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Significance of the Study – Examples and Writing Guide

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Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is the Significance of the Study?

• By DiscoverPhDs
• August 25, 2020

• what the significance of the study means,
• why it’s important to include in your research work,
• where you would include it in your paper, thesis or dissertation,
• how you write one
• and finally an example of a well written section about the significance of the study.

What does Significance of the Study mean?

The significance of the study is a written statement that explains why your research was needed. It’s a justification of the importance of your work and impact it has on your research field, it’s contribution to new knowledge and how others will benefit from it.

Why is the Significance of the Study important?

The significance of the study, also known as the rationale of the study, is important to convey to the reader why the research work was important. This may be an academic reviewer assessing your manuscript under peer-review, an examiner reading your PhD thesis, a funder reading your grant application or another research group reading your published journal paper. Your academic writing should make clear to the reader what the significance of the research that you performed was, the contribution you made and the benefits of it.

How do you write the Significance of the Study?

When writing this section, first think about where the gaps in knowledge are in your research field. What are the areas that are poorly understood with little or no previously published literature? Or what topics have others previously published on that still require further work. This is often referred to as the problem statement.

The introduction section within the significance of the study should include you writing the problem statement and explaining to the reader where the gap in literature is.

Then think about the significance of your research and thesis study from two perspectives: (1) what is the general contribution of your research on your field and (2) what specific contribution have you made to the knowledge and who does this benefit the most.

For example, the gap in knowledge may be that the benefits of dumbbell exercises for patients recovering from a broken arm are not fully understood. You may have performed a study investigating the impact of dumbbell training in patients with fractures versus those that did not perform dumbbell exercises and shown there to be a benefit in their use. The broad significance of the study would be the improvement in the understanding of effective physiotherapy methods. Your specific contribution has been to show a significant improvement in the rate of recovery in patients with broken arms when performing certain dumbbell exercise routines.

This statement should be no more than 500 words in length when written for a thesis. Within a research paper, the statement should be shorter and around 200 words at most.

Significance of the Study: An example

Building on the above hypothetical academic study, the following is an example of a full statement of the significance of the study for you to consider when writing your own. Keep in mind though that there’s no single way of writing the perfect significance statement and it may well depend on the subject area and the study content.

The statement of the significance of the study is used by students and researchers in academic writing to convey the importance of the research performed; this section is written at the end of the introduction and should describe the specific contribution made and who it benefits.

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How To Write Significance of the Study (With Examples) 

Whether you’re writing a research paper or thesis, a portion called Significance of the Study ensures your readers understand the impact of your work. Learn how to effectively write this vital part of your research paper or thesis through our detailed steps, guidelines, and examples.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

What is the significance of the study.

The Significance of the Study presents the importance of your research. It allows you to prove the study’s impact on your field of research, the new knowledge it contributes, and the people who will benefit from it.

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

Where Should I Put the Significance of the Study?

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The Significance of the Study is part of the first chapter or the Introduction. It comes after the research’s rationale, problem statement, and hypothesis.

Related: How to Make Conceptual Framework (with Examples and Templates)

Why Should I Include the Significance of the Study?

The purpose of the Significance of the Study is to give you space to explain to your readers how exactly your research will be contributing to the literature of the field you are studying 1 . It’s where you explain why your research is worth conducting and its significance to the community, the people, and various institutions.

How To Write Significance of the Study: 5 Steps

Below are the steps and guidelines for writing your research’s Significance of the Study.

1. Use Your Research Problem as a Starting Point

Your problem statement can provide clues to your research study’s outcome and who will benefit from it 2 .

Ask yourself, “How will the answers to my research problem be beneficial?”. In this manner, you will know how valuable it is to conduct your study. 

Let’s say your research problem is “What is the level of effectiveness of the lemongrass (Cymbopogon citratus) in lowering the blood glucose level of Swiss mice (Mus musculus)?”

Discovering a positive correlation between the use of lemongrass and lower blood glucose level may lead to the following results:

• Increased public understanding of the plant’s medical properties;
• Higher appreciation of the importance of lemongrass  by the community;
• Adoption of lemongrass tea as a cheap, readily available, and natural remedy to lower their blood glucose level.

Once you’ve zeroed in on the general benefits of your study, it’s time to break it down into specific beneficiaries.

2. State How Your Research Will Contribute to the Existing Literature in the Field

Think of the things that were not explored by previous studies. Then, write how your research tackles those unexplored areas. Through this, you can convince your readers that you are studying something new and adding value to the field.

3. Explain How Your Research Will Benefit Society

In this part, tell how your research will impact society. Think of how the results of your study will change something in your community. 

For example, in the study about using lemongrass tea to lower blood glucose levels, you may indicate that through your research, the community will realize the significance of lemongrass and other herbal plants. As a result, the community will be encouraged to promote the cultivation and use of medicinal plants.

4. Mention the Specific Persons or Institutions Who Will Benefit From Your Study

Using the same example above, you may indicate that this research’s results will benefit those seeking an alternative supplement to prevent high blood glucose levels.

5. Indicate How Your Study May Help Future Studies in the Field

You must also specifically indicate how your research will be part of the literature of your field and how it will benefit future researchers. In our example above, you may indicate that through the data and analysis your research will provide, future researchers may explore other capabilities of herbal plants in preventing different diseases.

Tips and Warnings

• Think ahead . By visualizing your study in its complete form, it will be easier for you to connect the dots and identify the beneficiaries of your research.
• Write concisely. Make it straightforward, clear, and easy to understand so that the readers will appreciate the benefits of your research. Avoid making it too long and wordy.
• Go from general to specific . Like an inverted pyramid, you start from above by discussing the general contribution of your study and become more specific as you go along. For instance, if your research is about the effect of remote learning setup on the mental health of college students of a specific university , you may start by discussing the benefits of the research to society, to the educational institution, to the learning facilitators, and finally, to the students.
• Seek help . For example, you may ask your research adviser for insights on how your research may contribute to the existing literature. If you ask the right questions, your research adviser can point you in the right direction.
• Revise, revise, revise. Be ready to apply necessary changes to your research on the fly. Unexpected things require adaptability, whether it’s the respondents or variables involved in your study. There’s always room for improvement, so never assume your work is done until you have reached the finish line.

Significance of the Study Examples

This section presents examples of the Significance of the Study using the steps and guidelines presented above.

Example 1: STEM-Related Research

Research Topic: Level of Effectiveness of the Lemongrass ( Cymbopogon citratus ) Tea in Lowering the Blood Glucose Level of Swiss Mice ( Mus musculus ).

Significance of the Study .

This research will provide new insights into the medicinal benefit of lemongrass ( Cymbopogon citratus ), specifically on its hypoglycemic ability.

Through this research, the community will further realize promoting medicinal plants, especially lemongrass, as a preventive measure against various diseases. People and medical institutions may also consider lemongrass tea as an alternative supplement against hyperglycemia. 

Moreover, the analysis presented in this study will convey valuable information for future research exploring the medicinal benefits of lemongrass and other medicinal plants.  

Example 2: Business and Management-Related Research

Research Topic: A Comparative Analysis of Traditional and Social Media Marketing of Small Clothing Enterprises.

Significance of the Study:

By comparing the two marketing strategies presented by this research, there will be an expansion on the current understanding of the firms on these marketing strategies in terms of cost, acceptability, and sustainability. This study presents these marketing strategies for small clothing enterprises, giving them insights into which method is more appropriate and valuable for them. 

Specifically, this research will benefit start-up clothing enterprises in deciding which marketing strategy they should employ. Long-time clothing enterprises may also consider the result of this research to review their current marketing strategy.

Furthermore, a detailed presentation on the comparison of the marketing strategies involved in this research may serve as a tool for further studies to innovate the current method employed in the clothing Industry.

Example 3: Social Science -Related Research.

Research Topic:  Divide Et Impera : An Overview of How the Divide-and-Conquer Strategy Prevailed on Philippine Political History.

Significance of the Study :

Through the comprehensive exploration of this study on Philippine political history, the influence of the Divide et Impera, or political decentralization, on the political discernment across the history of the Philippines will be unraveled, emphasized, and scrutinized. Moreover, this research will elucidate how this principle prevailed until the current political theatre of the Philippines.

In this regard, this study will give awareness to society on how this principle might affect the current political context. Moreover, through the analysis made by this study, political entities and institutions will have a new approach to how to deal with this principle by learning about its influence in the past.

In addition, the overview presented in this research will push for new paradigms, which will be helpful for future discussion of the Divide et Impera principle and may lead to a more in-depth analysis.

Example 4: Humanities-Related Research

Research Topic: Effectiveness of Meditation on Reducing the Anxiety Levels of College Students.

Significance of the Study: 

This research will provide new perspectives in approaching anxiety issues of college students through meditation. 

Specifically, this research will benefit the following:

 Community – this study spreads awareness on recognizing anxiety as a mental health concern and how meditation can be a valuable approach to alleviating it.

Academic Institutions and Administrators – through this research, educational institutions and administrators may promote programs and advocacies regarding meditation to help students deal with their anxiety issues.

Mental health advocates – the result of this research will provide valuable information for the advocates to further their campaign on spreading awareness on dealing with various mental health issues, including anxiety, and how to stop stigmatizing those with mental health disorders.

Parents – this research may convince parents to consider programs involving meditation that may help the students deal with their anxiety issues.

Students will benefit directly from this research as its findings may encourage them to consider meditation to lower anxiety levels.

Future researchers – this study covers information involving meditation as an approach to reducing anxiety levels. Thus, the result of this study can be used for future discussions on the capabilities of meditation in alleviating other mental health concerns.

Frequently Asked Questions

1. what is the difference between the significance of the study and the rationale of the study.

Both aim to justify the conduct of the research. However, the Significance of the Study focuses on the specific benefits of your research in the field, society, and various people and institutions. On the other hand, the Rationale of the Study gives context on why the researcher initiated the conduct of the study.

Let’s take the research about the Effectiveness of Meditation in Reducing Anxiety Levels of College Students as an example. Suppose you are writing about the Significance of the Study. In that case, you must explain how your research will help society, the academic institution, and students deal with anxiety issues through meditation. Meanwhile, for the Rationale of the Study, you may state that due to the prevalence of anxiety attacks among college students, you’ve decided to make it the focal point of your research work.

2. What is the difference between Justification and the Significance of the Study?

In Justification, you express the logical reasoning behind the conduct of the study. On the other hand, the Significance of the Study aims to present to your readers the specific benefits your research will contribute to the field you are studying, community, people, and institutions.

Suppose again that your research is about the Effectiveness of Meditation in Reducing the Anxiety Levels of College Students. Suppose you are writing the Significance of the Study. In that case, you may state that your research will provide new insights and evidence regarding meditation’s ability to reduce college students’ anxiety levels. Meanwhile, you may note in the Justification that studies are saying how people used meditation in dealing with their mental health concerns. You may also indicate how meditation is a feasible approach to managing anxiety using the analysis presented by previous literature.

3. How should I start my research’s Significance of the Study section?

– This research will contribute… – The findings of this research… – This study aims to… – This study will provide… – Through the analysis presented in this study… – This study will benefit…

Moreover, you may start the Significance of the Study by elaborating on the contribution of your research in the field you are studying.

4. What is the difference between the Purpose of the Study and the Significance of the Study?

The Purpose of the Study focuses on why your research was conducted, while the Significance of the Study tells how the results of your research will benefit anyone.

Suppose your research is about the Effectiveness of Lemongrass Tea in Lowering the Blood Glucose Level of Swiss Mice . You may include in your Significance of the Study that the research results will provide new information and analysis on the medical ability of lemongrass to solve hyperglycemia. Meanwhile, you may include in your Purpose of the Study that your research wants to provide a cheaper and natural way to lower blood glucose levels since commercial supplements are expensive.

5. What is the Significance of the Study in Tagalog?

In Filipino research, the Significance of the Study is referred to as Kahalagahan ng Pag-aaral.

• Draft your Significance of the Study. Retrieved 18 April 2021, from http://dissertationedd.usc.edu/draft-your-significance-of-the-study.html
• Regoniel, P. (2015). Two Tips on How to Write the Significance of the Study. Retrieved 18 April 2021, from https://simplyeducate.me/2015/02/09/significance-of-the-study/

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.

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How To Write a Significance Statement for Your Research

A significance statement is an essential part of a research paper. It explains the importance and relevance of the study to the academic community and the world at large. To write a compelling significance statement, identify the research problem, explain why it is significant, provide evidence of its importance, and highlight its potential impact on future research, policy, or practice. A well-crafted significance statement should effectively communicate the value of the research to readers and help them understand why it matters.

Updated on May 4, 2023

A significance statement is a clearly stated, non-technical paragraph that explains why your research matters. It’s central in making the public aware of and gaining support for your research.

Write it in jargon-free language that a reader from any field can understand. Well-crafted, easily readable significance statements can improve your chances for citation and impact and make it easier for readers outside your field to find and understand your work.

Read on for more details on what a significance statement is, how it can enhance the impact of your research, and, of course, how to write one.

What is a significance statement in research?

A significance statement answers the question: How will your research advance scientific knowledge and impact society at large (as well as specific populations)? 

You might also see it called a “Significance of the study” statement. Some professional organizations in the STEM sciences and social sciences now recommended that journals in their disciplines make such statements a standard feature of each published article. Funding agencies also consider “significance” a key criterion for their awards.

Read some examples of significance statements from the Proceedings of the National Academy of Sciences (PNAS) here .

Depending upon the specific journal or funding agency’s requirements, your statement may be around 100 words and answer these questions:

1. What’s the purpose of this research?

2. What are its key findings?

3. Why do they matter?

4. Who benefits from the research results?

Readers will want to know: “What is interesting or important about this research?” Keep asking yourself that question.

Where to place the significance statement in your manuscript

Most journals ask you to place the significance statement before or after the abstract, so check with each journal’s guide. 

This article is focused on the formal significance statement, even though you’ll naturally highlight your project’s significance elsewhere in your manuscript. (In the introduction, you’ll set out your research aims, and in the conclusion, you’ll explain the potential applications of your research and recommend areas for future research. You’re building an overall case for the value of your work.)

Developing the significance statement

The main steps in planning and developing your statement are to assess the gaps to which your study contributes, and then define your work’s implications and impact.

Identify what gaps your study fills and what it contributes

Your literature review was a big part of how you planned your study. To develop your research aims and objectives, you identified gaps or unanswered questions in the preceding research and designed your study to address them.

Go back to that lit review and look at those gaps again. Review your research proposal to refresh your memory. Ask:

• How have my research findings advanced knowledge or provided notable new insights?
• How has my research helped to prove (or disprove) a hypothesis or answer a research question?
• Why are those results important?

Consider your study’s potential impact at two levels: 

• What contribution does my research make to my field?
• How does it specifically contribute to knowledge; that is, who will benefit the most from it?

Define the implications and potential impact

As you make notes, keep the reasons in mind for why you are writing this statement. Whom will it impact, and why?

The first audience for your significance statement will be journal reviewers when you submit your article for publishing. Many journals require one for manuscript submissions. Study the author’s guide of your desired journal to see its criteria ( here’s an example ). Peer reviewers who can clearly understand the value of your research will be more likely to recommend publication. 

Second, when you apply for funding, your significance statement will help justify why your research deserves a grant from a funding agency . The U.S. National Institutes of Health (NIH), for example, wants to see that a project will “exert a sustained, powerful influence on the research field(s) involved.” Clear, simple language is always valuable because not all reviewers will be specialists in your field.

Third, this concise statement about your study’s importance can affect how potential readers engage with your work. Science journalists and interested readers can promote and spread your work, enhancing your reputation and influence. Help them understand your work.

You’re now ready to express the importance of your research clearly and concisely. Time to start writing.

How to write a significance statement: Key elements 

When drafting your statement, focus on both the content and writing style.

• In terms of content, emphasize the importance, timeliness, and relevance of your research results. 
• Write the statement in plain, clear language rather than scientific or technical jargon. Your audience will include not just your fellow scientists but also non-specialists like journalists, funding reviewers, and members of the public. 

Follow the process we outline below to build a solid, well-crafted, and informative statement. 

Get started

Some suggested opening lines to help you get started might be:

• The implications of this study are… 
• Building upon previous contributions, our study moves the field forward because…
• Our study furthers previous understanding about…

Alternatively, you may start with a statement about the phenomenon you’re studying, leading to the problem statement.

Include these components

Next, draft some sentences that include the following elements. A good example, which we’ll use here, is a significance statement by Rogers et al. (2022) published in the Journal of Climate .

1. Briefly situate your research study in its larger context . Start by introducing the topic, leading to a problem statement. Here’s an example:

‘Heatwaves pose a major threat to human health, ecosystems, and human systems.”

2. State the research problem.

“Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages.”

3. Tell what your study does to address it.

“We assess recent changes in the occurrence of simultaneous large heatwaves.”

4. Provide brief but powerful evidence to support the claims your statement is making , Use quantifiable terms rather than vague ones (e.g., instead of “This phenomenon is happening now more than ever,” see below how Rogers et al. (2022) explained it). This evidence intensifies and illustrates the problem more vividly:

“Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia.

5. Relate your study’s impact to the broader context , starting with its general significance to society—then, when possible, move to the particular as you name specific applications of your research findings. (Our example lacks this second level of application.) 

“Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.”

Refine your English

Don’t understate or overstate your findings – just make clear what your study contributes. When you have all the elements in place, review your draft to simplify and polish your language. Even better, get an expert AJE edit . Be sure to use “plain” language rather than academic jargon.

• Avoid acronyms, scientific jargon, and technical terms 
• Use active verbs in your sentence structure rather than passive voice (e.g., instead of “It was found that...”, use “We found...”)
• Make sentence structures short, easy to understand – readable
• Try to address only one idea in each sentence and keep sentences within 25 words (15 words is even better)
• Eliminate nonessential words and phrases (“fluff” and wordiness)

Enhance your significance statement’s impact

Always take time to review your draft multiple times. Make sure that you:

• Keep your language focused
• Provide evidence to support your claims
• Relate the significance to the broader research context in your field

After revising your significance statement, request feedback from a reading mentor about how to make it even clearer. If you’re not a native English speaker, seek help from a native-English-speaking colleague or use an editing service like AJE to make sure your work is at a native level.

Understanding the significance of your study

Your readers may have much less interest than you do in the specific details of your research methods and measures. Many readers will scan your article to learn how your findings might apply to them and their own research. 

Different types of significance

Your findings may have different types of significance, relevant to different populations or fields of study for different reasons. You can emphasize your work’s statistical, clinical, or practical significance. Editors or reviewers in the social sciences might also evaluate your work’s social or political significance.

Statistical significance means that the results are unlikely to have occurred randomly. Instead, it implies a true cause-and-effect relationship.

Clinical significance means that your findings are applicable for treating patients and improving quality of life.

Practical significance is when your research outcomes are meaningful to society at large, in the “real world.” Practical significance is usually measured by the study’s  effect size . Similarly, evaluators may attribute social or political significance to research that addresses “real and immediate” social problems.

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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

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Part of the Research in Mathematics Education book series (RME)

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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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
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• Academic Writing Style
• Choosing a Title
• Making an Outline
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• Executive Summary
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
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• 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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The significance of a study is its importance . It refers to the contribution(s) to and impact of the study on a research field. The significance also signals who benefits from the research findings and how.

Purpose of writing the significance of a study

A study’s significance should spark the interest of the reader. Researchers will be able to appreciate your work better when they understand the relevance and its (potential) impact. Peer reviewers also assess the significance of the work, which will influence the decision made (acceptance/rejection) on the manuscript. 

Sections in which the significance of the study is written

Introduction.

In the Introduction of your paper, the significance appears where you talk about the potential importance and impact of the study. It should flow naturally from the problem , aims and objectives, and rationale .

The significance is described in more detail in the concluding paragraph(s) of the Discussion or the dedicated Conclusions section. Here, you put the findings into perspective and outline the contributions of the findings in terms of implications and applications.

The significance may or may not appear in the abstract . When it does, it is written in the concluding lines of the abstract.

Significance vs. other introductory elements of your paper

In the Introduction…

• The problem statement outlines the concern that needs to be addressed.
• The research aim describes the purpose of the study.
• The objectives indicate how that aim will be achieved.
• The rationale explains why you are performing the study.
• The significance tells the reader how the findings affect the topic/broad field. In other words, the significance is about how much the findings matter.

How to write the significance of the study

A good significance statement may be written in different ways. The approach to writing it also depends on the study area. In the arts and humanities , the significance statement might be longer and more descriptive. In applied sciences , it might be more direct.

a. Suggested sequence for writing the significance statement

• Think of the gaps your study is setting out to address.
• Look at your research from general and specific angles in terms of its (potential) contribution .
• Once you have these points ready, start writing them, connecting them to your study as a whole.

b. Some ways to begin your statement(s) of significance

Here are some opening lines to build on:

• The particular significance of this study lies in the… 
• We argue that this study moves the field forward because…
• This study makes some important contributions to…
• Our findings deepen the current understanding about…

c. Don’ts of writing a significance statement

• Don’t make it too long .
• Don’t repeat any information that has been presented in other sections.
• Don’t overstate or exaggerat e the importance; it should match your actual findings.

Example of significance of a study

Note the significance statements highlighted in the following fictional study.

Significance in the Introduction

The effects of Miyawaki forests on local biodiversity in urban housing complexes remain poorly understood. No formal studies on negative impacts on insect activity, populations or diversity have been undertaken thus far. In this study, we compared the effects that Miyawaki forests in urban dwellings have on local pollinator activity. The findings of this study will help improve the design of this afforestation technique in a way that balances local fauna, particularly pollinators, which are highly sensitive to microclimatic changes.

Significance in the Conclusion

[…] The findings provide valuable insights for guiding and informing Miyawaki afforestation in urban dwellings. We demonstrate that urban planning and landscaping policies need to consider potential declines.

A study’s significance usually appears at the end of the Introduction and in the Conclusion to describe the importance of the research findings. A strong and clear significance statement will pique the interest of readers, as well as that of relevant stakeholders.

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• Study Background & Introduction

Q: What is meant by the significance of the study?

My study is about the lower grades of students.

Asked on 16 Jan, 2020

The significance of the study implies the importance of the study for the broader area of study, the specific question of the study, and the target group under study. In this case, the target group is students (whether of school, college, or university) and the broad area is the lower grades among these students. The specific question, I assume, will be around causes/factors, implications, or remedies for the lower grades. So, you will need to talk about how your study will be important or relevant for these various aspects of the study. The significance is written in the Introduction section of the paper, after providing the background (context) of the study. (Note that in the Discussion section, you will need to talk about the significance of the results – what the findings of the study mean for your study question – and you will need to do so in considerable detail.)

For more information on the significance of the study, you may refer to the following resources:

• What is significance of the study in research?
• What is the significance of a study and how is it stated in a research paper?

Answered by Editage Insights on 21 Jan, 2020

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Introduction

Research papers can be a real headache for college students . As a student, your research needs to be credible enough to support your thesis statement. You must also ensure you’ve discussed the literature review, findings, and results.

However, it’s also important to discuss the significance of your research . Your potential audience will care deeply about this. It will also help you conduct your research. By knowing the impact of your research, you’ll understand what important questions to answer.

If you’d like to know more about the impact of your research, read on! We’ll talk about why it’s important and how to discuss it in your paper.

What Is the Significance of Research?

This is the potential impact of your research on the field of study. It includes contributions from new knowledge from the research and those who would benefit from it. You should present this before conducting research, so you need to be aware of current issues associated with the thesis before discussing the significance of the research.

Why Does the Significance of Research Matter?

Potential readers need to know why your research is worth pursuing. Discussing the significance of research answers the following questions:

●  Why should people read your research paper ?

●  How will your research contribute to the current knowledge related to your topic?

●  What potential impact will it have on the community and professionals in the field?

Not including the significance of research in your paper would be like a knight trying to fight a dragon without weapons.

Where Do I Discuss the Significance of Research in My Paper?

As previously mentioned, the significance of research comes before you conduct it. Therefore, you should discuss the significance of your research in the Introduction section. Your reader should know the problem statement and hypothesis beforehand.

Steps to Discussing the Significance of Your Research

Discussing the significance of research might seem like a loaded question, so we’ve outlined some steps to help you tackle it.

Step 1: The Research Problem

The problem statement can reveal clues about the outcome of your research. Your research should provide answers to the problem, which is beneficial to all those concerned. For example, imagine the problem statement is, “To what extent do elementary and high school teachers believe cyberbullying affects student performance?”

Learning teachers’ opinions on the effects of cyberbullying on student performance could result in the following:

●  Increased public awareness of cyberbullying in elementary and high schools

●  Teachers’ perceptions of cyberbullying negatively affecting student performance

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●  Whether cyberbullying is more prevalent in elementary or high schools

The research problem will steer your research in the right direction, so it’s best to start with the problem statement.

Step 2: Existing Literature in the Field

Think about current information on your topic, and then find out what information is missing. Are there any areas that haven’t been explored? Your research should add new information to the literature, so be sure to state this in your discussion. You’ll need to know the current literature on your topic anyway, as this is part of your literature review section .

Step 3: Your Research’s Impact on Society

Inform your readers about the impact on society your research could have on it. For example, in the study about teachers’ opinions on cyberbullying, you could mention that your research will educate the community about teachers’ perceptions of cyberbullying as it affects student performance. As a result, the community will know how many teachers believe cyberbullying affects student performance.

You can also mention specific individuals and institutions that would benefit from your study. In the example of cyberbullying, you might indicate that school principals and superintendents would benefit from your research.

Step 4: Future Studies in the Field

Next, discuss how the significance of your research will benefit future studies, which is especially helpful for future researchers in your field. In the example of cyberbullying affecting student performance, your research could provide further opportunities to assess teacher perceptions of cyberbullying and its effects on students from larger populations. This prepares future researchers for data collection and analysis.

Discussing the significance of your research may sound daunting when you haven’t conducted it yet. However, an audience might not read your paper if they don’t know the significance of the research. By focusing on the problem statement and the research benefits to society and future studies, you can convince your audience of the value of your research.

Remember that everything you write doesn’t have to be set in stone. You can go back and tweak the significance of your research after conducting it. At first, you might only include general contributions of your study, but as you research, your contributions will become more specific.

You should have a solid understanding of your topic in general, its associated problems, and the literature review before tackling the significance of your research. However, you’re not trying to prove your thesis statement at this point. The significance of research just convinces the audience that your study is worth reading.

Finally, we always recommend seeking help from your research advisor whenever you’re struggling with ideas. For a more visual idea of how to discuss the significance of your research, we suggest checking out this video .

1. Do I need to do my research before discussing its significance?

No, you’re discussing the significance of your research before you conduct it. However, you should be knowledgeable about your topic and the related literature.

2. Is the significance of research the same as its implications?

No, the research implications are potential questions from your study that justify further exploration, which comes after conducting the research.

 3. Discussing the significance of research seems overwhelming. Where should I start?

We recommend the problem statement as a starting point, which reveals clues to the potential outcome of your research.

4. How can I get feedback on my discussion of the significance of my research?

Our proofreading experts can help. They’ll check your writing for grammar, punctuation errors, spelling, and concision. Submit a 500-word document for free today!

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Andrew G Myers Research Group

Professor Myers' research program involves the synthesis and study of complex molecules of importance in biology and human medicine. His group has developed laboratory synthetic routes to a broad array of complex natural products, including the ene-diyne antibiotics neocarzinostatin chromophore, dynemicin A, N1999A2, and kedarcidin chromophore, undertakings greatly complicated by the chemical instability of all members of the class. His laboratory developed the first practical synthetic route to the tetracycline antibiotics, allowing for the synthesis of more than three thousand fully synthetic analogs (compounds inaccessible by semi-synthesis: chemical modification of natural products) by a scalable process. A portfolio of clinical candidates for the treatment of infectious diseases, all fully synthetic tetracycline analogs, are currently in development at Tetraphase Pharmaceuticals, a company founded by Myers. In addition, the Myers' laboratory has developed short, practical and scalable synthetic routes to the saframycin, cytochalasin, stephacidin B-avrainvillamide, and trioxacarin classes of natural antiproliferative agents, in each case by the modular assembly of simple components of similar synthetic complexity. His group has reported synthetic routes to the natural products epoxybasmenone, cyanocycline, terpestacin, salinosporamides, and cortistatins A, J, K, and L. Increasingly, the Myers' laboratory is dedicated to the development of highly convergent synthetic pathways that (1) provide practical, scalable solutions for the construction of molecular classes multiplicatively expanded by (2) incorporation of modular variations. Myers and his students have also developed numerous reagents and procedures of general utility in the construction of complex molecules. These include the development of methodology for the preparation of highly enantiomerically enriched ketones, aldehydes, alcohols, carboxylic acids, organofluorine compounds, α-amino acids, and molecules containing quaternary carbon centers using pseudoephenamine and pseudoephedrine as chiral auxiliaries, a method for the reductive deoxygenation of alcohols that does not involve metal hydride reagents, methods for the stereoselective synthesis of alkenes from sulfonyl hydrazones, a stereospecific synthesis of allenes from propargylic alcohols, a 1,3-reductive transposition of allylic alcohols, a silicon-directed aldol addition reaction, a method for the reductive coupling of aldehydes and allylic alcohols, the discovery of the powerful reductant lithium amidotrihydroborate, the use α-amino aldehydes in synthesis, methods for the synthesis and transformation of diazo compounds, a highly diversifiable method for the synthesis of isoquinolines, as well as others. In addition they have identified and studied transformations of fundamental importance in chemistry such as the allene-ene-yne→α,3-dehydrotoluene, 1,6-didehydrotolu-ene[10]-annulene→1,5-naphthalenediyl, and neocarzinostatin biradical-forming cycloaromatization reactions, as well as the decarboxylative palladiation reaction.

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Knowledge of and attitudes towards medical research ethics among first year doctoral students in Slovenia at the Faculty of Medicine, University of Ljubljana

• S Grosek 1 , 2 ,
• D Pleterski Rigler 3 ,
• M Podbregar 4 , 5 &
• V Erčulj 6 , 7  

BMC Medical Education volume  23 , Article number:  828 ( 2023 ) Cite this article

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Metrics details

Research ethics and attitudes should be the main concern of those who are conducting and publishing research in medicine.

A cross-sectional study was conducted using a questionnaire among first year postgraduate doctoral students in Biomedicine at the Faculty of Medicine, University of Ljubljana during the academic year 2022/2023.

There were 54 out of 57 doctoral students included in the study, with a mean age (SD) of 29.7 (4.7) years, with predominantly female doctoral students, 66.7%. The number of correct answers out of 39 considered to illustrate students’ knowledge of medical research ethics was 31, meaning that they gave correct answers to 80% of all the questions. The mean number (SD) of correct answers was 18.9 (5.8), which significantly differed from 31 ( p  < 0.001). The previous experience of the doctoral students in research was significantly correlated with their knowledge of medical research ethics, even when controlling for the age, gender and workplace of respondents.

This study clearly showed that insufficient knowledge and a poor level of attitudes exist about the main questions pertaining to medical research ethics. Overall knowledge is well below the expected positive answers. Further studies are needed to compare the knowledge of doctoral students with that of their tutors and what implications this might have for further teaching of research ethics.

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Introduction

Planning and conducting research in medicine is inevitable in modern oriented evidence-based medicine if an improvement in treatment possibilities of more and more complex diseases is to be achieved [ 1 , 2 , 3 ]. However, research should be conducted by experienced as well as young researchers, who are or should be familiar with internationally agreed ethical principles and codes of integrity in research.

A long list of unethical human clinical trials in humans can be still extracted from the literature and media since research studies have begun to flourish, despite the sad history of unethical experimentation in the past. Despite many conscientious physicians and researchers alerted in the past about the occurrence of unethical experimentation in human subjects, it was not widely recognised until the Nuremberg trial, which showed the atrocities of German physicians in Nazi camps during the Second World War. The Nuremberg Code’s 10 points was part of the section of the judgement entitled “Permissible Medical Experiments”. It was only later that the Nuremberg Code became to some the most important document in the history of clinical research ethics [ 4 ]. Many of the critical points from the Nuremberg Code were expressed and cited in 1964 in the Declaration of Helsinki by the most prominent physicians in the World Medical Association. The Helsinki Declaration strongly emphasized the need for informed consent of participants in human research and ethical approval of such studies by research ethics committees, which should be established [ 5 ].

Despite international guidelines about research ethics, unethical behaviour in human research can be found daily in the media and scientific journals. To list only some of the most prominent: the Tuskegee Syphilis Study in the United States [ 6 ], Chinese researcher He Jiankui creating a gene-edited baby [ 7 ], Diederik Stapel, a Dutch social psychologist [ 8 ] deliberately misconducted studies over several years There are many others. In a study by Martinson et al. among American researchers in mid- and early-career, as many as 33% of researchers admitted to serious misconduct at least once during their scientific career [ 9 ].

A year before the Helsinki Declaration, in 1963 a Code of, now former, Yugoslav health professionals was adopted, in which it was mentioned that forced human experimentation is the worst violation of ethical principles, and human experimentation is only allowed if this medically and biologically justified and human subjects in experiments must be aware of the experiment and possible adverse effects and must voluntarily agree to participate [ 10 ].

In the Republic of Slovenia, ethics has been taught to undergraduate students at the Faculty of Medicine, University of Ljubljana since 1948 [ 11 ], and the first medical ethics committee was established in the second half of the1960s after the Declaration of Helsinki on human research came into effect [ 12 ]. This was among the first ethics committees in Europe, and its primary role was to evaluate the ethical adequacy of medical research projects by postgraduate doctoral students [ 11 ].

Teaching medical research ethics to undergraduate and postgraduate doctoral students at the Faculty of Medicine UL has a long tradition. In recent years, teaching for postgraduate students has even been strengthened to include 16 lectures covering all aspects of medical ethics and integrity in conducting human research. In 2022, we tried for the first time to evaluate knowledge of research ethics among Slovene doctoral students before they started lectures on research ethics and integrity. There is a scarcity of published research articles available at PubMed.gov under the key words: research ethics, postgraduate students, doctoral students [ 13 , 14 ], which shows that knowledge and attitudes about research ethics is inadequate. In the first nationwide study on facing and solving ethical dilemmas among healthcare professionals in Slovenia in 2015 and in Croatia in 2016 when were studied the most frequent ethical dilemmas among health professionals, 5,3% and 11,0% participants answered that they face ethical dilemmas while conducting research at their university hospitals [ 15 , 16 ].

We therefore hypothesized that knowledge among doctoral students and among those doctoral students who have had previous experience in conducting or participating in studies, with or without human subjects, is not satisfactory.

Study design

A cross-sectional study was performed during one afternoon, on Monday, November 14, 2022 at 16:00.

Study participants

We recruited members of the first year postgraduate doctoral students (PhD students) in Biomedicine at the Faculty of Medicine, University of Ljubljana.

Sampling method

The study aimed to include all students who were enrolled in the fall of 2022 in postgraduate doctoral study, i.e. 57 doctoral students. An online survey was conducted. The link to the questionnaire was sent by electronic mail to the students during the first hour of the course on medical ethics, which is part of the postgraduate course and consists of 16 lectures on different aspects of medical and research ethics and integrity. The PhD students were aware of this survey early, when they entered the postgraduate course. A questionnaire was sent to all students through their electronic mails, with access opened for the time of the study, i.e., 20 min, after which access was closed and blocked by November 14, 2022.

Data collection tool

We developed a questionnaire based on our study objectives, that knowledge among doctoral students and also among those doctoral students who have had previous experience in conducting or participating in studies, with or without human subjects, is not satisfactory . All four authors prepared the questionnaire. The questionnaire, with 48 questions, consisted of 2 parts, with closed type questions (3 available answers; Yes; No; I don’t know). The first part related to general and demographic variables (age, gender, affiliation: surgical specialisations, internal specialisations, other medical specialisations (paediatrician, psychiatrist, neurologist and others) and other healthcare professions (biology, biochemistry, pharmacy, registered medical nurse or technician, veterinary medicine, psychology and so on), whether they had already been included in any research activities before or after graduation from their faculty and, if yes, what types of research activities. The last three questions of the survey were whether they had read the Tasks of the Medical Ethics Committee of Republic Slovenia (MEC RS), whether they know what informed consent is and whether they have ever submitted an application for ethics approval at MEC RS. The second part of the questionnaire (39 questions) explored the knowledge of doctoral students about research ethics (S 1 _File_Questionnaire).

The basis for preparing the questionnaire was an article written by Korošec and Trontelj (2003) on legislation related to research ethics in Slovenia, as a new country that was preparing to join the European Union [ 17 ]. This article is available online at the web site of MEC RS. They divided legislation issues into eight sections: 1. International Instruments in Slovene Legislation, 2. National Review, 3. Research on Humans, 4. Research on Biological Material of Human Origin (blood, organs, tissues, cells, DNA), 5. Research on Human Embryos and Embryonic Stem Cells, 6. Personal data, 7. Genetic Data, 8. Research on Animals. We prepared questions from each of these sections on the most relevant matters presented in each section related to research ethics, except for Sect. 7, for which questions were omitted, because we considered that this section is already partially covered in Sect. 4, and this section would need a special newly designed questionnaire due to evolving changes and constant new possibilities of processing genetic data which may generate possible misuses, especially regarding the protection of the unauthorized use of genetic material for new research without the new permission of the ethics.

Means and standard deviations were calculated for numerical, frequencies and percentages for categorical variables. The normality of the distribution of continuous variables was tested by the Shapiro–Wilk test. One sample t-test was used to test the difference of the mean number of correct answers from 31 (corresponding to 80% of correct answers). An independent sample t-test was used to test the difference in the mean number of correct answers between the group of students with previous experience with medical research and those without it. Univariate logistic regression or the likelihood ratio test were used to test the association between the correct reply to each question on medical research ethics and the study group. Multiple linear regression was used to test the relationship between the demographic variables, work-related variables and previous experience in research and the number of correct answers to questions concerning medical research ethics. P  < 0.05 was considered statistically significant. SPSS version 28 was used for the statistical analysis.

There were 54 out of 57 doctoral students included in the study and their characteristics are summarized in Table 1 . Three students were unavailable to complete the questionnaire at the time of survey; they were either on sick leave or unable to connect to website. The mean (SD) age of respondents was 29.7 (4.7) years. There were 18 (33.3%) men in the sample. The workplace of 20 (37%) students was internal medicine, 10 (18.5%) surgery, 22 (40.7%) another medical areas, such as paediatrics, dental medicine or oncology and 2 (3.7%) were studying veterinary medicine. Overall, 33 (61.1%) students had previous experience with medical research, mainly as part of a thesis at the 1 st Bologna level (64.5%).

Figure  1 illustrates the number and percentage of students giving the correct answer to each of the questions pertaining to medical ethics. Most students (52; 98%) knew that a child should be at least 15 years old to be able to give consent to participate in research and that retrieved personal data in medical research should be protected (50; 94%). Students also knew that cloning of human beings is not allowed in Slovenia (47; 89%) and that medical ethics committee approval is necessary even when doing research pertaining to a doctoral thesis (43; 80%). The least known topics on research ethics are the content of the Menlo report (none of the students answered correctly) or Belmont report (4 students answered correctly), whether the medical ethics code also includes articles on animals (only 1 student answered correctly) and whether biomedical research is regulated by law (2 students answered correctly).

Frequency (%) of correct answers to questions relating to medical ethics (the columns represent percentages; MEC = Medical Ethics Committee)

The mean number of correct answers with 95% CI is summarized in Fig.  2 . The mean number (SD) of correct answers was 18.9 (5.8). The number of correct answers considered to illustrate students’ knowledge on medical ethics was 31; that means giving correct answers to 80% of all the questions. The obtained mean number of correct answers statistically significantly differed from 31 ( p  < 0.001).

Mean and 95% CI of the number of correct answers

Students with previous experience of medical research differed from those without experience in being knowledgeable about the content of the Helsinki Declaration (Table 2 ). They had ~ 5-times (95% CI: 1.4 – 15.8) higher odds of knowing the content of the Declaration than students without previous experience in research. They also had about 5-times (95% CI: 1.4 – 21.1) higher odds of answering correctly to the question about needing ethical approval when doing a multicentric clinical study. No other statistically significant associations between the number of correct answers and the group affiliation were found.

A comparison of the number of correct answers between the two study groups showed a statistically significant difference ( p  = 0.018; Fig.  3 ).

Box plots of the number of correct answers by study group

Previous experience in research was statistically significantly correlated with knowledge of medical research ethics, even when controlling for age, gender and workplace of respondents (Table 3 ).

This is the first Slovene study on ethical knowledge and attitudes of medical research ethics among postgraduate doctoral students of Biomedicine at the Faculty of Medicine, University of Ljubljana. This study clearly showed insufficient knowledge and unsatisfactory level of attitudes about the main questions pertaining to medical research ethics. Overall knowledge was well below the expected positive answers, which should have been 80% or above. Previous experience in research among the participants was statistically significantly related to knowledge of medical research ethics, even when controlling for age, gender and workplace of respondents.

As already said, the basis for preparing the questionnaire was an article written by Korošec and Trontelj on legislation related to research ethics in Slovenia, as a new country that was preparing to join the European Union, and which is available on the web site of MEC RS [ 17 ]. We were specifically interested in getting answers about seven topics; International Instruments in Slovenian Legislation, National Review, Research on Humans, Research on Human Embryos and Embryonic Stem Cells, Research on Biological Material of Human Origin, Research on Animals, and Personal Data.

Undergraduate medical studies in Slovenia have a strong emphasis on the deontological part of ethics and international instruments related to human research, such as Nuremberg, the Helsinki Declaration, the Oviedo report, and other codes, principles and declarations, but despite that previous teaching, positive answers to these questions were scaled the worst, on the lower half or bottom of the scaling (see Fig.  1 ).

One of the important issues in the ethics of medical research is for doctoral students to have enough knowledge before conducting their research projects. Young et al. showed in their study that knowledge of the principles of research ethics and that knowledge of basic research ethics concepts, including the Helsinki Declaration (87.5%) was good, but almost half lower than knowledge of the Nuremberg Code (52.4%) [ 18 ]. In present study knowledge of the Helsinki Declaration was positively answered by only 51% and of the Nuremberg principles by only 30%. Furthermore, when we tested these results between two groups of students, those with and those without previous experience of conducting or helping in research, those with prior experience had almost 5-times higher statistically significant odds of knowing the content of the Helsinki Declaration and 2.4-times higher odds for the Nuremberg principles, though this was not statistically significant. These results shows a great opportunity to improve knowledge of the both groups of doctoral students. Informed consent was extremely important for participants in the Young study, with 100% of them answering positively to that question, However, in practice, informed consent was obtained in only 52.4% of cases [ 18 ]. In our study, 78% of the doctoral students answered that informed consent is required by law. When we asked whether informed consent is required for specific situations, i.e., when informed consent is not sufficient (70%) or needed for archived biological samples (65%), archived personal medical data (57%), when consent is not required (20%) and when participation can proceed without prior consent (17%), the positive answers were much lower. Results on national review, research on humans, research on human embryos and embryonic stem cells showed a dichotomous distribution, i.e., the answers of participants to some questions from these three topics were very good, while for some worse. However, a high percentage of participants knew that cloning of human beings and embryo retrieval only for research is not allowed, although they did not know whether research on early embryos is allowed or not in Slovenia, and if yes, with what exemptions.

In Europe, a recent study by Abdi et al. among members of the League of European Research Universities (LERU) to map out the content, format, frequency, duration, timing and compulsory status of their training programmes and the characteristics of instructors of onsite courses, revealed substantial variation in educational materials among the studied institutions [ 19 ]. The European Code of Conduct for Research Integrity specifies that good research practices are based on basic principles of research integrity, which are intended to guide researchers in their work. The most important principles quoted are reliability, honesty in the development of all phases of research, respect for colleagues and responsibility for research from conception to publication (ALLEA 2018) [ 20 ]. Research institutions and organizations ensure that researchers receive thorough training in research design, methodology and analysis [ 21 ]. American philosophers, in the book Principles of Medical Ethics, wrote about moral virtues that should be inherent to medical professionals, and the same is also true for researchers. Among the five moral virtues for them, the most important is professional integrity, i.e., in research, research integrity [ 22 ]. Because moral virtues, e.g., integrity, can be taught, it is important to educate researchers about research integrity.

The current study has some strengths, being the first in Slovenia comprehensively to tackle the issue of knowledge and attitudes of doctoral students about research ethics. It clearly has the limitation of being done in only one centre and having a relatively small number of participants. A future study with next set of doctoral students is justified, which should include doctoral tutors before and after classes on research ethics in the postgraduate study of Biomedicine.

This means that before being involved in conducting studies, doctoral students should have passed a test of knowledge, attitudes and integrity. Lack of knowledge of research ethics may lead to misconduct of researchers and a lack of integrity. However, despite the results of the presented study, the national MEC receives ethically prepared doctoral theses, which is probably a reflection of the adequate work of the mentors of the doctoral students.

Availability of data and materials

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

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Aubert Bonn N, Godecharle S, Dierickx K. European Universities’ Guidance on Research Integrity and Misconduct. J Empir Res Hum Res Ethics. 2017;12(1):33–44. https://doi.org/10.1177/1556264616688980 .

Beauchamp TL, Childress JF. Moral Character. In: Principles of Biomedical Ethics. 6th Ed. Oxford: Oxford University Press, Inc.; 2009. p. 30–61.

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Acknowledgements

The authors wish to thank to all the doctoral students of Biomedicine at the Faculty of Medicine, University of Ljubljana who participated in this survey. Special thanks to Assistant Matevž Trdan, MD for preparing the online questionnaire for the survey.

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The Medical Ethics Committee of Republic Slovenia approved the anonymous online survey in its present form and waived the need of the informed consent signature for participants.

By opening and filling in the questionnaire, the participants in the online survey agreed (consented) to participate in an anonymous survey.

This research was partially financed by the research program Terciar (Research No. 20210074) of the University Medical Center Ljubljana, Slovenia. None of the authors received any funding for their intelectuall work. The financer had no role in the preparation of this manuscript.

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SG, MP, VE and DPR concieved of the present idea. SG, MP, VE and DPR developed the questionnaire conception and design. VE provided statistics. All authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors have read and approved the manuscript.

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Ethical approval was approved from the Medical Ethics Committee of Republic Slovenia (no. of approval: 0120–384/2022/4; September 27, 2022). Original approval in Slovene and translated in English is available as Supplementary File (S 2 _File).

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Grosek, S., Pleterski Rigler, D., Podbregar, M. et al. Knowledge of and attitudes towards medical research ethics among first year doctoral students in Slovenia at the Faculty of Medicine, University of Ljubljana. BMC Med Educ 23 , 828 (2023). https://doi.org/10.1186/s12909-023-04809-w

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November 3, 2023

• Comparing Statistical Significance with Clinical Relevance: The Fragility Index vs. The Relative Risk Index

A recent study compared different metrics for evaluating the fragility and clinical relevance of research findings. The fragility index, which measures how easily p-values flip from significant to nonsignificant, strongly correlated with p-values in simulated 2×2 contingency tables. This suggests the fragility index provides minimal insight beyond p-values alone. In contrast, the relative risk index, which quantifies divergence from therapeutic equivalence, showed only weak correlation with p-values. The relative risk index appears to assess something distinct from statistical significance, more closely related to clinical relevance. This novel metric warrants further real-world testing but may provide a complementary tool to p-values and fragility indices for appraising research robustness.

Citation: Heston TF. Statistical significance versus clinical relevance: a head-to-head comparison of the fragility index and relative risk index. Cureus. 2023;15(10):e47741. DOI: 10.7759/cureus.47741.

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Children's Health

How lack of independent play is impacting children's mental health.

Kathryn Fox

Juana Summers

NPR's Juana Summers speaks with research professor Peter Gray about the connection between the decline of children's mental health and the decline of independent play.

JUANA SUMMERS, HOST:

We've been hearing a lot about the mental health crisis among children. Researchers have looked at a number of reasons, from social media use to isolation during the pandemic. But a recent commentary published in the Journal of Pediatrics looked at another factor - the decline of independent activity and play for children. Peter Gray is the lead author of that piece. For years, he's been following the trend of declining mental health in kids and the declining levels of independent play. He joins us now. Welcome.

PETER GRAY: I'm very happy to be here.

SUMMERS: So, Peter, how is it that you and your co-author started to focus on the decline of independent play as a potential factor when it comes to the mental health crisis that we're seeing among kids?

GRAY: Well, I've actually been studying play for many, many years and what play does for children, how children acquire confidence and abilities and make friends through play. And I've also, for a long time, been aware of the fact that over the past 50 to 70 years, there has been a continuous decline in children's opportunities to play freely, away from adult intervention and control. So at some point, I began to put those findings together with the observation that, over this same period, the last 50 to 70 years - I mean, everybody is concerned about the most recent increase in anxiety, depression, even suicide among young people. But the mental health crisis really has long preceded COVID, and it has long preceded the internet.

SUMMERS: When you're talking about this decline of independent play that you're dating back to nearly half a century, do you have any sense of where the roots of that are? What changed?

GRAY: I think a lot of things changed. Television changed things. It brought kids inside, isolated them somewhat. Another thing that changed, and I think more significantly, is that over time, we began to develop the view that children develop best when they are guided and controlled by adults. This resulted in an increased amount of schooling and increased emphasis on schooling and the development of organized sports for kids and in other adult-directed activities outside of school, leaving less and less time for free play. In addition to that, beginning particularly in the 1980s, we developed a fear of allowing children to be outdoors unguarded by an adult. What was normal parenting before the 1980s of just sending your kids outdoors to play, that began to become regarded as negligent parenting because of fear that something terrible would happen to them.

SUMMERS: OK. So let's unspool this out a little bit here. What is the connection between that less independent time and independent play that you're describing and the declining mental health among kids that we're seeing? How do those two things correlate?

GRAY: Play, to me and to most play researchers, is something that children do themselves. It's not something that is organized by an adult. It's something that - where children decide what they're going to do and control what they're going to do and solve the problems as they're doing it. That's how children develop the kinds of character traits that allow them to ultimately become independent adults. They learn that - they learn how to deal with peers without an adult intervening. They learn how to deal with minor bullying. There are always going to be bullies around.

SUMMERS: Right.

GRAY: But if you're always protected from bullies by some adult, you're not learning how to deal with that yourself. If we're not allowing these kinds of things to happen with young children, they're not learning that they can solve their own problems, they can take control of their lives. And when you believe that you cannot, then you kind of develop a victim attitude, like anything can happen at any time and there's nothing I can do about it. And that's an attitude that sets you up for anxiety and depression.

SUMMERS: I imagine if you're a parent or a caregiver for a child who's listening to this conversation, they might be asking themselves, what can I do? What are ways that I can give my child more independence, more time for that solo play that you're saying is so important? What would you tell those people?

GRAY: So within the neighborhood, a group of parents might decide, look, let's - every Friday afternoon, let's all send our kids outdoors. Just send them outdoors. Leave the cellphone inside. And there's going to be other kids out there. And maybe you have one parent out there, or ideally a grandparent, out there just for safety. And you let the kids play. It takes initiative. It's not necessarily that easy to do, but I know of families that do that.

SUMMERS: Peter Gray is a research professor of psychology and neuroscience at Boston College. Thanks so much for being here.

GRAY: Thank you for having me.

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This paper is in the following e-collection/theme issue:

Published on 26.10.2023 in Vol 25 (2023)

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study

Authors of this article:

Original Paper

• Xulin Yang 1 * , BEng   ; 
• Hang Qiu 1, 2 * , PhD   ; 
• Liya Wang 2 , MM   ; 
• Xiaodong Wang 3 , MD  

1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China

3 Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China

*these authors contributed equally

Corresponding Author:

Hang Qiu, PhD

School of Computer Science and Engineering

University of Electronic Science and Technology of China

No.2006, Xiyuan Ave, West Hi-Tech Zone

Chengdu, 611731

Phone: 86 28 61830278

Email: [email protected]

Background: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling.

Objective: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival.

Methods: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance.

Results: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival.

Conclusions: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer death worldwide, with 1.9 million new cases and 0.93 million deaths estimated in 2020, accounting for 10% of the global cancer incidence and 9.4% of all cancer-caused deaths [ 1 , 2 ]. With high morbidity and mortality, CRC is an important component of health care expenditure and imposes a heavy burden on families and society [ 3 ]. Precise survival prediction for patients with CRC will help clinicians optimize treatment measures, improve survival rates, and reduce the disease burden of patients [ 3 , 4 ]. Therefore, obtaining precise survival predictions for patients with CRC and understanding what affects these predictions are critical for identifying targeted interventions in the clinical setting.

The Cox proportional hazards (CPH) model [ 5 ] is the most commonly used statistical method for survival analysis, which has been widely applied to predict prognosis for patients with CRC due to its ease of use and interpretation [ 6 , 7 ]. To deal with high-dimensional data, based on the basic CPH model, some variant models of CPH were proposed, such as Lasso-Cox [ 8 ], EN-Cox [ 9 ], and robust CPH with nonlinearities and interactions [ 6 ]. In recent years, machine learning (ML), especially ensemble learning and deep learning (DL), has proven to be a great complement to traditional statistical methods in many health care applications [ 10 - 12 ]. A large body of studies has attempted to use ML models to predict CRC survival [ 4 , 13 , 14 ]. For instance, Pourhoseingholi et al [ 4 ] compared the performance of traditional and ensemble ML models for predicting the 5-year survival of patients with CRC. The results showed that the ensemble voting model achieved an area under the receiver operating characteristic curve of 0.96, which was the best result. Al-Bahrani et al [ 14 ] used deep neural networks to predict 1-year, 2-year, and 5-year survival for patients with CRC. The deep neural networks model achieved an average area under the receiver operating characteristic curve of 0.87, which is higher than the 0.85 reported by Stojadinovic et al [ 15 ].

Although ML-based approaches have shown great potential in CRC survival prediction, the vast majority of existing studies did not include time-to-event data and have only considered binary outcomes, which may incur the risk of bias in prediction accuracy [ 16 , 17 ]. Some time-to-event ML models, such as random survival forest (RSF) [ 18 ], gradient boosting machine (GBM) [ 19 ], DeepSurv [ 20 ], DeepHit [ 21 ], neural net-extended time-dependent Cox model (Cox-Time) [ 22 ], and neural multitask logistic regression (N-MTLR) [ 23 ], have shown promising performances in several prognostic studies on breast cancer [ 10 , 24 ], oral cavity cancer [ 16 ], and lung cancer [ 11 , 23 ]; however, it is not clear whether these models have the same advantages in CRC survival prediction. Moreover, due to the “black box” nature of ML models, the predictions made by these models are opaque, meaning that the importance of input features to the output is unclear, which limits the clinical applications of ML approaches. Therefore, it is essential to adopt effective methods to increase the transparency of ML models in the medical domain.

Given the high incidence of CRC and the lack of a reliable study on modeling time-to-event survival data of CRC using ML-based approaches, this study seeks to contribute to the existing body of knowledge by evaluating the performance of time-to-event ML models in predicting CRC-specific survival and by combining ML models with the SHapley Additive exPlanations (SHAP) method [ 25 ] to provide transparent predictions for clinical application.

Data Collection

We collected data from patients with CRC from the Database of Colorectal Cancer (DACCA) of West China Hospital, Sichuan University. This database includes patient demographics, diagnosis, tumor, treatment, and follow-up information. Specifically, the features collected included age at diagnosis, gender, marriage, BMI, operation time, preoperative carcinoembryonic antigen (CEA), number of positive lymph nodes (PLNs), dystrophy, obstruction, intussusception, intestinal perforation, diabetes, hypertension, differentiation, tumor-node-metastasis (TNM) staging based on the 8th edition of American Joint Committee on Cancer (TNM staging), morphologic type, histologic type, R0 resection, neoadjuvant treatment, cardiac function, anemia, perineural invasion, and tumor location.

The date of the last follow-up for this study was October 11, 2021. CRC cases were identified by the International Classification of Diseases, Tenth Revision codes (C18, C19, and C20). After discharge, the clinician would follow up with the patient regularly according to the patient’s condition and record the survival information. The inclusion criteria were as follows: (1) aged 15-99 years; (2) first diagnosed with CRC between December 28, 2012, and December 27, 2019; and (3) follow-up time ≥1 month.

Ethics Approval

This study was approved by the Ethics Committee of West China Hospital, Sichuan University (2021-155). Because this study was a retrospective study design and all data were analyzed anonymously, the requirement to obtain informed consent was removed.

Study Outcomes

The outcome of this study was CRC-specific survival, which was defined as the number of months from diagnosis to death from CRC or the end of follow-up, whichever occurred first.

Data Preprocessing and Feature Selection

Features with a missing ratio of more than 30% were excluded [ 26 , 27 ] because they provided limited information. Missing data were assumed missing at random and were imputed 5 times in the package “miceforest” [ 28 ] by multiple imputation by chained equations, which helps minimize bias. The imputation model contained all candidate predictor variables. Imputations were performed within the cross-validation loop, and we developed an imputation model on the training set and used it to impute missing values on the training and testing sets, respectively. Because the dimensions were different, numerical features, ordinal categorical features, and nominal categorical features were processed using zero-mean normalization, integer encoding, and one-hot encoding, respectively.

We performed feature selection by combining the results of 2 different approaches: one based on the algorithm and the other based on clinical experience. For the algorithm-based approach, we used multivariate Cox regression to select features significantly associated with CRC-specific survival [ 16 ]. Features with P values <.05 were considered significantly associated with survival. For the clinical experience–based approach, clinical experts identified 6 features (age, preoperative CEA, PLN, TNM staging, R0 resection, and neoadjuvant treatment) as the most relevant to CRC-specific survival based on their clinical experience. The final feature set was the union of the feature sets selected by the above 2 approaches. We aimed to develop parsimonious models that contain only relevant and easily accessible features, appropriately preventing models from overfitting [ 29 ].

Model Development

A total of 6 time-to-event ML models with 2 based on ensemble learning (RSF and GBM) and 4 based on DL (DeepSurv, DeepHit, Cox-Time, and N-MTLR) were developed to predict CRC-specific survival. These models were selected according to their promising performances reported in previous studies [ 16 , 23 , 30 ].

RSF is an ensemble learning algorithm similar to bagging [ 31 ], which consists of survival trees [ 18 ]. RSF grows survival trees by randomly selecting features and then splits nodes using candidate features to maximize the survival difference between child nodes. GBM is a gradient boosting–based ensemble learning algorithm consisting of base learners. GBM sequentially builds base learners in a greedy stage-wise fashion to minimize the weighted risk function [ 19 ]. DeepSurv is a DL-based algorithm that extends CPH to handle nonlinear effects between input features and clinical events. DeepSurv consists of multiple hidden layers and is trained with modern techniques, such as batch normalization and gradient descent optimization algorithms [ 20 ]. DeepHit is a DL-based nonproportional hazards algorithm that uses multitask learning to handle competition between events. DeepHit consists of a shared subnetwork and 1 or more cause-specific subnetworks [ 21 ]. Cox-Time is a DL-based algorithm that treats time as a regular covariate to model interactions between time and the other covariates. N-MTLR is a DL-based algorithm that builds different neural networks on different time intervals to estimate the probability of the event of interest occurring in each interval. RSF, GBM, DeepHit, Cox-Time, and N-MTLR algorithms have no proportional hazards assumption. To explore the difference in performance between the time-to-event ML model and the CPH model, we developed a robust CPH model with nonlinearities and interactions based on a study by Hippisley-Cox and Coupland [ 6 ]. A sample size calculation was performed using the “pmsampsize” [ 32 ] package in R for the CPH model, and the total required sample size was 555 patients. In each cross-validation loop, we had 1725 patients in the training set, meaning that our sample size was sufficient for modeling a reliable CPH model.

To tune all the time-to-event ML models’ hyper-parameters, we performed a Bayesian search [ 33 ] with stratified 5-fold cross-validation in the training set. The hyper-parameter search space of the ML models is shown in Multimedia Appendix 1 .

Evaluation of Model Performance

The discriminative ability of models was evaluated by the time-dependent concordance index (C td ) [ 34 ], which is the ratio of correctly distinguished pairs to all pairs. A C td value of 1 represents perfect discrimination, whereas a value of 0.5 represents random guessing. The Brier score [ 35 ] measures the distance between a patient’s survival status and the predicted probability of survival. The integrated Brier score (IBS) is the integral of the Brier score at all available times. The calibration ability of models was evaluated with IBS, where the smaller the IBS value of the model, the better its calibration ability. Additionally, we assessed the calibration of 5-year CRC-specific survival by comparing the observed survival probability at 5 years with the predicted survival probability.

Decision curve analysis is a statistical method to evaluate whether a model has utility in supporting clinical decisions by calculating the net benefit at different threshold probabilities [ 27 ]. Therefore, we used decision curve analysis to evaluate the net benefits of models for CRC survival at 5 years at a range of clinically reasonable risk thresholds (10%-30%) [ 36 ].

All models were evaluated in stratified 5-fold cross-validation [ 37 ] repeated 5 times, as shown in Figure 1 . During the inner stratified 5-fold cross-validation loop, we trained time-to-event ML models with different hyperparameter configurations on the inner training set and calculated their C td on the inner testing set. The configuration that yielded the highest average C td was chosen as the best hyperparameter configuration. During the outer 5 times stratified 5-fold cross-validation loop, the performance of the optimized time-to-event ML models was estimated on the outer testing data.

Model Explanation

Model transparency is critical to the application of models in the medical domain. Therefore, to make time-to-event ML models more transparent, we introduced SHAP, which is a model-agnostic post hoc explanation algorithm that has been widely applied to explain ML models [ 10 , 38 , 39 ].

The 5-year survival is a metric commonly used in medical science to evaluate the effects of surgery and treatment. Thus, we adopted SHAP to explore important factors affecting 5-year CRC-specific survival. In this study, all testing data were selected to calculate the SHA P value of each feature to obtain the importance ranking of features.

Sensitivity Analysis

Sensitivity analyses were performed to examine the predictive stability of the models for different subgroups. Model performance was evaluated in the subgroups, focusing on patients in different age groups (<65 years and ≥65 years) [ 40 ] and patients of different sex.

Statistical Analysis

Categorical and Boolean features were presented as frequencies and percentages, and numerical features were presented as the median (25th and 75th percentiles). A Wilcoxon rank sum test was used to assess the difference in performance between the models. A 2-sided P value <.05 was considered statistically significant.

All analyses and calculations were performed using R (version 4.2.2; R Core Team) and Python (version 3.8.7; Python Software Foundation). This study followed the Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research [ 41 ] and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis [ 42 ] statement.

Patient Characteristics

A total of 2157 patients were included in this study. Statistical descriptions of these patients are presented in Multimedia Appendix 2 . The median age of the 2157 patients was 61 years, and 1301 (60.4%) patients were male. Tumors were completely resected (R0 resection) in 1503 (69.6%) patients, and the median operation time was 60 minutes. Tumor pathology in very few patients was characterized by squamous cell carcinoma, and tumors were moderately differentiated in 1388 (64.3%) patients. These patients had a median preoperative CEA of 3.8 ng/mL, and 1217 (56.4%) patients received neoadjuvant treatment. Follow-up durations ranged from 1 month to 104 months. During this period, 420 (19.5%) patients died from CRC, 36 (1.6%) patients died from other causes, and 1702 (78.9%) patients survived during follow-up.

Model Performance

The evaluation results of the time-to-event models are shown in Table 1 . Among the 6 time-to-event ML models, the average C td (0.789, 95% CI 0.779-0.799) of the DeepHit model is the highest and the average IBS (0.096, 95% CI 0.094-0.099) of the RSF model is the lowest, but these are not statistically significant ( Multimedia Appendices 3 and 4 ). Additionally, no significant performance differences were observed between the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models and the CPH model for C td and IBS ( Multimedia Appendix 5 ).

a Neural net-extended time-dependent Cox.

Figure 2 shows the difference between the predicted 5-year CRC-specific survival and the actual events. Overall, all models exhibited good 5-year survival calibration. The CPH, RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models slightly overestimated the 5-year survival rate, while the DeepHit model slightly underestimated the 5-year survival rate. In addition, the CPH, RSF, DeepSurv, Cox-Time, and N-MTLR models produced better 5-year survival calibrations than the DeepHit and GBM models.

Figure 3 displays the net benefit curves for CRC survival models at 5 years. Overall, all the CRC survival models had higher net benefits than the default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. In particular, the net benefit of the RSF model surpassed all other models.

Feature Importance

We applied SHAP to determine the effect of the input features on the 5-year CRC-specific survival. Figure 4 shows the importance ranking of the input features. The features are listed in a top-down order, with decreasing importance. The larger the mean SHAP absolute value of a feature, the more important that feature is. R0 resection, TNM staging, and PLN ranked among the top 3 in feature importance ranking for all ML models.

The C td and IBS of the GBM and DeepHit models remained stable in different age and sex groups ( Multimedia Appendix 6 ). The performance of the GBM, DeepSurv, DeepHit, and Cox-Time models has no statistical difference in different age stratifications, while the IBS of the CPH, RSF, and N-MTLR models in the age group ≥65 years is significantly lower than that in the age group <65 years. The performance of the GBM and DeepHit models has no statistical difference in different sex stratifications, while the IBS of the CPH, RSF, DeepSurv, Cox-Time, and N-MTLR models for female individuals is significantly lower than that for male individuals.

Principal Findings

In this study, we evaluated the performance of traditional (CPH) and ML-based (RSF, GBM, DeepSurv, DeepHit, Cox-Time, and N-MTLR) models for CRC-specific survival prediction and applied SHAP to make predictions of time-to-event ML models more transparent. We found that the DeepHit model demonstrated the best discriminative ability (C td 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (IBS 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Moreover, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the CPH model in terms of discrimination and calibration. The 5-year CRC-specific survival calibration plot showed all the ML models exhibited good calibration. Decision curves for 5-year CRC-specific survival showed that all the ML models had higher net benefits than the default strategies of treating all or no patients, and the RSF model had the highest net benefit. Similar results have been reported in other cancer survival studies. For example, Du et al [ 43 ] used several models, including CPH and RSF, to predict disease-specific survival in patients with oral and pharyngeal cancers. Their results showed that time-to-event ML algorithms, such as RSF, provide nonparametric alternatives to CPH to estimate the survival probability of patients with oral and pharyngeal cancers. Adeoye al [ 16 ] found that RSF, DeepSurv, DeepHit, and Cox-Time algorithms are successful in predicting oral cavity cancer prognosis. Our results showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival, and the RSF, GBM, Cox-Time, and N-MTLR nonproportional hazards algorithms could be used as nonparametric alternatives to CPH in CRC-specific survival prediction. Inconsistent with some previous studies [ 10 , 11 , 24 ], we did not find that the time-to-event ML models achieve better performance than the CPH model for CRC-specific prediction. One possible reason may be that the sample size of our data set is not large enough. ML approaches are data-driven approaches and may require truly “big data” to ensure their developed models avoid overfitting and their potential advantages (dealing with nonlinear relations and interactions) reach fruition [ 32 ]. Our data set is sufficient to develop a reliable CPH model, but larger sample sizes may be required when developing ML models. Another possible reason is that we only included a small number of features. The advantage of ML over traditional statistical methods is that it automatically deals with the interactions between numerous features based on data [ 44 ]. Therefore, if the number of features is too small, the advantage of ML will not be significant.

The results of the sensitivity analysis showed that the performance of the GBM and DeepHit models remained stable in different age and sex groups, while other models performed better in the age group ≥65 years and the female group. This may be related to a higher incidence of CRC deaths among individuals aged ≥65 years compared to those aged <65 years. This data set is unbalanced, so higher event (death from CRC) rates may lead to better performance. The proportion of female patients aged ≥65 years is higher than that of male patients, which may be one of the reasons why the models perform better in the female subgroup.

To the best of our knowledge, this is the first study to evaluate the discriminative ability and calibration ability of various time-to-event ML models trained with clinical features to predict CRC-specific survival based on data from Chinese patients with CRC. Censoring is an unavoidable problem in long-term survival prediction because patients are often lost to follow-up or die from unrelated causes. Although ML has been widely used in CRC survival prediction, many ML-based models ignore censoring because the default framework is to analyze binary outcomes rather than time-to-event survival outcomes, which may bias survival predictions. Time-to-event algorithms achieve a dynamic perception of survival predictions by providing estimates at various time points, and these algorithms can be better used for the survival monitoring of patients with CRC. However, how different ML-based time-to-event algorithms perform in terms of CRC-specific survival remains to be explored. The results of our study will fill this gap and provide a reference for subsequent researchers.

The predictions of ML models are opaque due to their “black box” nature. In this study, we used SHAP to make time-to-event ML models more transparent. SHAP is a model-agnostic ex post facto explanation method. The larger the SHA P value of a feature, the more influential it is on the model output. The visualization of feature importance showed that TNM staging, PLN, and preoperative CEA were important in predicting 5-year CRC-specific survival, which was consistent with those of previous works [ 3 , 4 , 6 ] and clinical experience. Additionally, we found R0 resection and operation time were important features in our study, which were rarely reported in the previous CRC literature. One possible reason for this result is that our model is based on data from Chinese patients with CRC, and it suggests that R0 resection and operation time may simply be valid independent predictors of CRC-specific survival in Chinese populations, suggesting that the features affecting the prediction of CRC-specific survival are different in different populations. The value of R0 resection and operation time in predicting CRC-specific survival is worthy of Chinese clinicians’ attention.

Limitations

This study has some limitations. First, the retrospective nature of this study resulted in some overly missing features, such as perineural invasion. However, the features available for modeling produced satisfactory and reasonable estimates on the test set. Second, the information collected in this study is structured clinical data; if combined with structured clinical data and unstructured clinical data, such as imaging and multiomics data, it may provide better prediction results. Third, as with other cancer survival studies [ 6 , 10 , 17 ], unbalanced survival data sets were not processed. Last, the time-to-event ML models were trained on single-center CRC survival data and need to be further validated in external data sets.

Conclusions

This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to CPH in estimating the survival probability of CRC patients. The transparent time-to-event ML models help clinicians more accurately predict the survival rate for patients with CRC and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Acknowledgments

This study was supported by the Key Research and Development Program of Sichuan Province (2021YFS0112 and 2022YFS0163) and the Technological Innovation Research and Development Project of Chengdu (2021-YF05-01214-SN).

Data Availability

The data sets analyzed during this study are not publicly available due to the personal information protection requirement of the ethics committee but are available from the corresponding author on reasonable request.

Authors' Contributions

XY and HQ contributed equally as the first authors. XY contributed to the formal analysis, visualization, and writing of the original draft. HQ contributed to the conceptualization, methodology, formal analysis, and the original draft. LW contributed to the review and editing of the manuscript. XW contributed to data curation as well as reviewing and editing the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

None declared.

The hyperparameter search space of machine learning models.

Features collected from the Database of Colorectal Cancer.

Wilcoxon rank sum test for the time-dependent concordance index between the DeepHit model and other models.

Wilcoxon rank sum test for the integrated Brier score between the random survival forest model and other models.

Wilcoxon rank sum test for pairwise comparison between the Cox proportional hazards model and other models.

Performance in stratified subgroups.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 18.11.22; peer-reviewed by D Gartner, A Clift; comments to author 11.01.23; revised version received 22.03.23; accepted 29.09.23; published 26.10.23

©Xulin Yang, Hang Qiu, Liya Wang, Xiaodong Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.10.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

ORIGINAL RESEARCH article

This article is part of the research topic.

Technological Developments in Point of Interest Recommendation for Smart and Sustainable Cities

Do Individuals' Resist Green Home Investment Decisions? An Empirical Study From Status Quo Bias and Inertia Perspective

• 1 VIT Business School, VIT University, India

The final, formatted version of the article will be published soon.

The study explores the influence of Status Quo Bias theory constructs and inertia on resistance to sustainable green home investment among Indian homeowners and prospective homebuyers.The collection of survey data through a questionnaire administered to 404 participants has involved, and data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS SEM). Factors such as loss aversion, transition costs, adherence to social norms, and self-efficacy to change significantly contribute to resistance. Inertia amplifies the relationship between transition costs, social norms, and self-efficacy to change, but does not mediate loss aversion. The research highlights the importance of providing clear information about the benefits of green home upgrades and offering incentives to reduce perceived costs and risks. It also addresses environmental sustainability concerns and the increasing importance of green home investment decisions in today's world.

Keywords: Status Quo Bias, Inertia, individual resistance, sustainability, Green home, Investment decision

Received: 16 Sep 2023; Accepted: 31 Oct 2023.

Copyright: © 2023 R and Perumandla. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Swamy Perumandla, VIT University, VIT Business School, Vellore, India

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