera et al. (2022) [ 17 ] under the condition of adjusting confounders in observational data.

In this study, we developed two processes to conduct the confounding-controlled high-dimensional mediation analysis. The detailed procedure is described in the following text.

Step 1: PS-based methods for adjusting confounders

Since there are always some baseline confounders in observational data, we integrate propensity score (PS) into mediators (and/or outcome) models to reduce the selection bias and acquire as accurate estimates of the mediation effect as possible. Due to the PS approaches allowing the inclusion of a large scale of confounders, PS is widely used in observational research.

PS is defined as the conditional probability that a study individual with baseline covariates \(C=\left({C}_{1},{C}_{2},\cdots ,{C}_{l}\right)\) would be exposed to certain study factors of interest [ 31 ]:

PS can be estimated by classic multivariable statistical methods such as logistic regression [ 32 ] or by machine learning methods such as random forest (RF) and generalized boosted model (GBM) [ 33 , 34 ]. In practice, logistic regression is the most commonly used. The PS of \(i\) th individual \({\pi }_{i}=P\left({X}_{i}=1|{C}_{1i},\cdots ,{C}_{li}\right)\) can be expressed as follows:

where \(\theta ={\left({\theta }_{1},{\theta }_{2},\cdots ,{\theta }_{l}\right)}^{T}\) represents the effect of the confounders on the exposure. Then we can adopt some PS-based techniques to adjust confounders such as matching [ 35 ], stratification [ 36 ], regression [ 31 ], and weighting [ 37 ]. Here, we focus on PS regression (PSR) and PS weighting [ 14 ] (PSW, also called IPW short for inverse probability weighting) techniques to adjust potential confounders between exposure, mediators and outcome.

PSR approach incorporates PS as a covariate into the original regression model to adjust for the probability of being exposed to study factors and to reduce confounding [ 32 ]. That is similar to taking all confounders as covariates in a classical regression approach which usually uses the linear regression model for continuous outcomes and the logistic regression model for binary outcomes [ 38 ]. For the PSR approach, we can estimate the effect through the models below:

The PSW approach constructs the inverse probability weights by taking the reciprocal of PS. For binary exposure, the weight of the exposed group \(X=1\) is given as \(\frac{1}{PS}\) , and that of the controlled group \(X=0\) as \(\frac{1}{1-PS}\) . For \(i\) th individual:

Then, we can estimate the coefficients of X in pathways \(X\to M\) and \(M\to Y\) by weighted estimation:

where \({\alpha }_{k,ipw}\) and \({\gamma }_{ipw}\) are the weighted estimation according to the \(ipw\) weight vector. However, the IPW often faces extreme PSs issue which may lead to extreme weights and result in biased estimates and excessive variance [ 23 , 24 ].

The overlap weighting (OW) approach was proposed to address the issue of extreme PSs [ 23 ]. The overlap weight is given as \(1-PS\) for the group \(X=1\) and \(PS\) for the group \(X=0\) . Note that, individuals with \(PS\) of 0.5 make the largest contribution to the effect estimate, and individuals with \(PS\) close to 0 and 1 make the smallest contribution. OW is likely to be beneficial in the presence of extreme tail weights [ 23 , 39 ]. For individual \(i\) :

Then, the effect estimation of OW is similar to that of the PSW procedure:

In the same way, \({\alpha }_{k,ow}\) and \({\gamma }_{ow}\) are the weighted estimation using \(ow\) weight vector.

Step 2: Confounding-controlled SIS approach for dimensionality reduction

The SIS procedure is a general technique to reduce accurately high dimensions to below sample size [ 40 ]. We adopt the SIS method to reduce dimension \(p\) from ultra-high dimension to moderate scale \(d=\left[\frac{2n}{\text{log}\left(n\right)}\right]\) [ 9 , 15 ].

In this study, we considered two preliminary screening strategies as described in HIMA [ 9 ] and HIMA2 [ 17 ], based on the effects of \(M\) on \(Y\) ( \({\beta }_{k}\) ) and the indirect effect \(\left|{\alpha }_{k}{\beta }_{k}\right|\) respectively. Because the indirect effects can be both positive and negative effects, to address the influence of the signs of the estimated indirect effects, the HIMA2 approach uses the absolute values of the indirect effect to obtain the size of the effect estimate regardless of the direction. This approach ensures that mediators with large effect size can be selected.

Due to the lack of screening accuracy in SIS based on indirect effects in the presence of confounders, we conducted the SIS screening based on the effects on the path \(M\to Y\) controlling confounding effects using the OW approach.

In simulation, we found that it is hard to select the true mediators based on \(\left|{\alpha }_{k}{\beta }_{k}\right|\) in the presence of confounding factors as applied in the original HIMA2 approach. So, we modified the frame of the HIMA2 method and both adopt SIS based on the effects on the path \(M\to Y\) \({\beta }_{k}\) in the preliminary screening to select the subset of potential mediators \({M}_{SIS}=\left\{{M}_{k}:{M}_{k} \text{i}\text{s} \text{a}\text{m}\text{o}\text{n}\text{g} \text{t}\text{h}\text{e} \text{t}\text{o}\text{p} d \text{l}\text{a}\text{r}\text{g}\text{e}\text{s}\text{t} \text{e}\text{f}\text{f}\text{e}\text{c}\text{t} of {\beta }_{k}\right\}\) .

Noticing that we need to adopt a two-step weighting method [ 14 ] to estimate \({\beta }_{k}\) for the PSW and OW methods.

First, \({\gamma }_{k,w}\) can be obtained from the following sub-model:

where \({\widehat{\gamma }}_{k,w}\) is the estimator of \({\gamma }_{k,ow}\) or \({\gamma }_{k,ipw}\) for each \({M}_{k}\) . In addition, the residual \({\widehat{e}}_{k}\) can be derived:

Then \({\beta }_{k}\) can be estimated by regressing \({\widehat{e}}_{k}\) on \({M}_{k}\) without weighting. Through the above SIS procedure, we can identify the important mediators and achieve the goal of dimensionality reduction.

Step 3: Penalized estimation

According to the HIMA procedure, after the preliminary selection of candidate mediators, further variable selection can be accomplished by the penalized estimation method. Here, we adopt the MCP [ 41 ] rather than other penalty functions, since the MCP approach has the oracle property which can select the correct model with probability tending to 1 as \(n\to \infty\) [ 15 , 41 , 42 ].

For the \(d\) -dimensional subset \({M}_{SIS}\) , we employed the MCP-penalized estimation to further select significant mediators set \({M}_{MCP}=\left\{{M}_{k}:{\beta }_{k}\ne 0,{M}_{k}\in {M}_{SIS}\right\}\) , MCP penalty function can be defined as below:

where \(\lambda >0\) is the regularization parameter which can be selected by AIC or BIC, and \(\delta >0\) is the tuning parameter which determines the concavity of MCP. The MCP procedure can be implemented through the R package ncvreg [ 43 ]. Through MCP penalty estimation, we filtered out the mediators with too weak effects by combining SIS and MCP procedures and then acquired the small number of mediators that needed to be tested. That will help to obtain more accurate effect estimates.

Following the original HIMA2 procedure, the penalized estimation adopts the de-biased Lasso method to get the estimator \({\widehat{\beta }}_{k}\) and standard error \({\widehat{\sigma }}_{{\beta }_{k}}\) . The sub-model of the de-biased Lasso method can be described below:

where \({\beta }_{SIS}\) denote the effects of \({M}_{k}\in {M}_{SIS}\) on \(Y\) . The corresponding P -values \({P}_{{\beta }_{k}}\) are given as:

where \({\Phi }\left(.\right)\) is the cumulative distribution function of standard normal distribution \(N\left(\text{0,1}\right)\) . The de-biased Lasso method can be implemented with the R package hdi .

Step 4: PS-based multiple mediation test

After MCP-based penalized estimation, we use the Joint significance test [ 3 , 44 ] (termed JS-uniform) to test the mediation effect of \({M}_{k}\in {M}_{MCP}\) . The Joint significance test considers the \({M}_{k}\) as a true mediator when \({\alpha }_{k}\) and \({\beta }_{k}\) is significant simultaneously. Here, \({\alpha }_{k}\) can be estimated through different confounding adjustment methods as shown in Eqs. 1 , 3 , 4 , and 5 . \({\beta }_{k}\) can be obtained using the linear regression with considering all confounders as covariates or only including PS (summary of all confounders) as a covariate.

In other words, that is based on the P -values for testing the path-specific effects \({H}_{0}:{\alpha }_{k}=0\) or \({H}_{0}:{\beta }_{k}=0\) . The raw P -value for the joint significance test [ 3 ] is defined below:

\(\begin{array}{c}{P}_{raw,k}=\text{max}\left({P}_{raw,{\alpha }_{k}}, {P}_{raw,{\beta }_{k}}\right),\#\end{array}\) where \({P}_{raw,{\alpha }_{k}}\) and \({P}_{raw,{\beta }_{k}}\) are the P -values for testing \({H}_{0}:{\alpha }_{k}=0\) and \({H}_{0}:{\beta }_{k}=0\) . \({P}_{raw,{\alpha }_{k}}\) and \({P}_{raw,{\beta }_{k}}\) can be obtained from the mediator model (e.g. Equations 1 , 3 , 4 , and 5 ) and outcome model (Eq. 2 ), respectively.

For the multiplicity (Type I error inflation) issue in multiple mediation testing, we adopted the Benjamini–Hochberg (BH) method [ 45 , 46 ] to acquire the adjusted \(p\) -values as below,

where \(q\) is the number of potential mediators in the set \({M}_{MCP}\) , and \({r}_{k}\) is the location number of \({P}_{raw,k}\) when all the P -values \({P}_{raw,k}\) are sorted ascending.

However, the Joint significance test assumes \({P}_{raw,k}\) follows a uniform null distribution. Although \({P}_{{\alpha }_{k}}\) and \({P}_{{\beta }_{k}}\) are each uniformly distributed, their maximum may not. Therefore, the Joint significance test results in a valid but overly conservative test with lower power [ 13 , 17 , 47 ].

Hence, we adopt the PS-based joint significance with mixture null distribution method [ 18 ] (termed JS-mixture) approach to conduct multiple mediation test after de-biased Lasso penalized estimation [ 17 , 48 ] referring to the classical HIMA2 procedure. The PS-based JS-mixture approach adopts a 3-component mixture distribution as below:

The estimated pointwise FDR for testing mediation can be computed as:

where \(t\in \left[\text{0,1}\right]\) , \({V}_{00}\left(t\right),{V}_{01}\left(t\right),{V}_{10}\left(t\right)\) denoting the numbers of the three types of false positives and \(R\left(t\right)={V}_{00}\left(t\right)+{V}_{01}\left(t\right)+{V}_{10}\left(t\right)+{V}_{11}\left(t\right)\) . The \({V}_{00}\left(t\right),{V}_{01}\left(t\right),{V}_{10}\left(t\right)\) and \(\widehat{FDR}\left(t\right)\) can be obtained using the R package HDMT .

We set the significance level of 0.05 for all the tests. The detailed processes of the proposed method are summarized in Fig. 2 .

figure 2

The overall workflow for high-dimensional mediation analysis under the adjusting for confounders condition

Simulation studies

Simulation design.

In this section, we conducted the simulation studies to evaluate the performance of the proposed method. The implementation of the simulation was based on R (version 4.3.0, R Foundation for Statistical Computing, Vienna, Austria) and RStudio (version 2023.9.0.463, RStudio: Integrated Development Environment for R, Boston, MA). The setting of simulation parameters was based on the published studies [ 9 , 14 , 16 ]. The number of replications in simulation study was set to be 500 for each combination of parameter setting referring to the replication times settings in published methodogical studies [ 9 , 14 , 15 , 16 , 17 , 19 , 49 ].

The model structure is shown in Fig. 1 . We consider 8 confounders \(C=\left({C}_{1},{C}_{2},\cdots ,{C}_{8}\right)\) affecting the relationship of \(X\) , \(M\) , \(Y\) , in which continuous confounders \({C}_{1}-{C}_{4}\) follow a multivariate normal distribution \(N\left(\mu ,{\Sigma }\right)\) with a mean vector \(\mu ={\left(\text{0,0},\text{0,0}\right)}^{T}\) and a covariance matrix \({\Sigma }\) :

The last four binary confounders \({C}_{5}-{C}_{8}\) are independently generated from the Binary distribution \(B\left(n,0.3\right)\) , where \(n\) is the sample size.

Then exposure \(X\) can be generated from Binary distribution \(B\left(n,{P}_{c}\right)\) , where \(n\) is the sample size, \({P}_{c}=1/\left(1+{e}^{-\left({\theta }^{T}C\right)}\right)\) , and \({\theta }^{T}=\left({\theta }_{1},{\theta }_{2},\cdots ,{\theta }_{8}\right)=\left(\text{0.2,0.2,0.3,0.3,0.2,0.2,0.3,0.3}\right)\) .

Mediators \(M\) and the outcome variable \(Y\) are generated according to Eqs. 1 and 2 , respectively. For simplicity, we set all the effects of \(C\) on \(M\) to be the same. Let \({\phi }_{k}={\left({\phi }_{k1},\cdots ,{\phi }_{k8}\right)}^{T}={\left(\text{0.2,0.2,0.3,0.3,0.2,0.2,0.3,0.3}\right)}^{T}\) represent the effect of C on M. Let \(\eta ={\left({\eta }_{1},{\eta }_{2},\cdots ,{\eta }_{8}\right)}^{T}={\left(\text{0.2,0.2,0.3,0.3,0.2,0.2,0.3,0.3}\right)}^{T}\) denote the effects of \(C\) on \(Y\) .

We set the first four potential mediators \({M}_{1}-{M}_{4}\) as the true significant mediators in this study. Let \(\alpha ={\left({\alpha }_{1},{\alpha }_{2},\cdots ,{\alpha }_{p}\right)}^{T}=\left(\text{0.4,0.4,0.5,0.5,0.5,0.5,0},0,\cdots ,0\right)\) ; \(\beta ={\left({\beta }_{1},{\beta }_{2},\cdots ,{\beta }_{p}\right)}^{T}=\left(\text{0.4,0.5,0.5,0.6,0},\text{0,0.5,0.5,0},\cdots ,0\right)\) . The elements of both \(\alpha\) and \(\beta\) are equal to zero except for the first eight elements, and the first four are the significant mediators. The mediation effect size of the true mediators \({M}_{1}-{M}_{4}\) is \({\alpha \beta }_{1-4}=\left(\text{0.16,0.2,0.25,0.3}\right)\) .

Let \(\gamma =0.5\) ; \(a=0.5\) ; \(a_k\sim U(0,1)\) , \(\epsilon\sim N(0,1)\) . The error term \({e}_{k}\) are generated from \(N\left(\text{0,1.2}\right)\) and the correlation between mediators mostly falls between 0.15 and 0.35.

To evaluate the impacts of sample size and potential mediators dimension, we set two sample size levels \(n=300, 500\) , and two dimension levels \(p\) =1000,10000.

In addition, we take the correlation between mediators into account in the condition of \(p\) =1000 dimension. We simulate the strong correlation between mediators by generating the error terms \({e}_{k}\) from \(N\left(0,{{\Sigma }}_{e}\right)\) , where \({{\Sigma }}_{e}={\left({\rho }^{\left|k-{k}^{{\prime }}\right|}\right)}_{k,{k}^{{\prime }}}\) . It means the correlation between two mediators will decrease as the absolute difference in mediators’ subscript \(\left|k-{k}^{{\prime }}\right|\) increases. We set four correlation levels \(\rho =0, 0.25, \text{0.5,0.75}\) with dimension \(p\) =1000 and sample size \(n=300, 500\) . In the simulation setting \(\rho =0, 0.25, \text{0.5,0.75}\) , the corresponding Pearson correlation coefficients between two adjacent mediators are around 0.4, 0.5, 0.7, and 0.8, respectively. We evaluated the performance of the mHIMA2 and PS-based HIMA by conducting 500 replications of simulated data sets for each scenario [ 9 , 14 , 15 , 16 , 17 , 19 , 49 ].

Simulation results

Simulation results are presented in Tables 1 and 2 . Evaluation of the performance of mediator selection of the proposed approach is shown in Table 1 by measuring the true positive rate (TPR) and false discovery proportion (FDP) of selection after the significance test for mediation effects. The mediators have higher TPR as the indirect effect increases (i.e., larger mediation effect, higher detection rate).

As presented in Table 1 . Under most settings, the mHIMA2 mediation test approach has a higher TPR than PS-based HIMA while a higher FDP at the same time. Overall, the mHIMA2 is more powerful than the PS-based HIMA and is less conservative in selecting significant mediators.

As shown in Table 1 , for the mHIMA2 mediation test approach, TPR is ranked as OW > IPW > PSR > RA, and FDP is not more than 0.1 and gradually decreases to close to 0.05 as the sample size increases. Among all models, the mHIMA2 mediation test approach with OW adjustment has the highest power and acceptable false positive level. When using the PS-based HIMA mediation test approach, TPR is ranked consistently as RA > PSR > OW > IPW, and all four models also keep FDP at an extremely low level.

Table 2 presents the estimation of mediation effects with the mean and mean square error (MSE). The estimators approach the true values as the mediation effect increases. All models tend to be more accurate as \(n\) gets larger and \(p\) gets smaller. Overall, the mHIMA2 mediation test approach has a smaller MSE than the PS-based HIMA approach in most cases. RA adjustment has a higher MSE than other adjustment methods especially when facing the large mediation effect, OW adjustment has the lower MSE among the four adjustment methods.

As shown in Table 2 , similarly, the mHIMA2 approach with OW adjustment has the smallest MSE among all models. Moreover, similar results can be seen in the different strong correlation settings in Table S1-S8 in the supplementary file. The mHIMA2 methods have lower MSE (i.e. more precise estimation) and apparently higher TPR. That means the de-biased Lasso technique in mHIMA2 methods performs better when handling the moderate correlation between mediators. However, the FDP of all models slightly increases as the correlation between mediators increases. When correlation among the mediators is strong (for example, \(r\) >0.7), all models suffer in terms of increased MSE.

Data application

Smoking is an important environmental factor affecting the immune system and blood cell composition [ 50 , 51 ]. Previous studies have demonstrated smokers had lower natural killer (NK) cell counts and activity [ 50 , 51 ]. Smoking has also been found to be associated with DNA methylation levels [ 52 ]. Meanwhile, DNA methylation levels have also been found to be associated with associated with human NK cell activation [ 53 , 54 ]. Therefore, DNA methylation may mediate the association between smoking and NK cell level. So we implemented the proposed high-dimensional mediation analysis methods to identify the specific functional CpG sites that may mediate the relationship between smoking and the estimated NK cell level.

Here we apply our method to the GSE117859 dataset obtained from the Gene Expression Omnibus (GEO) database. The aim of the study in which GSE117859 was originally measured is to explore the smoking-associated DNA methylation features linked to AIDS outcomes in the HIV-positive population [ 55 ]. The blood samples from the Veteran Aging Cohort Study (VACS) were collected in that study. The HumanMethylation450 BeadChip platform was used to measure the DNA methylation levels.

In total 608 samplesand 485,577 probes were included in the dataset. Clinical information such as age, sex, race, smoking history, adherence of antiretroviral therapy (ART), estimated CD4 T cells, estimated CD8 T cells, and estimated NK cells were collected. The estimated CD4/CD8/NK were obtained using a methylation-based cell type deconvolution algorithm proposed by Housman et al. [ 56 ]. To some extent, the estimated CD4 and CD8 levels can represent AIDS severity.

Smoking status was collected based on self-report. All included patients were classified into the smoker and the non-smoker groups according to their reported smoking history. After removing the individuals without available clinical information and DNAm sites with missing values, a total of 587 samples and 485,503 probes were included in the analysis.

We adjusted the potential confounders including age, race, adherence of antiretroviral therapy, estimated CD4 T cells, and estimated CD8 T cells. Demographic and clinical variables included in our analysis are presented in Table 3 .

The analysis results using the proposed mHIMA2 method are presented in Table 4 . Here, we mainly presented the CpGs mediators with a total effect proportion greater than 5%. Due to the limitation of text content, we didn’t present the whole summary results of the PS-based HIMA method, but that can be seen in Table S9 in the supplementary file.

As shown in Table 4 , we identified two methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene by most of mHIMA2 based methods. The similar result can be seen in Table S9 in the supplementary file. The existing studies have already demonstrated the site cg13917614 is associated with smoking [ 52 , 57 ]. Although we don’t find direct evidence that the CNP gene is associated with immune function based on the existing literature, relevant studies showed a link between CNP and inflammatory responses in which the mechanism remains further study [ 58 , 59 ].

The encoded protein of the LILRA2 gene can suppress innate immune response [ 60 , 61 ]. The results reveal that smoking will promote the demethylation of cg16893868, leading to an increase in gene LILRA2 expression and ultimately reducing the estimated NK cell level. It has been found that the remaining CpG sites cg20460771, cg03164561, cg03605454, cg09529165, and cg01500140 are all associated with smoking [ 11 , 52 , 62 , 63 , 64 ]. Further insights into the discovered CpG mediators in genome-wide epigenetic studies will be meaningful.

The causal relationship obtained in high-dimensional mediation analysis usually depends on no-confounding assumption. However, confounding is almost inevitable in observational studies owing to the lack of randomization of the baseline covariates in practice. Previous studies show the utilization of PS method such as PS-adjustment and IPW in high-dimensional mediation analysis, but those face the issue of extreme PS distribution.

In this article, we integrated OW approach into the high-dimensional mediation model, which can address extreme PS distribution and better adjust for confounding. Finally, we developed a high-dimensional mediation analysis workflow consisting of OW confounding adjustment, SIS, de-biased Lasso penalization for potential mediator screening, and the high-dimensional mediation test underlying the mixture null distribution of P -values.

Simulation results indicate that the mHIMA2 with OW approach presented in this study performs best among all the compared models with the highest TPR, acceptable FDP level, and the smallest MSE in mediating effect estimation. In addition, the mHIMA2 embedded de-biased Lasso method performs better when moderate correlations between mediators exist.

Simulation study also suggestedthe proposed method would perform better when the sample size was increased. This result suggests that when the proposed method is used for the analysis of mediating effects on real data, a sufficient sample size should also be ensured. Such a feature is also consistent with other existing methods [ 5 , 9 , 14 , 17 , 19 , 49 ]. Furthermore, the dimensionality of potential mediators has little effect on the performance of the proposed method.

In most of the previous studies [ 5 , 9 , 13 , 17 ], it didn’t take confounding adjustment into account in the SIS process. However, we adopted the PS-based method to adjust confounding, thus improving the accuracy of mediators screening. Moreover, it has been assumed that mediators are linearly independent of each other, but such an assumption is often not strictly valid in real data. The violation of the mediators’ independence assumption often affects the accuracy of mediators selection and precision of mediating effect estimation. The proposed method can effectively deal with this issue which can tolerate the correlation between the mediators and ensure the robustness of mediators selection, multiple mediation testing, and mediating effect estimation.

Similar to other two-step approaches, the error of the first model may be introduced and cumulated in the second step, because the first-step can not quarantee 100% correctness. To avoid this, we set a relatively loose screening criterion with \(d=2n/log(n)\) to select the top \(d\) largest effect mediators [ 15 , 16 , 17 , 49 ] in the first step to control false negative while avoiding the increase of false positive error according to the application recommendation of SIS approach. Though the errors cannot be totally avoid, this can reduce the error in the preliminary screening of mediators and prevent serious error cumulation in the second step to some extend. As shown in the simulation, the proposed two-step model performed well. Besides, previous published studies also have demonstrated the error cumulation issue in two-step models can be controlled well in the similar way as we did, and well not cause serious bias in the final results [ 14 , 65 , 66 , 67 , 68 , 69 , 70 ].

Meanwhile, we applied the proposed method to the dataset GSE117859 obtained from the GEO databases and identified several significant DNAm mediators, including the sites cg13917614, cg16893868, cg20460771, cg03164561, cg03605454, cg09529165, and cg01500140. Among them, site cg16893868 in LILRA2 gene has been demonstrated to be associated with smoking and immune function [ 60 , 61 ]. That indicates that the proposed method can identify reliable mediators in empirical data analysis.

The presence of confounding in observation studies always is a major challenge to obtaining causal relationships. Currently, most genetic studies are based on observational research without randomization of baseline characteristics. Particularly, the high-dimensional mediation analysis always faces some issues, such as the accuracy of the high-dimensional mediation selection and the low power of multiple mediation test [ 13 , 14 , 17 , 18 ]. Although the utilization of PSR and IPW offers a solution of confounding adjustment in classical HDMA workflow, it still faces the issue of extreme PS distribution.

The proposed OW-based method can provide a more precise and stable mediating effect estimation. However, the misspecification of the outcome model and PS model can not be avoid in practice. Hence, the doubly robust methods may be desirable to be applied in HDMA workflow in future study. Even if the JS-mixture method was proposed to improve the power of multiple mediation testing, other more powerful test methods still are appealing in large-scale genome-wide epigenetic studies [ 13 , 18 ]. Conducting further simulation and methodology studies to compare different powerful test methods may provide useful reference for future studies. It should also be noticed that the existence of unmeasured confounding is out of the scope of this paper. Previews published studies have provided serval applicable methods to deal with this issue [ 49 , 71 ].

Overall, the mHIMA2 with OW adjustment has sufficient power in selecting potential true mediators and obtaining precise estimation for mediation effects. It can be recommended in practical high-dimensional mediation analysis, especially in epigenetic study.

Availability of data and materials

The dataset GSE117859 obtained from GEO database in our real data analysis can be accessed (at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE117859) without limitation. Our procedure is implemented using the R software. The corresponding R code can be found at https://github.com/huww1998/CONF_mHIMA2.

Abbreviations

False discovery proportion

Gene Expression Omnibus

High-dimensional mediation analysis

Inverse probability weighting

Joint significant test with uniform distribution

Joint significance test with mixture null distribution

Mean square error

Modified HIMA2 model

Natural killer cell

Overlapping weighting

Propensity score regression adjustment

  • Propensity score

Sure independence screening

True positive rate

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This work was supported by the National Social Science Found of China (21CTJ009) and National Nature Science Foundation of China (81703325).

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Additional file 1: High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study. Simulation results of different correlation levels   \(\rho\) =0,0.25,0.5,0.75 with dimension \(\rho\) =1000 and sample size \(n\) =300,500 were presented in the Table S1-8. Analysis result using the PS-based HIMA methods was shown in Table S9.

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Hu, W., Chen, S., Cai, J. et al. High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study. BMC Med Res Methodol 24 , 125 (2024). https://doi.org/10.1186/s12874-024-02254-x

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Knowledge, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics: a cross-sectional study in Shanghai

  • Xinyue Zhang 1   na1 ,
  • Zhijia Tang 1   na1 ,
  • Yanxia Zhang 1   na1 ,
  • Wai Kei Tong 1 ,
  • Qian Xia 1 ,
  • Bing Han 1 &
  • Nan Guo 1  

BMC Health Services Research volume  24 , Article number:  677 ( 2024 ) Cite this article

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Pharmacist clinics offer professional pharmaceutical services that can improve public health outcomes. However, primary healthcare staff in China face various barriers and challenges in implementing such clinics. To identify existing problems and provide recommendations for the implementation of pharmacist clinics, this study aims to assess the knowledge, attitudes, and practices of pharmacist clinics among primary healthcare providers.

A cross-sectional survey based on the Knowledge-Attitude-Practice (KAP) model, was conducted in community health centers (CHCs) and private hospitals in Shanghai, China in May, 2023. Descriptive analytics and the Pareto principle were used to multiple-answer questions. Chi-square test, Fisher’s exact test, and binary logistic regression models were employed to identify factors associated with the knowledge, attitudes, and practices of pharmacist clinics.

A total of 223 primary practitioners participated in the survey. Our study revealed that most of them had limited knowledge (60.1%, n  = 134) but a positive attitude (82.9%, n  = 185) towards pharmacist clinics, with only 17.0% ( n  = 38) having implemented them. The primary goal of pharmacist clinics was to provide comprehensive medication guidance (31.5%, n  = 200), with medication education (26.3%, n  = 202) being the primary service, and special populations (24.5%, n  = 153) identified as key recipients. Logistic regression analysis revealed that education, age, occupation, position, work seniority, and institution significantly influenced their perceptions. Practitioners with bachelor’s degrees, for instance, were more likely than those with less education to recognize the importance of pharmacist clinics in medication guidance (aOR: 7.130, 95%CI: 1.809–28.099, p -value = 0.005) and prescription reviews (aOR: 4.675, 95% CI: 1.548–14.112, p -value = 0.006). Additionally, practitioners expressed positive attitudes but low confidence, with only 33.3% ( n  = 74) feeling confident in implementation. The confidence levels of male practitioners surpassed those of female practitioners ( p -value = 0.037), and practitioners from community health centers (CHCs) exhibited higher confidence compared to their counterparts in private hospitals ( p -value = 0.008). Joint physician-pharmacist clinics (36.8%, n  = 82) through collaboration with medical institutions (52.0%, n  = 116) emerged as the favored modality. Daily sessions were preferred (38.5%, n  = 86), and both registration and pharmacy service fees were considered appropriate for payment (42.2%, n  = 94). The primary challenge identified was high outpatient workload (30.9%, n  = 69).

Conclusions

Although primary healthcare practitioners held positive attitudes towards pharmacist clinics, limited knowledge, low confidence, and high workload contributed to the scarcity of their implementation. Practitioners with diverse sociodemographic characteristics, such as education, age, and institution, showed varying perceptions and practices regarding pharmacist clinics.

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Pharmacist clinics are specialized healthcare facilities that offer professional pharmaceutical services, such as medication therapy management, medication reconciliation, lifestyle counseling, and immunizations, for patients with chronic diseases or managing multiple drugs [ 1 ]. Through the provision of these services, pharmacist clinics aim to improve patient access to healthcare, optimize medication use, and improve overall public health outcomes.

Pharmacist clinics originated in the 1960s in the United States and have spread globally in recent decades [ 2 ], with a growing number of countries adopting this model of care. The World Health Organization (WHO) has recognized the importance of pharmacists in primary healthcare and encouraged the integration of pharmaceutical services into broader healthcare systems [ 3 ]. This integration facilitates the rational use of medication, thereby minimizing adverse drug events and medication errors, ultimately leading to better therapeutic outcomes. Moreover, pharmacist clinics offer medication guidance and education, which adjusts optimal medication dosage [ 4 ], enhances patient adherence [ 1 , 5 ], expands access to health care [ 6 ], and reduces treatment costs [ 7 ]. These clinics effectively bridge the communication gap between physicians and pharmacists [ 8 ], fostering interdisciplinary collaboration and integrated patient care [ 1 , 9 ].

The development of pharmacist clinics in China was initiated in the late 20th century, coinciding with the introduction of healthcare reforms by the Chinese government in the early 2000s. The release of “Opinions on Deepening the Reform of the Medical and Health System” [ 10 ] in 2009 highlighted the importance of pharmacist clinics and the crucial role of pharmacists in improving the quality and accessibility of healthcare services in primary settings. In 2020, the Chinese government released a guidance document titled “Opinions on Strengthening the Pharmaceutical Management of Medical Institutions and Promoting Rational Drug Use,” encouraging provinces to actively establish pharmacist clinics [ 11 ]. However, it wasn’t until 2021 that the General Office of the National Health Commission developed the “Guidelines for Pharmaceutical Outpatient Services in Medical Institutions” to standardize these pharmacist clinics [ 12 ]. Despite the progress made, primary medical staff in both developed and developing countries face various challenges, especially in developing countries [ 13 ], including a shortage of qualified pharmacists [ 14 , 15 ], limited recognition of pharmacists’ roles among healthcare professionals and the public [ 16 , 17 ], and the need for a more standardized approach to pharmaceutical care [ 18 ]. Additionally, these clinics are predominantly located in large general hospitals or specialized medical facilities, limiting their coverage to specific areas, such as antibiotics [ 19 ] and anticoagulants [ 20 ]. In rural areas, there is scarce awareness and discussion regarding the promotion of pharmacist clinics.

To date, most research on pharmacist clinics comes from countries like the United States, the UK, Canada, and Australia, focusing primarily on the outcomes of pharmacist interventions rather than the implementation challenges [ 1 , 4 , 21 , 22 , 23 , 24 ]. In China, only a few studies have assessed the current state of pharmacist clinics. Cai et al. [ 25 ], for instance, conducted a national survey revealing that just 10.03% of hospitals had pharmacist clinics. Wu et al. [ 26 ] investigated the establishment and operational details of pharmacist-managed clinics in Taiwan. However, there is no published research exploring optimal practices for setting up pharmacist clinics in China or identifying the barriers to establishing these clinics in primary healthcare settings. In this study, we aim to assess the awareness and understanding of pharmacist clinics among primary healthcare providers. We conducted a cross-sectional survey based on the Knowledge-Attitude-Practice (KAP) model to identify knowledge gaps and develop interventions to encourage interprofessional collaboration and enhance practice efficiency. The findings may also improve patient outcomes, healthcare delivery by streamlining the implementation process, and utilization of high-quality pharmaceutical services. Our ultimate goal was to overcome barriers to advancing pharmacist clinics within China’s healthcare system and offer insights for policymakers and healthcare authorities to integrate these clinics into primary healthcare settings, not only in China but potentially in other countries as well.

Survey instrument & selection criteria

Our study employed a structural equation model based on the Knowledge, Attitude, and Practice (KAP) theory [ 27 ] and relevant literature [ 28 , 29 , 30 , 31 ] to explore the relationships between various factors. Following the KAP principles, we developed a questionnaire consisting of 21 questions across three domains: (A) knowledge of pharmacist clinics, (B) attitudes towards pharmacist clinics, and (C) practices related to pharmacist clinics. Demographic information such as gender, age, education, occupation, position, seniority, department, and institution was collected through self-reporting.

The inclusion and exclusion criteria for the sampled respondents were as follows. Inclusion criteria: (1) Full-time primary healthcare practitioners attending a continuing education course at Minhang Hospital in Shanghai, China. This included physicians, pharmacists, nurses, and other primary healthcare practitioners. (2) Willingness to participate in the study and provide informed consent. Exclusion criteria: (1) Part-time employees or interns. (2) Non-medical staff. (3) Individuals who declined to sign the informed consent form.

Study population and data source

This study used data from a cross-sectional survey conducted in May, 2023, involving primary healthcare practitioners from 10 community health centers (CHCs) and 38 private hospitals in Shanghai, China. After excluding participants from secondary or tertiary hospitals ( n  = 9), nursing homes ( n  = 6), and other facilities such as welfare homes and school clinics ( n  = 9), a total of 223 eligible subjects were included.

Data collection

The sample size was optimized to range between 105 and 210, based on the recommended ratio of 5 to 10 respondents per item [ 32 , 33 ]. We also performed a pilot study in April, 2023 to ensure linguistic clarity and readability of the questionnaire. Twenty-six student volunteers from the School of Pharmacy at Fudan University were recruited to refine the questionnaire. Additionally, face-to-face interviews were conducted to further assess their understanding of the content. The final version was electronically distributed to participants during a continuing education course using a voluntary sampling approach. The full questionnaire is available in Supplementary Table 1 , and all data were anonymized.

Statistical analysis

Categorical variables were summarized using frequency counts (weighted percentage, %). The Chi-square test and Fisher’s exact test were used to assess differences in knowledge, attitude, and practice regarding pharmacist clinics across various sociodemographic characteristics. Descriptive analytics and the Pareto principle were applied to multiple-answer questions. In case of rejection of the null hypothesis, multiple pairwise comparisons would be conducted as confirmatory post hoc analysis using Bonferroni correction. Based on the univariate analysis results, we constructed binary logistic regression models to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) to reveal factors associated with perceived goals, service scope, and target recipients of pharmacist clinics.

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 20.0 (IBM Corp., Armonk, NY, USA). A two-sided p -value < 0.05 was considered statistically significant.

Demographics

As presented in Table  1 , a total of 223 primary healthcare practitioners participated in the survey, with 41.3% ( n  = 92) being male and 76.2% ( n  = 170) under 45 years old. The majority (84.3%, n  = 188) were physicians, while the remaining were pharmacists. Regarding educational qualifications, 82.5% ( n  = 184) of respondents held a bachelor’s degree or below. Furthermore, 91.9% ( n  = 205) held mid-level or lower positions, and 56.1% ( n  = 125) reported professional tenures of less than 10 years. Of these 223 practitioners, 36.8% ( n  = 82) were from public institutions (community health centers), and 63.2% ( n  = 141) were from private hospitals.

Knowledge of pharmacist clinics

Of primary care practitioners, 84.8% ( n  = 189) recognized pharmacist clinics, with 24.7% ( n  = 55) having strong familiarity. Figure  1 a-c showed practitioners’ views on the goals, services, and target recipients of these clinics. The primary goal was to provide comprehensive medication guidance (31.5%, n  = 200), with medication education (26.3%, n  = 202) being the primary service, and special populations (24.5%, n  = 153) identified as key recipients. Logistic regression results revealed several significant influential factors (Table  2 ).

figure 1

Pareto chart demonstrating respondents’ knowledge of pharmacist clinics

( a ) Perceived goals: A prescription reviews, B medication guidance, C time-saving, D conflict alleviation, E patient empowerment, F cost reduction, G role enhancement, H research, I training, and J no perceived value

( b ) Perceived service scope: A drug regimen adjustments, B medication reconciliation, C medication education on dosage, side effects, and interactions, D adherence interventions, E health promotion, F patient follow-ups

( c ) Perceived target recipients: A isolated/empty-nest patients, B special populations (e.g. elderly, children, pregnant, and liver/kidney-impaired), C economically disadvantaged patients, D patients suffering from adverse reactions, E patients needing test report interpretations, F frequent drug collectors (> 20 times/year), G patients with ≥ 2 chronic diseases, H patients with any chronic diseases, I patients on ≥ 5 medications, J high-risk drug users (e.g. psychotropic drugs, hormones, injections, and inhalants), K patients under contract with family physicians, and L all patients

Compared to those with less education, practitioners with bachelor’s degrees were more likely to see the role of pharmacist clinics in medication guidance (aOR: 7.130, 95%CI: 1.809–28.099, p -value = 0.005), prescription reviews (aOR: 4.675, 95% CI: 1.548–14.112, p -value = 0.006), and serving patients on high-risk drugs (aOR: 2.824, 95% CI: 1.090–7.316, p -value = 0.033).

Besides medication guidance (aOR: 7.303, 95%CI: 1.343–39.720, p -value = 0.021), practitioners with master’s or higher degrees preferred adherence interventions (aOR: 4.221, 95%CI: 1.339–13.300, p -value = 0.014), follow-up services (aOR: 3.125, 95%CI: 1.095–8.915, p -value = 0.033), and catering to patients with ≥ 2 chronic diseases (aOR: 6.401, 95%CI: 1.233–33.223, p -value = 0.027) or ≥ 5 medications (aOR: 3.987, 95%CI: 1.250-12.717, p -value = 0.019). Higher education was also inversely associated with emphasizing patients needing test report interpretations (aOR < 1, p -value < 0.05).

Younger practitioners, aged 18 to 30, considered pharmacist clinics as tools to mitigate physician-patient conflicts through improved communication compared to those aged ≥ 46 (aOR: 0.165, 95%CI: 0.028–0.988, p -value = 0.048).

Compared to physicians, pharmacists typically addressed all patients as recipients (aOR: 3.322, 95%CI: 1.031–10.703, p -value = 0.044), but were less likely to offer drug regimen adjustments (aOR: 0.210, 95%CI: 0.088-0.500, p -value < 0.001).

Junior and intermediate-level practitioners demonstrated a greater likelihood for follow-up services (aOR 1 : 5.832, 95%CI: 1.308–25.998, p -value = 0.021; aOR 2 : 3.99, 95%CI: 1.087–14.646, p -value = 0.037), and were less likely to target patients in need of test report interpretations (aOR 1 : 0.172, 95%CI: 0.038–0.781, p -value = 0.023; aOR 2 : 0.287, 95%CI: 0.082–0.997, p -value = 0.049) than their senior counterparts.

Work seniority

Practitioners with 10–19 years of work experience were significantly more likely to consider isolated/empty-nest patients as suitable recipients compared to those with < 5 years of experience (aOR: 3.328, 95%CI: 1.021–10.849, p -value = 0.046).

Institution

Practitioners from CHCs were more likely to view frequent drug collectors as suitable recipients compared to those from private hospitals (aOR: 0.359, 95%CI: 0.134–0.966, p -value = 0.043).

Attitude of pharmacist clinics

Necessity and confidence in implementing pharmacist clinics.

Table  3 showed that 82.9% ( n  = 185) of practitioners recognized the necessity of pharmacist clinics, but only 33.3% ( n  = 75) felt confident in their implementation. Male practitioners exhibited significantly higher confidence levels compared to female practitioners ( p  = 0.037), and practitioners from community health centers (CHCs) showed greater confidence relative to those practicing in private hospitals ( p  = 0.008).

Preferred mode of pharmacist clinics

As shown in Table  4 , the favored modality was found to be joint physician-pharmacist clinics (36.8%, n  = 82), through collaboration with medical institutions (52.0%, n  = 116). Daily sessions emerged as the preferred frequency ( n  = 86, 38.5%), with both registration and pharmacy service fees considered appropriate for payment (42.2%, n  = 94).

Furthermore, we explored the influence of different sociodemographic variables. Practitioners holding a master’s degree or higher demonstrated a preference for a clinic frequency of 2–4 times per week ( p -value = 0.015), along with acceptance of both registration and pharmacy service fees ( p -value < 0.001), compared to those with lower levels of education. Conversely, those with a junior college education or below were more willing to seek free services. Practitioners from CHCs exhibited a preference for weekly or 2–4 times per week clinics, whereas those from private hospitals favored daily or monthly sessions ( p -value < 0.001).

Practice of pharmacist clinics

As shown in Table  5 , there was a limited prevalence of pharmacist clinics within primary care institutions. Only 17.0% ( n  = 38) of practitioners reported the implementation of pharmacy clinics, mostly scheduled once a week (47.4%, n  = 18), with the primary challenge being a high outpatient workload (30.9%, n  = 69). Practitioners from CHCs demonstrated a significantly higher implementation frequency compared to those from private hospitals ( p -value < 0.001).

We further explored sociodemographic factors associated with challenges. Practitioners aged over 45 years ( P  = 0.020) and occupying senior/deputy senior positions ( p -value = 0.018) were more likely to consider the absence of fee collection mechanisms as the principal difficulty, as opposed to their younger counterparts and those in lower positions.

Our study aims to evaluate the perceptions, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics and to identify necessary changes. The findings unveiled a lack of knowledge and confidence among primary care providers, who are faced with barriers including high outpatient workloads and concerns related to professionalism. Collaborative models are preferred as they align with the current emphasis on multidisciplinary approaches in modern healthcare, which aim to achieve optimal population health [ 34 ]. Additionally, our findings highlight the impact of institution and gender on the perceptions of primary care providers.

In this study, more practitioners preferred joint physician-pharmacist clinics over traditional physician-led clinics (36.8%, n  = 82 vs. 24.2%, n  = 54), which is in line with a global focus on integrating pharmacists into the provision of patient-centered, coordinated, and comprehensive care [ 1 , 35 , 36 ]. Primary care physicians are in short supply, and studies unveiled that the shortage of primary care physicians has led to increased workloads and a greater demand for medication guidance services, especially among vulnerable patients aged 65 and above [ 37 , 38 , 39 , 40 ]. Our study showed the primary goals of pharmacist clinics were found to be prescription reviews (28.9%, n  = 183) and medication guidance (31.5%, n  = 200), which are critical in addressing concerns regarding poorly managed or duplicate prescriptions [ 41 , 42 ]. Integrating pharmaceutical services into primary care offers expedited access and convenience for patients, thereby releasing physicians to focus on more complex cases and reducing their workload [ 43 , 44 ]. These services also contribute to overall savings in healthcare and medication costs, as well as reduced general physician appointments, emergency department visits, and inappropriate drug use [ 45 , 46 ]. Our findings support the potential of pharmacist-led prescription reviews in reducing duplicate prescriptions [ 47 ], drug-related problems [ 48 ], and medication costs, without increasing physicians’ workload [ 49 ]. Moreover, pharmacist-led medication guidance provided to other professionals has been shown to reduce medication errors and inappropriate prescriptions compared to standard care [ 50 , 51 ]. The development of joint physician-pharmacist clinics may be an advantageous choice for the development of pharmacist clinics in the future.

Current evidence highlights the suboptimal quality of primary care in China [ 52 ], with previous research suggesting that inadequate education and training pose significant challenges in enhancing care quality [ 53 ]. Primary healthcare providers in China have reported being too busy for continued education, dissatisfaction with course content, and having unqualified supervisors [ 54 ]. This issue seems to be consistent in the United States [ 55 ], Canada [ 56 ], and Belgium [ 57 ]. Moreover, our study has identified high workload (30.9%, n  = 69) and insufficient professionalism (25.1%, n  = 56) as the top two challenges faced by pharmacist clinics. On the other hand, insufficient knowledge may contribute to negative attitudes [ 39 ].

In this study, a minority of practitioners (24.7%, n  = 55) demonstrated strong familiarity, and only 33.3% ( n  = 75) felt confident. While some global studies did not find a significant difference in clinical competence confidence between public and private practitioners [ 58 , 59 ], our study revealed that pharmacists from CHCs exhibited greater confidence in conducting pharmacist clinics compared to those from private hospitals, partially due to their greater exposure to training. Studies have also shown that community pharmacists, through enhanced training, can acquire expanded expertise and knowledge [ 60 , 61 ], leading to improved service quality in primary care [ 62 , 63 ]. Future efforts should focus on establishing a more efficient learning and continued education system for community practitioners in China [ 52 ].

Several impediments were identified by respondents, including limited patient volume (22.0%, n  = 49) and low staff motivation (6.3%, n  = 14). Despite the positive impact of pharmacists in outpatient settings on patient outcomes, the adoption of these services remains low [ 1 ]. Recent literature has highlighted public uncertainty about primary care specialties and skepticism regarding their capacity to deliver comprehensive care [ 64 ]. Evidence suggests a lack of awareness, demand, and utilization of community pharmacy services among patients [ 65 , 66 ]. Another barrier is the prevailing focus on quantity rather than quality of care, with job content and bonuses linked more to quantity than the quality of care delivered [ 52 , 67 ]. Financial conflicts over funding and the absence of fee collection may also hinder collaboration between pharmacists and other healthcare providers [ 43 , 68 ]. Additionally, the implementation of the zero-mark-up drug policy in China in 2011 caused a substantial decrease of about 40% in drug-related incomes [ 69 ]. Institutions responded by scaling back clinical care services to offset this profit loss [ 70 ], leading to an uptick in hospital visits for minor ailments and further burdening the healthcare system [ 53 ]. It is important to expand community pharmacy services by establishing reimbursement mechanisms to relieve the burden on general practice [ 71 ]. Countries like Australia, the UK, New Zealand, and Canada have established systems for pharmacist remuneration [ 72 ]. Payment models for pharmaceutical services typically include fee-for-service, where providers are compensated based on the services delivered (as seen in Australia, Canada, Belgium, and Japan), capitation, where providers receive a fixed amount per patient (as in the US, Thailand, and Denmark), and blended funding, which combines government and private payments (as in China, Australia, New Zealand, and Canada) [ 73 ]. Despite the existence of various payment models for pharmaceutical services, there is no standardized pricing for pharmacist clinics. Among 465 hospitals with pharmacist clinics, only 98 (21.08%) owned charging mechanisms [ 25 ]. Various studies have explored the willingness to pay (WTP) for pharmaceutical services in different countries. For instance, Porteous et al. [ 74 ] found a WTP of $69.19 for community practices in the UK. Tsao et al. [ 75 ] reported a WTP of $21.26 for medication therapy management in Canada, and in Brazil, the estimated WTP for comprehensive medication management was $17.75 [ 76 ].

Our findings also revealed gender-based disparities in the perceptions and implementation of pharmacist clinics. Female practitioners exhibited lower levels of confidence in conducting the clinics compared to males, consistent with previous research indicating that women in healthcare often perceive deficiencies in their abilities despite no differences in clinical performance between genders [ 77 ]. Additionally, female medical students reported higher levels of anxiety, stress, and self-doubt about their knowledge and performance [ 78 ]. However, in Australia and Ireland, females rated themselves higher than males in self-assessment tests [ 79 , 80 ]. Further investigations to explore potential confounding factors, such as cultural influences, may contribute to understanding these variations and better address the need to tailor pharmacist-managed clinic services based on institutional needs [ 81 ].

This research is geographically confined to Shanghai and solely captures the perspectives of practitioners, potentially limiting generalizability. Future studies should broaden their scope to encompass diverse practices and include patients’ perceptions. The cross-sectional design used in this study restricts the evaluation of cause-effect relationships, emphasizing the need for longitudinal investigations. Despite these limitations, to the best of the authors’ knowledge, this is the first quantitative study that has examined the knowledge, attitudes, and practice of practitioners regarding pharmacist clinics in primary settings based on real-world data in China. The identified challenges in conducting these clinics provide valuable insights for policymakers, researchers, and institutions in this field.

Although primary healthcare practitioners generally hold positive attitudes towards pharmacist clinics, limited knowledge and confidence, high workload, and other factors lead to the scarcity of such clinics. Practitioners with diverse sociodemographic backgrounds, especially those from different institutions and genders, exhibit varying perceptions of the forms of pharmacist clinics. Further exploration with lager samples from different regions and service recipients is necessary.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank all the participants in this research.

This study received funding from the Shanghai Committee of Science and Technology (Grant No. 22YF1439800) and the Shanghai Municipal Health Commission (Grant No. 20194Y0234).

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Xinyue Zhang, Zhijia Tang and Yanxia Zhang contributed equally to this work.

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Minhang Hospital & Department of Clinical Pharmacy, School of Pharmacy, Fudan University, 170 Xinsong Road, Shanghai, 201199, P.R. China

Xinyue Zhang, Zhijia Tang, Yanxia Zhang, Wai Kei Tong, Qian Xia, Bing Han & Nan Guo

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ZT and YZ designed the research, developed the questionnaire; WT and QX collected the data; XZ and ZT performed the statistical analysis and wrote the manuscript; BH and NG critically reviewed the statistical analysis, work, and this report. All authors read and approved the final manuscript.

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Zhang, X., Tang, Z., Zhang, Y. et al. Knowledge, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics: a cross-sectional study in Shanghai. BMC Health Serv Res 24 , 677 (2024). https://doi.org/10.1186/s12913-024-11136-3

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DOI : https://doi.org/10.1186/s12913-024-11136-3

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CSDE Affiliates  Avanti Adhia  (Nursing),  Michelle Shin  (Nursing), and  Teresa Ward  (Nursing) released an article with colleagues in  Nursing Outlook , titled “ Experiences of recruitment and retention in academia: A collaborative autoethnography of early-career faculty members of color “. The article was lead-authored by Omeid Heidari (Nursing) and included additional co-authors, Kaboni Gondwe (Nursing) and Daniel Suárez-Baquero (Nursing). Recruitment and retention of diverse faculty in schools of nursing continues to be an important challenge, but little has been written from the perspectives of early-career faculty of color on their decision to join academia and their retention. Authors aimed to understand the perspectives of a cluster hire of early-career faculty of color on their recruitment, mentorship and support received, and resources needed for long-term retention. Findings suggest strategies (e.g., targeted resources, diverse cluster hires, building community) to inform recruitment and retention of early-career faculty of color.

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In 2022, at the UN Biodiversity Conference, COP 15, in Montreal over 190 countries made what has been called “the biggest conservation commitment the world has ever seen.”  The Kunming-Montreal Global Biodiversity Framework called for the effective protection and management of 30% of the world’s terrestrial, inland water, and coastal and marine areas by the year 2030 — commonly known as the 30x30 target. While there has been progress toward reaching this ambitious goal of protecting 30% of land and seas on paper, just ahead of World Environment Day, the 2024 Environmental Performance Index (EPI) , an analysis by Yale researchers that provides a data-driven summary of the state of sustainability around the world, shows that in many cases such protections have failed to halt ecosystem loss or curtail environmentally destructive practices.

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 Denmark, the top ranked country in the 2022 EPI dropped to 10th place, as its pace of decarbonization slowed, highlighting that those early gains from implementing “low-hanging-fruit policies, such as switching to electricity generation from coal to natural gas and expanding renewable power generation are themselves insufficient,” the index notes. Emissions in the world’s largest economies such as the U.S. (which is ranked 34th) are falling too slowly or still rising — such as in China, Russia, and India, which is ranked 176th.

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  4. what is Research Problem

  5. Research Process: Problems & identification

  6. How to identify a research problem? What are the major sources of research problems? -22-Fiza Rajper

COMMENTS

  1. Identifying a Research Problem: A Step-by-Step Guide

    To identify a research problem, you need a systematic approach and a deep understanding of the subject area. Below are some steps to guide you in this process: Conduct a thorough literature review to understand what has been studied before. Identify gaps in the existing research that could form the basis of your study.

  2. PDF Identifying a Research Problem and Question, and Searching Relevant

    ong before you create a research proposal, let alone conduct your research, you need to identify a problem or phenomenon to address and then a question or questions to ask about the problem or phenomenon. This chapter first discusses the nature of a research problem, where you might get ideas for a problem to investigate,

  3. How to Define a Research Problem

    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.

  4. Q: How do I identify a research problem and properly state it?

    The problem statement is a crystallization - a focused expression - of the research problem. A good problem statement will do the following: Describe the problem (s) succinctly. Include a vision (solution) Suggest a method to solve the problem (s) Provide a hypothesis. Again, here is an excellent detailed article, with multiple examples and ...

  5. The Research Problem & Problem Statement

    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 ...

  6. How to Write a Problem Statement

    Step 3: Set your aims and objectives. Finally, the problem statement should frame how you intend to address the problem. Your goal here should not be to find a conclusive solution, but rather to propose more effective approaches to tackling or understanding it. The research aim is the overall purpose of your research.

  7. How to Define a Research Problem

    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.

  8. Finding Researchable Problems

    By the end of this module, readers should be able to: (1) Differentiate a research area of interest from a research topic and research problem; (2) Identify one's own area of interest; (3) Determining the most suitable topic to study; (4) Explain the importance of a research problem in a study; (3) Distinguish between a research problem and ...

  9. 1. Choosing a Research Problem

    Resources for Identifying a Research Problem. If you are having difficulty identifying a topic to study or need basic background information, the following web resources and databases can be useful: CQ Researcher-- a collection of single-themed public policy reports that provide an overview of an issue. Each report includes background ...

  10. The Research Problem/Question

    A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation.

  11. (PDF) Identifying and Formulating the Research Problem

    identify and determine the problem to study. Identifying a research problem is important. because, as the issue or concern in a particular setting that motivates and guides the need. Parlindungan ...

  12. Research Problems: How to Identify & Resolve

    2. Review the key factors involved. As a marketing researcher, you must work closely with your team of researchers to define and test the influencing factors and the wider context involved in your study. These might include demographic and economic trends or the business environment affecting the question at hand.

  13. How to Identify a Research Problem

    Stronger research problems are more likely to succeed in publication, presentation, and application. Supported by the Literature. Your research problem should be relevant to the field and supported by a number of recent peer-reviewed studies in the field. Even if you identify the problem based on the recommendation of one journal article or ...

  14. What is a Problem Statement? [with examples]

    The purpose of the problem statement is to identify the issue that is a concern and focus it in a way that allows it to be studied in a systematic way. It defines the problem and proposes a way to research a solution, or demonstrates why further information is needed in order for a solution to become possible.

  15. What is a Research Problem? Characteristics, Types, and Examples

    A research problem is at the heart of scientific inquiry. It guides the trajectory of an investigation, helping to define the research scope and identify the key questions that need to be answered. Read this detailed article to know more about what is a research problem, types, key characteristics, and how to define a research problem, with ...

  16. Problem Identification: The First Step in Evidence-Based Practice

    Problems and solutions are contextual, because every unit and facility differs in terms of structures, processes, and outcomes, as Donabedian described in his seminal work. 3 For that reason, potential solutions in the form of interventions must be implemented and evaluated as part of a quality improvement or research project to ensure that the ...

  17. A Practical Guide to Writing Quantitative and Qualitative Research

    To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe.9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying ...

  18. Writing Strong Research Questions

    A good research question is essential to guide your research paper, dissertation, or thesis. All research questions should be: Focused on a single problem or issue. Researchable using primary and/or secondary sources. Feasible to answer within the timeframe and practical constraints. Specific enough to answer thoroughly.

  19. Identifying the Research Problem

    The first step of conducting research is identifying a researchable problem. This chapter explains the procedures to identify the research problem. The procedure of research involves systematically investigating a subject matter to reveal facts or reach new conclusions. Research can be theory driven or problem driven.

  20. LibGuides: Research: Establishing the Problem Space

    The "problem space" of a study is a definition of the topic, the problem statements or research gaps mentioned by other researchers, and the steps other researchers took to answer the research question. The problem space is a way to identify and establish boundaries for your research, it helps to guide what should be included or excluded from ...

  21. Research Gap

    Here are some examples of research gaps that researchers might identify: Theoretical Gap Example: In the field of psychology, there might be a theoretical gap related to the lack of understanding of the relationship between social media use and mental health. Although there is existing research on the topic, there might be a lack of consensus ...

  22. Scholarly Articles: How can I tell?

    Methodology. The methodology section or methods section tells you how the author (s) went about doing their research. It should let you know a) what method they used to gather data (survey, interviews, experiments, etc.), why they chose this method, and what the limitations are to this method. The methodology section should be detailed enough ...

  23. New database features 250 AI tools that can enhance social science research

    In the database, we found 131 AI tools useful for literature reviews, summaries or writing. As many as 146 AI tools are useful for data collection or analysis, and 108 are useful for research ...

  24. High-dimensional mediation analysis for continuous outcome with

    Background Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA ...

  25. Knowledge, attitudes, and practices of primary healthcare practitioners

    Background Pharmacist clinics offer professional pharmaceutical services that can improve public health outcomes. However, primary healthcare staff in China face various barriers and challenges in implementing such clinics. To identify existing problems and provide recommendations for the implementation of pharmacist clinics, this study aims to assess the knowledge, attitudes, and practices of ...

  26. LGBTQI+ People and Substance Use

    People in LGBTQI+ communities can face stressful situations and environments like stigma and discrimination, harassment, and traumatic experiences. Coping with these issues may raise the likelihood of a person having substance use problems. NIDA supports research to help identify the particular challenges that sexual and gender minority people ...

  27. Scientists identify mechanism behind drug resistance in malaria

    The research sets the foundation for the development of better tools to study RNA modifications and their role in resistance while simultaneously opening new avenues for drug development. RNA-modifying enzymes, especially those linked to resistance, are currently understudied, and they are attractive targets for the development of new and more ...

  28. Adhia, Shin, Ward, and Colleagues Identify Strategies for Recruiting

    The article was lead-authored by Omeid Heidari (Nursing) and included additional co-authors, Kaboni Gondwe (Nursing) and Daniel Suárez-Baquero (Nursing). Recruitment and retention of diverse faculty in schools of nursing continues to be an important challenge, but little has been written from the perspectives of early-career faculty of color ...

  29. A Beginner's Guide to Starting the Research Process

    This describes who the problem affects, why research is needed, and how your research project will contribute to solving it. >>Read more about defining a research problem. Step 3: Formulate research questions. Next, based on the problem statement, you need to write one or more research questions. These target exactly what you want to find out.

  30. 2024 Environmental Performance Index: A Surprise Top Ranking, Global

    203-436-4842. The Baltic nation of Estonia is No. 1 in the 2024 rankings, while Denmark, one of the top ranked countries in the 2022 EPI dropped to 10th place, highlighting the challenges of reducing emissions in hard-to-decarbonize industries. Meanwhile, "paper parks" are proving a global challenge to international biodiversity commitments.