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Research Problem – Examples, Types and Guide

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Research Problem

Definition:

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

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

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

Exploratory problems

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

Explanatory Problems

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

Predictive Problems

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

Evaluative Problems

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

How to Define a Research Problem

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

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

Components of a Research Problem

The components of a research problem typically include the following:

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

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

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

Research Problem Examples in Sociology are as follows:

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

Research Problem Examples in Economics are as follows:

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

Political Science

Research Problem Examples in Political Science are as follows:

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

Environmental Science

Research Problem Examples in Environmental Science are as follows:

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

Research Problem Examples in Education are as follows:

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

Research Problem Examples in History are as follows:

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

Research Problem Examples in Business are as follows:

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

Research Problem Example for Students

An Example of a Research Problem for Students could be:

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

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

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

Another example of a research problem for students:

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

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

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

Applications of Research Problem

Applications of Research Problem are as follows:

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

Purpose of Research Problems

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

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

A well-formulated research problem should:

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

Characteristics of Research Problem

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

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

Advantages of Research Problem

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Research problem: Everything a market researcher needs to know

A research process includes several steps that assist individuals involved in a study in conducting successful testing. Defining a research problem is an essential step in any research and can help in outlining your study’s methodology.

LEARN ABOUT: Research Process Steps

In this article, we will define a research problem and go over everything a researcher should know.

Content Index

What is a research problem?

What is the research problem statement, what is the purpose of a research problem statement, characteristics of a research problem, characteristics of a research problem statement.

• Components of a research problem?

Steps to formulate a research problem

Marketing research problem example, research problem statement example, extensive research problem software, platform, and tool, top seven benefits of using a robust research software, advantages of formulating a research problem, how questionpro helps researchers solve research problems.

A research problem is a specific question, problem, or difficulty that needs to be investigated or analyzed.

It is a concise statement that expresses the difference between what is currently known and what needs to be known or the difference between a current situation and a desired state.

Examining research problems helps to identify the key concepts and terms of research. A research problem should be clear, concise, and specific enough to guide the process and contribute to the definition of research project objectives, methods, and outcomes. It is the foundation of any research project, and a well-formulated research problem is required for any research study to be successful.

A research problem statement is a brief and precise description of the problem that a researcher wishes to investigate. It defines the research’s focus and serves as a framework for developing research questions or hypotheses.

Typically, the problem statement begins with a broad topic or research area and then narrows down to a specific research question or problem. It should explain why the research is important, what gaps in knowledge or understanding exist, and what potential implications or applications the research may have.

A good research statement keeps the researcher focused and guides the research project’s development. It also assists other researchers in comprehending the scope and significance of the research, as well as identifying potential areas for collaboration or further investigation.

LEARN ABOUT:   Action Research

A problem statement in research seeks to achieve the following:

• Introduce the importance of the topic in the research proposal.
• Position the problem in an appropriate and particular context.
• Provide a framework to analyze and report results.

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Make sure to fulfill these essential characteristics to have an effective research problem. Due to the variety of research, we conduct, it is not possible to inculcate all these characteristics. However, ensure to consider and cover most of these characteristics to enable people to look at, examine, and understand the marketing research problem.

Covers the essential needs or issues

LEARN ABOUT: Market research vs marketing research

The problem is stated logically and clearly

The research project is based on actual facts and evidence (non-hypothetical), the research problem generates and encourages research questions, it fits the budget and time frame, sufficient data can be obtained, the problem has an unsatisfactory answer, or is it a new problem.

Here are the characteristics of a research problem statement:

• It must address the gap in knowledge.
• It must be significant to the extent that it contributes positively to the research
• It must help in further research
• The collected empirical data confirm the clarity and understandability of the research problem.
• It must be in the researcher’s interest should and suit his/her time, practical knowledge, research skills , and resources
• The problem-solving approach must be ethical
• Customary research methods can be applied

LEARN ABOUT: Theoretical Research

Components of a research problem

A research problem has the following components:

Research consumer

Research-consumers objective, alternative means to meet the objective, doubts in the selection of alternatives, there must be more than one environ­ment.

Here are the five basic steps to formulate a research problem:

Identify the broad research area

Divide the broad area into sub-areas.

• Profile of soccer players
• Profile of soccer clubs
• Level of soccer clubs
• Impact of the club on the city
• Revenue generating areas 
• Sponsors of the soccer clubs

Choose a sub-area

Formulate research questions, set research objectives.

Organizations and companies use marketing research problems to gauge the risks associated with launching a new product or service. They do not wish to spend money expanding a product line where research shows it will not succeed. A well-designed, well-executed marketing research study helps in identifying customer interests, consumer tastes, and preferences to help with decisions around the product or service.

A research question is the most important aspect of the research. You must spend time refining and assessing the research questions before getting started with the research activities. A research question must be straightforward, to the point, focused, and appropriately complex to capture the most relevant information.

Having difficulty writing research problems? Follow these examples to write a problem statement:

Incorrect: What are the effects of social media on people? Correct: What effect does use Facebook every day have on teenagers?

In the above example, the first research question is not specific enough to capture accurate feedback. Nobody knows what social media you’re talking about and what ‘people’ you’re referring to.

Let’s look at another marketing research problem example.

Incorrect: Who has a better healthcare system? The US or the UK? Correct: How do low-income earning people feel about the healthcare system, and how do the UK and the US compare?

The next research question is comprehensive and does not draw a definite conclusion about the healthcare systems of both countries.

The third example of how to write a problem statement is:

Incorrect: What will help political parties address the issues of low voter turnout? Correct: What communication strategies can political parties apply to increase voter turnout among people between the age of 25-30?

Again, comparing both statements, the second one is more direct and implies only a specific group of people, thus collecting actionable information.

Formulating a marketing research problem is just one crucial part of the research process. Another essential aspect of marketing research is using a robust market research software tool that aids in your research activity. 

For example, The Research methods knowledge base is a comprehensive web-based textbook that covers all the topics in a typical introductory undergraduate or graduate social research methods course. It covers the research question, measurement (surveys, scaling, qualitative, unobtrusive), research design (experimental and quasi-experimental research ), data analysis, and writing the research paper.

You can do a whole lot by choosing the right research platform to solve a specific problem. By using a  research repository , you can mitigate the need to think of research and a research problem as a decentralized process in your organization. 

Here are the benefits of using a robust research software tool

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Here are the advantages of formulating a research problem:

Understanding the research procedure

Determining the research objective, design the research process, lays the foundation for research.

QuestionPro provides a number of tools and features to assist researchers in solving research problems, including:

• Survey creation:  QuestionPro offers to skip logic, branching, randomization, and a range of research question types.
• Data Collection:  QuestionPro lets researchers collect data through email, social media, and embedded surveys on websites.
• Real-time Data Analytics:  QuestionPro’s real-time data analytics solutions help researchers solve research issues. Researchers may quickly spot patterns and make data-driven decisions using the platform’s strong analytics tools.
• Collaboration:  Researchers can invite team members to surveys and exchange data and analytic results, making collaboration and task completion easy with QuestionPro.
• Integration with Other Tools:  QuestionPro integrates with a variety of other tools, including Salesforce, Hubspot, and Google Analytics.

QuestionPro provides a comprehensive set of research tools to assist researchers in solving research problems. QuestionPro provides a complete solution that can help researchers tackle their research problems with ease, from survey creation to data collection, real-time data analytics, collaboration, and integration with other tools. Contact QuestionPro right away to get the best value for your research process!

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Defining a Research Problem

Defining a research problem is the fuel that drives the scientific process, and is the foundation of any research method and experimental design, from true experiment to case study.

This article is a part of the guide:

• Null Hypothesis
• Research Hypothesis
• Selecting Method
• Test Hypothesis

Browse Full Outline

• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

It is one of the first statements made in any research paper and, as well as defining the research area, should include a quick synopsis of how the hypothesis was arrived at.

Operationalization is then used to give some indication of the exact definitions of the variables, and the type of scientific measurements used.

This will lead to the proposal of a viable hypothesis . As an aside, when scientists are putting forward proposals for research funds, the quality of their research problem often makes the difference between success and failure.

Structuring the Research Problem

Look at any scientific paper, and you will see the research problem, written almost like a statement of intent.

Defining a research problem is crucial in defining the quality of the answers, and determines the exact research method used. A quantitative experimental design uses deductive reasoning to arrive at a testable hypothesis .

Qualitative research designs use inductive reasoning to propose a research statement.

Formulating the research problem begins during the first steps of the scientific process .

As an example, a literature review and a study of previous experiments, and research, might throw up some vague areas of interest.

Many scientific researchers look at an area where a previous researcher generated some interesting results, but never followed up. It could be an interesting area of research, which nobody else has fully explored.

A scientist may even review a successful experiment, disagree with the results , the tests used, or the methodology , and decide to refine the research process, retesting the hypothesis .

This is called the conceptual definition, and is an overall view of the problem. A science report will generally begin with an overview of the previous research and real-world observations. The researcher will then state how this led to defining a research problem.

The Operational Definitions

The operational definition is the determining the scalar properties of the variables .

For example, temperature, weight and time are usually well known and defined, with only the exact scale used needing definition. If a researcher is measuring abstract concepts, such as intelligence, emotions, and subjective responses, then a system of measuring numerically needs to be established, allowing statistical analysis and replication.

For example, intelligence may be measured with IQ and human responses could be measured with a questionnaire from ‘1- strongly disagree’, to ‘5 - strongly agree’.

Behavioral biologists and social scientists might design an ordinal scale for measuring and rating behavior. These measurements are always subjective, but allow statistics and replication of the whole research method. This is all an essential part of defining a research problem.

Examples of Defining a Research Problem

An anthropologist might find references to a relatively unknown tribe in Papua New Guinea. Through inductive reasoning , she arrives at the research problem and asks,

‘How do these people live and how does their culture relate to nearby tribes?’

She has found a gap in knowledge, and she seeks to fill it, using a qualitative case study , without a hypothesis.

The Bandura Bobo Doll Experiment is a good example of using deductive reasoning to arrive at a research problem and hypothesis.

Anecdotal evidence showed that violent behavior amongst children was increasing. Bandura believed that higher levels of violent adult role models on television, was a contributor to this rise. This was expanded into a hypothesis , and operationalization of the variables, and scientific measurement scale , led to a robust experimental design.

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Martyn Shuttleworth (Oct 2, 2008). Defining a Research Problem. Retrieved May 07, 2024 from Explorable.com: https://explorable.com/defining-a-research-problem

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How to Identify an Appropriate Research Problem

By Mansureh Kebritchi, Ph.D.

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

Where to Find a Research Problem

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

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

How to Evaluate a Research Problem 

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

Supported by the Literature

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

Significant

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

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

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

Specific and Clear

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

Researchable

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

References and More Information

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

• How To Formulate A Research Problem

Introduction

In the dynamic realm of academia, research problems serve as crucial stepping stones for groundbreaking discoveries and advancements. Research problems lay the groundwork for inquiry and exploration that happens when conducting research. They direct the path toward knowledge expansion.

In this blog post, we will discuss the different ways you can identify and formulate a research problem. We will also highlight how you can write a research problem, its significance in guiding your research journey, and how it contributes to knowledge advancement.

Understanding the Essence of a Research Problem

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

There are two ways you can consider what the purpose of your research problem is. The first way is that the research problem helps you define the scope of your study and break down what you should focus on in the research. The essence of this is to ensure that you embark on a relevant study and also easily manage it. 

The second way is that having a research problem helps you develop a step-by-step guide in your research exploration and execution. It directs your efforts and determines the type of data you need to collect and analyze. Furthermore, a well-developed research problem is really important because it contributes to the credibility and validity of your study.

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

Identifying a Research Problem

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

• Conduct a Literature Review: Before you dive into your research problem, ensure you get familiar with the existing literature in your field. Analyze gaps, controversies, and unanswered questions. This will help you identify areas where your research can make a meaningful contribution.
• Consult with Peers and Mentors: Participate in discussions with your peers and mentors to gain insights and feedback on potential research problems. Their perspectives can help you refine and validate your ideas.
• Define Your Research Objectives: Clearly outline the objectives of your study. What do you want to achieve through your research? What specific outcomes are you aiming for?

Formulating a Research Problem

Once you have identified the general area of interest and specific research objectives, you can then formulate your research problem. Things to consider when formulating a research problem:

• Clarity and Specificity: Your research problem should be concise, specific, and devoid of ambiguity. Avoid vague statements that could lead to confusion or misinterpretation.
• Originality: Strive to formulate a research problem that addresses a unique and unexplored aspect of your field. Originality is key to making a meaningful contribution to the existing knowledge.
• Feasibility: Ensure that your research problem is feasible within the constraints of time, resources, and available data. Unrealistic research problems can hinder the progress of your study.
• Refining the Research Problem: It is common for the research problem to evolve as you delve deeper into your study. Don’t be afraid to refine and revise your research problem if necessary. Seek feedback from colleagues, mentors, and experts in your field to ensure the strength and relevance of your research problem.

How Do You Write a Research Problem?

Steps to consider in writing a Research Problem:

• Select a Topic: The first step in writing a research problem is to select a specific topic of interest within your field of study. This topic should be relevant, and meaningful, and have the potential to contribute to existing knowledge.
• Conduct a Literature Review: Before formulating your research problem, conduct a thorough literature review to understand the current state of research on your chosen topic. This will help you identify gaps, controversies, or areas that need further exploration.
• Identify the Research Gap: Based on your literature review, pinpoint the specific gap or problem that your research aims to address. This gap should be something that has not been adequately studied or resolved in previous research.
• Be Specific and Clear: The research problem should be framed in a clear and concise manner. It should be specific enough to guide your research but broad enough to allow for meaningful investigation.
• Ensure Feasibility: Consider the resources and constraints available to you when formulating the research problem. Ensure that it is feasible to address the problem within the scope of your study.
• Align your Research Goals: The research problem should align with the overall goals and objectives of your study. It should be directly related to the research questions you intend to answer.

Related: How to Write a Problem Statement for your Research

Research Problem vs Research Questions

Research Problem: The research problem is a broad statement that outlines the overarching issue or gap in knowledge that your research aims to address. It provides the context and motivation for your study and helps establish its significance and relevance. The research problem is typically stated in the introduction section of your research proposal or thesis.

Research Questions: Research questions are specific inquiries that you seek to answer through your research. These questions are derived from the research problem and help guide the focus of your study. They are often more detailed and narrow in scope compared to the research problem. Research questions are usually listed in the methodology section of your research proposal or thesis.

Difference Between a Research Problem and a Research Topic

Research Problem: A research problem is a specific issue, gap, or question that requires investigation and can be addressed through research. It is a clearly defined and focused problem that the researcher aims to solve or explore. The research problem provides the context and rationale for the study and guides the research process. It is usually stated as a question or a statement in the introduction section of a research proposal or thesis.

Example of a Research Problem: “ What are the factors influencing consumer purchasing decisions in the online retail industry ?”

Research Topic: A research topic, on the other hand, is a broader subject or area of interest within a particular field of study. It is a general idea or subject that the researcher wants to explore in their research. The research topic is more general and does not yet specify a specific problem or question to be addressed. It serves as the starting point for the research, and the researcher further refines it to formulate a specific research problem.

Example of a Research Topic: “ Consumer behavior in the online retail industry.”

In summary, a research topic is a general area of interest, while a research problem is a specific issue or question within that area that the researcher aims to investigate.

Difference Between a Research Problem and Problem Statement

Research Problem: As explained earlier, a research problem is a specific issue, gap, or question that you as a researcher aim to address through your research. It is a clear and concise statement that defines the focus of the study and provides a rationale for why it is worth investigating.

Example of a Research Problem: “What is the impact of social media usage on the mental health and well-being of adolescents?”

Problem Statement: The problem statement, on the other hand, is a brief and clear description of the problem that you want to solve or investigate. It is more focused and specific than the research problem and provides a snapshot of the main issue being addressed.

Example of a Problem Statement: “ The purpose of this study is to examine the relationship between social media usage and the mental health outcomes of adolescents, with a focus on depression, anxiety, and self-esteem.”

In summary, a research problem is the broader issue or question guiding the study, while the problem statement is a concise description of the specific problem being addressed in the research. The problem statement is usually found in the introduction section of a research proposal or thesis.

Challenges and Considerations

Formulating a research problem involves several challenges and considerations that researchers should carefully address:

• Feasibility: Before you finalize a research problem, it is crucial to assess its feasibility. Consider the availability of resources, time, and expertise required to conduct the research. Evaluate potential constraints and determine if the research problem can be realistically tackled within the given limitations.
• Novelty and Contribution: A well-crafted research problem should aim to contribute to existing knowledge in the field. Ensure that your research problem addresses a gap in the literature or provides innovative insights. Review past studies to understand what has already been done and how your research can build upon or offer something new.
• Ethical and Social Implications: Take into account the ethical and social implications of your research problem. Research involving human subjects or sensitive topics requires ethical considerations. Consider the potential impact of your research on individuals, communities, or society as a whole. 
• Scope and Focus: Be mindful of the scope of your research problem. A problem that is too broad may be challenging to address comprehensively, while one that is too narrow might limit the significance of the findings. Strike a balance between a focused research problem that can be thoroughly investigated and one that has broader implications.
• Clear Objectives: Ensure that your research problem aligns with specific research objectives. Clearly define what you intend to achieve through your study. Having well-defined objectives will help you stay on track and maintain clarity throughout the research process.
• Relevance and Significance: Consider the relevance and significance of your research problem in the context of your field of study. Assess its potential implications for theory, practice, or policymaking. A research problem that addresses important questions and has practical implications is more likely to be valuable to the academic community and beyond.
• Stakeholder Involvement: In some cases, involving relevant stakeholders early in the process of formulating a research problem can be beneficial. This could include experts in the field, practitioners, or individuals who may be impacted by the research. Their input can provide valuable insights that can help you enhance the quality of the research problem.

In conclusion, understanding how to formulate a research problem is fundamental for you to have meaningful research and intellectual growth. Remember that a well-crafted research problem serves as the foundation for groundbreaking discoveries and advancements in various fields. It not only enhances the credibility and relevance of your study but also contributes to the expansion of knowledge and the betterment of society.

Therefore, put more effort into the process of identifying and formulating research problems with enthusiasm and curiosity. Engage in comprehensive literature reviews, observe your surroundings, and reflect on the gaps in existing knowledge. Lastly, don’t forget to be mindful of the challenges and considerations, and ensure your research problem aligns with clear objectives and ethical principles.

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How to formulate research problems?

June 16, 2023 4 min read

One of the most important steps in the research process is formulating a research problem. It establishes the framework for the whole study and directs the researcher in determining the research’s emphasis, scope, and goals. An effective research technique may be created with the support of a clearly defined research topic, which also aids in the generation of pertinent research questions.

This article will provide a general overview of the procedure involved in defining research problems, highlighting important considerations and steps researchers should take to formulate precise and insightful research problems.

What is a research problem?

It refers to a specific topic, problem, or knowledge gap that a researcher aims to study and address through a systematic inquiry. It establishes the foundation for a research project and guides the entire investigation.

When creating a research problem, researchers often start with a topic of interest before focusing on a particular issue or question. A substantial, relevant, and original challenge adds to the corpus of knowledge and has real-world applications.

A clearly stated research topic aids in the concentration of research resources and efforts, permits the development of an effective research technique, and directs the evaluation and interpretation of data acquired. It also helps in developing research goals and hypotheses by giving the investigation a distinct direction.

For instance, a research problem could be “What are the causes leading to the decline of bee populations in urban areas?” — This study challenge addresses a particular set of urban regions and draws attention to the problem of dwindling bee numbers. By focusing on this issue, researchers may analyze the various reasons for the loss, analyze how it affects the environment, and suggest conservation tactics.

Characteristics of an effective research problem

An effective research problem possesses several essential qualities that enhance its quality and suitability for examination. The key characteristics of a strong research problem are:

Significance

Should address an important issue or knowledge gap in the field of study, contributing to the existing body of knowledge.

Should be precisely stated, avoiding vague or overly general statements and providing a clear and concise description. This clarity enables the definition of research objectives and hypotheses and guides the research process.

Feasibility

Should be feasible in terms of the available time, resources, and skills. It can be realistically pursued, given the researcher’s capabilities and study circumstances. Sufficient data, research tools, and potential exploration paths should be reasonably accessible.

Should explore new facets, angles, or dimensions of the subject, offering fresh perspectives or approaches. This characteristic promotes intellectual progress and distinguishes the research from previous investigations.

Measurability

Should be formulated in a way that allows for empirical examination and the generation of quantifiable results. Data can be systematically collected and analyzed to answer the research questions or achieve the research goals, enhancing the objectivity and rigor of the research process.

Relevance and applicability

Should address relevant issues or help develop useful guidelines, regulations, or actions. It is more effective when it impacts multiple stakeholders and has the potential to produce practical results.

Interest and motivation

Should be intellectually engaging and interesting to the researcher and the academic community. It sparks curiosity and encourages further research, leading to high-quality research output.

Ethical consideration

Should adhere to ethical principles and rules, considering the welfare and rights of participants or subjects involved in the study.

ALSO READ: What is research design?

Types of research problems.

Research problems can be categorized into different types based on their nature and scope. The three most common types are:

Theoretical

It involves using theoretical frameworks, concepts, and models to investigate a subject or event. Theoretical research aims to extend existing knowledge, address unsolved disputes or gaps, or critique and evaluate preexisting theories.

It focuses on specific problems or challenges within a particular industry or sector and aims to provide practical solutions through systematic research. Applied research aims to bridge the gap between theory and practical application, optimizing existing processes, technologies, products, or services.

Action research combines research and action to address real-world issues. It encompasses problem-solving in various contexts, such as organizations, education, community development, policy implementation, and personal or professional development. Action research is flexible and can be tailored to different situations and issues.

Importance of research problems

Research problems play a vital role in shaping the direction and course of an investigation. They serve as the foundation for the entire research process, guiding researchers in their pursuit of knowledge and advancement in a specific field. The importance of research problems lies in the following:

Identifying knowledge gaps

Research problems help identify areas where knowledge is lacking or incomplete, highlighting the need for further investigation and addressing unanswered questions.

Providing direction

A well-defined research problem gives the research project focus and direction. It aids in the development of an effective research design, technique and the establishment of research objectives and questions.

Justifying the study’s significance

A clear research problem helps researchers justify the value and importance of their study by emphasizing its relevance, potential benefits, and contributions to the field.

Facilitating problem-solving and decision-making

Research problems often stem from real-world challenges or problems. By examining these problems, researchers can develop innovative ideas, methods, or strategies to solve practical issues or guide decision-making.

Advancing theory and knowledge

Research problems serve as a basis for developing new concepts, hypotheses, or models. By addressing research challenges, researchers contribute to understanding a subject, debunk preexisting beliefs, or propose new hypotheses.

Promoting intellectual curiosity and innovation

Research problems encourage intellectual curiosity and innovation by pushing researchers to explore fresh perspectives and methodologies. By encouraging critical thinking, generating original ideas, and developing unique research approaches, research problems foster innovation and creativity.

ALSO READ: The basics of market research

5 steps to formulate research problems.

Formulating research problems is a crucial initial step in conducting purposeful and targeted research. Here are five steps to follow:

Identify the broad research area

Determine the broad subject or field that interests you, considering discipline-specific topics or specific phenomena.

Conduct a literature review

Review existing literature and research in your chosen field to understand the current knowledge level and identify gaps or unsolved issues and areas requiring further research. Read relevant scholarly publications, books, and articles to gain a comprehensive understanding.

Narrow down the focus

Based on the literature review, select a specific component or subject within your chosen research field. Look for inconsistencies, contradictions, or open-ended questions in the existing literature that can present challenges for future research. Refine your research topic and focus it on a single problem or phenomenon.

Define clear objectives

Establish clear and concise research objectives that outline your investigation’s specific aims or outcomes. SMART (specific, measurable, attainable, relevant, and time-bound) objectives help maintain focus and guide the research process effectively.

Formulate research questions

Create distinct research questions or hypotheses that align with your research problem and objectives. Qualitative research often utilizes research questions, while quantitative research employs hypotheses. Ensure these inquiries or hypotheses are precise, concise, and aimed at addressing the stated research problem.

Remember that formulating research problems is an iterative process. As you learn more about the topic and develop new ideas, it can need several changes and improvements. You may establish a solid basis for your study and improve your chances of performing fruitful and influential research by adhering to these recommendations and continually improving your research problem.

Researchers can create precise and insightful research problems that add to the body of knowledge and progress in their particular fields of study by using the procedures described in this article. A research problem outlines the precise field of inquiry and knowledge gaps that the research attempts to address, defining the scope and objective of a study.

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Organizing Academic Research Papers: The Research Problem/Question

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

A research problem is a statement about an area of concern, a condition to be improved, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or in practice that points to the need for meaningful understanding and deliberate investigation. In some social science disciplines the research problem is typically posed in the form of a question. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study and the research questions or hypotheses to follow.
• Places the problem into a particular context that defines the parameters of what is to be investigated.
• Provides the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. The "So What?" question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What" question requires a commitment on your part to not only show that you have researched the material, but that you have thought about its significance.

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

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible statements],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question and key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's boundaries or parameters,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [regardless of the type of research, it is important to address the “so what” question by demonstrating that the research is not trivial],
• Does not have unnecessary jargon; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Castellanos, Susie. Critical Writing and Thinking . The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem. Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements . The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements . The Writing Lab and The OWL. Purdue University.  

Structure and Writing Style

I.  Types and Content

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

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

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study
• A declaration of originality [e.g., mentioning a knowledge void, which would be supported by the literature review]
• An indication of the central focus of the study, and
• An explanation of the study's significance or the benefits to be derived from an investigating the problem.

II.  Sources of Problems for Investigation

Identifying a problem to study can be challenging, not because there is a lack of issues that could be investigated, but due to pursuing a goal of formulating a socially relevant and researchable problem statement that is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these three broad sources of inspiration:

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

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

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

Personal Experience Your everyday experiences can give rise to worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society, your community, or in your neighborhood. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can often be derived from an extensive and thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps remain in our understanding of a topic. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied to different study sample [i.e., different groups of people]. Also, authors frequently conclude their studies by noting implications for further research; this can also be a valuable source of problems to investigate.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered and then gradually leads the reader to the more narrow questions you are posing. The statement need not be lengthy but a good research problem should incorporate the following features:

Compelling topic Simple curiosity is not a good enough reason to pursue a research study. The problem that you choose to explore must be important to you and to a larger community you share. The problem chosen must be one that motivates you to address it. Supports multiple perspectives The problem most be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. Researchable It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex  research project and realize that you don't have much to draw on for your research. Choose research problems that can be supported by the resources available to you. Not sure? Seek out help  from a librarian!

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about whereas a problem is something to solve or framed as a question that must be answered.

IV.  Mistakes to Avoid

Beware of circular reasoning . Don’t state that the research problem as simply the absence of the thing you are suggesting. For example, if you propose, "The problem in this community is that it has no hospital."

This only leads to a research problem where:

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

This is an example of a research problem that fails the "so what?" test because it does not reveal the relevance of why you are investigating the problem of having no hospital in the community [e.g., there's a hospital in the community ten miles away] and because the research problem does not elucidate the significance of why one should study the fact that no hospital exists in the community [e.g., that hospital in the community ten miles away has no emergency room].

Choosing and Refining Topics . Writing@CSU. Colorado State University; Ellis, Timothy J. and Yair Levy Nova Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem. Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question . The Writing Center. George Mason University; Invention: Developing a Thesis Statement . The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation . The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements . University College Writing Centre. University of Toronto; Trochim, William M.K. Problem Formulation . Research Methods Knowledge Base. 2006; Thesis and Purpose Statements . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements . The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements . The Writing Lab and The OWL. Purdue University.

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Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
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• Paragraph Development
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• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

• Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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5 Sources of a Research Problem: The Complete Guide

by  Antony W

February 20, 2022

In this guide, you will learn about the best sources of a research problem for your next project. 

The term research problem refers to a clear expression of an area of concern that requires a clear understanding and deliberate investigation. While it offers a broad proposition and a valuable question, a research problem doesn’t demonstrate how to do something.

It’s worth looking at a research problem for a number of reasons. It introduces a reader to the topic under investigation and orients to the importance of the study.

Besides allowing you to define the most important parameter to investigate in your paper, a research problem offers you a concise guide to come up with research questions , make relevant assumptions, and formulate a proposition .

More importantly, a research problem gives you a more comprehensive framework to conduct extensive studies and explain your findings.

Need help with your research paper, dissertation, or thesis but you have no idea where to start? Hire Help for Assessment for Assistance.

Type of Research Problems

There are four types of research problems that you need to know before we look at the sources of a research problem.

These are casuist, difference, descriptive and relational research problems.

1. Relational Research Problem

A relational research problem suggests the need to investigate the correlation between two or more variables.

It’s the researcher’s responsibility to investigate a number of precise characteristics and identify the relationship between them.

2. Casuist Research Problem

Casuist research problem has something to do with the determination of what’s right and what’s wrong.

It questions human conduct by looking closely at the moral dilemmas by means of careful differentiation of cases as well as the application of general rules.

3. Descriptive Research Problem

In this case, a researcher looks forward to investigating a “what is” kind of issue.

The goal of examining a descriptive research problem is to determine the underlying significance of an event or the existence of a situation.

It’s with the descriptive research problem that a researcher can discover understudied or hidden issues.

5. Difference Research Problem

A difference research problem focuses on the distinction between two or more groups.  More often than not, researchers use this type of problem to compare and contrast more than one phenomenon.

What are the Sources of Research Problems?

Now that you know the types of possible research problems that you can focus on in a term paper , let’s look at the sources that you can use to identify research problems.

From a research perspective, the kind of research problem that you wish to investigate should meet two conditions.

First, the problem has to be unique and not something other researchers have already looked into exhaustively. Second, the problem has to be concise enough to raise specific issues that you can address in a research paper .

With that said, below are five sources of a research problem:

1. Interviews

Interviews sessions can be significant sources of research problems. The method gives you an opportunity to have formal discussions and informal interactions with individuals who can provide useful insights into research and make findings more relevant to future research. 

Consider having discussions with experts in the field you wish to investigate. These professionals mat be healthcare service providers, business leaders, teachers, social workers, attorneys, and accountants to mention but a few examples.

By interacting with these experts, you’re able to identify real-world problems that researchers have either ignored or understudied in the academic space.

Moreover, interview sessions give you the opportunity to get some practical knowledge that can help you to design and conduct your studies.

2. Personal Experiences

Your everyday experiences are a good source of research problem.

You have to think critically about your personal experiences with an issue that affects your family, your personal life, or your community.

A research problem derived from personal experience can spring from any issue and from anywhere.

For example, you can construct a research problem from events that appear to be out of the ordinary or from community relationships that don’t have clear explanations.

3. Deductions from Theory

A deduction from theory refers to inferences a researcher makes from the generalizations of life in a society that a researcher knows very well.

A researcher takes the deduction, places them in an empirical frame, and then, based on a theory, they come up with a research problem and a hypothesis that suggests some findings based on given empirical results.

The research accounts for the relationship to observe if a theory summarizes the state of an affair.

A systematic investigation, which evaluates if the empirical information affirms or rejects the hypothesis , comes next.

4. Interdisciplinary Perspective

If you consider interdisciplinary perspective to identify a problem for a research study, you’ll have to look at scholarship and academic movements from outside your main area of investigation.

It’s an intellectually involving process, one that requires reviewing pertinent literature to discover unique avenues of exploration an analysis.

The benefit of using this approach to identify a research problem for your research paper assignment is that it presents an opportunity for you to understand complex issues with ease.

5. Relevant Literature

To generate a research problem from relevant literature, you first have to review research related to your area of interest.

Doing so allows you to find gaps on the topic, making it easy for you to understand just how much understudied your area of interest is.

Data collected from relevant literature is relevant because it helps to:

• Fill existing gaps in knowledge based on a specific research
• Determine if current studies can have implications on further research on the same issue
• See if it’s possible to conduct a similar study in a different area or apply the same in a different context
• Determine if the methods used in previous studies can be effective in solving future problems

We can’t stress enough on the value of existing literature. The results should point you towards an outstanding issue, give suggestion for future gaps, and make it possible to delineate gaps in existing knowledge.

Research Paper Writing Help

Finding a research problem is just one part of the research paper assignment. You have to develop a research question, formulate a hypothesis, write a thesis statement,  and then write your research paper. It can be a lot of work, which demands a lot of attention and time.

If you need help to brainstorm, research, and write your research paper, click the button below to place your order. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

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

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

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

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

Table of contents

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

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

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

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

Prevent plagiarism, run a free check.

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

Practical research problems

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

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

Examples of practical research problems

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

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

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

Theoretical research problems

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

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

Examples of theoretical research problems

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

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

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

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

Context and background

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

Specificity and relevance

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

Example of a specific research problem

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

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

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

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

I will compare …

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

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

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

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

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

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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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By Scott Lyon

May 1, 2024

Researchers recommend 32 best practices to stamp out a smoldering crisis that threatens to engulf all of science: thousands of AI-driven claims across dozens of fields that cannot be reproduced. Illustration courtesy Adobe Stock

AI holds the potential to help doctors find early markers of disease and policymakers to avoid decisions that lead to war. But a growing body of evidence has revealed deep flaws in how machine learning is used in science, a problem that has swept through dozens of fields and implicated thousands of erroneous papers.

Now an interdisciplinary team of 19 researchers, led by Princeton University computer scientists Arvind Narayanan and Sayash Kapoor, has published guidelines for the responsible use of machine learning in science.

“When we graduate from traditional statistical methods to machine learning methods, there are a vastly greater number of ways to shoot oneself in the foot,” said Narayanan , director of Princeton’s Center for Information Technology Policy and a professor of computer science . “If we don’t have an intervention to improve our scientific standards and reporting standards when it comes to machine learning-based science, we risk not just one discipline but many different scientific disciplines rediscovering these crises one after another.”

The authors say their work is an effort to stamp out this smoldering crisis of credibility that threatens to engulf nearly every corner of the research enterprise. A paper detailing their guidelines appeared May 1 in the journal Science Advances .

Because machine learning has been adopted across virtually every scientific discipline, with no universal standards safeguarding the integrity of those methods, Narayanan said the current crisis, which he calls the reproducibility crisis , could become far more serious than the replication crisis that emerged in social psychology more than a decade ago.

The good news is that a simple set of best practices can help resolve this newer crisis before it gets out of hand, according to the authors, who come from computer science, mathematics, social science and health research.

“This is a systematic problem with systematic solutions,” said Kapoor , a graduate student who works with Narayanan and who organized the effort to produce the new consensus-based checklist.

The checklist focuses on ensuring the integrity of research that uses machine learning. Science depends on the ability to independently reproduce results and validate claims. Otherwise, new work cannot be reliably built atop old work, and the entire enterprise collapses. While other researchers have developed checklists that apply to discipline-specific problems, notably in medicine, the new guidelines start with the underlying methods and apply them to any quantitative discipline.

One of the main takeaways is transparency. The checklist calls on researchers to provide detailed descriptions of each machine learning model, including the code, the data used to train and test the model, the hardware specifications used to produce the results, the experimental design, the project’s goals and any limitations of the study’s findings. The standards are flexible enough to accommodate a wide range of nuance, including private datasets and complex hardware configurations, according to the authors.

While the increased rigor of these new standards might slow the publication of any given study, the authors believe wide adoption of these standards would increase the overall rate of discovery and innovation, potentially by a lot.

“What we ultimately care about is the pace of scientific progress,” said sociologist Emily Cantrell , one of the lead authors, who is pursuing her Ph.D. at Princeton. “By making sure the papers that get published are of high quality and that they’re a solid base for future papers to build on, that potentially then speeds up the pace of scientific progress. Focusing on scientific progress itself and not just getting papers out the door is really where our emphasis should be.”

Kapoor concurred. The errors hurt. “At the collective level, it’s just a major time sink,” he said. That time costs money. And that money, once wasted, could have catastrophic downstream effects, limiting the kinds of science that attract funding and investment, tanking ventures that are inadvertently built on faulty science, and discouraging countless numbers of young researchers.

In working toward a consensus about what should be included in the guidelines, the authors said they aimed to strike a balance: simple enough to be widely adopted, comprehensive enough to catch as many common mistakes as possible.

They say researchers could adopt the standards to improve their own work; peer reviewers could use the checklist to assess papers; and journals could adopt the standards as a requirement for publication.

“The scientific literature, especially in applied machine learning research, is full of avoidable errors,” Narayanan said. “And we want to help people. We want to keep honest people honest.”

The paper, “ Consensus-based recommendations for machine-learning-based science ,” published on May 1 in Science Advances, included the following authors:

Sayash Kapoor, Princeton University; Emily Cantrell, Princeton University; Kenny Peng, Cornell University; Thanh Hien (Hien) Pham, Princeton University; Christopher A. Bail, Duke University; Odd Erik Gundersen, Norwegian University of Science and Technology; Jake M. Hofman, Microsoft Research; Jessica Hullman, Northwestern University; Michael A. Lones, Heriot-Watt University; Momin M. Malik, Center for Digital Health, Mayo Clinic; Priyanka Nanayakkara, Northwestern; Russell A. Poldrack, Stanford University; Inioluwa Deborah Raji, University of California-Berkeley; Michael Roberts, University of Cambridge; Matthew J. Salganik, Princeton University; Marta Serra-Garcia, University of California-San Diego; Brandon M. Stewart, Princeton University; Gilles Vandewiele, Ghent University; and Arvind Narayanan, Princeton University.

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Physical Fitness Linked to Better Mental Health in Young People

A new study bolsters existing research suggesting that exercise can protect against anxiety, depression and attention challenges.

By Matt Richtel

Physical fitness among children and adolescents may protect against developing depressive symptoms, anxiety and attention deficit hyperactivity disorder, according to a study published on Monday in JAMA Pediatrics.

The study also found that better performance in cardiovascular activities, strength and muscular endurance were each associated with greater protection against such mental health conditions. The researchers deemed this linkage “dose-dependent,” suggesting that a child or adolescent who is more fit may be accordingly less likely to experience the onset of a mental health disorder.

These findings come amid a surge of mental health diagnoses among children and adolescents, in the United States and abroad, that have prompted efforts to understand and curb the problem.

The new study, conducted by researchers in Taiwan, compared data from two large data sets: the Taiwan National Student Fitness Tests, which measures student fitness performance in schools, and the National Insurance Research Databases, which records medical claims, diagnoses prescriptions and other medical information. The researchers did not have access to the students’ names but were able to use the anonymized data to compare the students’ physical fitness and mental health results.

The risk of mental health disorder was weighted against three metrics for physical fitness: cardio fitness, as measured by a student’s time in an 800-meter run; muscle endurance, indicated by the number of situps performed; and muscle power, measured by the standing broad jump.

Improved performance in each activity was linked with a lower risk of mental health disorder. For instance, a 30-second decrease in 800-meter time was associated, in girls, with a lower risk of anxiety, depression and A.D.H.D. In boys, it was associated with lower anxiety and risk of the disorder.

An increase of five situps per minute was associated with lower anxiety and risk of the disorder in boys, and with decreased risk of depression and anxiety in girls.

“These findings suggest the potential of cardiorespiratory and muscular fitness as protective factors in mitigating the onset of mental health disorders among children and adolescents,” the researchers wrote in the journal article.

Physical and mental health were already assumed to be linked , they added, but previous research had relied largely on questionnaires and self-reports, whereas the new study drew from independent assessments and objective standards.

The Big Picture

The surgeon general, Dr. Vivek H. Murthy, has called mental health “the defining public health crisis of our time,” and he has made adolescent mental health central to his mission. In 2021 he issued a rare public advisory on the topic. Statistics at the time revealed alarming trends: From 2001 to 2019, the suicide rate for Americans ages 10 to 19 rose 40 percent, and emergency visits related to self-harm rose 88 percent.

Some policymakers and researchers have blamed the sharp increase on the heavy use of social media, but research has been limited and the findings sometimes contradictory. Other experts theorize that heavy screen use has affected adolescent mental health by displacing sleep, exercise and in-person activity, all of which are considered vital to healthy development. The new study appeared to support the link between physical fitness and mental health.

“The finding underscores the need for further research into targeted physical fitness programs,” its authors concluded. Such programs, they added, “hold significant potential as primary preventative interventions against mental disorders in children and adolescents.”

Matt Richtel is a health and science reporter for The Times, based in Boulder, Colo. More about Matt Richtel

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Since a nationwide Adderall shortage started, some people with A.D.H.D. have said their medication no longer helps with their symptoms. But there could be other factors at play .

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The disorder can put a strain on relationships. But there are ways to cope .

Though meditation can be beneficial to those with A.D.H.D., sitting still and focusing on breathing can be hard for them. These tips can help .

• Open access
• Published: 02 May 2024

Determination of drug-related problems in the hematology service: a prospective interventional study

• Aslınur Albayrak   ORCID: orcid.org/0000-0001-5862-4746 1 &
• Demircan Özbalcı 2  

BMC Cancer volume  24 , Article number:  552 ( 2024 ) Cite this article

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Patients with hematological malignancies often require multidrug therapy using a variety of antineoplastic agents and supportive care medications. This increases the risk of drug-related problems (DRPs). Determining DRPs in patients hospitalized in hematology services is important for patients to achieve their drug treatment goals and prevent adverse effects. This study aims to identify DRPs by the clinical pharmacist in the multidisciplinary team in patients hospitalized in the hematology service of a university hospital in Turkey.

This study was conducted prospectively between December 2022 and May 2023 in the hematology service of Suleyman Demirel University Research and Application Hospital in Isparta, Turkey. DRPs were determined using the Pharmaceutical Care Network Europe (PCNE) 9.1 Turkish version.

This study included 140 patients. Older age, longer hospital stay, presence of acute lymphoblastic leukemia, presence of comorbidities, higher number of medications used, and polypharmacy rate were statistically significantly higher in the DRP group than in the non-DRP group ( p  < 0.05). According to multivariate logistic regression analysis, the probability of DRP in patients with polypharmacy was statistically significant 7.921 times (95% CI: 3.033–20.689) higher than in patients without polypharmacy ( p  < 0.001).Every 5-day increase in the length of hospital stay increased the likelihood of DRP at a statistically significant level (OR = 1.476, 95% CI: 1.125–1.938 p  = 0.005). In this study, at least one DRP was detected in 69 (49.3%) patients and the total number of DRPs was 152. Possible or actual adverse drug events (96.7%) were the most common DRPs. The most important cause of DRPs was drug choice (94.7%), and the highest frequency within its subcategories was the combination of inappropriate drugs (93.4%).

Conclusions

This study shows the importance of including a clinical pharmacist in a multidisciplinary team in identifying and preventing DRPs in the hematology service.

Peer Review reports

Hematological malignancies include a variety of diseases such as Hodgkin lymphoma, non-Hodgkin lymphoma, leukemias, and multiple myeloma [ 1 ]. New treatment strategies were developed for all these diseases and the survival time of patients was increased [ 2 , 3 , 4 ]. Hematological cancer patients require combination therapy using a variety of antineoplastic agents and supportive care medications [ 5 ]. Polypharmacy is the use of multiple medications and is common in this patient group [ 6 ]. Polypharmacy increases the risk of drug-related problems (DRPs) [ 7 ]. DRPs are defined as an event or situation involving medication that interferes with desired health outcomes. DRPs include inappropriate dosage and method of administration, drug-drug interactions, drug omissions and monitoring deficiencies, and adverse drug reactions [ 8 , 9 ]. This may fail to achieve drug therapy goals or harm the patient [ 10 ]. It also causes prolonged hospital stay, readmission, and increased mortality [ 11 , 12 , 13 ].

Within a multidisciplinary team, clinical pharmacists can detect and prevent DRPs early through comprehensive medication review [ 9 , 14 ]. Clinical pharmacy services are pretty new in Turkey. Although there have been postgraduate programs (master’s degree, doctorate) related to clinical pharmacy for years, there has been a clinical pharmacy specialty program since 2018 [ 15 ]. Only graduates of the clinical specialty program can work in public hospitals [ 16 ]. Therefore, the number of clinical pharmacists actively working in hospitals is relatively low.

The contributions of clinical pharmacists in identifying and preventing DRPs have been demonstrated in many clinical departments [ 14 , 17 , 18 , 19 , 20 ]. However, studies on determining DRPs in patients with hematological malignancy are limited [ 5 , 9 , 21 , 22 , 23 ]. In a study conducted in an onco-hematology and bone marrow transplant unit in Brazil [ 23 ], the frequency of DRPs was found to be 135 (9%). 135 interventions were performed by the pharmacist and 90% were accepted. In a study conducted in France [ 9 ], 552 (12.6%) DRPs were found. Medication problems were mostly related to anti-infective agents, and oncologists’ acceptance of interventions was found to be high (96%). In a study conducted in Korea [ 5 ], a total of 1187 DRPs were identified in 438 (23.9%) of 1836 hospitalized patients with hematological malignancy. Pharmacists’ intervention was accepted by 88.3%. In a study examining the clinical and economic impact of pharmacist interventions in an outpatient hematology-oncology department in France [ 24 ], a total of 1970 pharmacist interventions were performed, corresponding to an average of 3.5 pharmacist interventions/patient, and the total cost savings was €175,563. The clinical pharmacist’s cost-benefit ratio was found to be €3.7 for every €1 invested.

As far as it is known, no study shows that DRPs are determined by the clinician in the hematology service in Turkey. Therefore, this study aims to determine drug-related problems by a clinical pharmacist within the multidisciplinary team in patients with a diagnosis of hematological malignancy hospitalized in the hematology services of a university hospital in Turkey.

Study design

This study was conducted prospectively between December 2022 and May 2023 in the hematology service of Suleyman Demirel University Research and Application Hospital in Isparta, Turkey.

All patients over the age of 18 who were hospitalized in the hematology service for more than 24 h were included in the study. Only the first hospitalization of each patient was evaluated. Informed consent was obtained from all participants before they participated in the study. Ethics Committee approval was obtained from Suleyman Demirel University Faculty of Medicine Clinical Research Ethics Committee (Approval No:274, Date:28.09.2022).

The service where the research was conducted had 15 beds and two physicians and assistant physicians were working. There was no stem cell transplant unit in the hospital. Isparta was a small city with a population of 449,777 [ 25 ]. The hospital and patient population where the study was conducted were smaller than the hospitals in Turkey’s metropolitan cities.

Sample size

The sample size was calculated based on the approximate number of patients admitted to the hematology service during the previous 6 months. With the Raosoft sample size calculator, the sample size was found to be minimum 123 with a population size of 180, 5% margin of error, 95% confidence interval and 50% distribution rate [ 26 ].

Data collection

The clinical pharmacist in the study was an academic, did not routinely work in this hospital, and was present at the hospital for this study. The clinical pharmacist performed comprehensive medication reviews of patients and provided interventions. The patients’ socio-demographic characteristics, history, diagnosis, comorbidities, medications used, laboratory test results, and interventions were recorded in the data collection form by the clinical pharmacist. The patients’ data were obtained from the hospital database, patient files, and patients. In general, interventions were made through verbal communication. UpToDate® and Sanford Guide to Antimicrobial Therapy Mobile® software were used for the interventions [ 27 , 28 ]. The Lexicomp Drug Interactions® tool, accessed via UpToDate®, was used to identify drug-drug interactions [ 29 ]. According to Lexicomp Drug Interactions®, drug interactions consist of five categories. A -no known interaction, B- no action required, C -monitor therapy, D- consider changing therapy, X- avoid combination. The presence of at least one of the risk levels C, D, and X was defined as a potential drug-drug interactions because it was clinically significant [ 30 , 31 , 32 ]. Polypharmacy was defined as the use of 5 or more medications [ 33 , 34 ].

DRPs were determined using the Pharmaceutical Care Network Europe (PCNE) 9.1 Turkish version. PCNE 9.1 has 3 primary fields for problems, 9 primary fields for causes, 5 primary fields for planned interventions, 3 primary fields for acceptance level (of interventions), and 4 primary fields for status of the problem. Problems include treatment effectiveness and safety, while reasons include drug selection, drug form dose selection, and treatment duration [ 35 ].

Statistical analysis

Statistical analysis was performed using SPSS 20. Continuous variables were expressed as median-interquartile range, and categorical variables were expressed as percentage and frequency. The normality of the data was analysed with the Kolmogorov-Smirnov test. The Mann-Whitney U test was used to compare continuous independent variables, and the Chi-Square test was used for categorical variables. The Pearson Chi-Square (> 25), the Continuity Correction (5–25), and the Fisher’s Exact test (< 5) were used according to the number of cases. Multiple logistic regression analysis was performed to determine the best predictor(s) which effect on the presence of DRP. Any variable whose univariable test had a p value < 0.10 was accepted as a candidate for the multivariable model along with all variables of known clinical importance. Odds ratios, 95% confidence intervals and Wald statistics for each independent variable were also calculated. A p-value smaller than 0.05 was considered statistically significant.

This study included 140 patients. Almost half (55%) of the patients were male and the median age was 65 (55–74) years. The median length of hospital stay was 8 (5–14) days. The median number of medications used by the patients was 6 (4–7). Polypharmacy was present in 67% of the patients. Older age, longer hospital stay, presence of acute lymphoblastic leukemia, presence of comorbidities, higher number of medications used, and polypharmacy rate were statistically significantly higher in the DRP group than in the non-DRP group ( p  < 0.05). Table  1 shows the socio-demographic and clinical characteristics of the patients.

At least one DRP was detected in 69 (49.3%) patients and the total number of DRPs was 152. Possible or actual adverse drug events (96.7%) were the most common DRPs. The most important cause of DRPs were drug choice (94.7%), and the highest frequency within its subcategories was the combination of inappropriate drugs (93.4%). Potential drug-drug interactions were detected in at least one C risk in 43 (30.7%) patients, at least one D risk in 11 (7.9%) patients, and at least one X risk in 6 patients (4.3%).

The clinical pharmacist performed 104 (68.4%) interventions on the prescriber, of which 100 (96.15%) were accepted and fully implemented. All 120 DRPs (78.9%) were resolved, and 28 DRPs (18.4%) were not possible or necessary to be resolved. Table  2 shows the classification of DRPs. Table  3 shows some examples of interventions performed by the clinical pharmacist. Anticancer drugs such as venetoclax, lenalidomide, and dasatinib were examples of potential drug-drug interactions. Table  4 shows the adverse effects that occurred. Drug-related nephrotoxicity was the most common adverse effect. Table  5 shows the results of the multivariate logistic regression analysis: factors most predictive of the presence of DRP. Polypharmacy and length of hospitalization were the most determinant factors in differentiating the groups with and without DRP, respectively. After adjustment for other factors, the likelihood of the presence of DRP was statistically significantly 7.921 folds (95% CI: 3.033–20.689) higher in patients with polypharmacy compared to patients without polypharmacy ( p  < 0.001). On the other hand, each 5-day increase in the duration of hospitalization continued to increase the likelihood of the presence of DRP by a statistically significant (OR = 1.476, 95% CI: 1.125–1.938 p  = 0.005).

In our study, 152 DRPs were identified and 120 DRPs were totally solved. This reveals the importance of involving the clinical pharmacist in a multidisciplinary team. The most common DRPs in our study were possible or actual adverse drug events. Since the patient population was generally elderly and cancer patients, they were exposed to polypharmacy and drug-drug interactions. Additionally, this was not surprising since the risk of exposure to possible or actual adverse drug events was high due to the anticancer medications they use [ 36 , 37 ]. Adverse drug events varied across studies. While this rate was 28.6% in the study conducted by Kim et al. [ 5 ] in the hematology service, it was 78.6% in the study conducted by Umar et al. [ 14 ] in the oncology service. Since Kim et al.‘s study [ 5 ] was retrospective, the rate of possible or actual adverse effects may have been found to be low. Additionally, although both studies used the PCNE classification system, it was not mentioned in Kim et al.‘s study which drug-drug interaction tool was used and which risk ratio for drug-drug interaction was considered clinically significant.

​In our study, most of the causes of DRPs were related to drug selection and their subgroup, inappropriate combination of drugs. Drug-drug interaction rates in the studies were 14.3%, 7.4%, 13.6%, and 73.2%, respectively [ 5 , 9 , 14 , 23 ]. Differences in this rate may be due to polypharmacy rates, differences in healthcare services, and different drug-drug interaction software [ 38 , 39 ]. Most of the potential drug-drug interactions in our study were at risk C (monitor therapy). Therefore, in some drug-drug interactions that required monitoring, only the physician was informed, and in others, intervention was recommended to the prescriber. Drug-drug interactions were mostly related to supportive medications. In our study, anticancer drugs such as venetoclax, lenalidomide, bortezomib, and dasatinib had potential drug-drug interactions. Venetoclax had potential drug-drug interactions with verapamil-trandolapril at increased risk of D. Verapamil-trandolapril is a CYP3A4 inhibitor [ 40 ], and concomitant use with venetoclax increases the concentration of venetoclax. It is recommended that the dose of venetoclax be reduced by 50% [ 29 , 41 , 42 , 43 ]. Also, there was a potential drug-drug interaction at risk X (avoid combination) between dasatinib and pantoprazole. Concomitant use of these two agents decreases the concentration of dasatinib [ 44 ]. Bortezomib had potential drug-drug interactions at risk level C with antihypertensive drugs and drugs used in the treatment of benign prostatic hyperplasia, such as tamsulosin [ 29 ]. Bortezomib may have a blood pressure-lowering effect, so if used concomitantly with an antihypertensive drug or another drug that can lower blood pressure, the patient should be monitored for hypotension [ 45 , 46 ]. In our study, there was also a potential drug-drug interaction between bortezomib and diltiazem at risk level C. Diltiazem, as a CYP3A4 inhibitor, may increase bortezomib concentration [ 40 ]. The bortezomib prescribing information emphasizes that in this case, it should be monitored for toxicity and dose reduction should be made if necessary [ 29 , 47 ]. In our study, there was a potential drug-drug interaction between lenalidomide and dexamethasone. When lenalidomide and dexamethasone are used together, venous thromboembolism prophylaxis should be considered, as the thrombogenic activity of lenalidomide may increase [ 29 , 48 , 49 ]. Additionally, potential drug-drug interactions with antiemetics and opioid-derived analgesics were frequently observed in our study. Identifying, monitoring, and intervening when necessary, drug-drug interactions are very important in cancer patients, and clinical pharmacists have important roles in this regard [ 50 , 51 ].

Dose selection was the second important DRP in our study. Renal dosage adjustment of drugs is significant, especially in patients who develop acute kidney injury [ 52 ]. Even if the drugs are started at the correct dose, the dose of the drugs should be monitored and adjusted when necessary in case of liver and renal dysfunction [ 52 , 53 ]. In our study, antimicrobials were among the drugs that required dosage adjustment according to renal function. This was due to the fact that although infectious disease physicians started antimicrobials at the correct dose, these doses were sometimes not followed up later.

Drug-induced nephrotoxicity was a common adverse event in our study, similar to other studies [ 17 , 54 ]. Also, venetoclax-related hyperuricemia, hyperkalemia and neutropenia were observed in some patients. In a study investigating the incidence of venetoclax-related toxicity risk in British Columbia, hyperkalemia and hyperphosphatemia were observed in 9 patients (27%), and hyperuricemia was observed in 7 patients (21%) [ 55 ]. In their study by Koehler et al., venetoclax-related hyperkalemia (31%) and hyperuricemia (5%) were observed [ 56 ]. In our study, one acute lymphoblastic leukemia patient had vincristine-induced neuropathy. Vincristine-induced neuropathy is a common side effect and its incidence is between 30 and 40% [ 57 ].

The clinical pharmacist’s acceptance rate of the interventions was good. In general, interventions regarding renal and hepatic dosing were accepted. The clinical pharmacist did not intervene in some cases that required monitoring (for example, category C drug interactions) and only informed the physician. These were evaluated as not possible or necessary to resolve the problem.

One of the strengths of the study is that the acceptability of the interventions was higher than other studies [ 5 , 18 , 23 , 58 ]. Additionally, our study was the first study in Turkey to reveal DRPs in detail in this vulnerable patient population in the hematology service. One of the limitations of our study is that it was conducted in a single center and with a small number of patients. In addition, the clinical pharmacist in the study was an academician and did not work full-time in the hospital, but worked at certain times of the day. This may have caused some DRPs not to be determined.

According to our study, a high frequency of DRPs and possible or actual adverse drug events were detected in patients. Older age, longer hospital stay, presence of acute lymphoblastic leukemia, presence of comorbidities, higher number of medications used, and polypharmacy rate were statistically significantly higher in the DRP group than in the non-DRP group According to the results of multiple logistic regression analysis, polypharmacy and length of hospital stay were the most determining factors in distinguishing between groups with and without DRP. The most common DRP was related to possible or actual adverse drug events. The most common cause of DRPs was drug selection and its subgroup, inappropriate combination of drugs. Also, our study shows the importance of including a clinical pharmacist in a multidisciplinary team in identifying and preventing DRPs in the hematology service.

Data availability

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

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Department of Clinical Pharmacy, Faculty of Pharmacy, Suleyman Demirel University, Isparta, Türkiye

Aslınur Albayrak

Department of Hematology, Faculty of Medicine, Suleyman Demirel University, Isparta, Türkiye

Demircan Özbalcı

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Study concept and design: AA, DÖ; Data Collection: AA; Analysis and interpretation of data: AA; Drafting of the manuscript: AA; Critical revision of the manuscript for important intellectual content: AA, DÖ. All the authors read and approved the final manuscript.

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Albayrak, A., Özbalcı, D. Determination of drug-related problems in the hematology service: a prospective interventional study. BMC Cancer 24 , 552 (2024). https://doi.org/10.1186/s12885-024-12291-w

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Shots - Health News

Hormones for menopause are safe, study finds. here's what changed.

Allison Aubrey

Low-dose estrogen can be taken orally, but it's also now available in patches, gels and creams. svetikd/Getty Images hide caption

Low-dose estrogen can be taken orally, but it's also now available in patches, gels and creams.

The benefits of hormone therapy for the treatment of menopause symptoms outweigh the risks. That's the conclusion of a new study published in the medical journal JAMA.

"Among women below the age of 60, we found hormone therapy has low risk of adverse events and [is] safe for treating bothersome hot flashes, night sweats and other menopausal symptoms, " says study author Dr. JoAnn Manson, chief of preventive medicine at Brigham and Women's Hospital. This is a departure from the advice many women have been given in the past.

The new analysis is based on two decades of follow-up data from the Women's Health Initiative study, which followed thousands of women taking hormone replacement therapy. The study was halted after it was found that women taking Prempro, which is a combination of estrogen and progestin, had higher risks of breast cancer and stroke.

A cheap drug may slow down aging. A study will determine if it works

"The findings were surprising," Manson says, pointing out that the reason the randomized trial was conducted was because scientists were trying to determine if hormone therapy decreased the risk of heart disease and other conditions.

After the initial findings came out, many women abruptly stopped the therapy. Prescriptions plummeted, and many healthcare providers still hesitate to recommend hormone therapy. But menopause experts say it's time to reconsider hormone therapy, because there's a lot known now that wasn't known two decades ago.

Most significantly, there are now different types of hormones — delivered at lower doses — that are shown to be safer.

"Women should know that hormone therapy is safe and beneficial," says Dr. Lauren Streicher , a clinical professor of obstetrics and gynecology at Northwestern University Feinberg School of Medicine.

Looking back, Dr. Streicher says, it's clear the Women's Health Initiative study was flawed and that some of the risks that were identified were linked to the type of hormones that women were given.

"We learned what not to do," Streicher says. The type of progestin used, known as medroxyprogesterone acetate , was "highly problematic," she says. This may have been linked to the increase in breast cancer seen among women in the earlier study. "So we don't prescribe that anymore," Streicher says.

Increasingly, other types of hormones are used, such as micronized progesterone which does not increase the risk of breast cancer, Streicher says. Micronized progesterone is a bioidentical hormone that has a molecular structure identical to the progesterone produced by women's ovaries, and tends to have fewer side effects.

Another problem with the study was the age of the women enrolled. Most of the women were over the age of 60, Streicher says. "And we know that there is a window of opportunity when it is the safest to start hormone therapy and that you get the most benefit." That window is typically between ages 50 and 60, she says.

Women who do strength training live longer. How much is enough?

Another risk identified in the Women's Health Initiative study, was an increased incidence of pulmonary embolism among women taking hormones. A pulmonary embolism is a blood clot that blocks blood flow to the lungs.

Since women in the study were taking estrogen orally, by pill, this may have increased their risk, Streicher says. A better option for people at risk of clots is to take estrogen through the skin, via a patch, a cream or gel.

"The advantage of a transdermal estrogen is that it is not metabolized by the liver," Streicher says. "And because it's not metabolized by the liver, we don't see that increase in blood clots."

With a range of hormone therapies available now, Dr Streicher says there's not a one-size fits all approach. "Hormone therapy is beneficial way beyond the benefits to just helping with hot flashes," she says. Ongoing research points to protection against bone loss and heart disease , too.

Streicher says women should talk to their healthcare providers about what options may best suit their needs.

Millions of women are 'under-muscled.' These foods help build strength

This story was edited by Jane Greenhalgh

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• hormone replacement therapy

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Make Decisions with a VC Mindset

• Ilya A. Strebulaev

Venture capitalists’ unique approach to investment and innovation has played a pivotal role in launching one-fifth of the largest U.S. public companies. And three-quarters of the largest U.S. companies founded in the past 50 years would not have existed or achieved their current scale without VC support.

The question is, Why? What makes venture firms so good at finding start-ups that go on to achieve tremendous success? What skills do they have that experienced, networked, and powerful large corporations lack?

The authors’ research reveals that the venture mindset is characterized by several principles: the individual over the group, disagreement over consensus, exceptions over dogma, and agility over bureaucracy. This article offers guidance to traditional firms in using the VC mindset to spur innovation.

The key is to embrace risk, disagreement, and agility.

Idea in Brief

The opportunity.

Venture capitalists’ unique approach to investment and innovation has played a pivotal role in launching one-fifth of the largest U.S. public companies, demonstrating the power of the venture mindset.

The Challenge

Traditional companies often struggle to replicate the success of venture firms because of their aversion to risk and failure and their preference for consensus and stability.

The Solution

When faced with market changes or disruptive technology, big companies should adopt the venture mindset, prioritizing the individual over the group, disagreement over consensus, exceptions over dogma, and agility over bureaucracy.

Venture investors are the hidden hand behind the most innovative companies surrounding us. According to research conducted by one of us (Ilya), venture capitalists were causally responsible for the launch of one-fifth of the 300 largest U.S. public companies in existence today. They have played an essential role in unlocking the power of the internet, the mobile revolution, and now artificial intelligence in all its forms. Apple, Google, Moderna, Netflix, Airbnb, OpenAI, Salesforce, Tesla, Uber, and Zoom—these firms disrupted entire industries despite initially having fewer resources and less support and experience than their mature, successful, cash-rich competitors. All these businesses could theoretically have emerged from within an established company—but they didn’t. Instead, they were financed and shaped by VCs. Indeed, we estimate that three-quarters of the largest U.S. companies founded in the past 50 years would not have existed or achieved their current scale without VC support.

• IS Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and a professor of finance at the Stanford Graduate School of Business. He is also the founder of the Stanford GSB Venture Capital Initiative and a research associate at the National Bureau of Economic Research.
• AD Alex Dang is a venture builder and a digital strategy adviser. He was a partner at McKinsey and EY and launched numerous businesses at Amazon.

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