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Delimitations in Research – Types, Examples and Writing Guide

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Delimitations

Definition:

Delimitations refer to the specific boundaries or limitations that are set in a research study in order to narrow its scope and focus. Delimitations may be related to a variety of factors, including the population being studied, the geographical location, the time period, the research design , and the methods or tools being used to collect data .

The Importance of Delimitations in Research Studies

Here are some reasons why delimitations are important in research studies:

• Provide focus : Delimitations help researchers focus on a specific area of interest and avoid getting sidetracked by tangential topics. By setting clear boundaries, researchers can concentrate their efforts on the most relevant and significant aspects of the research question.
• Increase validity : Delimitations ensure that the research is more valid by defining the boundaries of the study. When researchers establish clear criteria for inclusion and exclusion, they can better control for extraneous variables that might otherwise confound the results.
• Improve generalizability : Delimitations help researchers determine the extent to which their findings can be generalized to other populations or contexts. By specifying the sample size, geographic region, time frame, or other relevant factors, researchers can provide more accurate estimates of the generalizability of their results.
• Enhance feasibility : Delimitations help researchers identify the resources and time required to complete the study. By setting realistic parameters, researchers can ensure that the study is feasible and can be completed within the available time and resources.
• Clarify scope: Delimitations help readers understand the scope of the research project. By explicitly stating what is included and excluded, researchers can avoid confusion and ensure that readers understand the boundaries of the study.

Types of Delimitations in Research

Here are some types of delimitations in research and their significance:

Time Delimitations

This type of delimitation refers to the time frame in which the research will be conducted. Time delimitations are important because they help to narrow down the scope of the study and ensure that the research is feasible within the given time constraints.

Geographical Delimitations

Geographical delimitations refer to the geographic boundaries within which the research will be conducted. These delimitations are significant because they help to ensure that the research is relevant to the intended population or location.

Population Delimitations

Population delimitations refer to the specific group of people that the research will focus on. These delimitations are important because they help to ensure that the research is targeted to a specific group, which can improve the accuracy of the results.

Data Delimitations

Data delimitations refer to the specific types of data that will be used in the research. These delimitations are important because they help to ensure that the data is relevant to the research question and that the research is conducted using reliable and valid data sources.

Scope Delimitations

Scope delimitations refer to the specific aspects or dimensions of the research that will be examined. These delimitations are important because they help to ensure that the research is focused and that the findings are relevant to the research question.

How to Write Delimitations

In order to write delimitations in research, you can follow these steps:

• Identify the scope of your study : Determine the extent of your research by defining its boundaries. This will help you to identify the areas that are within the scope of your research and those that are outside of it.
• Determine the time frame : Decide on the time period that your research will cover. This could be a specific period, such as a year, or it could be a general time frame, such as the last decade.
• I dentify the population : Determine the group of people or objects that your study will focus on. This could be a specific age group, gender, profession, or geographic location.
• Establish the sample size : Determine the number of participants that your study will involve. This will help you to establish the number of people you need to recruit for your study.
• Determine the variables: Identify the variables that will be measured in your study. This could include demographic information, attitudes, behaviors, or other factors.
• Explain the limitations : Clearly state the limitations of your study. This could include limitations related to time, resources, sample size, or other factors that may impact the validity of your research.
• Justify the limitations : Explain why these limitations are necessary for your research. This will help readers understand why certain factors were excluded from the study.

When to Write Delimitations in Research

Here are some situations when you may need to write delimitations in research:

• When defining the scope of the study: Delimitations help to define the boundaries of your research by specifying what is and what is not included in your study. For instance, you may delimit your study by focusing on a specific population, geographic region, time period, or research methodology.
• When addressing limitations: Delimitations can also be used to address the limitations of your research. For example, if your data is limited to a certain timeframe or geographic area, you can include this information in your delimitations to help readers understand the limitations of your findings.
• When justifying the relevance of the study : Delimitations can also help you to justify the relevance of your research. For instance, if you are conducting a study on a specific population or region, you can explain why this group or area is important and how your research will contribute to the understanding of this topic.
• When clarifying the research question or hypothesis : Delimitations can also be used to clarify your research question or hypothesis. By specifying the boundaries of your study, you can ensure that your research question or hypothesis is focused and specific.
• When establishing the context of the study : Finally, delimitations can help you to establish the context of your research. By providing information about the scope and limitations of your study, you can help readers to understand the context in which your research was conducted and the implications of your findings.

Examples of Delimitations in Research

Examples of Delimitations in Research are as follows:

Research Title : “Impact of Artificial Intelligence on Cybersecurity Threat Detection”

Delimitations :

• The study will focus solely on the use of artificial intelligence in detecting and mitigating cybersecurity threats.
• The study will only consider the impact of AI on threat detection and not on other aspects of cybersecurity such as prevention, response, or recovery.
• The research will be limited to a specific type of cybersecurity threats, such as malware or phishing attacks, rather than all types of cyber threats.
• The study will only consider the use of AI in a specific industry, such as finance or healthcare, rather than examining its impact across all industries.
• The research will only consider AI-based threat detection tools that are currently available and widely used, rather than including experimental or theoretical AI models.

Research Title: “The Effects of Social Media on Academic Performance: A Case Study of College Students”

Delimitations:

• The study will focus only on college students enrolled in a particular university.
• The study will only consider social media platforms such as Facebook, Twitter, and Instagram.
• The study will only analyze the academic performance of students based on their GPA and course grades.
• The study will not consider the impact of other factors such as student demographics, socioeconomic status, or other factors that may affect academic performance.
• The study will only use self-reported data from students, rather than objective measures of their social media usage or academic performance.

Purpose of Delimitations

Some Purposes of Delimitations are as follows:

• Focusing the research : By defining the scope of the study, delimitations help researchers to narrow down their research questions and focus on specific aspects of the topic. This allows for a more targeted and meaningful study.
• Clarifying the research scope : Delimitations help to clarify the boundaries of the research, which helps readers to understand what is and is not included in the study.
• Avoiding scope creep : Delimitations help researchers to stay focused on their research objectives and avoid being sidetracked by tangential issues or data.
• Enhancing the validity of the study : By setting clear boundaries, delimitations help to ensure that the study is valid and reliable.
• Improving the feasibility of the study : Delimitations help researchers to ensure that their study is feasible and can be conducted within the time and resources available.

Applications of Delimitations

Here are some common applications of delimitations:

• Geographic delimitations : Researchers may limit their study to a specific geographic area, such as a particular city, state, or country. This helps to narrow the focus of the study and makes it more manageable.
• Time delimitations : Researchers may limit their study to a specific time period, such as a decade, a year, or a specific date range. This can be useful for studying trends over time or for comparing data from different time periods.
• Population delimitations : Researchers may limit their study to a specific population, such as a particular age group, gender, or ethnic group. This can help to ensure that the study is relevant to the population being studied.
• Data delimitations : Researchers may limit their study to specific types of data, such as survey responses, interviews, or archival records. This can help to ensure that the study is based on reliable and relevant data.
• Conceptual delimitations : Researchers may limit their study to specific concepts or variables, such as only studying the effects of a particular treatment on a specific outcome. This can help to ensure that the study is focused and clear.

Advantages of Delimitations

Some Advantages of Delimitations are as follows:

• Helps to focus the study: Delimitations help to narrow down the scope of the research and identify specific areas that need to be investigated. This helps to focus the study and ensures that the research is not too broad or too narrow.
• Defines the study population: Delimitations can help to define the population that will be studied. This can include age range, gender, geographical location, or any other factors that are relevant to the research. This helps to ensure that the study is more specific and targeted.
• Provides clarity: Delimitations help to provide clarity about the research study. By identifying the boundaries and limitations of the research, it helps to avoid confusion and ensures that the research is more understandable.
• Improves validity: Delimitations can help to improve the validity of the research by ensuring that the study is more focused and specific. This can help to ensure that the research is more accurate and reliable.
• Reduces bias: Delimitations can help to reduce bias by limiting the scope of the research. This can help to ensure that the research is more objective and unbiased.

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Scope and Delimitations in Research

Delimitations are the boundaries that the researcher sets in a research study, deciding what to include and what to exclude. They help to narrow down the study and make it more manageable and relevant to the research goal.

Updated on October 19, 2022

All scientific research has boundaries, whether or not the authors clearly explain them. Your study's scope and delimitations are the sections where you define the broader parameters and boundaries of your research.

The scope details what your study will explore, such as the target population, extent, or study duration. Delimitations are factors and variables not included in the study.

Scope and delimitations are not methodological shortcomings; they're always under your control. Discussing these is essential because doing so shows that your project is manageable and scientifically sound.

This article covers:

• What's meant by “scope” and “delimitations”
• Why these are integral components of every study
• How and where to actually write about scope and delimitations in your manuscript
• Examples of scope and delimitations from published studies

What is the scope in a research paper?

Simply put, the scope is the domain of your research. It describes the extent to which the research question will be explored in your study.

Articulating your study's scope early on helps you make your research question focused and realistic.

It also helps decide what data you need to collect (and, therefore, what data collection tools you need to design). Getting this right is vital for both academic articles and funding applications.

What are delimitations in a research paper?

Delimitations are those factors or aspects of the research area that you'll exclude from your research. The scope and delimitations of the study are intimately linked.

Essentially, delimitations form a more detailed and narrowed-down formulation of the scope in terms of exclusion. The delimitations explain what was (intentionally) not considered within the given piece of research.

Scope and delimitations examples

Use the following examples provided by our expert PhD editors as a reference when coming up with your own scope and delimitations.

Scope example

Your research question is, “What is the impact of bullying on the mental health of adolescents?” This topic, on its own, doesn't say much about what's being investigated.

The scope, for example, could encompass:

• Variables: “bullying” (dependent variable), “mental health” (independent variable), and ways of defining or measuring them
• Bullying type: Both face-to-face and cyberbullying
• Target population: Adolescents aged 12–17
• Geographical coverage: France or only one specific town in France

Delimitations example

Look back at the previous example.

Exploring the adverse effects of bullying on adolescents' mental health is a preliminary delimitation. This one was chosen from among many possible research questions (e.g., the impact of bullying on suicide rates, or children or adults).

Delimiting factors could include:

• Research design : Mixed-methods research, including thematic analysis of semi-structured interviews and statistical analysis of a survey
• Timeframe : Data collection to run for 3 months
• Population size : 100 survey participants; 15 interviewees
• Recruitment of participants : Quota sampling (aiming for specific portions of men, women, ethnic minority students etc.)

We can see that every choice you make in planning and conducting your research inevitably excludes other possible options.

What's the difference between limitations and delimitations?

Delimitations and limitations are entirely different, although they often get mixed up. These are the main differences:

This chart explains the difference between delimitations and limitations. Delimitations are the boundaries of the study while the limitations are the characteristics of the research design or methodology.

Delimitations encompass the elements outside of the boundaries you've set and depends on your decision of what yo include and exclude. On the flip side, limitations are the elements outside of your control, such as:

• limited financial resources
• unplanned work or expenses
• unexpected events (for example, the COVID-19 pandemic)
• time constraints
• lack of technology/instruments
• unavailable evidence or previous research on the topic

Delimitations involve narrowing your study to make it more manageable and relevant to what you're trying to prove. Limitations influence the validity and reliability of your research findings. Limitations are seen as potential weaknesses in your research.

Example of the differences

To clarify these differences, go back to the limitations of the earlier example.

Limitations could comprise:

• Sample size : Not large enough to provide generalizable conclusions.
• Sampling approach : Non-probability sampling has increased bias risk. For instance, the researchers might not manage to capture the experiences of ethnic minority students.
• Methodological pitfalls : Research participants from an urban area (Paris) are likely to be more advantaged than students in rural areas. A study exploring the latter's experiences will probably yield very different findings.

Where do you write the scope and delimitations, and why?

It can be surprisingly empowering to realize you're restricted when conducting scholarly research. But this realization also makes writing up your research easier to grasp and makes it easier to see its limits and the expectations placed on it. Properly revealing this information serves your field and the greater scientific community.

Openly (but briefly) acknowledge the scope and delimitations of your study early on. The Abstract and Introduction sections are good places to set the parameters of your paper.

Next, discuss the scope and delimitations in greater detail in the Methods section. You'll need to do this to justify your methodological approach and data collection instruments, as well as analyses

At this point, spell out why these delimitations were set. What alternative options did you consider? Why did you reject alternatives? What could your study not address?

Let's say you're gathering data that can be derived from different but related experiments. You must convince the reader that the one you selected best suits your research question.

Finally, a solid paper will return to the scope and delimitations in the Findings or Discussion section. Doing so helps readers contextualize and interpret findings because the study's scope and methods influence the results.

For instance, agricultural field experiments carried out under irrigated conditions yield different results from experiments carried out without irrigation.

Being transparent about the scope and any outstanding issues increases your research's credibility and objectivity. It helps other researchers replicate your study and advance scientific understanding of the same topic (e.g., by adopting a different approach).

How do you write the scope and delimitations?

Define the scope and delimitations of your study before collecting data. This is critical. This step should be part of your research project planning.

Answering the following questions will help you address your scope and delimitations clearly and convincingly.

• What are your study's aims and objectives?
• Why did you carry out the study?
• What was the exact topic under investigation?
• Which factors and variables were included? And state why specific variables were omitted from the research scope.
• Who or what did the study explore? What was the target population?
• What was the study's location (geographical area) or setting (e.g., laboratory)?
• What was the timeframe within which you collected your data ?
• Consider a study exploring the differences between identical twins who were raised together versus identical twins who weren't. The data collection might span 5, 10, or more years.
• A study exploring a new immigration policy will cover the period since the policy came into effect and the present moment.
• How was the research conducted (research design)?
• Experimental research, qualitative, quantitative, or mixed-methods research, literature review, etc.
• What data collection tools and analysis techniques were used? e.g., If you chose quantitative methods, which statistical analysis techniques and software did you use?
• What did you find?
• What did you conclude?

Useful vocabulary for scope and delimitations

When explaining both the scope and delimitations, it's important to use the proper language to clearly state each.

For the scope , use the following language:

• This study focuses on/considers/investigates/covers the following:
• This study aims to . . . / Here, we aim to show . . . / In this study, we . . .
• The overall objective of the research is . . . / Our objective is to . . .

When stating the delimitations, use the following language:

• This [ . . . ] will not be the focus, for it has been frequently and exhaustively discusses in earlier studies.
• To review the [ . . . ] is a task that lies outside the scope of this study.
• The following [ . . . ] has been excluded from this study . . .
• This study does not provide a complete literature review of [ . . . ]. Instead, it draws on selected pertinent studies [ . . . ]

Analysis of a published scope

In one example, Simione and Gnagnarella (2020) compared the psychological and behavioral impact of COVID-19 on Italy's health workers and general population.

Here's a breakdown of the study's scope into smaller chunks and discussion of what works and why.

Also notable is that this study's delimitations include references to:

• Recruitment of participants: Convenience sampling
• Demographic characteristics of study participants: Age, sex, etc.
• Measurements methods: E.g., the death anxiety scale of the Existential Concerns Questionnaire (ECQ; van Bruggen et al., 2017) etc.
• Data analysis tool: The statistical software R

Analysis of published scope and delimitations

Scope of the study : Johnsson et al. (2019) explored the effect of in-hospital physiotherapy on postoperative physical capacity, physical activity, and lung function in patients who underwent lung cancer surgery.

The delimitations narrowed down the scope as follows:

Refine your scope, delimitations, and scientific English

English ability shouldn't limit how clear and impactful your research can be. Expert AJE editors are available to assess your science and polish your academic writing. See AJE services here .

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Educational resources and simple solutions for your research journey

Decoding the Scope and Delimitations of the Study in Research

Scope and delimitations of the study are two essential elements of a research paper or thesis that help to contextualize and convey the focus and boundaries of a research study. This allows readers to understand the research focus and the kind of information to expect. For researchers, especially students and early career researchers, understanding the meaning and purpose of the scope and delimitation of a study is crucial to craft a well-defined and impactful research project. In this article, we delve into the core concepts of scope and delimitation in a study, providing insightful examples, and practical tips on how to effectively incorporate them into your research endeavors.

Table of Contents

What is scope and delimitation in research

The scope of a research paper explains the context and framework for the study, outlines the extent, variables, or dimensions that will be investigated, and provides details of the parameters within which the study is conducted. Delimitations in research , on the other hand, refer to the limitations imposed on the study. It identifies aspects of the topic that will not be covered in the research, conveys why these choices were made, and how this will affect the outcome of the research. By narrowing down the scope and defining delimitations, researchers can ensure focused research and avoid pitfalls, which ensures the study remains feasible and attainable.

Example of scope and delimitation of a study

A researcher might want to study the effects of regular physical exercise on the health of senior citizens. This would be the broad scope of the study, after which the researcher would refine the scope by excluding specific groups of senior citizens, perhaps based on their age, gender, geographical location, cultural influences, and sample sizes. These then, would form the delimitations of the study; in other words, elements that describe the boundaries of the research.

The purpose of scope and delimitation in a study

The purpose of scope and delimitation in a study is to establish clear boundaries and focus for the research. This allows researchers to avoid ambiguity, set achievable objectives, and manage their project efficiently, ultimately leading to more credible and meaningful findings in their study. The scope and delimitation of a study serve several important purposes, including:

• Establishing clarity: Clearly defining the scope and delimitation of a study helps researchers and readers alike understand the boundaries of the investigation and what to expect from it.
• Focus and relevance: By setting the scope, researchers can concentrate on specific research questions, preventing the study from becoming too broad or irrelevant.
• Feasibility: Delimitations of the study prevent researchers from taking on too unrealistic or unmanageable tasks, making the research more achievable.
• Avoiding ambiguity: A well-defined scope and delimitation of the study minimizes any confusion or misinterpretation regarding the research objectives and methods.

Given the importance of both the scope and delimitations of a study, it is imperative to ensure that they are mentioned early on in the research manuscript. Most experts agree that the scope of research should be mentioned as part of the introduction and the delimitations must be mentioned as part of the methods section. Now that we’ve covered the scope and delimitation meaning and purpose, we look at how to write each of these sections.

How to write the scope of the study in research

When writing the scope of the study, remain focused on what you hope to achieve. Broadening the scope too much might make it too generic while narrowing it down too much may affect the way it would be interpreted. Ensure the scope of the study is clear, concise and accurate. Conduct a thorough literature review to understand existing literature, which will help identify gaps and refine the scope of your study.

It is helpful if you structure the scope in a way that answers the Six Ws – questions whose answers are considered basic in information-gathering.

Why: State the purpose of the research by articulating the research objectives and questions you aim to address in your study.

What: Outline the specific topic to be studied, while mentioning the variables, concepts, or aspects central to your research; these will define the extent of your study.

Where: Provide the setting or geographical location where the research study will be conducted.

When : Mention the specific timeframe within which the research data will be collected.

Who : Specify the sample size for the study and the profile of the population they will be drawn from.

How : Explain the research methodology, research design, and tools and analysis techniques.

How to write the delimitations of a study in research

When writing the delimitations of the study, researchers must provide all the details clearly and precisely. Writing the delimitations of the study requires a systematic approach to narrow down the research’s focus and establish boundaries. Follow these steps to craft delimitations effectively:

• Clearly understand the research objectives and questions you intend to address in your study.
• Conduct a comprehensive literature review to identify gaps and areas that have already been extensively covered. This helps to avoid redundancies and home in on a unique issue.
• Clearly state what aspects, variables, or factors you will be excluding in your research; mention available alternatives, if any, and why these alternatives were rejected.
• Explain how you the delimitations were set, and they contribute to the feasibility and relevance of your study, and how they align with the research objectives.
• Be sure to acknowledge limitations in your research, such as constraints related to time, resources, or data availability.

Being transparent ensures credibility, while explaining why the delimitations of your study could not be overcome with standard research methods backed up by scientific evidence can help readers understand the context better.

Differentiating between delimitations and limitations

Most early career researchers get confused and often use these two terms interchangeably which is wrong. Delimitations of a study refer to the set boundaries and specific parameters within which the research is carried out. They help narrow down your focus and makes it more relevant to what you are trying to prove.

Meanwhile, limitations in a study refer to the validity and reliability of the research being conducted. They are those elements of your study that are usually out of your immediate control but are still able to affect your findings in some way. In other words, limitation are potential weaknesses of your research.

In conclusion, scope and delimitation of a study are vital elements that shape the trajectory of your research study. The above explanations will have hopefully helped you better understand the scope and delimitations meaning, purpose, and importance in crafting focused, feasible, and impactful research studies. Be sure to follow the simple techniques to write the scope and delimitations of the study to embark on your research journey with clarity and confidence. Happy researching!

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• How to write a research paper

Last updated

11 January 2024

Reviewed by

With proper planning, knowledge, and framework, completing a research paper can be a fulfilling and exciting experience. 

Though it might initially sound slightly intimidating, this guide will help you embrace the challenge. 

By documenting your findings, you can inspire others and make a difference in your field. Here's how you can make your research paper unique and comprehensive.

• What is a research paper?

Research papers allow you to demonstrate your knowledge and understanding of a particular topic. These papers are usually lengthier and more detailed than typical essays, requiring deeper insight into the chosen topic.

To write a research paper, you must first choose a topic that interests you and is relevant to the field of study. Once you’ve selected your topic, gathering as many relevant resources as possible, including books, scholarly articles, credible websites, and other academic materials, is essential. You must then read and analyze these sources, summarizing their key points and identifying gaps in the current research.

You can formulate your ideas and opinions once you thoroughly understand the existing research. To get there might involve conducting original research, gathering data, or analyzing existing data sets. It could also involve presenting an original argument or interpretation of the existing research.

Writing a successful research paper involves presenting your findings clearly and engagingly, which might involve using charts, graphs, or other visual aids to present your data and using concise language to explain your findings. You must also ensure your paper adheres to relevant academic formatting guidelines, including proper citations and references.

Overall, writing a research paper requires a significant amount of time, effort, and attention to detail. However, it is also an enriching experience that allows you to delve deeply into a subject that interests you and contribute to the existing body of knowledge in your chosen field.

• How long should a research paper be?

Research papers are deep dives into a topic. Therefore, they tend to be longer pieces of work than essays or opinion pieces. 

However, a suitable length depends on the complexity of the topic and your level of expertise. For instance, are you a first-year college student or an experienced professional? 

Also, remember that the best research papers provide valuable information for the benefit of others. Therefore, the quality of information matters most, not necessarily the length. Being concise is valuable.

Following these best practice steps will help keep your process simple and productive:

1. Gaining a deep understanding of any expectations

Before diving into your intended topic or beginning the research phase, take some time to orient yourself. Suppose there’s a specific topic assigned to you. In that case, it’s essential to deeply understand the question and organize your planning and approach in response. Pay attention to the key requirements and ensure you align your writing accordingly. 

This preparation step entails

Deeply understanding the task or assignment

Being clear about the expected format and length

Familiarizing yourself with the citation and referencing requirements 

Understanding any defined limits for your research contribution

Where applicable, speaking to your professor or research supervisor for further clarification

2. Choose your research topic

Select a research topic that aligns with both your interests and available resources. Ideally, focus on a field where you possess significant experience and analytical skills. In crafting your research paper, it's crucial to go beyond summarizing existing data and contribute fresh insights to the chosen area.

Consider narrowing your focus to a specific aspect of the topic. For example, if exploring the link between technology and mental health, delve into how social media use during the pandemic impacts the well-being of college students. Conducting interviews and surveys with students could provide firsthand data and unique perspectives, adding substantial value to the existing knowledge.

When finalizing your topic, adhere to legal and ethical norms in the relevant area (this ensures the integrity of your research, protects participants' rights, upholds intellectual property standards, and ensures transparency and accountability). Following these principles not only maintains the credibility of your work but also builds trust within your academic or professional community.

For instance, in writing about medical research, consider legal and ethical norms , including patient confidentiality laws and informed consent requirements. Similarly, if analyzing user data on social media platforms, be mindful of data privacy regulations, ensuring compliance with laws governing personal information collection and use. Aligning with legal and ethical standards not only avoids potential issues but also underscores the responsible conduct of your research.

3. Gather preliminary research

Once you’ve landed on your topic, it’s time to explore it further. You’ll want to discover more about available resources and existing research relevant to your assignment at this stage. 

This exploratory phase is vital as you may discover issues with your original idea or realize you have insufficient resources to explore the topic effectively. This key bit of groundwork allows you to redirect your research topic in a different, more feasible, or more relevant direction if necessary. 

Spending ample time at this stage ensures you gather everything you need, learn as much as you can about the topic, and discover gaps where the topic has yet to be sufficiently covered, offering an opportunity to research it further. 

4. Define your research question

To produce a well-structured and focused paper, it is imperative to formulate a clear and precise research question that will guide your work. Your research question must be informed by the existing literature and tailored to the scope and objectives of your project. By refining your focus, you can produce a thoughtful and engaging paper that effectively communicates your ideas to your readers.

5. Write a thesis statement

A thesis statement is a one-to-two-sentence summary of your research paper's main argument or direction. It serves as an overall guide to summarize the overall intent of the research paper for you and anyone wanting to know more about the research.

A strong thesis statement is:

Concise and clear: Explain your case in simple sentences (avoid covering multiple ideas). It might help to think of this section as an elevator pitch.

Specific: Ensure that there is no ambiguity in your statement and that your summary covers the points argued in the paper.

Debatable: A thesis statement puts forward a specific argument––it is not merely a statement but a debatable point that can be analyzed and discussed.

Here are three thesis statement examples from different disciplines:

Psychology thesis example: "We're studying adults aged 25-40 to see if taking short breaks for mindfulness can help with stress. Our goal is to find practical ways to manage anxiety better."

Environmental science thesis example: "This research paper looks into how having more city parks might make the air cleaner and keep people healthier. I want to find out if more green spaces means breathing fewer carcinogens in big cities."

UX research thesis example: "This study focuses on improving mobile banking for older adults using ethnographic research, eye-tracking analysis, and interactive prototyping. We investigate the usefulness of eye-tracking analysis with older individuals, aiming to spark debate and offer fresh perspectives on UX design and digital inclusivity for the aging population."

6. Conduct in-depth research

A research paper doesn’t just include research that you’ve uncovered from other papers and studies but your fresh insights, too. You will seek to become an expert on your topic––understanding the nuances in the current leading theories. You will analyze existing research and add your thinking and discoveries.  It's crucial to conduct well-designed research that is rigorous, robust, and based on reliable sources. Suppose a research paper lacks evidence or is biased. In that case, it won't benefit the academic community or the general public. Therefore, examining the topic thoroughly and furthering its understanding through high-quality research is essential. That usually means conducting new research. Depending on the area under investigation, you may conduct surveys, interviews, diary studies , or observational research to uncover new insights or bolster current claims.

7. Determine supporting evidence

Not every piece of research you’ve discovered will be relevant to your research paper. It’s important to categorize the most meaningful evidence to include alongside your discoveries. It's important to include evidence that doesn't support your claims to avoid exclusion bias and ensure a fair research paper.

8. Write a research paper outline

Before diving in and writing the whole paper, start with an outline. It will help you to see if more research is needed, and it will provide a framework by which to write a more compelling paper. Your supervisor may even request an outline to approve before beginning to write the first draft of the full paper. An outline will include your topic, thesis statement, key headings, short summaries of the research, and your arguments.

9. Write your first draft

Once you feel confident about your outline and sources, it’s time to write your first draft. While penning a long piece of content can be intimidating, if you’ve laid the groundwork, you will have a structure to help you move steadily through each section. To keep up motivation and inspiration, it’s often best to keep the pace quick. Stopping for long periods can interrupt your flow and make jumping back in harder than writing when things are fresh in your mind.

10. Cite your sources correctly

It's always a good practice to give credit where it's due, and the same goes for citing any works that have influenced your paper. Building your arguments on credible references adds value and authenticity to your research. In the formatting guidelines section, you’ll find an overview of different citation styles (MLA, CMOS, or APA), which will help you meet any publishing or academic requirements and strengthen your paper's credibility. It is essential to follow the guidelines provided by your school or the publication you are submitting to ensure the accuracy and relevance of your citations.

11. Ensure your work is original

It is crucial to ensure the originality of your paper, as plagiarism can lead to serious consequences. To avoid plagiarism, you should use proper paraphrasing and quoting techniques. Paraphrasing is rewriting a text in your own words while maintaining the original meaning. Quoting involves directly citing the source. Giving credit to the original author or source is essential whenever you borrow their ideas or words. You can also use plagiarism detection tools such as Scribbr or Grammarly to check the originality of your paper. These tools compare your draft writing to a vast database of online sources. If you find any accidental plagiarism, you should correct it immediately by rephrasing or citing the source.

12. Revise, edit, and proofread

One of the essential qualities of excellent writers is their ability to understand the importance of editing and proofreading. Even though it's tempting to call it a day once you've finished your writing, editing your work can significantly improve its quality. It's natural to overlook the weaker areas when you've just finished writing a paper. Therefore, it's best to take a break of a day or two, or even up to a week, to refresh your mind. This way, you can return to your work with a new perspective. After some breathing room, you can spot any inconsistencies, spelling and grammar errors, typos, or missing citations and correct them. 

• The best research paper format 

The format of your research paper should align with the requirements set forth by your college, school, or target publication. 

There is no one “best” format, per se. Depending on the stated requirements, you may need to include the following elements:

Title page: The title page of a research paper typically includes the title, author's name, and institutional affiliation and may include additional information such as a course name or instructor's name. 

Table of contents: Include a table of contents to make it easy for readers to find specific sections of your paper.

Abstract: The abstract is a summary of the purpose of the paper.

Methods : In this section, describe the research methods used. This may include collecting data , conducting interviews, or doing field research .

Results: Summarize the conclusions you drew from your research in this section.

Discussion: In this section, discuss the implications of your research . Be sure to mention any significant limitations to your approach and suggest areas for further research.

Tables, charts, and illustrations: Use tables, charts, and illustrations to help convey your research findings and make them easier to understand.

Works cited or reference page: Include a works cited or reference page to give credit to the sources that you used to conduct your research.

Bibliography: Provide a list of all the sources you consulted while conducting your research.

Dedication and acknowledgments : Optionally, you may include a dedication and acknowledgments section to thank individuals who helped you with your research.

• General style and formatting guidelines

Formatting your research paper means you can submit it to your college, journal, or other publications in compliance with their criteria.

Research papers tend to follow the American Psychological Association (APA), Modern Language Association (MLA), or Chicago Manual of Style (CMOS) guidelines.

Here’s how each style guide is typically used:

Chicago Manual of Style (CMOS):

CMOS is a versatile style guide used for various types of writing. It's known for its flexibility and use in the humanities. CMOS provides guidelines for citations, formatting, and overall writing style. It allows for both footnotes and in-text citations, giving writers options based on their preferences or publication requirements.

American Psychological Association (APA):

APA is common in the social sciences. It’s hailed for its clarity and emphasis on precision. It has specific rules for citing sources, creating references, and formatting papers. APA style uses in-text citations with an accompanying reference list. It's designed to convey information efficiently and is widely used in academic and scientific writing.

Modern Language Association (MLA):

MLA is widely used in the humanities, especially literature and language studies. It emphasizes the author-page format for in-text citations and provides guidelines for creating a "Works Cited" page. MLA is known for its focus on the author's name and the literary works cited. It’s frequently used in disciplines that prioritize literary analysis and critical thinking.

To confirm you're using the latest style guide, check the official website or publisher's site for updates, consult academic resources, and verify the guide's publication date. Online platforms and educational resources may also provide summaries and alerts about any revisions or additions to the style guide.

Citing sources

When working on your research paper, it's important to cite the sources you used properly. Your citation style will guide you through this process. Generally, there are three parts to citing sources in your research paper: 

First, provide a brief citation in the body of your essay. This is also known as a parenthetical or in-text citation. 

Second, include a full citation in the Reference list at the end of your paper. Different types of citations include in-text citations, footnotes, and reference lists. 

In-text citations include the author's surname and the date of the citation. 

Footnotes appear at the bottom of each page of your research paper. They may also be summarized within a reference list at the end of the paper. 

A reference list includes all of the research used within the paper at the end of the document. It should include the author, date, paper title, and publisher listed in the order that aligns with your citation style.

10 research paper writing tips:

Following some best practices is essential to writing a research paper that contributes to your field of study and creates a positive impact.

These tactics will help you structure your argument effectively and ensure your work benefits others:

Clear and precise language:  Ensure your language is unambiguous. Use academic language appropriately, but keep it simple. Also, provide clear takeaways for your audience.

Effective idea separation:  Organize the vast amount of information and sources in your paper with paragraphs and titles. Create easily digestible sections for your readers to navigate through.

Compelling intro:  Craft an engaging introduction that captures your reader's interest. Hook your audience and motivate them to continue reading.

Thorough revision and editing:  Take the time to review and edit your paper comprehensively. Use tools like Grammarly to detect and correct small, overlooked errors.

Thesis precision:  Develop a clear and concise thesis statement that guides your paper. Ensure that your thesis aligns with your research's overall purpose and contribution.

Logical flow of ideas:  Maintain a logical progression throughout the paper. Use transitions effectively to connect different sections and maintain coherence.

Critical evaluation of sources:  Evaluate and critically assess the relevance and reliability of your sources. Ensure that your research is based on credible and up-to-date information.

Thematic consistency:  Maintain a consistent theme throughout the paper. Ensure that all sections contribute cohesively to the overall argument.

Relevant supporting evidence:  Provide concise and relevant evidence to support your arguments. Avoid unnecessary details that may distract from the main points.

Embrace counterarguments:  Acknowledge and address opposing views to strengthen your position. Show that you have considered alternative arguments in your field.

7 research tips 

If you want your paper to not only be well-written but also contribute to the progress of human knowledge, consider these tips to take your paper to the next level:

Selecting the appropriate topic: The topic you select should align with your area of expertise, comply with the requirements of your project, and have sufficient resources for a comprehensive investigation.

Use academic databases: Academic databases such as PubMed, Google Scholar, and JSTOR offer a wealth of research papers that can help you discover everything you need to know about your chosen topic.

Critically evaluate sources: It is important not to accept research findings at face value. Instead, it is crucial to critically analyze the information to avoid jumping to conclusions or overlooking important details. A well-written research paper requires a critical analysis with thorough reasoning to support claims.

Diversify your sources: Expand your research horizons by exploring a variety of sources beyond the standard databases. Utilize books, conference proceedings, and interviews to gather diverse perspectives and enrich your understanding of the topic.

Take detailed notes: Detailed note-taking is crucial during research and can help you form the outline and body of your paper.

Stay up on trends: Keep abreast of the latest developments in your field by regularly checking for recent publications. Subscribe to newsletters, follow relevant journals, and attend conferences to stay informed about emerging trends and advancements. 

Engage in peer review: Seek feedback from peers or mentors to ensure the rigor and validity of your research . Peer review helps identify potential weaknesses in your methodology and strengthens the overall credibility of your findings.

• The real-world impact of research papers

Writing a research paper is more than an academic or business exercise. The experience provides an opportunity to explore a subject in-depth, broaden one's understanding, and arrive at meaningful conclusions. With careful planning, dedication, and hard work, writing a research paper can be a fulfilling and enriching experience contributing to advancing knowledge.

How do I publish my research paper? 

Many academics wish to publish their research papers. While challenging, your paper might get traction if it covers new and well-written information. To publish your research paper, find a target publication, thoroughly read their guidelines, format your paper accordingly, and send it to them per their instructions. You may need to include a cover letter, too. After submission, your paper may be peer-reviewed by experts to assess its legitimacy, quality, originality, and methodology. Following review, you will be informed by the publication whether they have accepted or rejected your paper. 

What is a good opening sentence for a research paper? 

Beginning your research paper with a compelling introduction can ensure readers are interested in going further. A relevant quote, a compelling statistic, or a bold argument can start the paper and hook your reader. Remember, though, that the most important aspect of a research paper is the quality of the information––not necessarily your ability to storytell, so ensure anything you write aligns with your goals.

Research paper vs. a research proposal—what’s the difference?

While some may confuse research papers and proposals, they are different documents. 

A research proposal comes before a research paper. It is a detailed document that outlines an intended area of exploration. It includes the research topic, methodology, timeline, sources, and potential conclusions. Research proposals are often required when seeking approval to conduct research. 

A research paper is a summary of research findings. A research paper follows a structured format to present those findings and construct an argument or conclusion.

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Online Guide to Writing and Research

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• Online Guide to Writing

Structuring the Research Paper

Formal research structure.

These are the primary purposes for formal research:

enter the discourse, or conversation, of other writers and scholars in your field

learn how others in your field use primary and secondary resources

find and understand raw data and information

For the formal academic research assignment, consider an organizational pattern typically used for primary academic research.  The pattern includes the following: introduction, methods, results, discussion, and conclusions/recommendations.

Usually, research papers flow from the general to the specific and back to the general in their organization. The introduction uses a general-to-specific movement in its organization, establishing the thesis and setting the context for the conversation. The methods and results sections are more detailed and specific, providing support for the generalizations made in the introduction. The discussion section moves toward an increasingly more general discussion of the subject, leading to the conclusions and recommendations, which then generalize the conversation again.

Sections of a Formal Structure

The introduction section.

Many students will find that writing a structured  introduction  gets them started and gives them the focus needed to significantly improve their entire paper. 

Introductions usually have three parts:

presentation of the problem statement, the topic, or the research inquiry

purpose and focus of your paper

summary or overview of the writer’s position or arguments

In the first part of the introduction—the presentation of the problem or the research inquiry—state the problem or express it so that the question is implied. Then, sketch the background on the problem and review the literature on it to give your readers a context that shows them how your research inquiry fits into the conversation currently ongoing in your subject area. 

In the second part of the introduction, state your purpose and focus. Here, you may even present your actual thesis. Sometimes your purpose statement can take the place of the thesis by letting your reader know your intentions. 

The third part of the introduction, the summary or overview of the paper, briefly leads readers through the discussion, forecasting the main ideas and giving readers a blueprint for the paper. 

The following example provides a blueprint for a well-organized introduction.

Example of an Introduction

Entrepreneurial Marketing: The Critical Difference

In an article in the Harvard Business Review, John A. Welsh and Jerry F. White remind us that “a small business is not a little big business.” An entrepreneur is not a multinational conglomerate but a profit-seeking individual. To survive, he must have a different outlook and must apply different principles to his endeavors than does the president of a large or even medium-sized corporation. Not only does the scale of small and big businesses differ, but small businesses also suffer from what the Harvard Business Review article calls “resource poverty.” This is a problem and opportunity that requires an entirely different approach to marketing. Where large ad budgets are not necessary or feasible, where expensive ad production squanders limited capital, where every marketing dollar must do the work of two dollars, if not five dollars or even ten, where a person’s company, capital, and material well-being are all on the line—that is, where guerrilla marketing can save the day and secure the bottom line (Levinson, 1984, p. 9).

By reviewing the introductions to research articles in the discipline in which you are writing your research paper, you can get an idea of what is considered the norm for that discipline. Study several of these before you begin your paper so that you know what may be expected. If you are unsure of the kind of introduction your paper needs, ask your professor for more information.  The introduction is normally written in present tense.

THE METHODS SECTION

The methods section of your research paper should describe in detail what methodology and special materials if any, you used to think through or perform your research. You should include any materials you used or designed for yourself, such as questionnaires or interview questions, to generate data or information for your research paper. You want to include any methodologies that are specific to your particular field of study, such as lab procedures for a lab experiment or data-gathering instruments for field research. The methods section is usually written in the past tense.

THE RESULTS SECTION

How you present the results of your research depends on what kind of research you did, your subject matter, and your readers’ expectations. 

Quantitative information —data that can be measured—can be presented systematically and economically in tables, charts, and graphs. Quantitative information includes quantities and comparisons of sets of data. 

Qualitative information , which includes brief descriptions, explanations, or instructions, can also be presented in prose tables. This kind of descriptive or explanatory information, however, is often presented in essay-like prose or even lists.

There are specific conventions for creating tables, charts, and graphs and organizing the information they contain. In general, you should use them only when you are sure they will enlighten your readers rather than confuse them. In the accompanying explanation and discussion, always refer to the graphic by number and explain specifically what you are referring to; you can also provide a caption for the graphic. The rule of thumb for presenting a graphic is first to introduce it by name, show it, and then interpret it. The results section is usually written in the past tense.

THE DISCUSSION SECTION

Your discussion section should generalize what you have learned from your research. One way to generalize is to explain the consequences or meaning of your results and then make your points that support and refer back to the statements you made in your introduction. Your discussion should be organized so that it relates directly to your thesis. You want to avoid introducing new ideas here or discussing tangential issues not directly related to the exploration and discovery of your thesis. The discussion section, along with the introduction, is usually written in the present tense.

THE CONCLUSIONS AND RECOMMENDATIONS SECTION

Your conclusion ties your research to your thesis, binding together all the main ideas in your thinking and writing. By presenting the logical outcome of your research and thinking, your conclusion answers your research inquiry for your reader. Your conclusions should relate directly to the ideas presented in your introduction section and should not present any new ideas.

You may be asked to present your recommendations separately in your research assignment. If so, you will want to add some elements to your conclusion section. For example, you may be asked to recommend a course of action, make a prediction, propose a solution to a problem, offer a judgment, or speculate on the implications and consequences of your ideas. The conclusions and recommendations section is usually written in the present tense.

Key Takeaways

• For the formal academic research assignment, consider an organizational pattern typically used for primary academic research. 
•  The pattern includes the following: introduction, methods, results, discussion, and conclusions/recommendations.

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Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Nature of Research

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

Writing Handbooks

Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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How To Write Scope and Delimitation of a Research Paper (With Examples)

An effective research paper or thesis has a well-written Scope and Delimitation.  This portion specifies your study’s coverage and boundaries.

Not yet sure about how to write your research’s Scope and Delimitation? Fret not, as we’ll guide you through the entire writing process through this article.

Related: How To Write Significance of the Study (With Examples)

Table of Contents

What is the scope and delimitation of a research paper.

The “Scope and Delimitation” section states the concepts and variables your study covered. It tells readers which things you have included and excluded in your analysis.

This portion tells two things: 1

• The study’s “Scope” – concepts and variables you have explored in your research and;
• The study’s “Delimitation” – the “boundaries” of your study’s scope. It sets apart the things included in your analysis from those excluded.

For example, your scope might be the effectiveness of plant leaves in lowering blood sugar levels. You can “delimit” your study only to the effect of gabi leaves on the blood glucose of Swiss mice.

Where Should I Put the Scope and Delimitation?

This portion is in Chapter 1, usually after the “Background of the Study.”

Why Should I Write the Scope and Delimitation of My Research Paper?

There’s a lot to discover in a research paper or thesis. However, your resources and time dedicated to it are scarce. Thus, given these constraints, you have to narrow down your study. You do this in the Scope and Delimitation.

Suppose you’re studying the correlation between the quantity of organic fertilizer and plant growth . Experimenting with several types of plants is impossible because of several limitations. So, you’ve decided to use one plant type only. 

Informing your readers about this decision is a must. So, you have to state it in your Scope and Delimitation. It also acts as a “disclaimer” that your results are inapplicable to the entire plant kingdom.

What Is the Difference Between Delimitation and Limitation?

People often use the terms “Delimitation” and “Limitation” interchangeably. However, these words differ 2 .

Delimitation refers to factors you set to limit your analysis. It delineates those that are included in your research and those that are excluded. Remember, delimitations are within your control. 

Meanwhile, limitations are factors beyond your control that may affect your research’s results.  You can think of limitations as the “weaknesses” of your study. 

Let’s go back to our previous example. Due to some constraints, you’ve only decided to examine one plant type: dandelions. This is an example of a delimitation since it limits your analysis to dandelions only and not other plant types. Note that the number of plant types used is within your control. 

Meanwhile, your study cannot state that a higher quantity of organic fertilizer is the sole reason for plant growth. That’s because your research’s focus is only on correlation. Since this is already beyond your control, then this is a limitation. 

How To Write Scope and Delimitation: Step-by-Step Guide

To write your research’s Scope and Delimitation section, follow these steps:

1. Review Your Study’s Objectives and Problem Statement

Your study’s coverage relies on its objectives. Thus, you can only write this section if you know what you’re researching. Furthermore, ensure that you understand the problems you ought to answer. 

Once you understand the abovementioned things, you may start writing your study’s Scope and Delimitation.

2. State the Key Information To Explain Your Study’s Coverage and Boundaries

a. The Main Objective of the Research

This refers to the concept that you’re focusing on in your research. Some examples are the following:

• level of awareness or satisfaction of a particular group of people
• correlation between two variables
• effectiveness of a new product
• comparison between two methods/approaches
• lived experiences of several individuals

It’s helpful to consult your study’s Objectives or Statement of the Problem section to determine your research’s primary goal.

b. Independent and Dependent Variables Included

Your study’s independent variable is the variable that you manipulate. Meanwhile, the dependent variable is the variable whose result depends upon the independent variable. Both of these variables must be clear and specific when indicated. 

Suppose you study the relationship between social media usage and students’ language skills. These are the possible variables for the study:

• Independent Variable: Number of hours per day spent on using Facebook
• Dependent Variable: Grade 10 students’ scores in Quarterly Examination in English. 

Note how specific the variables stated above are. For the independent variable, we narrow it down to Facebook only. Since there are many ways to assess “language skills,” we zero in on the students’ English exam scores as our dependent variable. 

c. Subject of the Study

This refers to your study’s respondents or participants. 

In our previous example, the research participants are Grade 10 students. However, there are a lot of Grade 10 students in the Philippines. Thus, we have to select from a specific school only—for instance, Grade 10 students from a national high school in Manila. 

d. Timeframe and Location of the Study

Specify the month(s), quarter(s), or year(s) as the duration of your study. Also, indicate where you will gather the data required for your research. 

e. Brief Description of the Study’s Research Design and Methodology

You may also include whether your research is quantitative or qualitative, the sampling method (cluster, stratified, purposive) applied, and how you conducted the experiment.

Using our previous example, the Grade 10 students can be selected using stratified sampling. Afterward, the researchers may obtain their English quarterly exam scores from their respective teachers. You can add these things to your study’s Scope and Delimitation. 

3. Indicate Which Variables or Factors Are Not Covered by Your Research

Although you’ve already set your study’s coverage and boundaries in Step 2, you may also explicitly mention things you’ve excluded from your research. 

Returning to our previous example, you can state that your assessment will not include the vocabulary and oral aspects of the English proficiency skill. 

Examples of Scope and Delimitation of a Research Paper

1. scope and delimitation examples for quantitative research.

a. Example 1

Research Title

    A Study on the Relationship of the Extent of Facebook Usage on the English Proficiency Level of Grade 10 Students of Matagumpay High School

Scope and Delimitation

(Main Objective)

This study assessed the correlation between the respondents’ duration of Facebook usage and their English proficiency level. 

(Variables used)

The researchers used the number of hours per day of using Facebook and the activities usually performed on the platform to assess the respondents’ extent of Facebook usage. Meanwhile, the respondents’ English proficiency level is limited to their quarterly English exam scores. 

(Subject of the study)

A sample of fifty (50) Grade 10 students of Matagumpay High School served as the study’s respondents. 

(Timeframe and location)

This study was conducted during the Second Semester of the School Year 2018 – 2019 on the premises of Matagumpay High School in Metro Manila. 

(Methodology)

The respondents are selected by performing stratified random sampling to ensure that there will be ten respondents from five Grade 10 classes of the school mentioned above. The researchers administered a 20-item questionnaire to assess the extent of Facebook usage of the selected respondents. Meanwhile, the data for the respondents’ quarterly exam scores were acquired from their English teachers. The collected data are handled with the utmost confidentiality. Spearman’s Rank Order Correlation was applied to quantitatively assess the correlation between the variables.

(Exclusions)

This study didn’t assess other aspects of the respondents’ English proficiency, such as English vocabulary and oral skills. 

Note: The words inside the parentheses in the example above are guides only. They are not included in the actual text.

b. Example 2

  Level of Satisfaction of Grade 11 Students on the Implementation of the Online Learning Setup of Matagumpay High School for SY 2020 – 2021

This study aims to identify students’ satisfaction levels with implementing online learning setups during the height of the COVID-19 pandemic.

Students’ satisfaction was assessed according to teachers’ pedagogy, school policies, and learning materials used in the online learning setup. The respondents included sixty (60) Grade 11 students of Matagumpay High School who were randomly picked. The researchers conducted the study from October 2020 to February 2021. 

Online platforms such as email and social media applications were used to reach the respondents. The researchers administered a 15-item online questionnaire to measure the respondents’ satisfaction levels. Each response was assessed using a Likert Scale to provide a descriptive interpretation of their answers. A weighted mean was applied to determine the respondents’ general satisfaction. 

This study did not cover other factors related to the online learning setup, such as the learning platform used, the schedule of synchronous learning, and channels for information dissemination.

2. Scope and Delimitation Examples for Qualitative Research

  Lived Experiences of Public Utility Vehicle (PUV) Drivers of Antipolo City Amidst the Continuous June 2022 Oil Price Hikes

This research focused on the presentation and discussion of the lived experiences of PUV drivers during the constant oil price hike in June 2022.

The respondents involved are five (5) jeepney drivers from Antipolo City who agreed to be interviewed. The researchers assessed their experiences in terms of the following: (1) daily net income; (2) duration and extent of working; (3) alternative employment opportunity considerations; and (4) mental and emotional status. The respondents were interviewed daily at their stations on June 6 – 10, 2022. 

In-depth one-on-one interviews were used for data collection.  Afterward, the respondents’ first-hand experiences were drafted and annotated with the researchers’ insights. 

The researchers excluded some factors in determining the respondents’ experiences, such as physical and health conditions and current family relationship status. 

 A Study on the Perception of the Residents of Mayamot, Antipolo City on the Political and Socioeconomic Conditions During the Post-EDSA Period (1986 – 1996)

This research aims to discuss the perception of Filipinos regarding the political and socioeconomic economic conditions during the post-EDSA period, specifically during the years 1986 – 1996. 

Ten (10) residents of Mayamot, Antipolo City, who belonged to Generation X (currently 40 – 62 years old), were purposively selected as the study’s respondents. The researchers asked them about their perception of the following aspects during the period mentioned above (1) performance of national and local government; (2) bureaucracy and government services; (3) personal economic and financial status; and (4) wage purchasing power. 

The researchers conducted face-to-face interviews in the respondents’ residences during the second semester of AY 2018 – 2019. The responses were written and corroborated with the literature on the post-EDSA period. 

The following factors were not included in the research analysis: political conflicts and turmoils, the status of the legislative and judicial departments, and other macroeconomic indicators. 

Tips and Warnings

1. use the “5ws and 1h” as your guide in understanding your study’s coverage.

• Why did you write your study?  
• What variables are included?
• Who are your study’s subject
• Where did you conduct the study?
• When did your study start and end?
• How did you conduct the study?

2. Use key phrases when writing your research’s scope

• This study aims to … 
• This study primarily focuses on …
• This study deals with … 
• This study will cover …
• This study will be confined…

3. Use key phrases when writing factors beyond your research’s delimitations

• The researcher(s) decided to exclude …
• This study did not cover….
• This study excluded … 
• These variables/factors were excluded from the study…

4. Don’t forget to ask for help

Your research adviser can assist you in selecting specific concepts and variables suitable to your study. Make sure to consult him/her regularly. 

5. Make it brief

No need to make this section wordy. You’re good to go if you meet the “5Ws and 1Hs”. 

Frequently Asked Questions

1. what are scope and delimitation in tagalog.

In a Filipino research ( pananaliksik ), Scope and Delimitation is called “ Saklaw at Delimitasyon”. 

Here’s an example of Scope and Delimitation in Filipino:

Pamagat ng Pananaliksik

Epekto Ng Paggamit Ng Mga Digital Learning Tools Sa Pag-Aaral Ng Mga Mag-Aaral Ng Mataas Na Paaralan Ng Matagumpay Sa General Mathematics

Sakop at Delimitasyon ng Pag-aaral

Nakatuon ang pananaliksik na ito sa epekto ng paggamit ng mga digital learning aids sa pag-aaral ng mga mag-aaral.

Ang mga digital learning tools na kinonsidera sa pag-aaral na ito ay Google Classroom, Edmodo, Kahoot, at mga piling bidyo mula YouTube. Samantala, ang epekto sa pag-aaral ng mga mag-aaral ng mga nabanggit na digital learning tools ay natukoy sa pamamagitan ng kanilang (1) mga pananaw hinggil sa benepisyo nito sa kanilang pag-aaral sa General Mathematics at (2) kanilang average grade sa asignaturang ito.

Dalawampu’t-limang (25) mag-aaral mula sa Senior High School ng Mataas na Paaralan ng Matagumpay ang pinili para sa pananaliksik na ito. Sila ay na-interbyu at binigyan ng questionnaire noong Enero 2022 sa nasabing paaralan. Sinuri ang resulta ng pananaliksik sa pamamagitan ng mga instrumentong estadistikal na weighted mean at Analysis of Variance (ANOVA). Hindi saklaw ng pananaliksik na ito ang ibang mga aspeto hinggil sa epekto ng online learning aids sa pag-aaral gaya ng lebel ng pag-unawa sa aralin at kakayahang iugnay ito sa araw-araw na buhay. 

2. The Scope and Delimitation should consist of how many paragraphs?

Three or more paragraphs will suffice for your study’s Scope and Delimitation. Here’s our suggestion on what you should write for each paragraph:

Paragraph 1: Introduction (state research objective) Paragraph 2: Coverage and boundaries of the research (you may divide this section into 2-3 paragraphs) Paragraph 3 : Factors excluded from the study

• University of St. La Salle. Unit 3: Lesson 3 Setting the Scope and Limitation of a Qualitative Research [Ebook] (p. 12). Retrieved from https://www.studocu.com/ph/document/university-of-st-la-salle/senior-high-school/final-sg-pr1-11-12-unit-3-lesson-3-setting-the-scope-and-limitation-of-a-qualitative-research/24341582
• Theofanidis, D., & Fountouki, A. (2018). Limitations and Delimitations in the Research Process. Perioperative Nursing (GORNA), 7(3), 155–162. doi: 10.5281/zenodo.2552022

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

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How to Write the Scope of the Study

• By DiscoverPhDs
• August 26, 2020

What is the Scope of the Study?

The scope of the study refers to the boundaries within which your research project will be performed; this is sometimes also called the scope of research. To define the scope of the study is to define all aspects that will be considered in your research project. It is also just as important to make clear what aspects will not be covered; i.e. what is outside of the scope of the study.

Why is the Scope of the Study Important?

The scope of the study is always considered and agreed upon in the early stages of the project, before any data collection or experimental work has started. This is important because it focuses the work of the proposed study down to what is practically achievable within a given timeframe.

A well-defined research or study scope enables a researcher to give clarity to the study outcomes that are to be investigated. It makes clear why specific data points have been collected whilst others have been excluded.

Without this, it is difficult to define an end point for a research project since no limits have been defined on the work that could take place. Similarly, it can also make the approach to answering a research question too open ended.

How do you Write the Scope of the Study?

In order to write the scope of the study that you plan to perform, you must be clear on the research parameters that you will and won’t consider. These parameters usually consist of the sample size, the duration, inclusion and exclusion criteria, the methodology and any geographical or monetary constraints.

Each of these parameters will have limits placed on them so that the study can practically be performed, and the results interpreted relative to the limitations that have been defined. These parameters will also help to shape the direction of each research question you consider.

The term limitations’ is often used together with the scope of the study to describe the constraints of any parameters that are considered and also to clarify which parameters have not been considered at all. Make sure you get the balance right here between not making the scope too broad and unachievable, and it not being too restrictive, resulting in a lack of useful data.

The sample size is a commonly used parameter in the definition of the research scope. For example, a research project involving human participants may define at the start of the study that 100 participants will be recruited. This number will be determined based on an understanding of the difficulty in recruiting participants to studies and an agreement of an acceptable period of time in which to recruit this number.

Any results that are obtained by the research group can then be interpreted by others with the knowledge that the study was capped to 100 participants and an acceptance of this as a limitation of the study. In other words, it is acknowledged that recruiting 100 rather than 1,000 participants has limited the amount of data that could be collected, however this is an acceptable limitation due to the known difficulties in recruiting so many participants (e.g. the significant period of time it would take and the costs associated with this).

Example of a Scope of the Study

The follow is a (hypothetical) example of the definition of the scope of the study, with the research question investigating the impact of the COVID-19 pandemic on mental health.

Whilst the immediate negative health problems related to the COVID-19 pandemic have been well documented, the impact of the virus on the mental health (MH) of young adults (age 18-24 years) is poorly understood. The aim of this study is to report on MH changes in population group due to the pandemic.

The scope of the study is limited to recruiting 100 volunteers between the ages of 18 and 24 who will be contacted using their university email accounts. This recruitment period will last for a maximum of 2 months and will end when either 100 volunteers have been recruited or 2 months have passed. Each volunteer to the study will be asked to complete a short questionnaire in order to evaluate any changes in their MH.

From this example we can immediately see that the scope of the study has placed a constraint on the sample size to be used and/or the time frame for recruitment of volunteers. It has also introduced a limitation by only opening recruitment to people that have university emails; i.e. anyone that does not attend university will be excluded from this study.

This may be an important factor when interpreting the results of this study; the comparison of MH during the pandemic between those that do and do not attend university, is therefore outside the scope of the study here. We are also told that the methodology used to assess any changes in MH are via a questionnaire. This is a clear definition of how the outcome measure will be investigated and any other methods are not within the scope of research and their exclusion may be a limitation of the study.

The scope of the study is important to define as it enables a researcher to focus their research to within achievable parameters.

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Organizing Your Social Sciences Research Paper

• The Research Problem/Question
• 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
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• 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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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

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

Importance of...

The purpose of a problem statement is to:

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

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

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

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

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

Structure and Writing Style

I.  Types and Content

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

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

A problem statement in the social sciences should contain :

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

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

II.  Sources of Problems for Investigation

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

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

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

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

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

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

III.  What Makes a Good Research Statement?

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

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

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

IV.  Asking Analytical Questions about the Research Problem

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

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

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

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

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

V.  Mistakes to Avoid

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

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

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

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

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• Biochem Med (Zagreb)
• v.31(1); 2021 Feb 15

Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies

Ceyhan ceran serdar.

1 Medical Biology and Genetics, Faculty of Medicine, Ankara Medipol University, Ankara, Turkey

Murat Cihan

2 Ordu University Training and Research Hospital, Ordu, Turkey

Doğan Yücel

3 Department of Medical Biochemistry, Lokman Hekim University School of Medicine, Ankara, Turkey

Muhittin A Serdar

4 Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.

Introduction

Statistical analysis is a crucial part of a research. A scientific study must include statistical tools in the study, beginning from the planning stage. Developed in the last 20-30 years, information technology, along with evidence-based medicine, increased the spread and applicability of statistical science. Although scientists have understood the importance of statistical analysis for researchers, a significant number of researchers admit that they lack adequate knowledge about statistical concepts and principles ( 1 ). In a study by West and Ficalora, more than two-thirds of the clinicians emphasized that “the level of biostatistics education that is provided to the medical students is not sufficient” ( 2 ). As a result, it was suggested that statistical concepts were either poorly understood or not understood at all ( 3 , 4 ). Additionally, intentionally or not, researchers tend to draw conclusions that cannot be supported by the actual study data, often due to the misuse of statistics tools ( 5 ). As a result, a large number of statistical errors occur affecting the research results.

Although there are a variety of potential statistical errors that might occur in any kind of scientific research, it has been observed that the sources of error have changed due to the use of dedicated software that facilitates statistics in recent years. A summary of main statistical errors frequently encountered in scientific studies is provided below ( 6 - 13 ):

• Flawed and inadequate hypothesis;
• Improper study design;
• Lack of adequate control condition/group;
• Spectrum bias;
• Overstatement of the analysis results;
• Spurious correlations;
• Inadequate sample size;
• Circular analysis (creating bias by selecting the properties of the data retrospectively);
• Utilization of inappropriate statistical studies and fallacious bending of the analyses;
• p-hacking ( i.e. addition of new covariates post hoc to make P values significant);
• Excessive interpretation of limited or insignificant results (subjectivism);
• Confusion (intentionally or not) of correlations, relationships, and causations;
• Faulty multiple regression models;
• Confusion between P value and clinical significance; and
• Inappropriate presentation of the results and effects (erroneous tables, graphics, and figures).

Relationship among sample size, power, P value and effect size

In this review, we will concentrate on the problems associated with the relationships among sample size, power, P value, and effect size (ES). Practical suggestions will be provided whenever possible. In order to understand and interpret the sample size, power analysis, effect size, and P value, it is necessary to know how the hypothesis of the study was formed. It is best to evaluate a study for Type I and Type II errors ( Figure 1 ) through consideration of the study results in the context of its hypotheses ( 14 - 16 ).

Illustration of Type I and Type II errors.

A statistical hypothesis is the researcher’s best guess as to what the result of the experiment will show. It states, in a testable form the proposition the researcher plans to examine in a sample to be able to find out if the proposition is correct in the relevant population. There are two commonly used types of hypotheses in statistics. These are the null hypothesis (H0) and the alternative (H1) hypothesis. Essentially, the H1 is the researcher’s prediction of what will be the situation of the experimental group after the experimental treatment is applied. The H0 expresses the notion that there will be no effect from the experimental treatment.

Prior to the study, in addition to stating the hypothesis, the researcher must also select the alpha (α) level at which the hypothesis will be declared “supported”. The α represents how much risk the researcher is willing to take that the study will conclude H1 is correct when (in the full population) it is not correct (and thus, the null hypothesis is really true). In other words, alpha represents the probability of rejecting H0 when it actually is true. (Thus, the researcher has made an error by reporting that the experimental treatment makes a difference, when in fact, in the full population, that treatment has no effect.)

The most common α level chosen is 0.05, meaning the researcher is willing to take a 5% chance that a result supporting the hypothesis will be untrue in the full population. However, other alpha levels may also be appropriate in some circumstances. For pilot studies, α is often set at 0.10 or 0.20. In studies where it is especially important to avoid concluding a treatment is effective when it actually is not, the alpha may be set at a much lower value; it might be set at 0.001 or even lower. Drug studies are examples for studies that often set the alpha at 0.001 or lower because the consequences of releasing an ineffective drug can be extremely dangerous for patients.

Another probability value is called “the P value”. The P value is simply the obtained statistical probability of incorrectly accepting the alternate hypothesis. The P value is compared to the alpha value to determine if the result is “statistically significant”, meaning that with high probability the result found in the sample will also be true in the full population. If the P value is at or lower than alpha, H1 is accepted. If it is higher than alpha, the H1 is rejected and H0 is accepted instead.

There are actually two types of errors: the error of accepting H1 when it is not true in the population; this is called a Type I error; and is a false positive. The alpha defines the probability of a Type I error. Type I errors can happen for many reasons, from poor sampling that results in an experimental sample quite different from the population, to other mistakes occurring in the design stage or implementation of the research procedures. It is also possible to make an erroneous decision in the opposite direction; by incorrectly rejecting H1 and thus wrongly accepting H0. This is called a Type II error (or a false negative). The β defines the probability of a Type II error. The most common reason for this type of error is small sample size, especially when combined with moderately low or low effect sizes. Both small sample sizes and low effect sizes reduce the power in the study.

Power, which is the probability of rejecting a false null hypothesis, is calculated as 1-β (also expressed as “1 - Type II error probability”). For a Type II error of 0.15, the power is 0.85. Since reduction in the probability of committing a Type II error increases the risk of committing a Type I error (and vice versa ), a delicate balance should be established between the minimum allowed levels for Type I and Type II errors. The ideal power of a study is considered to be 0.8 (which can also be specified as 80%) ( 17 ). Sufficient sample size should be maintained to obtain a Type I error as low as 0.05 or 0.01 and a power as high as 0.8 or 0.9.

However, when power value falls below < 0.8, one cannot immediately conclude that the study is totally worthless. In parallel with this, the concept of “cost-effective sample size” has gained importance in recent years ( 18 ).

Additionally, the traditionally chosen alpha and beta error limits are generally arbitrary and are being used as a convention rather than being based on any scientific validity. Another key issue for a study is the determination, presentation and discussion of the effect size of the study, as will be discussed below in detail.

Although increasing the sample size is suggested to decrease the Type II errors, it will increase the cost of the project and delay the completion of the research activities in a foreseen period of time. In addition, it should not be forgotten that redundant samples may cause ethical problems ( 19 , 20 ).

Therefore, determination of the effective sample size is crucial to enable an efficient study with high significance, increasing the impact of the outcome. Unfortunately, information regarding sample size calculations are not often provided by clinical investigators in most diagnostic studies ( 21 , 22 ).

Calculation of the sample size

Different methods can be utilized before the onset of the study to calculate the most suitable sample size for the specific research. In addition to manual calculation, various nomograms or software can be used. The Figure 2 illustrates one of the most commonly used nomograms for sample size estimation using effect size and power ( 23 ).

Nomogram for sample size and power, for comparing two groups of equal size. Gaussian distributions assumed. Standardized difference (effect size) and aimed power values are initially selected on the nomogram. The line connecting these values cross the significance level region of the nomogram. The intercept at the appropriate significance value presents the required sample size for the study. In the above example, for effect size = 1, power = 0.8 and alpha value = 0.05, the sample size is found to be 30. (Adapted from reference 16 ).

Although manual calculation is preferred by the experts of the subject, it is a bit complicated and difficult for the researchers that are not statistics experts. In addition, considering the variety of the research types and characteristics, it should be noted that a great number of calculations will be required with too many variables ( Table 1 ) ( 16 , 24 - 30 ).

In recent years, numerous software and websites have been developed which can successfully calculate sample size in various study types. Some of the important software and websites are listed in Table 2 and are evaluated based both on the remarks stated in the literature and on our own experience, with respect to the content, ease of use, and cost ( 31 , 32 ). G-Power, R, and Piface stand out among the listed software in terms of being free-to use. G-Power is a free-to use tool that be used to calculate statistical power for many different t-tests, F-tests, χ 2 tests, z-tests and some exact tests. R is an open source programming language which can be tailored to meet individual statistical needs, by adding specific program modules called packages onto a specific base program. Piface is a java application specifically designed for sample size estimation and post-hoc power analysis. The most professional software is PASS (Power Analysis and Sample Size). With PASS, it is possible to analyse sample size and power for approximately 200 different study types. In addition, many websites provide substantial aid in calculating power and sample size, basing their methodology on scientific literature.

The sample size or the power of the study is directly related to the ES of the study. What is this important ES? The ES provides important information on how well the independent variable or variables predict the dependent variable. Low ES means that, independent variables don’t predict well because they are only slightly related to the dependent variable. Strong ES means that, independent variables are very good predictors of the dependent variable. Thus, ES is clinically important for evaluating how efficiently the clinicians can predict outcomes from the independent variables.

The scale of the ES values for different types of statistical tests conducted in different study types are presented in Table 3 .

In order to evaluate the effect of the study and indicate its clinical significance, it is very important to evaluate the effect size along with statistical significance. P value is important in the statistical evaluation of the research. While it provides information on presence/absence of an effect, it will not account for the size of the effect. For comprehensive presentation and interpretation of the studies, both effect size and statistical significance (P value) should be provided and considered.

It would be much easier to understand ES through an example. For example, assume that independent sample t-test is used to compare total cholesterol levels for two groups having normal distribution. Where X, SD and N stands for mean, standard deviation and sample size, respectively. Cohen’s d ES can be calculated as follows:

Mean (X), mmol/L Standard deviation (SD) Sample size (N)

Group 1 6.5 0.5 30

Group 2 5.2 0.8 30

Cohen d ES results represents: 0.8 large, 0.5 medium, 0.2 small effects). The result of 1.94 indicates a very large effect. Means of the two groups are remarkably different.

In the example above, the means of the two groups are largely different in a statistically significant manner. Yet, clinical importance of the effect (whether this effect is important for the patient, clinical condition, therapy type, outcome, etc .) needs to be specifically evaluated by the experts of the topic.

Power, alpha values, sample size, and ES are closely related with each other. Let us try to explain this relationship through different situations that we created using G-Power ( 33 , 34 ).

The Figure 3 shows the change of sample size depending on the ES changes (0.2, 1 and 2.5, respectively) provided that the power remains constant at 0.8. Arguably, case 3 is particularly common in pre-clinical studies, cell culture, and animal studies (usually 5-10 samples in animal studies or 3-12 samples in cell culture studies), while case 2 is more common in clinical studies. In clinical, epidemiological or meta-analysis studies, where the sample size is very large; case 1, which emphasizes the importance of smaller effects, is more commonly observed ( 33 ).

Relationship between effect size and sample size. P – power. ES - effect size. SS - sample size. The required sample size increases as the effect size decreases. In all cases, P value is set to 0.8. The sample sizes (SS) when ES is 0.2, 1, or 2.5; are 788, 34 and 8, respectively. The graphs at the bottom represent the influence of change in the sample size on the power.

In Figure 4 , case 4 exemplifies the change in power and ES values when the sample size is kept constant ( i.e. as low as 8). As can be seen here, in studies with low ES, working with few samples will mean waste of time, redundant processing, or unnecessary use of laboratory animals.

Relationship between effect size and power. Two different cases are schematized where the sample size is kept constant either at 8 or at 30. When the sample size is kept constant, the power of the study decreases as the effect size decreases. When the effect size is 2.5, even 8 samples are sufficient to obtain power = ~0.8. When the effect size is 1, increasing sample size from 8 to 30 significantly increases the power of the study. Yet, even 30 samples are not sufficient to reach a significant power value if effect size is as low as 0.2.

Likewise, case 5 exemplifies the situation where the sample size is kept constant at 30. In this case, it is important to note that when ES is 1, the power of the study will be around 0.8. Some statisticians arbitrarily regard 30 as a critical sample size. However, case 5 clearly demonstrates that it is essential not to underestimate the importance of ES, while deciding on the sample size.

Especially in recent years, where clinical significance or effectiveness of the results has outstripped the statistical significance; understanding the effect size and power has gained tremendous importance ( 35 – 38 ).

Preliminary information about the hypothesis is eminently important to calculate the sample size at intended power. Usually, this is accomplished by determining the effect size from the results of a previous study or a preliminary study. There are software available which can calculate sample size using the effect size

We now want to focus on sample size and power analysis in some of the most common research areas.

Determination of sample size in pre-clinical studies

Animal studies are the most critical studies in terms of sample size. Especially due to ethical concerns, it is vital to keep the sample size at the lowest sufficient level. It should be noted that, animal studies are radically different from human studies because many animal studies use inbred animals having extremely similar genetic background. Thus, far fewer animals are needed in the research because genetic differences that could affect the study results are kept to a minimum ( 39 , 40 ).

Consequently, alternative sample size estimation methodologies were suggested for each study type ( 41 - 44 ). If the effect size is to be determined using the results from previous or preliminary studies, sample size estimation may be performed using G-Power. In addition, Table 4 may also be used for easy estimation of the sample size ( 40 ).

In addition to sample size estimations that may be computed according to Table 4 , formulas stated in Table 1 and the websites mentioned in Table 2 may also be utilized to estimate sample size in animal studies. Relying on previous studies pose certain limitations since it may not always be possible to acquire reliable “pooled standard deviation” and “group mean” values.

Arifin et al. proposed simpler formulas ( Table 5 ) to calculate sample size in animal studies ( 45 ). In group comparison studies, it is possible to calculate the sample size as follows: N = (DF/k)+1 (Eq. 4).

Based on acceptable range of the degrees of freedom (DF), the DF in formulas are replaced with the minimum ( 10 ) and maximum ( 20 ). For example, in an experimental animal study where the use of 3 investigational drugs are tested minimum number of animals that will be required: N = (10/3)+1 = 4.3; rounded up to 5 animals / group, total sample size = 5 x 3 = 15 animals. Maximum number of animals that will be required: N = (20/3)+1 = 7.7; rounded down to 7 animals / group, total sample size = 7 x 3 = 21 animals.

In conclusion, for the recommended study, 5 to 7 animals per group will be required. In other words, a total of 15 to 21 animals will be required to keep the DF within the range of 10 to 20.

In a compilation where Ricci et al. reviewed 15 studies involving animal models, it was noted that the sample size used was 10 in average (between 6 and 18), however, no formal power analysis was reported by any of the groups. It was striking that, all studies included in the review have used parametric analysis without prior normality testing ( i.e. Shapiro-Wilk) to justify their statistical methodology ( 46 ).

It is noteworthy that, unnecessary animal use could be prevented by keeping the power at 0.8 and selecting one-tailed analysis over two-tailed analysis with an accepted 5% risk of making type I error as performed in some pharmacological studies, reducing the number of required animals by 14% ( 47 ).

Neumann et al. proposed a group-sequential design to minimize animal use without a decrease in statistical power. In this strategy, researchers started the experiments with only 30% of the animals that were initially planned to be included in the study. After an interim analysis of the results obtained with 30% of the animals, if sufficient power is not reached, another 30% is included in the study. If results from this initial 60% of the animals provide sufficient statistical power, then the rest of the animals are excused from the study. If not, the remaining animals are also included in the study. This approach was reported to save 20% of the animals in average, without leading to a decrease in statistical power ( 48 ).

Alternative sample size estimation strategies are implemented for animal testing in different countries. As an example, a local authority in southwestern Germany recommended that, in the absence of a formal sample size estimation, less than 7 animals per experimental group should be included in pilot studies and the total number of experimental animals should not exceed 100 ( 48 ).

On the other hand, it should be noted that, for a sample size of 8 to 10 animals per group, statistical significance will not be accomplished unless a large or very large ES (> 2) is expected ( 45 , 46 ). This problem remains as an important limitation for animal studies. Software like G-Power can be used for sample size estimation. In this case, results obtained from a previous or a preliminary study will be required to be used in the calculations. However, even when a previous study is available in literature, using its data for a sample size estimation will still pose an uncertainty risk unless a clearly detailed study design and data is provided in the publication. Although researchers suggested that reliability analyses could be performed by methods such as Markov Chain Monte Carlo, further research is needed in this regard ( 49 ).

The output of the joint workshop held by The National Institutes of Health (NIH), Nature Publishing Group and Science; “Principles and Guidelines for Reporting Preclinical Research” that was published in 2014, has since been acknowledged by many organizations and journals. This guide has shed significant light on studies using biological materials, involving animal studies, and handling image-based data ( 50 ).

Another important point regarding animal studies is the use of technical repetition (pseudo replication) instead of biological repetition. Technical repetition is a specific type of repetition where the same sample is measured multiple times, aiming to probe the noise associated with the measurement method or the device. Here, no matter how many times the same sample is measured, the actual sample size will remain the same. Let us assume a research group is investigating the effect of a therapeutic drug on blood glucose level. If the researchers measure the blood glucose level of 3 mice receiving the actual treatment and 3 mice receiving placebo, this would be a biological repetition. On the other hand, if the blood glucose level of a single mouse receiving the actual treatment and the blood glucose level of a single mouse receiving placebo are each measured 3 times, this would be technical repetition. Both designs will provide 6 data points to calculate P value, yet the P value obtained from the second design would be meaningless since each treatment group will only have one member ( Figure 5 ). Multiple measurements on single mice are pseudo replication; therefore do not contribute to N. No matter how ingenious, no statistical analysis method can fix incorrectly selected replicates at the post-experimental stage; replicate types should be selected accurately at the design stage. This problem is a critical limitation, especially in pre-clinical studies that conduct cell culture experiments. It is very important for critical assessment and evaluation of the published research results ( 51 ). This issue is mostly underestimated, concealed or ignored. It is striking that in some publications, the actual sample size is found to be as low as one. Experiments comparing drug treatments in a patient-derived stem cell line are specific examples for this situation. Although there may be many technical replications for such experiments and the experiment can be repeated several times, the original patient is a single biological entity. Similarly, when six metatarsals are harvested from the front paws of a single mouse and cultured as six individual cultures, another pseudo replication is practiced where the sample size is actually 1, instead of 6 ( 52 ). Lazic et al . suggested that almost half of the studies (46%) had mistaken pseudo replication (technical repeat) for genuine replication, while 32% did not provide sufficient information to enable evaluation of appropriateness of the sample size ( 53 , 54 ).

Technical vs biological repeat.

In studies providing qualitative data (such as electrophoresis, histology, chromatography, electron microscopy), the number of replications (“number of repeats” or “sample size”) should explicitly be stated.

Especially in pre-clinical studies, standard error of the mean (SEM) is frequently used instead of SD in some situations and by certain journals. The SEM is calculated by dividing the SD by the square root of the sample size (N). The SEM will indicate how variable the mean will be if the whole study is repeated many times. Whereas the SD is a measure of how scattered the scores within a set of data are. Since SD is usually higher than SEM, researchers tend to use SEM. While SEM is not a distribution criterion; there is a relation between SEM and 95% confidence interval (CI). For example, when N = 3, 95% CI is almost equal to mean ± 4 SEM, but when N ≥ 10; 95% CI equals to mean ± 2 SEM. Standard deviation and 95% CI can be used to report the statistical analysis results such as variation and precision on the same plot to demonstrate the differences between test groups ( 52 , 55 ).

Given the attrition and unexpected death risk of the laboratory animals during the study, the researchers are generally recommended to increase the sample size by 10% ( 56 ).

Sample size calculation for some genetic studies

Sample size is important for genetic studies as well. In genetic studies, calculation of allele frequencies, calculation of homozygous and heterozygous frequencies based on Hardy-Weinberg principle, natural selection, mutation, genetic drift, association, linkage, segregation, haplotype analysis are carried out by means of probability and statistical models ( 57 - 62 ). While G-Power is useful for basic statistics, substantial amount of analyses can be conducted using genetic power calculator ( http://zzz.bwh.harvard.edu/gpc/ ) ( 61 , 62 ). This calculator, which provides automated power analysis for variance components (VC) quantitative trait locus (QTL) linkage and association tests in sibships, and other common tests, is significantly effective especially for genetics studies analysing complex diseases.

Case-control association studies for single nucleotide polymorphisms (SNPs) may be facilitated using OSSE web site ( http://osse.bii.a-star.edu.sg/ ). As an example, let us assume the minor allele frequencies of an SNP in cases and controls are approximately 15% and 7% respectively. To have a power of 0.8 with 0.05 significance, the study is required to include 239 samples both for cases and controls, adding up to 578 samples in total ( Figure 6 ).

Interface of Online Sample Size Estimator (OSSE) Tool. (Available at: http://osse.bii.a-star.edu.sg/ ).

Hong and Park have proposed tables and graphics in their article for facilitating sample size estimation ( 57 ). With the assumption of 5% disease prevalence, 5% minor allele frequency and complete linkage disequilibrium (D’ = 1), the sample size in a case-control study with a single SNP marker, 1:1 case-to-control ratio, 0.8 statistical power, and 5% type I error rate can be calculated according to the genetic models of inheritance (allelic, additive, dominant, recessive, and co-dominant models) and the odd ratios of heterozygotes/rare homozygotes ( Table 6 ). As demonstrated by Hong and Park among all other types of inheritance, dominant inheritance requires the lowest sample size to achieve 0.8 statistical power. Whereas, testing a single SNP in a recessive inheritance model requires a very large sample size even with a high homozygote ratio, that is practically challenging with a limited budget ( 57 ). The Table 6 illustrates the difficulty in detecting a disease allele following a recessive mode of inheritance with moderate sample size.

Sample size and power analyses in clinical studies

In clinical research, sample size is calculated in line with the hypothesis and study design. The cross-over study design and parallel study design apply different approaches for sample size estimation. Unlike pre-clinical studies, a significant number of clinical journals necessitate sample size estimation for clinical studies.

The basic rules for sample size estimation in clinical trials are as follows ( 63 , 64 ):

• Error level (alpha): It is generally set as < 0.05. The sample size should be increased to compensate for the decrease in the effect size.
• Power must be > 0.8: The sample size should be increased to increase the power of the study. The higher the power, the lower the risk of missing an actual effect.

The relationship among clinical significance, statistical significance, power and effect size. In the example above, in order to provide a clinically significant effect, a treatment is required to trigger at least 0.5 mmol/L decreases in cholesterol levels. Four different scenarios are given for a candidate treatment, each having different mean total cholesterol change and 95% confidence interval. ES - effect size. N – number of participant. Adapted from reference 65 .

• Similarity and equivalence: The sample size required demonstrating similarity and equivalence is very low.

Sample size estimation can be performed manually using the formulas in Table 1 as well as software and websites in Table 2 (especially by G-Power). However, all of these calculations require preliminary results or previous study outputs regarding the hypothesis of interest. Sample size estimations are difficult in complex or mixed study designs. In addition: a) unplanned interim analysis, b) planned interim analysis and

• adjustments for common variables may be required for sample size estimation.

In addition, post-hoc power analysis (possible with G-Power, PASS) following the study significantly facilitates the evaluation of the results in clinical studies.

A number of high-quality journals emphasize that the statistical significance is not sufficient on its own. In fact, they would require evaluation of the results in terms of effect size and clinical effect as well as statistical significance.

In order to fully comprehend the effect size, it would be useful to know the study design in detail and evaluate the effect size with respect to the type of the statistical tests conducted as provided in Table 3 .

Hence, the sample size is one of the critical steps in planning clinical trials, and any negligence or shortcomings in its estimate may lead to rejection of an effective drug, process, or marker. Since statistical concepts have crucial roles in calculating the sample size, sufficient statistical expertise is of paramount importance for these vital studies.

Sample size, effect size and power calculation in laboratory studies

In clinical laboratories, software such as G-Power, Medcalc, Minitab, and Stata can be used for group comparisons (such as t-tests, Mann Whitney U, Wilcoxon, ANOVA, Friedman, Chi-square, etc. ), correlation analyses (Pearson, Spearman, etc .) and regression analyses.

Effect size that can be calculated according to the methods mentioned in Table 3 is important in clinical laboratories as well. However, there are additional important criteria that must be considered while investigating differences or relationships. Especially the guidelines (such as CLSI, RiliBÄK, CLIA, ISO documents) that were established according to many years of experience, and results obtained from biological variation studies provide us with essential information and critical values primarily on effect size and sometimes on sample size.

Furthermore, in addition to the statistical significance (P value interpretation), different evaluation criteria are also important for the assessment of the effect size. These include precision, accuracy, coefficient of variation (CV), standard deviation, total allowable error, bias, biological variation, and standard deviation index, etc . as recommended and elaborated by various guidelines and reference literature ( 66 - 70 ).

In this section, we will assess sample size, effect size, and power for some analysis types used in clinical laboratories.

Sample size in method and device comparisons

Sample size is a critical determinant for Linear, Passing Bablok, and Deming regression studies that are predominantly being used in method comparison studies. Sample size estimations for the Passing-Bablok and Deming method comparison studies are exemplified in Table 7 and Table 8 respectively. As seen in these tables, sample size estimations are based on slope, analytical precision (% CV), and range ratio (c) value ( 66 , 67 ). These tables might seem quite complicated for some researchers that are not familiar with statistics. Therefore, in order to further simplify sample size estimation; reference documents and guidelines have been prepared and published. As stated in CLSI EP09-A3 guideline, the general recommendation for the minimum sample size for validation studies to be conducted by the manufacturer is 100; while the minimum sample size for user-conducted verification is 40 ( 68 ). In addition, these documents clearly explain the requirements that should be considered while collecting the samples for method/device comparison studies. For instance, samples should be homogeneously dispersed covering the whole detection range. Hence, it should be kept in mind that randomly selected 40-100 sample will not be sufficient for impeccable method comparison ( 68 ).

Additionally, comparison studies might be carried out in clinical laboratories for other purposes; such as inter-device, where usage of relatively few samples is suggested to be sufficient. For method comparison studies to be conducted using patient samples; sample size estimation, and power analysis methodologies, in addition to the required number of replicates are defined in CLSI document EP31-A-IR. The critical point here is to know the values of constant difference, within-run standard deviation, and total sample standard deviation ( 69 ). While studies that compare devices having high analytical performance would suffice lower sample size; studies comparing devices with lower analytical performance would require higher sample size.

Lu et al. used maximum allowed differences for calculating sample sizes that would be required in Bland Altman comparison studies. This type of sample size estimation, which is critically important in laboratory medicine, can easily be performed using Medcalc software ( 70 ).

Sample size in lot to lot variation studies

It is acknowledged that lot-to-lot variation may influence the test results. In line with this, method comparison is also recommended to monitor the performance of the kit in use, between lot changes. To aid in the sample size estimation of these studies; CLSI has prepared the EP26-A guideline “User evaluation of between-reagent lot variation; approved guideline”, which provides a methodology like EP31-A-IR ( 71 , 72 ).

The Table 9 presents sample size and power values of a lot-to-lot variation study comparing glucose measurements at 3 different concentrations. In this example, if the difference in the glucose values measured by different lots is > 0.2 mmol/L, > 0.58 mmol/L and > 1.16 mmol/L at analyte concentrations of 2.77 mmol/L, 8.32 mmol/L and 16.65 mmol/L respectively, lots would be confirmed to be different. In a scenario where one sample is used for each concentration; if the lot-to-lot variation results obtained from each of the three different concentrations are lower than the rejection limits (meaning that the precision values for the tested lots are within the acceptance limits), then the lot variation is accepted to lie within the acceptance range. While the example for glucose measurements presented in the guideline suggests that “1 sample” would be sufficient at each analyte concentration, it should be noted that sample size might vary according to the number to devices to be tested, analytical performance results of the devices ( i.e. precision), total allowable error, etc. For different analytes and scenarios ( i.e. for occasions where one sample/concentration is not sufficient), researchers need to refer CLSI EP26-A ( 71 ).

Some researchers find CLSI EP26-A and CLSI EP31 rather complicated for estimating the sample size in lot-to-lot variation and method comparison studies (which are similar to a certain extent). They instead prefer to use the sample size (number of replicates) suggested by Mayo Laboratories. Mayo Laboratories decided that lot-to-lot variation studies may be conducted using 20 human samples where the data are analysed by Passing-Bablok regression and accepted according to the following criteria: a) slope of the regression line will lie between 0.9 and 1.1; b) R2 coefficient of determination will be > 0.95; c) the Y-intercept of the regression line will be < 50% of the lowest reportable concentration, d) difference of the means between reagent lots will be < 10% ( 73 ).

Sample size in verification studies

Acceptance limits should be defined before the verification and validation studies. These could be determined according to clinical cut-off values, biological variation, CLIA criteria, RiliBÄK criteria, criteria defined by the manufacturer, or state of the art criteria. In verification studies, the “sample size” and the “minimum proportion of the observed samples required to lie within the CI limits” are proportional. For instance, for a 50-sample study, 90% of the samples are required to lie within the CI limits for approval of the verification; while for a 200-sample study, 93% is required ( Table 10 ). In an example study whose total allowable error (TAE) is specified as 15%; 50 samples were measured. Results of the 46 samples (92% of all samples) lied within the TAE limit of 15%. Since the proportion of the samples having results within the 15% TAE limit (92% of the samples) exceeds the minimum proportion required to lie within the TAE limits (90% of the samples), the method is verified ( 74 ).

Especially in recent years, researchers tend to use CLSI EP15-A3 or alternative strategies relying on EP15-A3, for verification analyses. While the alternative strategies diverge from each other in many ways, most of them necessitate a sample size of at least 20 ( 75 - 78 ). Yet, for bias studies, especially for the ones involving External Quality Control materials, even lower sample sizes ( i.e. 10) may be observed ( 79 ). Verification still remains to be one of the critical problems for clinical laboratories. It is not possible to find a single criteria and a single verification method that fits all test methods ( i.e. immunological, chemical, chromatographical, etc. ).

While sample size for qualitative laboratory tests may vary according to the reference literature and the experimental context, CLSI EP12 recommends at least 50 positive and 50 negative samples, where 20% of the samples from each group are required to fall within cut-off value +/- 20% ( 80 , 81 ). According to the clinical microbiology validation/verification guideline Cumitech 31A, the minimum number of the samples in positive and negative groups is 100/each group for validation studies, and 10/each group for verification studies ( 82 ).

Sample size in diagnostic and prognostic studies

ROC analysis is the most important statistical analysis in diagnostic and prognostic studies. Although sample size estimation for ROC analyses might be slightly complicated; Medcalc, PASS, and Stata may be used to facilitate the estimation process. Before the actual size estimations, it is a prerequisite for the researcher to calculate potential area under the curve (AUC) using data from previous or preliminary studies. In addition, size estimation may also be calculated manually according to Table 1 , or using sensitivity (or TPF) and 1-specificity (FPF) values according to Table 11 which is adapted from CLSI EP24-A2 ( 83 , 84 ).

As is known, X-axis of the ROC curve is FPF, and Y-axis is TPF. While TPF represents sensitivity, FPF represents 1-specificity. Utilizing Table 11 , for a 0.85 sensitivity, 0.90 specificity and a maximum allowable error of 5% (L = 0.05), 196 positive and 139 negative samples are required. For the scenarios not included in this table, reader should refer to the formulas given under “diagnostic prognostic studies” subsection of Table 1 .

Standards for reporting of diagnostic accuracy studies (STARD) checklist may be followed for diagnostic studies. It is a powerful checklist whose application is explained in detail by Cohen et al. and Flaubaut et al. ( 85 , 86 ). This document suggests that, readers demand to understand the anticipated precision and power of the study and whether authors were successful in recruiting the sufficient number of participants; therefore it is critical for the authors to explain the intended sample size of their study and how it was determined. For this reason, in diagnostic and prognostic studies, sample size and power should clearly be stated.

As can be seen here, the critical parameters for sample size estimation are AUC, specificity and sensitivity, and their 95% CI values. The table 12 demonstrates the relationship of sample size with sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV); the lower the sample size, the higher is the 95% CI values, leading to increase in type II errors ( 87 ). As can be seen here, confidence interval is narrowed as the sample size increases, leading to a decrease in type II errors.

Like all sample size calculations, preliminary information is required for sample size estimations in diagnostic and prognostic studies. Yet, variation occurs among sample size estimates that are calculated according to different reference literature or guidelines. This variation is especially prominent depending on the specific requirements of different countries and local authorities.

While sample size calculations for ROC analyses may easily be performed via Medcalc, the method explained by Hanley et al. and Delong et al. may be utilized to calculate sample size in studies comparing different ROC curves ( 88 , 89 ).

Sample size for reference interval determination

Both IFCC working groups and the CLSI guideline C28-A3c offer suggestions regarding sample size estimations in reference interval studies ( 90 - 93 ). These references mainly suggest at least 120 samples should be included for each study sub-group ( i.e., age-group, gender, race, etc. ). In addition, the guideline also states that, at least 20 samples should be studied for verification of the determined reference intervals.

Since extremes of the observed values may under/over-represent the actual percentile values of a population in nonparametric studies, care should be taken not to rely solely on the extreme values while determining the nonparametric 95% reference interval. Reed et al. suggested a minimum sample size of 120 to be used for 90% CI, 146 for 95% CI, and 210 for 99% CI (93). Linnet proposed that up to 700 samples should be obtained for results having highly skewed distributions ( 94 ). The IFCC Committee on Reference Intervals and Decision Limits working group recommends a minimum of 120 reference subjects for nonparametric methods, to obtain results within 90% CI limits ( 90 ).

Due to the inconvenience of the direct method, in addition to the challenges encountered using paediatric and geriatric samples as well as the samples obtained from complex biological fluids ( i.e. cerebrospinal fluid); indirect sample size estimations using patient results has gained significant importance in recent years. Hoffmann method, Bhattacharya method or their modified versions may be used for indirect determination of the reference intervals ( 95 - 101 ). While a specific sample size is not established, sample size between 1000 and 10.000 is recommended for each sub-group. For samples that cannot be easily acquired ( i.e. paediatric and geriatric samples, and complex biological fluids), sample sizes as low as 400 may be used for each sub-group ( 92 , 100 ).

Sample size in survey studies

The formulations given on Table 1 and the websites mentioned on Table 2 will be particularly useful for sample size estimations in survey studies which are dependent primarily on the population size ( 101 ).

Three critical aspects should be determined for sample size determination in survey studies:

• Population size

• Confidence Interval (CI) of 95% means that, when the study is repeated, with 95% probability, the same results will be obtained. Depending on the hypothesis and the study aim, confidence interval may lie between 90% and 99%. Confidence interval below 90% is not recommended.

For a given CI, sample size and ME is inversely proportional; sample size should be increased in order to obtain a narrower ME. On the contrary, for a fixed ME, CI and sample size is directly proportional; in order to obtain a higher CI, the sample size should be increased. In addition, sample size is directly proportional to the population size; higher sample size should be used for a larger population. A variation in ME causes a more drastic change in sample size than a variation in CI. As exemplified in Table 13 , for a population of 10,000 people, a survey with a 95% CI and 5% ME would require at least 370 samples. When CI is changed from 95% to 90% or 99%, the sample size which was 370 initially would change into 264 or 623 respectively. Whereas, when ME is changed from 5% to 10% or 1%; the sample size which was initially 370 would change into 96 or 4900 respectively. For other ME and CI levels, the researcher should refer to the equations and software provided on Table 1 and Table 2 .

The situation is slightly different for the survey studies to be conducted for problem detection. It would be most appropriate to perform a preliminary survey with a small sample size, followed by a power analysis, and completion of the study using the appropriate number of samples estimated based on the power analysis. While 30 is suggested as a minimum sample size for the preliminary studies, the optimal sample size can be determined using the formula suggested in Table 14 which is based on the prevalence value ( 103 ). It is unlikely to reach a sufficient power for revealing of uncommon problems (prevalence 0.02) at small sample sizes. As can be seen on the table, in the case of 0.02 prevalence, a sample size of 30 would yield a power of 0.45. In contrast, frequent problems ( i.e. prevalence 0.30) were discovered with higher power (0.83) even when the sample size was as low as 5. For situations where power and prevalence are known, effective sample size can easily be estimated using the formula in Table 1 .

Does big sample size always increase the impact of a study?

While larger sample size may provide researchers with great opportunities, it may create problems in interpretation of statistical significance and clinical impact. Especially in studies with big sample sizes, it is critically important for the researchers not to rely only on the magnitude of the regression (or correlation) coefficient, and the P value. The study results should be evaluated together with the effect size, study efficiencies ( i.e. basic research, clinical laboratory, and clinical studies) and confidence interval levels. Monte Carlo simulations could be utilized for statistical evaluations of the big data results ( 18 , 104 ).

As a result, sample size estimation is a critical step for scientific studies and may show significant differences according to research types. It is important that sample size estimation is planned ahead of the study, and may be performed through various routes:

• If a similar previous study is available, or preliminary results of the current study are present, their results may be used for sample size estimations via the websites and software mentioned in Table 1 and Table 2 . Some of these software may also be used to calculate effect size and power.
• If the magnitude of the measurand variation that is required for a substantial clinical effect is available ( i.e. significant change is 0.51 mmol/L for cholesterol, 26.5 mmol/L for creatinine, etc. ), it may be used for sample size estimation ( Figure 7 ). Presence of Total Allowable Error, constant and critical differences, biological variations, reference change value (RCV), etc. will further aid in sample size estimation process. Free software (especially G-Power) and web sites presented on Table 2 will facilitate calculations.
• If effect size can be calculated by a preliminary study, sample size estimations may be performed using the effect size ( via G-Power, Table 4 , etc. )
• In the absence of a previous study, if a preliminary study cannot be performed, an effect size may be initially estimated and be used for sample size estimations
• If none of the above is available or possible, relevant literature may be used for sample size estimation.
• For clinical laboratories, especially CLSI documents and guidelines may prove useful for sample size estimation ( Table 9,11 ​ 9,11 ).

Sample size estimations may be rather complex, requiring advanced knowledge and experience. In order to properly appreciate the concept and perform precise size estimation, one should comprehend properties of different study techniques and relevant statistics to certain extend. To assist researchers in different fields, we aimed to compile useful guidelines, references and practical software for calculating sample size and effect size in various study types. Sample size estimation and the relationship between P value and effect size are key points for comprehension and evaluation of biological studies. Evaluation of statistical significance together with the effect size is critical for both basic science, and clinical and laboratory studies. Therefore, effect size and confidence intervals should definitely be provided and its impact on the laboratory/clinical results should be discussed thoroughly.

Potential conflict of interest

None declared.

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• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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