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How to Write a Research Question: Types and Examples 

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

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

What is a research question?

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

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

A good research question has the following features: 

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

A poorly formulated research question poses several risks. 1   

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

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

Types of research questions

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

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

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

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

• Qualitative research questions  

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

• Mixed-methods studies  

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

What makes a good research question

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

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

Steps for developing a good research question

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

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

How to write a research question

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

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 
• Example 2 

References:  

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

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Home » Research Questions – Types, Examples and Writing Guide

Research Questions – Types, Examples and Writing Guide

Table of Contents

Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Formulation of Research Question – Stepwise Approach

Simmi k. ratan.

Department of Pediatric Surgery, Maulana Azad Medical College, New Delhi, India

1 Department of Community Medicine, North Delhi Municipal Corporation Medical College, New Delhi, India

2 Department of Pediatric Surgery, Batra Hospital and Research Centre, New Delhi, India

Formulation of research question (RQ) is an essentiality before starting any research. It aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. It is, therefore, pertinent to formulate a good RQ. The present paper aims to discuss the process of formulation of RQ with stepwise approach. The characteristics of good RQ are expressed by acronym “FINERMAPS” expanded as feasible, interesting, novel, ethical, relevant, manageable, appropriate, potential value, publishability, and systematic. A RQ can address different formats depending on the aspect to be evaluated. Based on this, there can be different types of RQ such as based on the existence of the phenomenon, description and classification, composition, relationship, comparative, and causality. To develop a RQ, one needs to begin by identifying the subject of interest and then do preliminary research on that subject. The researcher then defines what still needs to be known in that particular subject and assesses the implied questions. After narrowing the focus and scope of the research subject, researcher frames a RQ and then evaluates it. Thus, conception to formulation of RQ is very systematic process and has to be performed meticulously as research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

I NTRODUCTION

A good research question (RQ) forms backbone of a good research, which in turn is vital in unraveling mysteries of nature and giving insight into a problem.[ 1 , 2 , 3 , 4 ] RQ identifies the problem to be studied and guides to the methodology. It leads to building up of an appropriate hypothesis (Hs). Hence, RQ aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. A good RQ helps support a focused arguable thesis and construction of a logical argument. Hence, formulation of a good RQ is undoubtedly one of the first critical steps in the research process, especially in the field of social and health research, where the systematic generation of knowledge that can be used to promote, restore, maintain, and/or protect health of individuals and populations.[ 1 , 3 , 4 ] Basically, the research can be classified as action, applied, basic, clinical, empirical, administrative, theoretical, or qualitative or quantitative research, depending on its purpose.[ 2 ]

Research plays an important role in developing clinical practices and instituting new health policies. Hence, there is a need for a logical scientific approach as research has an important goal of generating new claims.[ 1 ]

C HARACTERISTICS OF G OOD R ESEARCH Q UESTION

“The most successful research topics are narrowly focused and carefully defined but are important parts of a broad-ranging, complex problem.”

A good RQ is an asset as it:

• Details the problem statement
• Further describes and refines the issue under study
• Adds focus to the problem statement
• Guides data collection and analysis
• Sets context of research.

Hence, while writing RQ, it is important to see if it is relevant to the existing time frame and conditions. For example, the impact of “odd-even” vehicle formula in decreasing the level of air particulate pollution in various districts of Delhi.

A good research is represented by acronym FINERMAPS[ 5 ]

Interesting.

• Appropriate
• Potential value and publishability
• Systematic.

Feasibility means that it is within the ability of the investigator to carry out. It should be backed by an appropriate number of subjects and methodology as well as time and funds to reach the conclusions. One needs to be realistic about the scope and scale of the project. One has to have access to the people, gadgets, documents, statistics, etc. One should be able to relate the concepts of the RQ to the observations, phenomena, indicators, or variables that one can access. One should be clear that the collection of data and the proceedings of project can be completed within the limited time and resources available to the investigator. Sometimes, a RQ appears feasible, but when fieldwork or study gets started, it proves otherwise. In this situation, it is important to write up the problems honestly and to reflect on what has been learned. One should try to discuss with more experienced colleagues or the supervisor so as to develop a contingency plan to anticipate possible problems while working on a RQ and find possible solutions in such situations.

This is essential that one has a real grounded interest in one's RQ and one can explore this and back it up with academic and intellectual debate. This interest will motivate one to keep going with RQ.

The question should not simply copy questions investigated by other workers but should have scope to be investigated. It may aim at confirming or refuting the already established findings, establish new facts, or find new aspects of the established facts. It should show imagination of the researcher. Above all, the question has to be simple and clear. The complexity of a question can frequently hide unclear thoughts and lead to a confused research process. A very elaborate RQ, or a question which is not differentiated into different parts, may hide concepts that are contradictory or not relevant. This needs to be clear and thought-through. Having one key question with several subcomponents will guide your research.

This is the foremost requirement of any RQ and is mandatory to get clearance from appropriate authorities before stating research on the question. Further, the RQ should be such that it minimizes the risk of harm to the participants in the research, protect the privacy and maintain their confidentiality, and provide the participants right to withdraw from research. It should also guide in avoiding deceptive practices in research.

The question should of academic and intellectual interest to people in the field you have chosen to study. The question preferably should arise from issues raised in the current situation, literature, or in practice. It should establish a clear purpose for the research in relation to the chosen field. For example, filling a gap in knowledge, analyzing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches, or testing theories within a specific population are some of the relevant RQs.

Manageable (M): It has the similar essence as of feasibility but mainly means that the following research can be managed by the researcher.

Appropriate (A): RQ should be appropriate logically and scientifically for the community and institution.

Potential value and publishability (P): The study can make significant health impact in clinical and community practices. Therefore, research should aim for significant economic impact to reduce unnecessary or excessive costs. Furthermore, the proposed study should exist within a clinical, consumer, or policy-making context that is amenable to evidence-based change. Above all, a good RQ must address a topic that has clear implications for resolving important dilemmas in health and health-care decisions made by one or more stakeholder groups.

Systematic (S): Research is structured with specified steps to be taken in a specified sequence in accordance with the well-defined set of rules though it does not rule out creative thinking.

Example of RQ: Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? This question fulfills the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant.

Types of research question

A RQ can address different formats depending on the aspect to be evaluated.[ 6 ] For example:

• Existence: This is designed to uphold the existence of a particular phenomenon or to rule out rival explanation, for example, can neonates perceive pain?
• Description and classification: This type of question encompasses statement of uniqueness, for example, what are characteristics and types of neuropathic bladders?
• Composition: It calls for breakdown of whole into components, for example, what are stages of reflux nephropathy?
• Relationship: Evaluate relation between variables, for example, association between tumor rupture and recurrence rates in Wilm's tumor
• Descriptive—comparative: Expected that researcher will ensure that all is same between groups except issue in question, for example, Are germ cell tumors occurring in gonads more aggressive than those occurring in extragonadal sites?
• Causality: Does deletion of p53 leads to worse outcome in patients with neuroblastoma?
• Causality—comparative: Such questions frequently aim to see effect of two rival treatments, for example, does adding surgical resection improves survival rate outcome in children with neuroblastoma than with chemotherapy alone?
• Causality–Comparative interactions: Does immunotherapy leads to better survival outcome in neuroblastoma Stage IV S than with chemotherapy in the setting of adverse genetic profile than without it? (Does X cause more changes in Y than those caused by Z under certain condition and not under other conditions).

How to develop a research question

• Begin by identifying a broader subject of interest that lends itself to investigate, for example, hormone levels among hypospadias
• Do preliminary research on the general topic to find out what research has already been done and what literature already exists.[ 7 ] Therefore, one should begin with “information gaps” (What do you already know about the problem? For example, studies with results on testosterone levels among hypospadias
• What do you still need to know? (e.g., levels of other reproductive hormones among hypospadias)
• What are the implied questions: The need to know about a problem will lead to few implied questions. Each general question should lead to more specific questions (e.g., how hormone levels differ among isolated hypospadias with respect to that in normal population)
• Narrow the scope and focus of research (e.g., assessment of reproductive hormone levels among isolated hypospadias and hypospadias those with associated anomalies)
• Is RQ clear? With so much research available on any given topic, RQs must be as clear as possible in order to be effective in helping the writer direct his or her research
• Is the RQ focused? RQs must be specific enough to be well covered in the space available
• Is the RQ complex? RQs should not be answerable with a simple “yes” or “no” or by easily found facts. They should, instead, require both research and analysis on the part of the writer
• Is the RQ one that is of interest to the researcher and potentially useful to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic
• Is the RQ researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
• Is the RQ measurable and will the process produce data that can be supported or contradicted?
• Is the RQ too broad or too narrow?
• Create Hs: After formulating RQ, think where research is likely to be progressing? What kind of argument is likely to be made/supported? What would it mean if the research disputed the planned argument? At this step, one can well be on the way to have a focus for the research and construction of a thesis. Hs consists of more specific predictions about the nature and direction of the relationship between two variables. It is a predictive statement about the outcome of the research, dictate the method, and design of the research[ 1 ]
• Understand implications of your research: This is important for application: whether one achieves to fill gap in knowledge and how the results of the research have practical implications, for example, to develop health policies or improve educational policies.[ 1 , 8 ]

Brainstorm/Concept map for formulating research question

• First, identify what types of studies have been done in the past?
• Is there a unique area that is yet to be investigated or is there a particular question that may be worth replicating?
• Begin to narrow the topic by asking open-ended “how” and “why” questions
• Evaluate the question
• Develop a Hypothesis (Hs)
• Write down the RQ.

Writing down the research question

• State the question in your own words
• Write down the RQ as completely as possible.

For example, Evaluation of reproductive hormonal profile in children presenting with isolated hypospadias)

• Divide your question into concepts. Narrow to two or three concepts (reproductive hormonal profile, isolated hypospadias, compare with normal/not isolated hypospadias–implied)
• Specify the population to be studied (children with isolated hypospadias)
• Refer to the exposure or intervention to be investigated, if any
• Reflect the outcome of interest (hormonal profile).

Another example of a research question

Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? Apart from fulfilling the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant, it also details about the intervention done (topical skin application of oil), rationale of intervention (as a skin barrier), population to be studied (preterm infants), and outcome (reduces hypothermia).

Other important points to be heeded to while framing research question

• Make reference to a population when a relationship is expected among a certain type of subjects
• RQs and Hs should be made as specific as possible
• Avoid words or terms that do not add to the meaning of RQs and Hs
• Stick to what will be studied, not implications
• Name the variables in the order in which they occur/will be measured
• Avoid the words significant/”prove”
• Avoid using two different terms to refer to the same variable.

Some of the other problems and their possible solutions have been discussed in Table 1 .

Potential problems and solutions while making research question

G OING B EYOND F ORMULATION OF R ESEARCH Q UESTION–THE P ATH A HEAD

Once RQ is formulated, a Hs can be developed. Hs means transformation of a RQ into an operational analog.[ 1 ] It means a statement as to what prediction one makes about the phenomenon to be examined.[ 4 ] More often, for case–control trial, null Hs is generated which is later accepted or refuted.

A strong Hs should have following characteristics:

• Give insight into a RQ
• Are testable and measurable by the proposed experiments
• Have logical basis
• Follows the most likely outcome, not the exceptional outcome.

E XAMPLES OF R ESEARCH Q UESTION AND H YPOTHESIS

Research question-1.

• Does reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients?

Hypothesis-1

• Reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients
• In pediatric patients with esophageal atresia, gap of <2 cm between two segments of the esophagus and proper mobilization of proximal pouch reduces the morbidity and mortality among such patients.

Research question-2

• Does application of mitomycin C improves the outcome in patient of corrosive esophageal strictures?

Hypothesis-2

In patients aged 2–9 years with corrosive esophageal strictures, 34 applications of mitomycin C in dosage of 0.4 mg/ml for 5 min over a period of 6 months improve the outcome in terms of symptomatic and radiological relief. Some other examples of good and bad RQs have been shown in Table 2 .

Examples of few bad (left-hand side column) and few good (right-hand side) research questions

R ESEARCH Q UESTION AND S TUDY D ESIGN

RQ determines study design, for example, the question aimed to find the incidence of a disease in population will lead to conducting a survey; to find risk factors for a disease will need case–control study or a cohort study. RQ may also culminate into clinical trial.[ 9 , 10 ] For example, effect of administration of folic acid tablet in the perinatal period in decreasing incidence of neural tube defect. Accordingly, Hs is framed.

Appropriate statistical calculations are instituted to generate sample size. The subject inclusion, exclusion criteria and time frame of research are carefully defined. The detailed subject information sheet and pro forma are carefully defined. Moreover, research is set off few examples of research methodology guided by RQ:

• Incidence of anorectal malformations among adolescent females (hospital-based survey)
• Risk factors for the development of spontaneous pneumoperitoneum in pediatric patients (case–control design and cohort study)
• Effect of technique of extramucosal ureteric reimplantation without the creation of submucosal tunnel for the preservation of upper tract in bladder exstrophy (clinical trial).

The results of the research are then be available for wider applications for health and social life

C ONCLUSION

A good RQ needs thorough literature search and deep insight into the specific area/problem to be investigated. A RQ has to be focused yet simple. Research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

How to Write Compelling Research Questions

Are you ready to take your research to the next level? Crafting a powerful research question is the key to unlocking the full potential of your academic journey. It's like having a trusty compass that guides you through the vast wilderness of knowledge, ensuring you stay on track and reach your destination.

In this article, we'll walk you through the essential steps to develop a research question that packs a punch. From identifying your research topic to refining and evaluating your question, we've got you covered. Along the way, we'll explore what makes a good research question and share some helpful research questions examples to inspire you. So, whether you're a student, writer, or just curious about how to form a research question, join us as we embark on this exciting adventure of developing research questions that will elevate your research to new heights!

Identify Your Research Topic

Alright, let's dive into the exciting world of identifying your research topic! Picture yourself as an adventurer, ready to explore uncharted territories of knowledge. But before you embark on this thrilling journey, you need to choose a destination that sparks your curiosity and aligns with your goals.

Brainstorming Your Interests

• Start by brainstorming a list of subjects that pique your interest. Consider areas that haven't been thoroughly explored or present challenges within your field.
• Ask yourself questions like: What fascinates me? What problems do I want to solve? What knowledge gaps exist in my area of study?
• Engage in discussions with peers, professors, or experts to gain fresh perspectives and refine your ideas.

Conducting Preliminary Research

Once you have a general topic in mind, it's time to do some background reading to narrow down your focus:

As you explore these resources, consider the following:

• Look for keywords and concepts that social scientists use to discuss your topic
• Identify specific cases or examples that can make your ideas more concrete
• Determine what aspects of the topic you want to focus on and find an angle to contribute through your project

Evaluating Your Topic

Before finalizing your research topic, ensure it meets the following criteria:

• Aligns with the assignment requirements and guidelines
• Has a substantial body of accessible and manageable related research
• Is interesting, relevant, and worthy of the time invested
• Allows for finding sufficient information in books or scholarly journals
• Fits your future professional path and enhances your skills

Remember, your research topic is not set in stone. It may evolve as you delve deeper into the research process. Embrace the opportunity to discover new insights and modify your topic accordingly.

Now that you've identified a captivating research topic, you're ready to embark on the next stage of your research adventure: conducting preliminary research to further refine your focus and develop a powerful research question.

Conduct Preliminary Research

Now that you've identified your research topic, it's time to dive deeper and conduct some preliminary research. This crucial step will help you narrow down your focus, identify key concepts, and lay the groundwork for developing a powerful research question.

• Use search engines like Google Scholar or your library's online catalog to find relevant articles, books, and other resources related to your topic.
• Explore Wikipedia to gain a broad understanding of your topic and discover potential subtopics or related areas of interest.
• As you read through your initial search results, take note of frequently used terms, phrases, and concepts related to your topic.
• Create a list of these key terms to help guide your further research and refine your focus.
• Assess the quantity and quality of the resources you've found so far.
• Determine if there is sufficient information available to support your research or if you need to adjust your topic's scope.
• Diversify your research by exploring various types of sources, such as:
• As you review the literature, look for areas where there is a lack of information or where scholars disagree.
• These gaps and debates can help you identify potential research questions and contribute to the existing body of knowledge.
• Based on your preliminary research, consider narrowing down your topic to a more specific focus.
• A well-defined and focused topic will make it easier to develop a clear and concise research question.

Remember, conducting preliminary research is an iterative process. As you learn more about your topic, you may need to adjust your focus, search for additional sources, or explore new angles. Embrace this process of discovery and let your curiosity guide you towards a compelling research question.

Define Your Research Problem

Alright, now that you've conducted some preliminary research and have a better understanding of your topic, it's time to define your research problem. This is where the real fun begins!

• Start by asking "how" and "why" questions about your general topic.
• For example, instead of asking, "Does social media affect mental health?" try, "How does social media impact the mental health of teenagers?"
• These types of questions encourage deeper exploration and analysis.
• Narrow down your research problem to a particular aspect of the broader topic.
• Consider focusing on a specific place, time, or group of people.
• Specify the aspects you will address and those you will not.
• Your research problem should be complex enough to require research and analysis, not just a simple yes/no answer.
• It should also be significant to you and potentially to others, addressing the "so what" factor.

Crafting the Research Question

Alright, now that you've defined your research problem, it's time to craft a powerful research question that will guide your study. A well-formulated research question should be clear, focused, and complex, avoiding simple yes/no answers and requiring research and analysis.

Characteristics of a Strong Research Question

A good research question exhibits the following characteristics:

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

The PICO(T) Framework

When structuring your research question, consider using the PICO(T) framework:

Avoiding Common Mistakes

To ensure your research question is strong, avoid these common mistakes:

• Ambiguity: Use clear and specific language to avoid confusion
• Assumption: Avoid making assumptions or using loaded language
• Scope: Keep the scope of your question manageable and relevant

Formulating Your Question

When crafting your research question, consider the following formulations:

• Describing and exploring: "What are the characteristics of...?"
• Explaining and testing: "What is the relationship between...?"
• Evaluating and acting: "How effective is...?"

Remember, developing a research question is an iterative process that involves continuously updating your knowledge on the topic and refining your ideas. As you progress through your research, you may need to adjust your question to better align with your findings and insights.

Examples of Research Questions

• Descriptive: "What are the main challenges faced by small businesses during a pandemic?"
• Comparative: "How does the effectiveness of online learning compare to traditional classroom learning?"
• Correlational: "Is there a relationship between social media use and anxiety levels in teenagers?"
• Exploratory: "What factors contribute to the success of remote work arrangements?"
• Explanatory: "How does regular exercise impact cognitive function in older adults?"
• Evaluation: "To what extent do diversity and inclusion initiatives improve employee satisfaction and retention?"

By crafting a strong research question that is clear, focused, and complex, you'll set the foundation for a successful research project that contributes to your field of study and provides valuable insights.

Refine and Evaluate Your Question

Congratulations on crafting a powerful research question! Now, it's time to refine and evaluate your question to ensure it's the best it can be. Let's dive in and make your research question shine!

Determining Relationships and Selecting Variables

• Identify how variables are related to one another and how these relationships may contribute to your research problem.
• Summarize how you plan to consider and use these variables and how they might influence the study results.

Asking Critical Open-Ended Questions

Narrow down your research question by asking the following:

• Who is involved in your research?
• What specific aspects are you investigating?
• When and where will your research take place?
• How will you conduct your study?
• Why is this research important?

Apply these criteria to make your question more generative, relevant, original, and less obvious.

Seeking Feedback and Revising

Remember, receiving feedback and revising is a valuable step in creating impactful and precise research.

Evaluating Interestingness and Feasibility

Consider the following factors when evaluating your research question:

• Is the answer in doubt?
• Does it fill a gap in the research literature?
• Does it have important practical implications?
• Do you have enough time and money?
• Do you possess the necessary technical knowledge and skills?
• Do you have access to special equipment and research participants?

Striving for Simplicity and Precision

• Make your research question as specific and concise as possible.
• Seek input from experts, mentors, and colleagues to refine your question further.
• Consider how your research question influences factors such as methodology, sample size, data collection, and analysis.

By refining and evaluating your research question, you'll set the stage for a successful research project that contributes valuable insights to your field of study. Remember, a well-crafted research question is central to a well-written paper, sparking interest and leading to new or rethought perspectives.

Developing a powerful research question is the cornerstone of any successful research project. By identifying your research topic, conducting preliminary research, defining your research problem, and crafting a focused question, you'll set the foundation for a meaningful and impactful study. Remember to refine and evaluate your question, seeking feedback from mentors and peers to ensure its relevance and feasibility.

As you embark on your research journey, let your curiosity be your guide. Embrace the process of discovery, and don't be afraid to refine your question as you uncover new insights. By crafting a research question that is clear, focused, and complex, you'll contribute valuable knowledge to your field and make a lasting impact on the world of research. So go forth, intrepid researcher, and let your powerful research question be the beacon that illuminates your path to success!

What are the essential steps to formulate a research question?

To formulate a research question, follow these steps:

• Choose a general subject area or consider the one that has been assigned to you.
• Narrow down the topic to a more specific aspect of the general subject.
• Brainstorm various questions that could be asked about this narrower topic.
• Select the question that you find most intriguing or compelling.

How can I create a strong research question?

To develop a strong research question, you should:

• Conduct initial research on your topic to understand the current discourse.
• Identify a problem or a knowledge gap within your field.
• Ensure your question aims to contribute to ongoing debates within your field or society.

What is the five-step process for writing a research question?

When crafting a research question, you should:

• Select a broad topic of interest.
• Engage in preliminary research to gain background information.
• Keep in mind the audience for whom the research is intended.
• Develop a list of possible questions related to the topic.
• Review and refine the list of questions.
• Formulate the final research question.

What are the five key elements of an effective research question?

An effective research question typically includes the following five components:

• Population: the group you are studying.
• Intervention: the variable or treatment you are considering.
• Comparator: the standard or control you are comparing the intervention to.
• Outcome: the expected result or effect of the intervention.
• Time frame: the period during which the study is conducted.These components can be remembered through the acronyms PICOT and FINER, which stand for Population, Intervention, Comparator, Outcome, Time frame, and Feasible, Interesting, Novel, Ethical, Relevant, respectively.

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How to write qualitative research questions.

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

What is qualitative research?

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

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

What are research questions?

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

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

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

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

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

Discover the fundamentals of qualitative research

Quantitative vs. qualitative research questions

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

Quantitative research questions

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

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

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

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

• What is the connection?

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

For example:

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

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

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

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

Qualitative research questions

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

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

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

A few example qualitative research questions:

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

Qualitative research question types

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

• Exploratory questions

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

• Explanatory questions

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

• Descriptive questions

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

• Predictive questions

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

Why are good qualitative research questions important?

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

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

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

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

Developing good qualitative research questions

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

1. Keep it specific

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

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

Weak example: Do people feel different about gender now?

2. Be original

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

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

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

3. Make it research-worthy

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

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

Weak example: What languages are spoken in India?

4. Focus your question

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

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

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

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

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

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

Weak example: How can hate crimes be prevented?

5. Ask something researchable

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

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

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

Related resources

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

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How to conduct a meta-analysis in eight steps: a practical guide

• Open access
• Published: 30 November 2021
• Volume 72 , pages 1–19, ( 2022 )

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• Christopher Hansen 1 ,
• Holger Steinmetz 2 &
• Jörn Block 3 , 4 , 5  

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Avoid common mistakes on your manuscript.

1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php .

The macros of David B. Wilson can be downloaded from: http://mason.gmu.edu/~dwilsonb/ .

The macros of Field and Gillet ( 2010 ) can be downloaded from: https://www.discoveringstatistics.com/repository/fieldgillett/how_to_do_a_meta_analysis.html .

The tutorials can be found via: https://www.metafor-project.org/doku.php .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: https://www.erim.eur.nl/research-support/meta-essentials/ .

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Hansen, C., Steinmetz, H. & Block, J. How to conduct a meta-analysis in eight steps: a practical guide. Manag Rev Q 72 , 1–19 (2022). https://doi.org/10.1007/s11301-021-00247-4

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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

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39 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

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I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

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I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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What’s in a Qualitative Research Question?

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

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

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

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

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

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

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

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

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

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

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

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

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

Other Qualitative Research Question Examples

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

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

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

Unclear : How do students experience progressing to college?

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

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

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• Next: Trustworthiness of Qualitative Data >>
• Last Updated: May 16, 2024 8:25 AM
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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Research Question

Ai generator.

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

What is a Research Question?

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

Types of Research Questions

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

1. Descriptive Research Questions

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

• What are the common symptoms of depression in teenagers?

2. Comparative Research Questions

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

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

3. Correlational Research Questions

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

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

4. Causal Research Questions

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

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

5. Exploratory Research Questions

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

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

6. Predictive Research Questions

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

• Can high school GPA predict college success?

7. Evaluative Research Questions

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

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

8. Ethnographic Research Questions

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

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

9. Case Study Research Questions

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

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

10. Phenomenological Research Questions

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

• What is the experience of living with chronic pain?

Research Question Format

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

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

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

Research Question Examples

Research questions in business.

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

Research Questions in Education

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

Research Questions in Health Care

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

Research Questions in Computer Science

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

Research Questions in Psychology

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

More Research Question Examples

Research question examples for students.

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

Research Question Examples for College Students

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

Research Question Examples in Statistics

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

Research Question Examples in Socialogy

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

Research Question Examples in Biology

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

Research Question Examples in History

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

Importance of Research Questions

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

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

Research Question vs. Hypothesis

Chracteristics of research questions.

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

1. Clear and Specific

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

2. Focused and Researchable

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

3. Complex and Analytical

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

4. Relevant and Significant

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

5. Feasible and Practical

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

6. Original and Novel

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

8. Open-Ended

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

9. Aligned with Research Goals

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

10. Based on Prior Research

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

Benefits of Research Question

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

1. Guides the Research Process

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

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

2. Clarifies the Purpose of the Study

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

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

3. Determines the Research Design

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

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

4. Enhances Literature Review

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

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

5. Focuses Data Collection

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

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

6. Improves Data Analysis

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

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

7. Facilitates Hypothesis Formation

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

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

8. Supports Result Interpretation

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

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

9. Enhances Reporting and Presentation

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

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

10. Encourages Critical Thinking

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

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

How to Write a Research Question

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

Identify a Broad Topic:

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

Conduct Preliminary Research:

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

Narrow Down the Topic:

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

Consider the Scope:

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

Determine the Research Type:

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

Formulate the Question:

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

Ensure Feasibility:

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

Review and Refine:

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

What is a research question?

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

Why is a research question important?

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

How do you formulate a research question?

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

What makes a good research question?

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

Can a research question change?

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

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

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

How specific should a research question be?

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

What are examples of good research questions?

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

Can a research question be too broad?

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

What role does a research question play in literature reviews?

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

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

In our increasingly competitive digital age, setting the right data analysis and critical thinking questions is essential to the ongoing growth and evolution of your business. It is not only important to gather your business’s existing information but you should also consider how to prepare your data to extract the most valuable insights possible.

That said, with endless rafts of data to sift through, arranging your insights for success isn’t always a simple process. Organizations may spend millions of dollars on collecting and analyzing information with various data analysis tools , but many fall flat when it comes to actually using that data in actionable, profitable ways.

Here we’re going to explore how asking the right data analysis and interpretation questions will give your analytical efforts a clear-cut direction. We’re also going to explore the everyday data questions you should ask yourself to connect with the insights that will drive your business forward with full force.

Let’s get started.

Data Is Only As Good As The Questions You Ask

The truth is that no matter how advanced your IT infrastructure is, your data will not provide you with a ready-made solution unless you ask it specific questions regarding data analysis.

To help transform data into business decisions, you should start preparing the pain points you want to gain insights into before you even start data gathering. Based on your company’s strategy, goals, budget, and target customers you should prepare a set of questions that will smoothly walk you through the online data analysis and enable you to arrive at relevant insights.

For example, you need to develop a sales strategy and increase revenue. By asking the right questions, and utilizing sales analytics software that will enable you to mine, manipulate and manage voluminous sets of data, generating insights will become much easier. An average business user and cross-departmental communication will increase its effectiveness, decreasing the time to make actionable decisions and, consequently, providing a cost-effective solution.

Before starting any business venture, you need to take the most crucial step: prepare your data for any type of serious analysis. By doing so, people in your organization will become empowered with clear systems that can ultimately be converted into actionable insights. This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy.

 “Today, big data is about business disruption. Organizations are embarking on a battle not just for success but for survival. If you want to survive, you need to act.” – Capgemini and EMC² in their study Big & Fast Data: The Rise of Insight-Driven Business .

This quote might sound a little dramatic. However, consider the following statistics pulled from research developed by Forrester Consulting and Collibra:

• 84% of correspondents report that data at the center stage of developing business strategies is critical
• 81% of correspondents realized an advantage in growing revenue
• 8% admit an advantage in improving customers' trust
• 58% of "data intelligent" organizations are more likely to exceed revenue goals

Based on this survey, it seems that business professionals believe that data is the ultimate cure for all their business ills. And that's not a surprise considering the results of the survey and the potential that data itself brings to companies that decide to utilize it properly. Here we will take a look at data analysis questions examples and explain each in detail.

19 Data Analysis Questions To Improve Your Business Performance In The Long Run

What are data analysis questions, exactly? Let’s find out. While considering the industry you’re in, and competitors your business is trying to outperform, data questions should be clearly defined. Poor identification can result in faulty interpretation, which can directly affect business efficiency, and general results, and cause problems.

Here at datapine, we have helped solve hundreds of analytical problems for our clients by asking big data questions. All of our experience has taught us that data analysis is only as good as the questions you ask. Additionally, you want to clarify these questions regarding analytics now or as soon as possible – which will make your future business intelligence much clearer. Additionally, incorporating a decision support system software can save a lot of the company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems.

That’s why we’ve prepared this list of data analysis questions examples – to be sure you won’t fall into the trap of futile, “after the fact” data processing, and to help you start with the right mindset for proper data-driven decision-making while gaining actionable business insights.

1) What exactly do you want to find out?

It’s good to evaluate the well-being of your business first. Agree company-wide on what KPIs are most relevant for your business and how they already develop. Research different KPI examples and compare them to your own. Think about what way you want them to develop further. Can you influence this development? Identify where changes can be made. If nothing can be changed, there is no point in analyzing data. But if you find a development opportunity, and see that your business performance can be significantly improved, then a KPI dashboard software could be a smart investment to monitor your key performance indicators and provide a transparent overview of your company’s data.

The next step is to consider what your goal is and what decision-making it will facilitate. What outcome from the analysis you would deem a success? These introductory examples of analytical questions are necessary to guide you through the process and focus on key insights. You can start broad, by brainstorming and drafting a guideline for specific questions about the data you want to uncover. This framework can enable you to delve deeper into the more specific insights you want to achieve.

Let’s see this through an example and have fun with a little imaginative exercise.

Let’s say that you have access to an all-knowing business genie who can see into the future. This genie (who we’ll call Data Dan) embodies the idea of a perfect data analytics platform through his magic powers.

Now, with Data Dan, you only get to ask him three questions. Don’t ask us why – we didn’t invent the rules! Given that you’ll get exactly the right answer to each of them, what are you going to ask it?  Let’s see….

Talking With A Data Genie

You: Data Dan! Nice to meet you, my friend. Didn’t know you were real.

Data Dan: Well, I’m not actually. Anyways – what’s your first data analysis question?

You: Well, I was hoping you could tell me how we can raise more revenue in our business.

Data Dan: (Rolls eyes). That’s a pretty lame question, but I guess I’ll answer it. How can you raise revenue? You can do partnerships with some key influencers, you can create some sales incentives, and you can try to do add-on services to your most existing clients. You can do a lot of things. Ok, that’s it. You have two questions left.

You: (Panicking) Uhhh, I mean – you didn’t answer well! You just gave me a bunch of hypotheticals!

Data Dan: I exactly answered your question. Maybe you should ask for better ones.

You: (Sweating) My boss is going to be so mad at me if I waste my questions with a magic business genie. Only two left, only two left… OK, I know! Genie – what should I ask you to make my business the most successful?

Data Dan: OK, you’re still not good at this, but I’ll be nice since you only have one data question left.  Listen up buddy – I’m only going to say this once.

The Key To Asking Good Analytical Questions

Data Dan: First of all, you want your questions to be extremely specific. The more specific it is, the more valuable (and actionable) the answer is going to be. So, instead of asking, “How can I raise revenue?”, you should ask: “What are the channels we should focus more on in order to raise revenue while not raising costs very much, leading to bigger profit margins?”. Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?”

These key questions to ask when analyzing data can define your next strategy in developing your organization. We have used a marketing example, but every department and industry can benefit from proper data preparation. By using a multivariate analysis, different aspects can be covered and specific inquiries defined.

2) What standard KPIs will you use that can help?

OK, let’s move on from the whole genie thing. Sorry, Data Dan! It’s crucial to know what data analysis questions you want to ask from the get-go. They form the bedrock for the rest of this process.

Think about it like this: your goal with business intelligence is to see reality clearly so that you can make profitable decisions to help your company thrive. The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality.

Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. For example, let’s say you want to see which of your PPC campaigns last quarter did the best. As Data Dan reminded us, “did the best” is too vague to be useful. Did the best according to what? Driving revenue? Driving profit? Giving the most ROI? Giving the cheapest email subscribers?

All of these KPI examples can be valid choices. You just need to pick the right ones first and have them in agreement company-wide (or at least within your department).

Let’s see this through a straightforward example.

You are a retail company and want to know what you sell, where, and when – remember the specific questions for analyzing data? In the example above, it is clear that the amount of sales performed over a set period tells you when the demand is higher or lower – you got your specific KPI answer. Then you can dig deeper into the insights and establish additional sales opportunities, and identify underperforming areas that affect the overall sales of products.

It is important to note that the number of KPIs you choose should be limited as monitoring too many can make your analysis confusing and less efficient. As the old analytics saying goes, just because you can measure something, it doesn't mean you should. We recommended sticking to a careful selection of 3-6 KPIs per business goal, this way, you'll avoid getting distracted by meaningless data.

The criteria to pick your KPIs is they should be attainable, realistic, measurable in time, and directly linked to your business goals. It is also a good practice to set KPI targets to measure the progress of your efforts.

Now let’s proceed to one of the most important data questions to ask – the data source.

3) Where will your data come from?

Our next step is to identify data sources you need to dig into all your data, pick the fields that you’ll need, leave some space for data you might potentially need in the future, and gather all the information in one place. Be open-minded about your data sources in this step – all departments in your company, sales, finance, IT, etc., have the potential to provide insights.

Don’t worry if you feel like the abundance of data sources makes things seem complicated. Our next step is to “edit” these sources and make sure their data quality is up to par, which will get rid of some of them as useful choices.

Right now, though, we’re just creating the rough draft. You can use CRM data, data from things like Facebook and Google Analytics, or financial data from your company – let your imagination go wild (as long as the data source is relevant to the questions you’ve identified in steps 1 and It could also make sense to utilize business intelligence software , especially since datasets in recent years have expanded in so much volume that spreadsheets can no longer provide quick and intelligent solutions needed to acquire a higher quality of data.

Another key aspect of controlling where your data comes from and how to interpret it effectively boils down to connectivity. To develop a fluent data analytics environment, using data connectors is the way forward.

Digital data connectors will empower you to work with significant amounts of data from several sources with a few simple clicks. By doing so, you will grant everyone in the business access to valuable insights that will improve collaboration and enhance productivity.

3.5) Which scales apply to your different datasets?

WARNING: This is a bit of a “data nerd out” section. You can skip this part if you like or if it doesn’t make much sense to you.

You’ll want to be mindful of the level of measurement for your different variables, as this will affect the statistical techniques you will be able to apply in your analysis.

There are basically 4 types of scales:

*Statistics Level Measurement Table*

• Nominal – you organize your data in non-numeric categories that cannot be ranked or compared quantitatively.

Examples: – Different colors of shirts – Different types of fruits – Different genres of music

• Ordinal – GraphPad gives this useful explanation of ordinal data:

“You might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain than a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order. Another example would be movie ratings, from 0 to 5 stars.”

• Interval – in this type of scale, data is grouped into categories with order and equal distance between these categories.

Direct comparison is possible. Adding and subtracting is possible, but you cannot multiply or divide the variables. Example: Temperature ratings. An interval scale is used for both Fahrenheit and Celsius.

Again, GraphPad has a ready explanation: “The difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees.”

• Ratio –  has the features of all three earlier scales.

Like a nominal scale, it provides a category for each item, items are ordered like on an ordinal scale and the distances between items (intervals) are equal and carry the same meaning.

With ratio scales, you can add, subtract, divide, multiply… all the fun stuff you need to create averages and get some cool, useful data. Examples: height, weight, revenue numbers, leads, and client meetings.

4) Will you use market and industry benchmarks?  

In the previous point, we discussed the process of defining the data sources you’ll need for your analysis as well as different methods and techniques to collect them. While all of those internal sources of information are invaluable, it can also be a useful practice to gather some industry data to use as benchmarks for your future findings and strategies. 

To do so, it is necessary to collect data from external sources such as industry reports, research papers, government studies, or even focus groups and surveys performed on your targeted customer as a market research study to extract valuable information regarding the state of the industry in general but also the position each competitor occupies in the market. 

In doing so, you’ll not only be able to set accurate benchmarks for what your company should be achieving but also identify areas in which competitors are not strong enough and exploit them as a competitive advantage. For example, you can perform a market research survey to analyze the perception customers have about your brand and your competitors and generate a report to analyze the findings, as seen in the image below. 

**click to enlarge**

This market research dashboard is displaying the results of a survey on brand perception for 8 outdoor brands. Respondents were asked different questions to analyze how each brand is recognized within the industry. With these answers, decision-makers are able to complement their strategies and exploit areas where there is potential. 

5) Is the data in need of cleaning?

Insights and analytics based on a shaky “data foundation” will give you… well, poor insights and analytics. As mentioned earlier, information comes from various sources, and they can be good or bad. All sources within a business have a motivation for providing data, so the identification of which information to use and from which source it is coming should be one of the top questions to ask about data analytics.

Remember – your data analysis questions are designed to get a clear view of reality as it relates to your business being more profitable. If your data is incorrect, you’re going to be seeing a distorted view of reality.

That’s why your next step is to “clean” your data sets in order to discard wrong, duplicated, or outdated information. This is also an appropriate time to add more fields to your data to make it more complete and useful. That can be done by a data scientist or individually, depending on the size of the company.

An interesting survey comes from CrowdFlower , a provider or a data enrichment platform among data scientists. They have found out that most data scientists spend:

• 60% of their time organizing and cleaning data (!).
• 19% is spent on collecting datasets.
• 9% is spent mining the data to draw patterns.
• 3% is spent on training the datasets.
• 4% is spent refining the algorithms.
• 5% of the time is spent on other tasks.

57% of them consider the data cleaning process the most boring and least enjoyable task. If you are a small business owner, you probably don’t need a data scientist, but you will need to clean your data and ensure a proper standard of information.

Yes, this is annoying, but so are many things in life that are very important.

When you’ve done the legwork to ensure your data quality, you’ll have built yourself the useful asset of accurate data sets that can be transformed, joined, and measured with statistical methods. But, cleaning is not the only thing you need to do to ensure data quality, there are more things to consider which we’ll discuss in the next question. 

6) How can you ensure data quality?

Did you know that poor data quality costs the US economy up to $3.1 trillion yearly? Taking those numbers into account it is impossible to ignore the importance of this matter. Now, you might be wondering, what do I do to ensure data quality?

We already mentioned making sure data is cleaned and prepared to be analyzed is a critical part of it, but there is more. If you want to be successful on this matter, it is necessary to implement a carefully planned data quality management system that involves every relevant data user in the organization as well as data-related processes from acquisition to distribution and analysis.  

Some best practices and key elements of a successful data quality management process include: 

• Carefully clean data with the right tools. 
• Tracking data quality metrics such as the rate of errors, data validity, and consistency, among others. 
• Implement data governance initiatives to clearly define the roles and responsibilities for data access and manipulation 
• Ensure security standards for data storage and privacy are being implemented 
• Rely on automation tools to clean and update data to avoid the risk of manual human error 

These are only a couple of the many actions you can take to ensure you are working with the correct data and processes. Ensuring data quality across the board will save your business a lot of money by avoiding costly mistakes and bad-informed strategies and decisions. 

7) Which statistical analysis techniques do you want to apply?

There are dozens of statistical analysis techniques that you can use. However, in our experience, these 3 statistical techniques are most widely used for business:

• Regression Analysis – a statistical process for estimating the relationships and correlations among variables.

More specifically, regression helps understand how the typical value of the dependent variable changes when any of the independent variables is varied, while the other independent variables are held fixed.

In this way, regression analysis shows which among the independent variables are related to the dependent variable, and explores the forms of these relationships. Usually, regression analysis is based on past data, allowing you to learn from the past for better decisions about the future.

• Cohort Analysis – it enables you to easily compare how different groups, or cohorts, of customers, behave over time.

For example, you can create a cohort of customers based on the date when they made their first purchase. Subsequently, you can study the spending trends of cohorts from different periods in time to determine whether the quality of the average acquired customer is increasing or decreasing over time.

Cohort analysis tools give you quick and clear insight into customer retention trends and the perspectives of your business.

• Predictive & Prescriptive Analysis – in short, it is based on analyzing current and historical datasets to predict future possibilities, including alternative scenarios and risk assessment.

Methods like artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA), time series, seasonal naïve approach, and data mining find wide application in data analytics nowadays.

• Conjoint analysis: Conjoint analytics is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services.

This type of analytics is incredibly valuable, as it will give you the insight required to see how your business’s products are really perceived by your audience, giving you the tools to make targeted improvements that will offer a competitive advantage.

• Cluster analysis: Cluster or 'clustering' refers to the process of grouping a set of objects or datasets. With this type of analysis, objects are placed into groups (known as a cluster) based on their values, attributes, or similarities.

This branch of analytics is often seen when working with autonomous applications or trying to identify particular trends or patterns.

We’ve already explained them and recognized them among the biggest business intelligence trends for 2022. Your choice of method should depend on the type of data you’ve collected, your team’s skills, and your resources.

8) What ETL procedures need to be developed (if any)?

One of the crucial questions to ask when analyzing data is if and how to set up the ETL process. ETL stands for Extract-Transform-Load, a technology used to read data from a database, transform it into another form and load it into another database. Although it sounds complicated for an average business user, it is quite simple for a data scientist. You don’t have to do all the database work, but an ETL service does it for you; it provides a useful tool to pull your data from external sources, conform it to demanded standards, and convert it into a destination data warehouse. These tools provide an effective solution since IT departments or data scientists don’t have to manually extract information from various sources, or you don’t have to become an IT specialist to perform complex tasks.

*ETL data warehouse*

If you have large data sets, and today most businesses do, it would be wise to set up an ETL service that brings all the information your organization is using and can optimize the handling of data.

9) What limitations will your analysis process have (if any)?

This next question is fundamental to ensure success in your analytical efforts. It requires you to put yourself in all the potential worst-case scenarios so you can prepare in advance and tackle them immediately with a solution. Some common limitations can be related to the data itself such as not enough sample size in a survey or research, lack of access to necessary technologies, and insufficient statistical power, among many others, or they can be related to the audience and users of the analysis such as lack of technical knowledge to understand the data. 

No matter which of these limitations you might face, identifying them in advance will help you be ready for anything. Plus, it will prevent you from losing time trying to find a solution for an issue, something that is especially valuable in a business context in which decisions need to be made as fast as possible.   

10) Who are the final users of your analysis results?

Another of the significant data analytics questions refers to the end-users of our analysis. Who are they? How will they apply your reports? You must get to know your final users, including:

• What they expect to learn from the data
• What their needs are
• Their technical skills
• How much time they can spend analyzing data?

Knowing the answers will allow you to decide how detailed your data report will be and what data you should focus on.

Remember that internal and external users have diverse needs. If the reports are designed for your own company, you more or less know what insights will be useful for your staff and what level of data complexity they can struggle through.

However, if your reports will also be used by external parties, remember to stick to your corporate identity. The visual reports you provide them with should be easy-to-use and actionable. Your final users should be able to read and understand them independently, with no IT support needed.

Also: think about the status of the final users. Are they junior members of the staff or part of the governing body? Every type of user has diverse needs and expectations.

11) How will the analysis be used?

Following on the latest point, after asking yourself who will use your analysis, you also need to ask yourself how you’re actually going to put everything into practice. This will enable you to arrange your reports in a way that transforms insight into action.

Knowing which questions to ask when analyzing data is crucial, but without a plan of informational action, your wonderfully curated mix of insights may as well be collecting dust on the virtual shelf. Here, we essentially refer to the end-use of your analysis. For example, when building reports, will you use it once as a standalone tool, or will you embed it for continual analytical use?

Embedded analytics is essentially a branch of BI technology that integrates professional dashboards or platforms into your business's existing applications to enhance its analytical scope and abilities. By leveraging the power of embedded dashboards , you can squeeze the juice out of every informational touchpoint available to your organization, for instance, by delivering external reports and dashboard portals to your external stakeholders to share essential information with them in a way that is interactive and easy to understand. 

Another key aspect of considering how you’re going to use your reports is to understand which mediums will work best for different kinds of users. In addition to embedded reports, you should also consider whether you want to review your data on a mobile device, as a file export, or even printed to mull through your newfound insights on paper. Considering and having these options at your disposal will ensure your analytical efforts are dynamic, flexible, and ultimately more valuable.

The bottom line? Decide how you’re going to use your insights in a practical sense, and you will set yourself on the path to data enlightenment. 

12) What data visualizations should you choose?

Your data is clean and your calculations are done, but you are not finished yet. You can have the most valuable insights in the world, but if they’re presented poorly, your target audience won’t receive the impact from them that you’re hoping for.

And we don’t live in a world where simply having the right data is the end-all, be-all. You have to convince other decision-makers within your company that this data is:

• Urgent to act upon

Effective presentation aids in all of these areas. There are dozens of data charts to choose from and you can either thwart all your data-crunching efforts by picking the wrong data visualization (like displaying a time evolution on a pie chart) or give it an additional boost by choosing the right types of graphs .

There are a number of online data visualization tools that can get the hard work done for you. These tools can effectively prepare the data and interpret the outcome. Their ease of use and self-service application in testing theories, analyzing changes in consumer buying behavior, leverage data for analytical purposes without the assistance of analysts or IT professionals have become an invaluable resource in today’s data management practice.

By being flexible enough to personalize its features to the end-user and adjust to your prepared questions for analyzing data, the tools enable a voluminous analysis that can help you not to overlook any significant issue of the day or the overall business strategy.

Dynamic modern dashboards are far more powerful than their static counterparts. You can reach out and interact with the information before you while gaining access to accurate real-time data at a glance. With interactive dashboards, you can also access your insights via mobile devices with the swipe of a screen or the click of a button 24/7. This will give you access to every single piece of analytical data you will ever need.

13) What kind of software will help?

Continuing on our previous point, there are some basic and advanced tools that you can utilize. Spreadsheets can help you if you prefer a more traditional, static approach, but if you need to tinker with the data on your own, perform basic and advanced analysis on a regular basis, and have real-time insights plus automated reports, then modern and professional tools are the way to go.

With the expansion of business intelligence solutions , data analytics questions to ask have never been easier. Powerful features such as basic and advanced analysis, countless chart types, quick and easy data source connection, and endless possibilities to interact with the data as questions arise, enable users to simplify oftentimes complex processes. No matter the analysis type you need to perform, the designated software will play an essential part in making your data alive and "able to speak."

Moreover, modern software will not require continuous manual updates of the data but it will automatically provide real-time insights that will help you answer critical questions and provide a stable foundation and prerequisites for good analysis.

14) What advanced technologies do you have at your disposal?

When you're deciding on which analysis question to focus on, considering which advanced or emerging technologies you have at your disposal is always essential.

By working with the likes of artificial intelligence (AI), machine learning (ML), and predictive analytics, you will streamline your data questions analysis strategies while gaining an additional layer of depth from your information.

The above three emerging technologies are interlinked in the sense that they are autonomous and aid business intelligence (BI) across the board. Using AI technology, it’s possible to automate certain data curation and analytics processes to boost productivity and hone in on better-quality insights.

By applying ML innovations, you can make your data analysis dashboards smarter with every single action or interaction, creating a self-improving ecosystem where you consistently boost the efficiency as well as the informational value of your analytical efforts with minimal human intervention.

From this ecosystem will emerge the ability to utilize predictive analytics to make accurate projections and develop organizational strategies that push you ahead of the competition. Armed with the ability to spot visual trends and patterns, you can nip any emerging issues or inefficiencies in the bud while playing on your current strengths for future gain.

With datapine, you can leverage the power of autonomous technologies by setting up data alerts that will notify you of a variety of functions - the kind that will help you exceed your business goals, as well as identify emerging patterns and particular numeric or data-driven thresholds. These BI features armed with cutting-edge technology will optimize your analytical activities in a way that will foster innovation and efficiency across the business.

15) How regularly should you check your data? 

Once you’ve answered all of the previous questions you should be 80% on the right track to be successful with your analytical efforts. That being said, data analytics is a never-ending process that requires constant monitoring and optimization. This leads us to our next question: how regularly should you check your data? 

There is no correct answer to this question as the frequency will depend on the goals of your analysis and the type of data you are tracking. In a business setting, there will be reports that contain data that you’ll need to track on a daily basis and in real-time since they influence the immediate performance of your organization for example, the marketing department might want to track the performance of their paid campaigns on a daily basis to optimize them and make the most out of their marketing budget. 

Likewise, there are other areas that can benefit from monthly tracking to extract more in-depth conclusions. For example, the customer service team might want to track the number of issues by channel on a monthly basis to identify patterns that can help them optimize their service. 

Modern data analysis tools provide users with the ability to automatically update their data as soon as it is generated. This alleviates the pain of having to manually check the data for new insights while significantly reducing the risk of human error. That said, no matter what frequency of monitoring you choose, it is also important to constantly check your data and analytical strategies to see if they still make sense for the current situation of the business. More on this in the next question. 

16) What else do you need to know?

Before finishing up, one of the crucial questions to ask about data analytics is how to verify the results. Remember that statistical information is always uncertain even if it is not reported in that way. Thinking about which information is missing and how you would use more information if you had it could be one point to consider. That way you can identify potential information that could help you make better decisions. Keep also in mind that by using simple bullet points or spreadsheets, you can overlook valuable information that is already established in your business strategy.

Always go back to the original objectives and make sure you look at your results in a holistic way. You will want to make sure your end result is accurate and that you haven’t made any mistakes along the way. In this step, important questions for analyzing data should be focused on:

• Does is it make sense on a general level?
• Are the measures I’m seeing in line with what I already know about the business?

Your end result is equally important as your process beforehand. You need to be certain that the results are accurate, verify the data, and ensure that there is no space for big mistakes. In this case, there are some data analysis types of questions to ask such as the ones we mentioned above. These types of questions will enable you to look at the bigger picture of your analytical efforts and identify any points that need more adjustments or additional details to work on.

You can also test your analytical environment against manual calculations and compare the results. If there are extreme discrepancies, there is something clearly wrong, but if the results turn accurate, then you have established a healthy data environment. Doing such a full-sweep check is definitely not easy, but in the long term, it will bring only positive results. Additionally, if you never stop questioning the integrity of your data, your analytical audits will be much healthier in the long run.

17) How can you create a data-driven culture?

Dirty data is costing you.

Whether you are a small business or a large enterprise, the data tell its story, and you should be able to listen. Preparing questions to ask about data analytics will provide a valuable resource and a roadmap to improved business strategies. It will also enable employees to make better departmental decisions and, consequently, create a cost-effective business environment that can help your company grow. Dashboards are a great way to establish such a culture, like in our financial dashboard example below:

In order to truly incorporate this data-driven approach to running the business, all individuals in the organization, regardless of the department they work in, need to know how to start asking the right data analytics questions.

They need to understand why it is important to conduct data analysis in the first place.

However, simply wishing and hoping that others will conduct data analysis is a strategy doomed to fail. Frankly, asking them to use data analysis (without showing them the benefits first) is also unlikely to succeed.

Instead, lead by example. Show your internal users that the habit of regular data analysis is a priceless aid for optimizing your business performance. Try to create a beneficial dashboard culture in your company.

Data analysis isn’t a means to discipline your employees and find who is responsible for failures, but to empower them to improve their performance and self-improve.

18) Are you missing anything, and is the data meaningful enough?

Once you’ve got your data analytics efforts off the ground and started to gain momentum, you should take the time to explore all of your reports and visualizations to see if there are any informational gaps you can fill.

Hold collaborative meetings with department heads and senior stakeholders to vet the value of your KPIs, visualizations, and data reports. You might find that there is a particular function you’ve brushed over or that a certain piece of data might be better displayed in a different format for greater insight or clarity.

Making an effort to keep track of your return on investment (ROI) and rates of improvements in different areas will help you paint a panoramic picture that will ultimately let you spot any potential analytical holes or data that is less meaningful than you originally thought.

For example, if you’re tracking sales targets and individual rep performance, you will have enough information to make improvements to the department. But with a collaborative conversation and a check on your departmental growth or performance, you might find that also throwing customer lifetime value and acquisition costs into the mix will offer greater context while providing additional insight. 

While this is one of the most vital ongoing data analysis questions to ask, you would be amazed at how many decision-makers overlook it: look at the bigger picture, and you will gain an edge on the competition.

19) How can you keep improving the analysis strategy?

When it comes to business questions for analytics, it’s essential to consider how you can keep improving your reports, processes, or visualizations to adapt to the landscape around you.

Regardless of your niche or sector, in the digital age, everything is in constant motion. What works today may become obsolete tomorrow. So, when prioritizing which questions to ask for analysis, it’s vital to decide how you’re going to continually evolve your reporting efforts.

If you’ve paid attention to business questions for data analysis number 18 (“Am I missing anything?” and “Is my data meaningful enough?”), you already have a framework for identifying potential gaps or weaknesses in your data analysis efforts. To take this one step further, you should explore every one of your KPIs or visualizations across departments and decide where you might need to update particular targets, modify your alerts, or customize your visualizations to return insights that are more relevant to your current situation.

You might, for instance, decide that your warehouse KPI dashboard needs to be customized to drill down further into total on-time shipment rates due to recent surges in customer order rates or operational growth. 

There is a multitude of reasons you will need to tweak or update your analytical processes or reports. By working with the right BI technology while asking yourself the right questions for analyzing data, you will come out on top time after time.

Start Your Analysis Today!

We just outlined a 19-step process you can use to set up your company for success through the use of the right data analysis questions.

With this information, you can outline questions that will help you to make important business decisions and then set up your infrastructure (and culture) to address them on a consistent basis through accurate data insights. These are good data analysis questions and answers to ask when looking at a data set but not only, as you can develop a good and complete data strategy if you utilize them as a whole. Moreover, if you rely on your data, you can only reap benefits in the long run and become a data-driven individual, and company.

To sum it up, here are the most important data questions to ask:

• What exactly do you want to find out? 
• What standard KPIs will you use that can help? 
• Where will your data come from? 
• Will you use market benchmarks?
• Is your data in need of cleaning?
• How can you ensure data quality? 
• Which statistical analysis techniques do you want to apply? 
• What ETL procedures need to be developed (if any?) 
• What limitations will your analysis process have (if any)?
• Who are the final users of your analysis results? 
• How will your analysis be used? 
• What data visualization should you choose? 
• What kind of software will help? 
• What advanced technologies do you have at your disposal? 
• What else do you need to know?
• How regularly should you check your data?
• How can you create a data-driven culture? 
• Are you missing anything, and is the data meaningful enough? 
• How can you keep improving the analysis strategy? 

Weave these essential data analysis question examples into your strategy, and you will propel your business to exciting new heights.

To start your own analysis, you can try our software for a 14-day trial - completely free!

• Open access
• Published: 24 May 2024

Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns

• Ulugbek Khudayberdievich Mirzaev 1 , 2 ,
• Serge Ouoba 1 , 3 ,
• Zayar Phyo 1 ,
• Chanroth Chhoung 1 ,
• Akuffo Golda Ataa 1 ,
• Aya Sugiyama 1 ,
• Tomoyuki Akita 1 &
• Junko Tanaka 1  

BMC Infectious Diseases volume  24 , Article number:  525 ( 2024 ) Cite this article

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The burden of hepatitis E in Southeast Asia is substantial, influenced by its distinct socio-economic and environmental factors, as well as variations in healthcare systems. The aim of this study was to assess the pooled seroprevalence of hepatitis E across countries within the Southeast Asian region by the UN division.

The study analyzed 66 papers across PubMed, Web of Science, and Scopus databases, encompassing data from of 44,850 individuals focusing on anti-HEV seroprevalence. The investigation spanned nine countries, excluding Brunei and East Timor due to lack of data. The pooled prevalence of anti-HEV IgG was determined to be 21.03%, with the highest prevalence observed in Myanmar (33.46%) and the lowest in Malaysia (5.93%). IgM prevalence was highest in Indonesia (12.43%) and lowest in Malaysia (0.91%). The study stratified populations into high-risk (farm workers, chronic patients) and low-risk groups (general population, blood donors, pregnant women, hospital patients). It revealed a higher IgG—28.9%, IgM—4.42% prevalence in the former group, while the latter group exhibited figures of 17.86% and 3.15%, respectively, indicating occupational and health-related vulnerabilities to HEV.

A temporal analysis (1987–2023), indicated an upward trend in both IgG and IgM prevalence, suggesting an escalating HEV burden.

These findings contribute to a better understanding of HEV seroprevalence in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Research Question

Investigate the seroprevalence of hepatitis E virus (HEV) in Southeast Asian countries focusing on different patterns, timelines, and population cohorts.

Sporadic Transmission of IgG and IgM Prevalence:

• Pooled anti-HEV IgG prevalence: 21.03%

• Pooled anti-HEV IgM prevalence: 3.49%

Seroprevalence among specific groups:

High-risk group (farm workers and chronic patients):

• anti-HEV IgG: 28.9%

• anti-HEV IgM: 4.42%

Low-risk group (general population, blood donors, pregnant women, hospital patients):

• anti-HEV IgG: 17.86%

• anti-HEV IgM: 3.15%

Temporal Seroprevalence of HEV:

Anti-HEV IgG prevalence increased over decades (1987–1999; 2000–2010; 2011–2023): 12.47%, 18.43%, 29.17% as an anti-HEV IgM prevalence: 1.92%, 2.44%, 5.27%

Provides a comprehensive overview of HEV seroprevalence in Southeast Asia.

Highlights variation in seroprevalence among different population groups.

Reveals increasing trend in HEV seroprevalence over the years.

Distinguishes between sporadic and epidemic cases for a better understanding of transmission dynamics.

Peer Review reports

Introduction

Hepatitis E is a major global health concern caused by the hepatitis E virus (HEV), which is a small, nonenveloped, single-stranded, positive-sense RNA virus belonging to the Paslahepevirus genus in the Hepeviridae family. There are eight genotypes of HEV: HEV-1 and HEV-2 infect only humans, HEV-3, HEV-4, and HEV-7 infect both humans and animals, while HEV-5, HEV-6, and HEV-8 infect only animals [ 1 ].

HEV infections affect millions of people worldwide each year, resulting in a significant number of symptomatic cases and deaths. In 2015, the World Health Organization (WHO) reported approximately 44,000 deaths from hepatitis E, accounting for 3.3% of overall mortality attributed to viral hepatitis [ 2 ]. The primary mode of transmission for hepatitis E is through the fecal–oral route. Outbreaks of the disease are often associated with heavy rainfall and flooding [ 3 , 4 ]. Additionally, sporadic cases can occur due to poor sanitation, vertical transmission, blood transfusion or close contact with infected animals, which serve as hosts for the virus [ 5 ]. Southeast Asia carries a substantial burden of hepatitis E, influenced by its unique socio-economic and environmental factors as well as variations in healthcare systems. Understanding the seroprevalence of hepatitis E in this region is crucial for implementing targeted public health interventions and allocating resources. To achieve the effective control and prevention of HEV, it is required to address the waterborne transmission and considering the specific characteristics of each region. By taking these measures, healthcare authorities can work towards reducing the global impact of hepatitis E on public health. Systematic reviews and meta-analyses on hepatitis E play a crucial role in synthesizing and integrating existing research findings, providing comprehensive insights into the epidemiology, transmission, and burden of the disease, thereby aiding evidence-based decision-making and public health strategies [ 6 , 7 ].

Recent systematic reviews and meta-analysis conducted on hepatitis E have varied in their scope or were limited by a smaller number of source materials [ 8 , 9 ]. The objective of this study was to determine the pooled seroprevalence of hepatitis E in countries within Southeast Asia by aggregating findings from a multitude of primary studies conducted across the region.

To commence this systematic review and meta-analysis, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and used the PRISMA assessment checklist [Supplementary Table  1 ]. The study included pertinent research conducted within the population of Southeast Asian countries, as outlined by the United Nations [ 10 ], and perform a meta-analysis on the seroprevalence of hepatitis E in this specific region.

PICOT assessment

In this systematic review and meta-analysis, the eligible population comprised individuals from the Southeast Asia region, irrespective of age, gender, ethnic characteristics, or specific chronic diseases. However, studies involving populations outside the designated countries, travelers, migrants, animal species studies, and those lacking clear descriptions of the study population were excluded.

Intervention and comparison

Intervention and comparison are not applicable to the prevalence studies.

Anti-HEV antibodies positivity either total antibodies or IgG or IgM among the Southeast Asian countries' population was assessed.

All studies conducted between 1987 and 2023 were included in this meta-analysis.

Search strategy

To conduct the data search, we utilized three databases, namely “PubMed”, “Scopus”, and “Web of Science”. The search terms comprised keywords related to the Hepatitis E virus, such as “Hepatitis E virus” OR “Hepatitis E” OR “HEV” AND names of each country “Brunei”, “Cambodia”, “Timor-Leste” OR “East-Timor”, “Laos” OR “Lao PDR”, “Indonesia”, “Malaysia”, “Myanmar” OR “Burma”, “Philippines”, “Singapore”, “Thailand”, “Vietnam” and “Southeast Asia”.

The search process in the databases finished on May 29 th , 2023, with two members of the study team conducting independent searches. Subsequently, the search results were unified. A grey literature search was performed from June 25 th to 30 th , 2023, by examining the references of review manuscripts and conference materials, along with using specific keywords in the Google Scholar database. Notably, during the gray literature search, additional studies from the Philippines that were initially missing in the first search were identified and included. Moreover, due to the diverse language expertise of the team, studies in Russian and French related to Cambodia and Vietnam were also considered for inclusion.

After applying the inclusion and exclusion criteria, each article selected for this systematic review (SR) was considered relevant. The quality assessment of each article was conducted using specific JBI critical appraisal instruments [ 11 ] [Supplementary Table  2 ].

Sporadic transmission of HEV infection

For the systematic review and meta-analysis of sporadic infection of HEV, we divided the study population into cohorts by countries, by risk of acquiring HEV—low and high risk. The low risk cohort included the general population (apparently healthy individuals, students, some ethnic populations, or individuals included in original studies as “general population”), blood donors, pregnant women, and hospital patients, while pig farmers, those with chronic hepatitis, HIV positive patients, and solid organ transplant patients in the high-risk group.

Lastly, we analyzed data in three decades—1987–1999, 2000–2010, and 2011–2023—to reveal seroprevalence rates over time.

Epidemic outbreaks of HEV infection

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation.

Statistical analysis

A meta-analysis of proportions was conducted using the 'meta' and 'metafor' packages in the R statistical software. To account for small proportions, the Freeman-Tukey double arcsine method was applied to transform the data. The Dersimonian and Laird method, which employs a random-effects model, was utilized for the meta-analysis, and the results were presented in a forest plot. Confidence intervals (CIs) for the proportions of individual studies were computed using the Clopper-Pearson method.

Heterogeneity was evaluated using the Cochran Q test and quantified by the I 2 index. Heterogeneity was considered significant if the p -value of the Cochran Q test was below 0.05.

For the assessment of publication bias, a funnel plot displaying the transformed proportions against the sample size was created. The symmetry of the plot was examined using the Egger test ( p  < 0.1).

The initial search yielded 1641 articles, which covered 9 out of 11 Southeast Asia countries. We couldn't find any information on hepatitis E from Brunei. We excluded a study from East Timor because it focused on the wrong population (US Army troops). The final screening resulted in the selection of 57 relevant studies, and the grey literature search added 9 more papers that met our inclusion criteria (Fig.  1 ). Among 9 papers through a grey literature, two relevant studies from the Philippines [ 12 , 13 ], one each from Indonesia [ 14 ] and Lao PDR [ 15 ], one study covered both Vietnam and Cambodia [ 16 ], one study provided HEV seroepidemiology information for Myanmar, Thailand, and Vietnam [ 17 ], two studies reported in Russian [ 18 , 19 ] (from Vietnam) and one reported in French [ 16 ] (from Vietnam and Cambodia). In total, our analysis included 66 papers from which we extracted data. This involved a total of 44,850 individuals (Table  1 ).

Flowchart of the identification, inclusion, and exclusion of the study. Table under flowchart informing about the studies which were found by the initial search in databases

Sporadic transmission IgG and IgM prevalence in Southeast Asian countries (excluding outbreak settings)

The sporadic cases involving 42,248 participants out of 44,850 participants (the remaining 2,602 people are considered in the “ Epidemic outbreaks ” section) from Southeast Asian countries the pooled prevalence of IgG was found to be 21.03%, while for IgM, it was 3.49% among 34,480 individuals who were tested (Fig. 2 ). Among these countries, Myanmar registered the highest pooled prevalence of IgG at 33.46%, while Malaysia had the lowest at 5.93%. For IgM prevalence, Indonesia had the highest rate at 12.43%, and Malaysia again had the lowest at 0.91% (Table  2 ) [Supplementary Figures  1 and 6 ].

Forest plot of meta-analysis of the prevalence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) in Southeast Asian countries. The plot includes the number of study participants for each country

Seroprevalence among specific groups

High risk of acquiring hev.

The high-risk group, which included farm workers and chronic patients, demonstrated a pooled anti-HEV IgG prevalence of 28.9%, with IgM prevalence at 4.42% [Supplementary Figures  2 and 8 ].

Chronic patients

This group, comprising individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest prevalence of pooled IgG among all cohorts, standing at 29.2%. Additionally, IgM prevalence was 3.9% [Supplementary Figures  2 and 7 ].

Farm workers

Farm workers were divided into several subgroups based on exposure to animals (reservoirs of HEV), including pig or ruminant farmers, slaughterhouse workers, butchers, and meat retailers. Among this group, the highest IgG prevalence was observed at 28.4%, while the pooled IgM level was 6.21% [Supplementary Figures  2 and 7 ].

Low risk of acquiring HEV

The low-risk group, comprising the general population, blood donors, pregnant women, and hospital patients, exhibited anti-HEV IgG and IgM prevalence of 17.86% and 3.15%, respectively. [Supplementary Figures  2 and 9 ].

General population

The general population in Southeast Asian countries, represented by 22,571 individuals, showed a presence of IgG in 21.4% of them. IgM was tested in 10,304 participants, and 2.63% of acute infection cases were identified [Supplementary Figures  2 and 7 ].

Blood donors

Blood donors, as a selected subgroup of the general population, exhibit differences in health status, age, gender distribution, and representativeness, warranting separate assessment. Among blood donors in Southeast Asian countries, the pooled prevalence of IgG and IgM were found to be 11.77% and 0.83%, respectively [Supplementary Figures  2 and 7 ].

Pregnant women

Pregnant women considered a vulnerable group regarding disease consequences, demonstrated an anti-HEV IgG prevalence of 18.56% among 1,670 individuals included in the study. Furthermore, 1.54% of them tested positive for anti-HEV IgM [Supplementary Figures  2 and 7 ].

Hospital patients

A group of 18,792 patients who visited hospitals with clinical signs of acute infection, jaundice, high temperature, and elevated liver enzymes, showed anti-HEV IgG and IgM prevalence of 16.3% and 4.45%, respectively [Supplementary Figures  2 and 7 ].

Temporal seroprevalence of HEV

Given the studies' long duration, the data was presented by decades: 1987–1999, 2000–2010, and 2011–2023. The prevalence of IgG showed an upward trend over these decades, with rates of 12.47%, 18.43%, and 29.17%. Similarly, for IgM, the prevalence rates were 1.92%, 2.44%, and 5.27% for the first, second, and third decades, respectively (Fig. 3 ).

The prevalence of anti-HEV IgG and IgM in Southeast Asian countries throughout the decades

Evaluating the trend of seroprevalence over decades within the same population and country proved challenging due to the limited availability of research papers. Consequently, we assessed anti-HEV antibody prevalence over decades, considering population cohorts and individual countries.

In Fig.  4 , we can see that all population groups show a consistent increase in the prevalence of both IgG and IgM antibodies over the decades. Figure  5 , we analyze the prevalence of anti-HEV antibodies in different countries over time, except for Indonesia and Malaysia, where we observe an increase in prevalence.

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within population cohorts across Southeast Asian nations divided by decades. The population cohorts delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Blood donors group has the anti-HEV IgM only for the last decade

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within countries of Southeast Asia divided by decades. The countries delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Philippines has the anti-HEV IgG antibodies information only for the first decade. Philippines, Myanmar, Singapore have anti-HEV IgM information only for single decade

Some studies lacked information on the collection time of the samples [ 13 , 19 , 41 , 48 , 59 , 62 , 64 , 82 ]. In these studies, the pooled IgG and IgM prevalence was 26.5% and 4.75%, respectively [Supplementary Figures  3 , 4 , 5 , 10 , 11 , 12 ].

Epidemic outbreaks

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation. The outbreaks occurred between 1987 and 1998 in several Southeast Asian countries, namely Indonesia [ 31 , 33 , 34 ], Vietnam [ 77 ], and Myanmar [ 54 ] [Supplementary Figure  13 ]. These outbreak investigations involved a total of 2,602 individuals, with most participants from Indonesia (2,292 individuals). The studies were mainly conducted using a case–control design. Among the participants, 876 were considered controls, while 1,726 were classified as cases. The pooled prevalence of total anti-HEV immunoglobulins was estimated as 61.6% (95% CI 57.1–66) (Table  2 ).

Assessment of publication bias

We checked for publication bias using a funnel plot and Egger's test. Both the studies on anti-HEV IgG and IgM showed asymmetry with Egger's test indicating a p -value less than 0.001 for both cases (Fig. 6 ).

Funnel plot of anti-HEV IgG ( A ) and anti-HEV IgM prevalence. Double arcsine transformed proportion of individual studies is plotted against the sample size. The distribution of studies in the funnel plot revealed the presence of publication bias

A paper search yielded varying numbers of manuscripts from Southeast Asian countries. The Philippines had the fewest studies, while Thailand had the highest with 15 studies. No data was found for Brunei Darussalam and East Timor or Timor Leste on the human species.

The results of this study provide valuable insights into the seroprevalence of IgG and IgM antibodies against HEV in different populations across Southeast Asian countries. Understanding the prevalence of these antibodies is essential for assessing the burden of HEV infection and identifying high-risk groups.

The extensive analysis of anti-HEV IgG prevalence in this study covered a wide range of population groups in Southeast Asia, including the general population, blood donors, pregnant women, hospital patients, farm workers, and chronic patients. The results unveiled an overall pooled prevalence of 21.03%, indicating significant exposure to the Hepatitis E virus among individuals in the region at some point in their lives. Moreover, a consistent increase in IgG prevalence was observed over the years, with the highest prevalence occurring in the most recent decade (2011–2023). This suggests a progressive rise in HEV exposure within the region.

Upon examining the prevalence data across different decades and population cohorts, a uniform upward trend in HEV antibody prevalence became apparent across all groups. Several factors could be assessed as potential contributors to this trend:

Notably, the expanding population in Southeast Asian nations during this timeframe increased the number of individuals at risk of Hepatitis E infection.

The rapid urbanization, characterized by the migration from rural to urban areas, led to higher population density and conditions conducive to Hepatitis E virus transmission [ 84 ]. Access to clean drinking water and adequate sanitation facilities emerged as critical factors in preventing Hepatitis E. Regions with inadequate infrastructure, particularly in water and sanitation, faced an elevated risk due to contaminated water sources. Climate-related events, such as heavy rainfall and flooding, significantly impacted waterborne diseases like Hepatitis E. The increasing frequency and severity of such events emphasized the importance of considering climate-related factors in assessing prevalence trends [ 85 ]. Consumption of contaminated or undercooked meat, particularly pork, was identified as a source of Hepatitis E transmission. Changes in food consumption habits over time may have contributed to changes in seroprevalence [ 86 ]. Limited access to healthcare facilities in certain areas exacerbated the spread of Hepatitis E. Increased awareness together with advances in medical research and the establishment of robust surveillance systems likely improved the detection and reporting of Hepatitis E cases, contributing to the observed increase in seroprevalence [ 87 , 88 , 89 ]. These multifaceted factors have likely played a collective role in shaping the changing landscape of Hepatitis E seroprevalence in Southeast Asian nations over the past decades. The upward trend emphasizes the importance of continued monitoring, intervention, and public health measures to mitigate the spread of Hepatitis E in the region.

Among specific populations, pregnant women exhibited an IgG prevalence of 18.56%, indicating that a considerable number of pregnant individuals have been exposed to HEV. Pregnant women are particularly vulnerable to the consequences of HEV infection, as it can lead to severe outcomes for both the mother and the foetus.

Hospital patients with clinical signs of acute infection showed an IgG prevalence of 16.3%, suggesting that HEV is still a significant cause of acute hepatitis cases in the hospital setting. Similarly, farm workers, especially those exposed to animals (reservoirs of HEV), had a high prevalence of IgG (28.4%), highlighting the occupational risk associated with zoonotic transmission.

Chronic patients, including individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest pooled IgG prevalence among all cohorts at 29.2%. This finding underscores the importance of monitoring HEV infection in immunocompromised individuals, as they may develop chronic HEV infection, which can lead to severe liver complications.

The prevalence of IgM antibodies, which are indicative of recent or acute HEV infection, was lower overall compared to IgG. The general population showed an IgM prevalence of 2.63% among acute infection cases. Among hospital patients exhibiting clinical signs of acute infection, the prevalence of IgM antibodies indicative of recent or acute HEV infection was higher at 4.45%.

Farm workers, particularly those exposed to animals, demonstrated the highest IgM prevalence at 6.21%. This finding highlights the occupational risk of acquiring acute HEV infection in this population due to direct or indirect contact with infected animals.

The study also identified a high-risk group, consisting of farm workers and chronic patients, with a pooled IgG prevalence of 28.9% and an IgM prevalence of 4.42%. This group is particularly susceptible to HEV infection and requires targeted interventions to reduce transmission and prevent severe outcomes.

Overall, this study provides valuable data on the seroprevalence of HEV antibodies in different populations in Southeast Asian countries. It highlights the importance of continued surveillance and public health interventions to control HEV transmission, especially in vulnerable groups. Understanding the prevalence trends over time can aid in developing effective strategies for the prevention and management of HEV infections in the region. However, further research and studies are warranted to explore the underlying factors contributing to the observed seroprevalence trends and to design targeted interventions to reduce HEV transmission in specific populations. Among the countries of Southeast Asia Myanmar was the most for HEV infection, while Malaysia registered the lowest seroprevalence.

This study has some limitations that we should be aware of. We looked at studies in three languages (English, Russian, and French), but we couldn't find data from two out of the 11 countries. This means we might not have a complete picture of the disease's prevalence in the whole region.

The way we divided the groups based on occupation or status could be questioned. Different criteria might give us different results, so it's something we need to consider. Another challenge is that the study covers a long time from 1989 to 2023 by published research and involves many different countries. This makes it difficult to compare the results because the tests used, and the diagnostic abilities might have changed over time and vary across countries.

Despite these limitations, our study presents a detailed epidemiologic report of combined seroprevalence data for HEV in Southeast Asian countries following the UN division. It gives us a basic understanding of the disease's prevalence in the region and offers some insights into potential risk factors. However, to get a more accurate picture, future research should address these limitations and include data from all countries in the region. Furthermore, certain countries such as Myanmar and the Philippines have not reported HEV prevalence data since 2006 and 2015, respectively. The absence of recent HEV prevalence reports from certain countries raises concerns about the availability of up-to-date epidemiological data for assessing the current status of hepatitis E virus infections in these regions.

Our comprehensive analysis study involving Southeast Asian countries provides significant insights into the seroprevalence of hepatitis E virus (HEV) infection in this region and in various populations. The rates of anti-HEV antibodies observed among different groups, as well as the increasing trend in seroprevalence over decades, emphasize the dynamic nature of HEV transmission in the region. These findings contribute to a better understanding of HEV prevalence across countries, populations, and time periods in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Availability of data and materials

All data generated or analyzed during this study were included in this paper either in the results or supplementary information.

Abbreviations

Hepatitis E Virus

Preferred reporting items for systematic review and meta-analysis

Enzyme-Linked Immunosorbent Essay

Hepatitis E virus Immunoglobulin G

Hepatitis E Virus Immunoglobulin M

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Acknowledgements

The authors would like to thank all researchers of the primary research included in this study.

This work was supported by Project Research Center for Epidemiology and Prevention of Viral Hepatitis and Hepatocellular Carcinoma, Hiroshima University led by Prof. Junko Tanaka (PI).

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Ulugbek Khudayberdievich Mirzaev, Serge Ouoba, Ko Ko, Zayar Phyo, Chanroth Chhoung, Akuffo Golda Ataa, Aya Sugiyama, Tomoyuki Akita & Junko Tanaka

Department of Hepatology, Research Institute of Virology, Tashkent, Uzbekistan

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UM, TA, and JT conceptualized the study. UM and SO contributed to developing the study design and data acquisition. UM, CC, ZP, AG, SO, and JT analysed and interpreted the data. UM, KK, and AS drafted the manuscript. TA, AS, KK, SO, and JT contributed to the intellectual content of the manuscript. All authors read and approved the final manuscript. JT and TA shared the co-correspondence. 

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Mirzaev, U.K., Ouoba, S., Ko, K. et al. Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns. BMC Infect Dis 24 , 525 (2024). https://doi.org/10.1186/s12879-024-09349-2

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• Published: 14 May 2024

2023 summer warmth unparalleled over the past 2,000 years

• Jan Esper   ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
• Max Torbenson   ORCID: orcid.org/0000-0003-2720-2238 1 &
• Ulf Büntgen 2 , 3 , 4  

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• Climate change
• Palaeoclimate

Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

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Department of Geography, Johannes Gutenberg University, Mainz, Germany

Jan Esper & Max Torbenson

Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

Jan Esper & Ulf Büntgen

Department of Geography, University of Cambridge, Cambridge, United Kingdom

Ulf Büntgen

Department of Geography, Masaryk University, Brno, Czech Republic

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Esper, J., Torbenson, M. & Büntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature (2024). https://doi.org/10.1038/s41586-024-07512-y

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Published : 14 May 2024

DOI : https://doi.org/10.1038/s41586-024-07512-y

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