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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is the Significance of a Study? Examples and Guide

If you’re reading this post you’re probably wondering: what is the significance of a study?

No matter where you’re at with a piece of research, it is a good idea to think about the potential significance of your work. And sometimes you’ll have to explicitly write a statement of significance in your papers, it addition to it forming part of your thesis.

In this post I’ll cover what the significance of a study is, how to measure it, how to describe it with examples and add in some of my own experiences having now worked in research for over nine years.

If you’re reading this because you’re writing up your first paper, welcome! You may also like my how-to guide for all aspects of writing your first research paper .

Looking for guidance on writing the statement of significance for a paper or thesis? Click here to skip straight to that section.

What is the Significance of a Study?

For research papers, theses or dissertations it’s common to explicitly write a section describing the significance of the study. We’ll come onto what to include in that section in just a moment.

However the significance of a study can actually refer to several different things.

Working our way from the most technical to the broadest, depending on the context, the significance of a study may refer to:

• Within your study: Statistical significance. Can we trust the findings?
• Wider research field: Research significance. How does your study progress the field?
• Commercial / economic significance: Could there be business opportunities for your findings?
• Societal significance: What impact could your study have on the wider society.
• And probably other domain-specific significance!

We’ll shortly cover each of them in turn, including how they’re measured and some examples for each type of study significance.

But first, let’s touch on why you should consider the significance of your research at an early stage.

Why Care About the Significance of a Study?

No matter what is motivating you to carry out your research, it is sensible to think about the potential significance of your work. In the broadest sense this asks, how does the study contribute to the world?

After all, for many people research is only worth doing if it will result in some expected significance. For the vast majority of us our studies won’t be significant enough to reach the evening news, but most studies will help to enhance knowledge in a particular field and when research has at least some significance it makes for a far more fulfilling longterm pursuit.

Furthermore, a lot of us are carrying out research funded by the public. It therefore makes sense to keep an eye on what benefits the work could bring to the wider community.

Often in research you’ll come to a crossroads where you must decide which path of research to pursue. Thinking about the potential benefits of a strand of research can be useful for deciding how to spend your time, money and resources.

It’s worth noting though, that not all research activities have to work towards obvious significance. This is especially true while you’re a PhD student, where you’re figuring out what you enjoy and may simply be looking for an opportunity to learn a new skill.

However, if you’re trying to decide between two potential projects, it can be useful to weigh up the potential significance of each.

Let’s now dive into the different types of significance, starting with research significance.

Research Significance

What is the research significance of a study.

Unless someone specifies which type of significance they’re referring to, it is fair to assume that they want to know about the research significance of your study.

Research significance describes how your work has contributed to the field, how it could inform future studies and progress research.

Where should I write about my study’s significance in my thesis?

Typically you should write about your study’s significance in the Introduction and Conclusions sections of your thesis.

It’s important to mention it in the Introduction so that the relevance of your work and the potential impact and benefits it could have on the field are immediately apparent. Explaining why your work matters will help to engage readers (and examiners!) early on.

It’s also a good idea to detail the study’s significance in your Conclusions section. This adds weight to your findings and helps explain what your study contributes to the field.

On occasion you may also choose to include a brief description in your Abstract.

What is expected when submitting an article to a journal

It is common for journals to request a statement of significance, although this can sometimes be called other things such as:

• Impact statement
• Significance statement
• Advances in knowledge section

Here is one such example of what is expected:

Impact Statement:  An Impact Statement is required for all submissions.  Your impact statement will be evaluated by the Editor-in-Chief, Global Editors, and appropriate Associate Editor. For your manuscript to receive full review, the editors must be convinced that it is an important advance in for the field. The Impact Statement is not a restating of the abstract. It should address the following: Why is the work submitted important to the field? How does the work submitted advance the field? What new information does this work impart to the field? How does this new information impact the field? Experimental Biology and Medicine journal, author guidelines

Typically the impact statement will be shorter than the Abstract, around 150 words.

Defining the study’s significance is helpful not just for the impact statement (if the journal asks for one) but also for building a more compelling argument throughout your submission. For instance, usually you’ll start the Discussion section of a paper by highlighting the research significance of your work. You’ll also include a short description in your Abstract too.

How to describe the research significance of a study, with examples

Whether you’re writing a thesis or a journal article, the approach to writing about the significance of a study are broadly the same.

I’d therefore suggest using the questions above as a starting point to base your statements on.

• Why is the work submitted important to the field?
• How does the work submitted advance the field?
• What new information does this work impart to the field?
• How does this new information impact the field?

Answer those questions and you’ll have a much clearer idea of the research significance of your work.

When describing it, try to clearly state what is novel about your study’s contribution to the literature. Then go on to discuss what impact it could have on progressing the field along with recommendations for future work.

Potential sentence starters

If you’re not sure where to start, why not set a 10 minute timer and have a go at trying to finish a few of the following sentences. Not sure on what to put? Have a chat to your supervisor or lab mates and they may be able to suggest some ideas.

• This study is important to the field because…
• These findings advance the field by…
• Our results highlight the importance of…
• Our discoveries impact the field by…

Now you’ve had a go let’s have a look at some real life examples.

Statement of significance examples

A statement of significance / impact:

Impact Statement This review highlights the historical development of the concept of “ideal protein” that began in the 1950s and 1980s for poultry and swine diets, respectively, and the major conceptual deficiencies of the long-standing concept of “ideal protein” in animal nutrition based on recent advances in amino acid (AA) metabolism and functions. Nutritionists should move beyond the “ideal protein” concept to consider optimum ratios and amounts of all proteinogenic AAs in animal foods and, in the case of carnivores, also taurine. This will help formulate effective low-protein diets for livestock, poultry, and fish, while sustaining global animal production. Because they are not only species of agricultural importance, but also useful models to study the biology and diseases of humans as well as companion (e.g. dogs and cats), zoo, and extinct animals in the world, our work applies to a more general readership than the nutritionists and producers of farm animals. Wu G, Li P. The “ideal protein” concept is not ideal in animal nutrition.  Experimental Biology and Medicine . 2022;247(13):1191-1201. doi: 10.1177/15353702221082658

And the same type of section but this time called “Advances in knowledge”:

Advances in knowledge: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders. Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change. Size changes of at least 6 mm or more in  T 1  weighted or  T 2  weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change. Wennmann M, Grözinger M, Weru V, et al. Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma [published online ahead of print, 2023 Apr 12].  Br J Radiol . 2023;20220745. doi: 10.1259/bjr.20220745

Other examples of research significance

Moving beyond the formal statement of significance, here is how you can describe research significance more broadly within your paper.

Describing research impact in an Abstract of a paper:

Three-dimensional visualisation and quantification of the chondrocyte population within articular cartilage can be achieved across a field of view of several millimetres using laboratory-based micro-CT. The ability to map chondrocytes in 3D opens possibilities for research in fields from skeletal development through to medical device design and treatment of cartilage degeneration. Conclusions section of the abstract in my first paper .

In the Discussion section of a paper:

We report for the utility of a standard laboratory micro-CT scanner to visualise and quantify features of the chondrocyte population within intact articular cartilage in 3D. This study represents a complimentary addition to the growing body of evidence supporting the non-destructive imaging of the constituents of articular cartilage. This offers researchers the opportunity to image chondrocyte distributions in 3D without specialised synchrotron equipment, enabling investigations such as chondrocyte morphology across grades of cartilage damage, 3D strain mapping techniques such as digital volume correlation to evaluate mechanical properties  in situ , and models for 3D finite element analysis  in silico  simulations. This enables an objective quantification of chondrocyte distribution and morphology in three dimensions allowing greater insight for investigations into studies of cartilage development, degeneration and repair. One such application of our method, is as a means to provide a 3D pattern in the cartilage which, when combined with digital volume correlation, could determine 3D strain gradient measurements enabling potential treatment and repair of cartilage degeneration. Moreover, the method proposed here will allow evaluation of cartilage implanted with tissue engineered scaffolds designed to promote chondral repair, providing valuable insight into the induced regenerative process. The Discussion section of the paper is laced with references to research significance.

How is longer term research significance measured?

Looking beyond writing impact statements within papers, sometimes you’ll want to quantify the long term research significance of your work. For instance when applying for jobs.

The most obvious measure of a study’s long term research significance is the number of citations it receives from future publications. The thinking is that a study which receives more citations will have had more research impact, and therefore significance , than a study which received less citations. Citations can give a broad indication of how useful the work is to other researchers but citations aren’t really a good measure of significance.

Bear in mind that us researchers can be lazy folks and sometimes are simply looking to cite the first paper which backs up one of our claims. You can find studies which receive a lot of citations simply for packaging up the obvious in a form which can be easily found and referenced, for instance by having a catchy or optimised title.

Likewise, research activity varies wildly between fields. Therefore a certain study may have had a big impact on a particular field but receive a modest number of citations, simply because not many other researchers are working in the field.

Nevertheless, citations are a standard measure of significance and for better or worse it remains impressive for someone to be the first author of a publication receiving lots of citations.

Other measures for the research significance of a study include:

• Accolades: best paper awards at conferences, thesis awards, “most downloaded” titles for articles, press coverage.
• How much follow-on research the study creates. For instance, part of my PhD involved a novel material initially developed by another PhD student in the lab. That PhD student’s research had unlocked lots of potential new studies and now lots of people in the group were using the same material and developing it for different applications. The initial study may not receive a high number of citations yet long term it generated a lot of research activity.

That covers research significance, but you’ll often want to consider other types of significance for your study and we’ll cover those next.

Statistical Significance

What is the statistical significance of a study.

Often as part of a study you’ll carry out statistical tests and then state the statistical significance of your findings: think p-values eg <0.05. It is useful to describe the outcome of these tests within your report or paper, to give a measure of statistical significance.

Effectively you are trying to show whether the performance of your innovation is actually better than a control or baseline and not just chance. Statistical significance deserves a whole other post so I won’t go into a huge amount of depth here.

Things that make publication in  The BMJ  impossible or unlikely Internal validity/robustness of the study • It had insufficient statistical power, making interpretation difficult; • Lack of statistical power; The British Medical Journal’s guide for authors

Calculating statistical significance isn’t always necessary (or valid) for a study, such as if you have a very small number of samples, but it is a very common requirement for scientific articles.

Writing a journal article? Check the journal’s guide for authors to see what they expect. Generally if you have approximately five or more samples or replicates it makes sense to start thinking about statistical tests. Speak to your supervisor and lab mates for advice, and look at other published articles in your field.

How is statistical significance measured?

Statistical significance is quantified using p-values . Depending on your study design you’ll choose different statistical tests to compute the p-value.

A p-value of 0.05 is a common threshold value. The 0.05 means that there is a 1/20 chance that the difference in performance you’re reporting is just down to random chance.

• p-values above 0.05 mean that the result isn’t statistically significant enough to be trusted: it is too likely that the effect you’re showing is just luck.
• p-values less than or equal to 0.05 mean that the result is statistically significant. In other words: unlikely to just be chance, which is usually considered a good outcome.

Low p-values (eg p = 0.001) mean that it is highly unlikely to be random chance (1/1000 in the case of p = 0.001), therefore more statistically significant.

It is important to clarify that, although low p-values mean that your findings are statistically significant, it doesn’t automatically mean that the result is scientifically important. More on that in the next section on research significance.

How to describe the statistical significance of your study, with examples

In the first paper from my PhD I ran some statistical tests to see if different staining techniques (basically dyes) increased how well you could see cells in cow tissue using micro-CT scanning (a 3D imaging technique).

In your methods section you should mention the statistical tests you conducted and then in the results you will have statements such as:

Between mediums for the two scan protocols C/N [contrast to noise ratio] was greater for EtOH than the PBS in both scanning methods (both  p  < 0.0001) with mean differences of 1.243 (95% CI [confidence interval] 0.709 to 1.778) for absorption contrast and 6.231 (95% CI 5.772 to 6.690) for propagation contrast. … Two repeat propagation scans were taken of samples from the PTA-stained groups. No difference in mean C/N was found with either medium: PBS had a mean difference of 0.058 ( p  = 0.852, 95% CI -0.560 to 0.676), EtOH had a mean difference of 1.183 ( p  = 0.112, 95% CI 0.281 to 2.648). From the Results section of my first paper, available here . Square brackets added for this post to aid clarity.

From this text the reader can infer from the first paragraph that there was a statistically significant difference in using EtOH compared to PBS (really small p-value of <0.0001). However, from the second paragraph, the difference between two repeat scans was statistically insignificant for both PBS (p = 0.852) and EtOH (p = 0.112).

By conducting these statistical tests you have then earned your right to make bold statements, such as these from the discussion section:

Propagation phase-contrast increases the contrast of individual chondrocytes [cartilage cells] compared to using absorption contrast. From the Discussion section from the same paper.

Without statistical tests you have no evidence that your results are not just down to random chance.

Beyond describing the statistical significance of a study in the main body text of your work, you can also show it in your figures.

In figures such as bar charts you’ll often see asterisks to represent statistical significance, and “n.s.” to show differences between groups which are not statistically significant. Here is one such figure, with some subplots, from the same paper:

In this example an asterisk (*) between two bars represents p < 0.05. Two asterisks (**) represents p < 0.001 and three asterisks (***) represents p < 0.0001. This should always be stated in the caption of your figure since the values that each asterisk refers to can vary.

Now that we know if a study is showing statistically and research significance, let’s zoom out a little and consider the potential for commercial significance.

Commercial and Industrial Significance

What are commercial and industrial significance.

Moving beyond significance in relation to academia, your research may also have commercial or economic significance.

Simply put:

• Commercial significance: could the research be commercialised as a product or service? Perhaps the underlying technology described in your study could be licensed to a company or you could even start your own business using it.
• Industrial significance: more widely than just providing a product which could be sold, does your research provide insights which may affect a whole industry? Such as: revealing insights or issues with current practices, performance gains you don’t want to commercialise (e.g. solar power efficiency), providing suggested frameworks or improvements which could be employed industry-wide.

I’ve grouped these two together because there can certainly be overlap. For instance, perhaps your new technology could be commercialised whilst providing wider improvements for the whole industry.

Commercial and industrial significance are not relevant to most studies, so only write about it if you and your supervisor can think of reasonable routes to your work having an impact in these ways.

How are commercial and industrial significance measured?

Unlike statistical and research significances, the measures of commercial and industrial significance can be much more broad.

Here are some potential measures of significance:

Commercial significance:

• How much value does your technology bring to potential customers or users?
• How big is the potential market and how much revenue could the product potentially generate?
• Is the intellectual property protectable? i.e. patentable, or if not could the novelty be protected with trade secrets: if so publish your method with caution!
• If commercialised, could the product bring employment to a geographical area?

Industrial significance:

What impact could it have on the industry? For instance if you’re revealing an issue with something, such as unintended negative consequences of a drug , what does that mean for the industry and the public? This could be:

• Reduced overhead costs
• Better safety
• Faster production methods
• Improved scaleability

How to describe the commercial and industrial significance of a study, with examples

Commercial significance.

If your technology could be commercially viable, and you’ve got an interest in commercialising it yourself, it is likely that you and your university may not want to immediately publish the study in a journal.

You’ll probably want to consider routes to exploiting the technology and your university may have a “technology transfer” team to help researchers navigate the various options.

However, if instead of publishing a paper you’re submitting a thesis or dissertation then it can be useful to highlight the commercial significance of your work. In this instance you could include statements of commercial significance such as:

The measurement technology described in this study provides state of the art performance and could enable the development of low cost devices for aerospace applications. An example of commercial significance I invented for this post

Industrial significance

First, think about the industrial sectors who could benefit from the developments described in your study.

For example if you’re working to improve battery efficiency it is easy to think of how it could lead to performance gains for certain industries, like personal electronics or electric vehicles. In these instances you can describe the industrial significance relatively easily, based off your findings.

For example:

By utilising abundant materials in the described battery fabrication process we provide a framework for battery manufacturers to reduce dependence on rare earth components. Again, an invented example

For other technologies there may well be industrial applications but they are less immediately obvious and applicable. In these scenarios the best you can do is to simply reframe your research significance statement in terms of potential commercial applications in a broad way.

As a reminder: not all studies should address industrial significance, so don’t try to invent applications just for the sake of it!

Societal Significance

What is the societal significance of a study.

The most broad category of significance is the societal impact which could stem from it.

If you’re working in an applied field it may be quite easy to see a route for your research to impact society. For others, the route to societal significance may be less immediate or clear.

Studies can help with big issues facing society such as:

• Medical applications : vaccines, surgical implants, drugs, improving patient safety. For instance this medical device and drug combination I worked on which has a very direct route to societal significance.
• Political significance : Your research may provide insights which could contribute towards potential changes in policy or better understanding of issues facing society.
• Public health : for instance COVID-19 transmission and related decisions.
• Climate change : mitigation such as more efficient solar panels and lower cost battery solutions, and studying required adaptation efforts and technologies. Also, better understanding around related societal issues, for instance this study on the effects of temperature on hate speech.

How is societal significance measured?

Societal significance at a high level can be quantified by the size of its potential societal effect. Just like a lab risk assessment, you can think of it in terms of probability (or how many people it could help) and impact magnitude.

Societal impact = How many people it could help x the magnitude of the impact

Think about how widely applicable the findings are: for instance does it affect only certain people? Then think about the potential size of the impact: what kind of difference could it make to those people?

Between these two metrics you can get a pretty good overview of the potential societal significance of your research study.

How to describe the societal significance of a study, with examples

Quite often the broad societal significance of your study is what you’re setting the scene for in your Introduction. In addition to describing the existing literature, it is common to for the study’s motivation to touch on its wider impact for society.

For those of us working in healthcare research it is usually pretty easy to see a path towards societal significance.

Our CLOUT model has state-of-the-art performance in mortality prediction, surpassing other competitive NN models and a logistic regression model … Our results show that the risk factors identified by the CLOUT model agree with physicians’ assessment, suggesting that CLOUT could be used in real-world clinicalsettings. Our results strongly support that CLOUT may be a useful tool to generate clinical prediction models, especially among hospitalized and critically ill patient populations. Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

In other domains the societal significance may either take longer or be more indirect, meaning that it can be more difficult to describe the societal impact.

Even so, here are some examples I’ve found from studies in non-healthcare domains:

We examined food waste as an initial investigation and test of this methodology, and there is clear potential for the examination of not only other policy texts related to food waste (e.g., liability protection, tax incentives, etc.; Broad Leib et al., 2020) but related to sustainable fishing (Worm et al., 2006) and energy use (Hawken, 2017). These other areas are of obvious relevance to climate change… AI-Based Text Analysis for Evaluating Food Waste Policies

The continued development of state-of-the art NLP tools tailored to climate policy will allow climate researchers and policy makers to extract meaningful information from this growing body of text, to monitor trends over time and administrative units, and to identify potential policy improvements. BERT Classification of Paris Agreement Climate Action Plans

Top Tips For Identifying & Writing About the Significance of Your Study

• Writing a thesis? Describe the significance of your study in the Introduction and the Conclusion .
• Submitting a paper? Read the journal’s guidelines. If you’re writing a statement of significance for a journal, make sure you read any guidance they give for what they’re expecting.
• Take a step back from your research and consider your study’s main contributions.
• Read previously published studies in your field . Use this for inspiration and ideas on how to describe the significance of your own study
• Discuss the study with your supervisor and potential co-authors or collaborators and brainstorm potential types of significance for it.

Now you’ve finished reading up on the significance of a study you may also like my how-to guide for all aspects of writing your first research paper .

Writing an academic journal paper

I hope that you’ve learned something useful from this article about the significance of a study. If you have any more research-related questions let me know, I’m here to help.

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Understanding relevance of health research: considerations in the context of research impact assessment

Mark j. dobrow.

1 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College Street, 4th Floor, Toronto, ON M5T 3M6 Canada

Fiona A. Miller

2 Alberta Innovates - Health Solutions, Edmonton, Alberta Canada

Adalsteinn D. Brown

Associated data.

Not applicable. No datasets were generated or analysed during the development of the article.

With massive investment in health-related research, above and beyond investments in the management and delivery of healthcare and public health services, there has been increasing focus on the impact of health research to explore and explain the consequences of these investments and inform strategic planning. Relevance is reflected by increased attention to the usability and impact of health research, with research funders increasingly engaging in relevance assessment as an input to decision processes. Yet, it is unclear whether relevance is a synonym for or predictor of impact, a necessary condition or stage in achieving it, or a distinct aim of the research enterprise. The main aim of this paper is to improve our understanding of research relevance, with specific objectives to (1) unpack research relevance from both theoretical and practical perspectives, and (2) outline key considerations for its assessment.

Our approach involved the scholarly strategy of review and reflection. We prepared a draft paper based on an exploratory review of literature from various fields, and gained from detailed and insightful analysis and critique at a roundtable discussion with a group of key health research stakeholders. We also solicited review and feedback from a small sample of expert reviewers.

Conclusions

Research relevance seems increasingly important in justifying research investments and guiding strategic research planning. However, consideration of relevance has been largely tacit in the health research community, often depending on unexplained interpretations of value, fit and potential for impact. While research relevance seems a necessary condition for impact – a process or component of efforts to make rigorous research usable – ultimately, relevance stands apart from research impact. Careful and explicit consideration of research relevance is vital to gauge the overall value and impact of a wide range of individual and collective research efforts and investments. To improve understanding, this paper outlines four key considerations, including how research relevance assessments (1) orientate to, capture and compare research versus non-research sources, (2) consider both instrumental versus non-instrumental uses of research, (3) accommodate dynamic temporal-shifting perspectives on research, and (4) align with an intersubjective understanding of relevance.

Various levels of government in Canada collectively invest multiple billions of dollars in health-related research per annum, above and beyond investments in the management and delivery of healthcare and public health services. In recognition of this sizeable collective commitment, much work has focused on the impact of health research to explore and explain the consequences of these investments and inform strategic planning. Relevance is tacit in the increased attention to the usability and impact of health research. Additionally, research funders increasingly engage in relevance assessment as an input to decision processes; yet, it is unclear whether relevance is a synonym for or predictor of impact, a necessary condition or stage in achieving it, or a distinct aim of the research enterprise. Therefore, the main aim of this paper is to improve our understanding of research relevance as it relates to research quality and research impact, with specific objectives to (1) unpack research relevance from both theoretical and practical perspectives, and (2) outline key considerations for the assessment of research relevance.

Globally, there has been increasing critical assessment of the value of health research investments [ 1 – 3 ], with growing interest in research impact assessment (RIA) in the health sector [ 4 – 6 ]. RIA focuses on understanding how research activity can directly and indirectly advance knowledge, influence decision-making, and effect health and socio-economic outcomes, with a small but growing body of work seeking to develop better measures to evaluate (and ideally attribute) the returns on health research investments [ 6 ]. The Canadian Academy of Health Sciences (CAHS) released a comprehensive report on the subject in 2009 that presented a call for action, with a number of recommendations including establishing collaborative efforts among Canadian research funders to advance frameworks and sets of indicators and metrics for health research impact [ 4 ]. The CAHS impact framework [ 4 ], which drew on the Buxton and Hanney [ 7 ] ‘payback model’, among others, has provided a thoughtful starting point for considering the impact of health research in Canada. Subsequent work by Alberta Innovates – Health Solutions (AIHS) on a Research to Impact Framework (described in Graham et al. [ 8 ]) provides further insights on operationalising RIA frameworks for health research in Canada.

These initiatives are part of a broadly discussed shift in approaches to knowledge production, from an emphasis on investigator-initiated, curiosity driven work judged and guided by scientists, to expanded approaches to knowledge production, drawing on a wider set of actors and approaches, and emphasising relevance and usability. This shift from science produced by and for scientists to knowledge production that is “ socially distributed, application-oriented, trans-disciplinary, and subject to multiple accountabilities ” [ 9 ] has been characterised as a shift from ‘mode 1’ to ‘mode 2’ knowledge regimes. In the language of mode 2, interest in research ‘impact’ expresses a concern for application or consequence, and – in the economic language of return on investment – a concern that the yield is at least equal to the investment in the research itself. Extending this reasoning, interest in research ‘relevance’ may reflect a concern for accountability – linking research to the actor(s) for whom the research is performed and who will, ideally, put it to use.

In Canada, interest in research impact and relevance appears to have been felt most forcefully in the context of health services and policy research, which has long been encouraged to orient to the needs of policymakers, health system planners and related decision makers. More recently, there has been increased attention to ensuring that all forms of health research are ‘patient oriented’ – that is, that the research is prioritised, conducted and applied in ways that are accountable to this important end user. This call has been picked up on several fronts, including by the Canadian Institutes of Health Research (CIHR), which released its Strategy for Patient-Oriented Research (SPOR) in 2011. The SPOR vision “…is to demonstrably improve health outcomes and enhance patients’ health care experience through integration of evidence at all levels in the health care system ” [ 10 ]. In some respects, it represents a fundamental re-orientation for the primary funder of health research in Canada.

Though relevance is tacit in attention to research impact and the wider concern with mode 2 knowledge production, explicit attention to the meaning or measurement of research relevance is limited. The CAHS and AIHS frameworks, for example, acknowledge ‘relevance’ of health research but do not clearly define the term nor describe approaches for assessing it [ 4 , 8 ]. Rather, these frameworks emphasise the role of broad stakeholder engagement approaches and feedback mechanisms as methods for addressing relevance. For example, the AIHS framework notes the challenge of, and need to, move “ …beyond the collection of traditional scientific indicators […] to include measures of greater interest to the broader stakeholder community… ” [ 8 ] without stating explicitly how “ greater interest ” or related concepts such as relevance should be judged. As currently constructed, these RIA frameworks provide important advances in how we think about the impact of health research, but they were not intended to provide guidance specifically to the assessment of the relevance of health research.

Despite this lack of specific guidance on research relevance from a scholarly or measurement perspective, attention to it as a practical component of health research funding and organisation is evolving. There is, for example, growing use of ‘relevance assessment’ by research funders. The Canadian Health Services Research Foundation, in particular, was an innovator in incorporating relevance review into its applied research funding programmes, including promoting partnerships and knowledge translation (KT) with health system stakeholders [ 11 ]. Current applications for funding from the Institute of Gender and Health at CIHR go through ‘relevance review’ ( http://www.cihr-irsc.gc.ca/e/45212.html ). Similarly, applications for Ontario’s Health System Research Fund are judged based on ‘internal review of relevance and impact’ ( http://www.health.gov.on.ca/en/pro/ministry/research/cihr.aspx ). However, given the lack of conceptual clarity on research relevance, and in particular, how relevance assessment aligns with and differs from impact assessment, there is a critical gap in our understanding that has implications for both its contemporary and ongoing application and our ability to make sound research investment decisions.

This work was commissioned by the Ontario SPOR SUPPORT (Support for People and Patient-Oriented Research and Trials) Unit (OSSU) – one of several units established at provincial and regional levels across Canada to work with CIHR in pursuing the SPOR. Like other research organisations, OSSU saw the need to consider the relevance of the research it supported, and it established both scientific and relevance advisory committees as part of its original governance structure [ 12 ], tasking the latter to “ …develop a measure, or small set of strategic measures, that serves to inspire the Ontario research, implementation, provider and patient communities to come together to make a difference for patients ” [ 12 ]. In the spirit of research and scholarship, OSSU then asked what exactly this commitment to research ‘relevance’ entailed.

Our approach to answering this question involved the scholarly strategy of review and reflection. As with the early investigations into research impact assessment, we were surprised to find so little reflexive attention to the topic within the health research community [ 13 ]. We prepared a draft paper based on an exploratory review of literature from various fields, and gained from detailed and insightful analysis and critique at a roundtable discussion with a small group of key health research stakeholders. We also solicited review and feedback from a small sample of expert reviewers.

The structure of our paper is as follows. First, to ‘unpack’ the concept of relevance, we review theoretical literature and then consider practical work both from within and outside the health sector, to ask what has been argued and concluded about the nature of relevance and its appropriate assessment. Next, we outline a series of forward-looking considerations for assessing research relevance and conclude with reflections on how research relevance assessment fits with evolving interest in RIA.

Unpacking relevance

Theoretical perspectives.

Before considering the relevance of health research, we need to step back and consider what we mean by the term ‘relevance’. A range of descriptors is often used to define relevance, including ‘pertinent to…’, ‘bearing upon…’, ‘connected with…’, or ‘appropriate to…’, ‘…the matter at hand’, as well as ‘germane’, ‘apropos’, ‘material’, ‘applicable’ and ‘satisfactory’. A large body of dedicated theoretical work on relevance, drawn from many fields and perspectives, such as computer science, information science, statistics/probability theory, artificial intelligence, cognitive science, epistemology, linguistics and jurisprudence [ 14 ], reflects its importance but also the challenge for establishing a common understanding of the term [ 14 , 15 ]. For example, Gärdenfors [ 16 ], in his discussion on the logic of relevance, noted that “ …relevance ought to be a central concept in the philosophy of science… ” given the position that “ …it is only relevant information that is of any importance… ” (p. 351). However, from a ‘research’ relevance perspective, the theoretical work on relevance has been linked to ‘information’, ‘evidence’, ‘reasoning’, ‘argument’ and ‘decision’ [ 15 – 18 ], each presenting variable framing that impedes practical definition or consistent comprehension of the term. Floridi [ 14 ] recently suggested that existing theories are “ …utterly useless when it comes to establish the actual relevance of some specific piece of information ” (p. 69), and goes on to advance a ‘subjectivist’ interpretation, with relevance judged by the questioner. While a subjectivist approach to relevance is intuitively appealing, its contribution to the assessment of research relevance presents particular challenges that we will discuss later in the paper.

Another approach to unpacking relevance is to consider the theoretical model behind the broad-based research strategies that have governed research investments and policies in high-income countries since the end of the Second World War. For the better part of the 20th century, a linear model was the dominant conceptual framework, whereby basic research was viewed as a necessary input for applied research, which then led to development and production [ 19 , 20 ]. In the late 1990s, an alternate thesis was introduced when Stokes proposed a new model for broad-based research strategy – known as Pasteur’s Quadrant – that highlighted the conceptual relationship between the ‘quest to understand’ and ‘practical needs’ [ 21 ]. While some research is clearly focused on advances in basic research (e.g. Niels Bohr’s foundational research on atomic structure and quantum theory), and some research is clearly focused on applied problems (e.g. Thomas Edison’s practical inventions), Stokes emphasised the potential for use-inspired basic research (e.g. Louis Pasteur’s foundational research on microbiology that addressed contemporaneous population health challenges). Pasteur’s Quadrant invokes consideration of ‘relevance’ with some commentators framing the two-by-two relationship as the relevance for advancement of basic knowledge and the relevance for immediate application [ 22 ]. Stokes’ model adds conceptual insight on the role of relevance when considering the value of research to society, however, it was not intended to specifically conceptualise the term and does not distinguish it from other related concepts such as research impact or value. Therefore, to provide further insights, we next consider relevance in practical settings.

Health sector perspectives

In the health sector, the idea that research should be ‘relevant’ is commonplace. Commitments to ‘knowledge translation’ and the ‘knowledge to action cycle’ [ 23 ] emphasise issues of relevance and provide considerable insight into approaches to ensuring research usability and use. At the same time, the health research community has given disproportionate attention to issues of research quality, with an emphasis on internal validity that may downplay external validity and suggest some tension between rigour and relevance. Thus, though the concept of relevance is of central importance to the health research enterprise, the failure to unpack it or explore it both theoretically and practically leaves room for misunderstanding and misapplication.

In the health sector, research relevance often arises as a practical question of the ‘fit’ between a body of knowledge or research approach and a specific field or issue (e.g. public health, primary healthcare, healthcare access, genomics, alternative healthcare, healthcare reform in rural areas). The results of two recent International Society for Pharmacoeconomics and Outcomes Research task forces take this approach. The task forces developed questionnaires to assess the relevance and credibility of research other than randomised controlled trials (e.g. observational research, meta-network analysis) to inform healthcare decision-making [ 24 , 25 ]. Both make similar observations about relevance, reinforcing the subjectivist approach noted earlier, and can be summarised by the following statement by Berger et al. [ 24 ]:

“ Relevance addresses whether the results of the study/apply [sic] to the setting of interest to the decision maker. It addresses issues of external validity similar to the population, interventions, comparators, outcomes, and setting framework from evidence based medicine. There is no correct answer for relevance. Relevance is determined by each decision maker, and the relevance assessment determined by one decision maker will not necessarily apply to other decision makers. Individual studies may be designed with the perspective of particular decision makers in mind (e.g. payer or provider) ” (p.148, emphasis added).

Research relevance in health is also noted in discussion and debate regarding the value of qualitative research relative to the more established forms of quantitative health research. For example, Mays and Pope [ 26 ] suggest that qualitative research can be assessed “… by two broad criteria: validity and relevance ”. Their further discussion provides some insight into the several ways that research might be relevant, suggesting that:

“[r] esearch can be relevant when it either adds to knowledge or increases the confidence with which existing knowledge is regarded. Another important dimension of relevance is the extent to which findings can be generalised beyond the setting in which they were generated ” [ 27 ].

The work of Mays and Pope positions research relevance amidst the longstanding tension between internal and external validity. This tension reflects opposing foci on internal validity as the quality/rigour of research methodology and external validity as the applicability/transferability of research to other settings or contexts. While external validity is not the only measure of relevance – as research may remain relevant to some contexts even when not generalisable to others – it is an important component, and one that has not always attracted sufficient attention. For example, the Canadian health research community has focused considerable practical attention on internal validity as a critical component of evidence for clinical and health policy decisions. Evidence-based medicine, the Cochrane Collaboration, the Canadian and United States task forces on preventive healthcare/services and a long list of aligned groups have developed and established many tools to assess the quality of research evidence (e.g. GRADE [ 28 ]), with a predominant focus on issues of internal validity, and an emphasis on evidence hierarchies that is sometimes seen to be incompatible with ‘real world’ relevance. The relative lack of similar approaches or tools that focus on external validity in health research is notable, though movements to marshal evidence in support of sound public policy, such as the Campbell Collaboration, have attended to issues of external validity in other areas of health and social policy [ 29 ]. Further, there are emerging approaches and tools for documenting the external validity of health research and facilitating its use [ 30 ]. For example, WHO has supported the development of workbooks to contextualise health systems guidance for different contexts [ 31 ] and the field of local applicability and transferability of research has emerged to facilitate the adaptation of interventions from one setting to another, including the development of some well-documented tools like RE-AIM [ 32 ].

Alongside these emerging approaches and tools sits the established field of KT. KT has a strong history in Canada with a distinctive feature being a reliance on stakeholder engagement to support a commitment to improve research relevance. For example, the AIHS framework relies heavily on KT and stakeholder engagement approaches as part of its RIA, describing the mobilisation of knowledge through “ …a process of interactions, feedback, and engagement using a variety of mechanisms (e.g. collaborations, partnerships, networks, knowledge brokering) with relevant target audiences (i.e. actors and performers) across the health sector ” ([ 8 ] p. 362). Experience in stakeholder engagement, particularly with clinical, management and policy decision-makers, has become fairly extensive and there is now increased attention on engaging patients as core stakeholders in health research. If relevance is truly subjective, then KT efforts (including engagement, dissemination, promotion, communication) would appear to represent reasonable approaches for articulating, conveying and improving research relevance. However, if there are underlying elements of relevance that are more universal, then there is a risk that KT efforts – and subjectivist approaches to ensuring relevance – are akin to commercial marketing or communication strategies where the aim is to ‘sell’ more product and/or generate more influence that may not align with a more objective lens.

In sum, the health research community in Canada has a longstanding history of critically appraising research quality based on study design and research methodology, with greater emphasis on internal rather than external validity. As the same time, there is established expertise in KT, emphasising engagement with research users and adaptation to settings or contexts of use – approaches that may imply a subjectivist interpretation of relevance. Thus, while relevance is an important concept for the health research enterprise, its use is largely tacit and taken for granted.

Non-health sector perspectives

To unpack relevance further we consider some non-health sector perspectives that give attention to the term, often with formal definitions or taxonomies established. Examples include the legal, financial accounting, education and web search (information retrieval) sectors, each of which are briefly described below.

From a legal perspective, relevance has a specific meaning that relates to the admissibility of evidence in terms of its probative value (i.e. the extent to which evidence contributes to proving an important matter of fact) [ 33 ]. For example, a common objection to legal testimony or evidence is that it is ‘irrelevant’ [ 34 ]. Legal processes for considering the admissibility or legal-relevance of evidence are firmly established, requiring explicit declaration of evidentiary sources and direct consideration of that evidence as it relates both to a specific case and related historical precedents, something that is undeveloped in the health sector [ 35 ]. It is the formality, explicitness and retrospective nature of this process, which is directly associated with a specific case (or decision), that is characteristic of the consideration of relevance in the legal context.

Financial accounting provides another perspective on relevance. In this field, relevance is viewed as a fundamental component of generally accepted accounting principles. Relevance and materiality are emphasised such that accountants and auditors focus on financial information that meets the decision-making needs of users and is expected to affect their decisions. In financial accounting, ‘value relevance’ provides a more focused perspective on relevance, defined as “ …the ability of information disclosed by financial statements to capture and summarise firm value. Value relevance can be measured through the statistical relations between information presented by financial statements and stock market values or returns ” [ 36 ]. Similar to the legal perspective, the financial accounting perspective on relevance is set with a formal context, where the focal point (i.e. financial performance) is clear and principles (i.e. generally accepted accounting principles) and processes (i.e. financial reporting and auditing) are clearly established and monitored.

Education provides a slightly more expansive approach to operationalising relevance, given the more general aim of the enterprise. In the United States, the Glossary of Education Reform [ 37 ] notes that “ …the term relevance typically refers to learning experiences that are either directly applicable to the personal aspirations, interests, or cultural experiences of students (personal relevance) or that are connected in some way to real-world issues, problems, and contexts (life relevance) ”. They further state that “ personal relevance occurs when learning is connected to an individual student’s interests, aspirations, and life experiences ”, while “ life relevance occurs when learning is connected in some way to real-world issues, problems, and contexts outside of school ”. A similar framing of relevance in this context suggests that it “…extends the learning beyond the classroom by teaching students to apply what they are learning to real world situations ” [ 38 ]. While the education sector also makes numerous references to a ‘rigour and relevance’ dyad [ 39 ] in contrast to the dominance of the internal validity focus in healthcare, it is the prominent dual focus on ‘personal’ relevance (with its subjectivist orientation) and ‘life’ or ‘real world’ relevance (with its more universal orientation) that seems to most clearly define the education sector’s perspective on relevance.

One of the most intensive and competitive sectors focusing on relevance is the web search (or information retrieval) field. This includes dominant search engines such as Google and Bing, as well as a wide range of commercial and social media sites such as Amazon, eBay, Facebook and LinkedIn, that compete either directly or indirectly on their ability to identify relevant information in response to user queries. Therefore, the ability of these organisations to advance the theory and practice related to relevance is fundamental to their success. For example, Google was built upon the effectiveness of its search algorithm, which is in a constant state of evolution. Both explicit and implicit approaches to assess relevance are used to contribute to search algorithm refinements [ 40 ]. The explicit approach focuses on ‘relevance ratings’, whereby evaluators (e.g. human raters) are contracted to assess the degree of ‘helpfulness’ of search results paired to specific search queries [ 41 ]. The implicit approach to assess relevance monitors and aggregates search behaviour of millions of users who are likely unaware that their behaviour is being assessed. Google has more recently advanced ‘personalised relevance’, which uses past individual search behaviour to personalise/tailor future search results for the same individual. Pariser has critiqued this concept as “ the filter bubble ” [ 42 ], warning that Google’s intent to optimise search algorithms for personal relevance creates a “ …personal ecosystem of information… ” that limits the diversity of search results and promotes insularity. This personal relevance is situated within the pervasiveness of social media, which facilitates the advancement of ‘social relevance’. Personal and social relevance highlight two important orientations towards relevance – one built on increasingly detailed understanding of individual preferences and the other reflecting the growing power and increasing accessibility of crowd-sourced perspectives. Overall, web search has made important contributions to how we understand and operationalise relevance, including the use of increasingly sophisticated explicit and implicit feedback mechanisms and the ability to draw upon and analyse big data sets. Web search has also exposed the contrasting orientations of personal and social relevance that underscore the challenges of combining or integrating different relevance assessments.

These non-health sector perspectives on relevance highlight several considerations. First, they reinforce general findings that point to perspective, decision context, timeliness and precision of focus or ‘fit’ as key elements of relevance. Additionally, they highlight a few distinctive considerations. The formalistic contexts of financial accounting and law emphasise issues such as precedent and legitimacy, implying that relevance in a research sense might require the demonstration of some legitimate or credible association between research and its use or user, among other considerations. Further, the complex consumerist world of social media highlights some of the challenges of a purely subjectivist definition of relevance. Whereas the International Society for Pharmacoeconomics and Outcomes Research guidance takes a subjectivist stance in suggesting that, “[t] here is no correct answer for relevance ” [ 24 ], the “ filter bubble ” criticised by Pariser [ 42 ] suggests otherwise. Relevance solely to the personally-perceived interests of a research user is unlikely to adequately serve the collective commitments to health and health equity that are especially germane to the health research enterprise.

Forward-looking considerations for assessing the relevance of health research

To this point, we have endeavoured to unpack relevance from theoretical and practical perspectives. In light of these insights and in the context of persistent interest in research impact assessment and evolving interest in research relevance, we now turn to some specific forward-looking considerations for research relevance assessment (RRA).

Relevance of research versus everything else

The first consideration for RRA is the acknowledgement that research is only one of many sources of insight to inform the needs or actions of research users. A research user is influenced by a wide range of political, legal, media, economic and other contextual information, interactions and experiences, as well as prevailing organisational governance, leadership, culture and values that all serve to complement (and often dominate) any insights that might be derived from research [ 43 ]. This reality implies that ‘relevance’ has a different meaning for researchers and research users. Researchers are typically interested in the relevance of a specific research product or activity for identifiable actions of (potentially) multiple research users; relevance is here judged relative to both the perceived needs of research users, and the extent and content of other related research. In contrast, research users are typically focused on identifying multiple relevant inputs to guide a specific action, only some of which may be research; relevance is here judged relative to both the research user’s needs and the form and content of the other inputs.

Given these distinct orientations to research relevance, RRA needs to be explicit about its comparative lens. Clear distinctions should be made between relevance based on the merits of the research product or activity (researcher lens) and relevance based on the relative value of research compared to other research and non-research sources (research user lens). RRA provides an opportunity to build more robust ways to characterise and assess the contribution of research to research users, including a more systematic and transparent articulation of anticipated research uses (akin to the Research Councils UK’s ‘Pathways to Impact’ [ 44 ] or descriptions of planned study design and methodological approach published in study protocols/registrations for randomised controlled trials or systematic reviews).

Beyond instrumental uses of research

The considerations noted above rely heavily on instrumental uses of research. Theoretically derived definitions of relevance, such as Floridi’s [ 14 ], tend to focus on the response to a specified question. This suggests a direct and tangible connection between research and its ‘use’. However, as Weiss [ 43 ] and others have observed, most types of research use are not instrumental, where use is documented and explicitly addresses a specific query or challenge for a research user. Rather, research use tends to be more conceptual, where use is indirect and evolves over time, or symbolic, where use may be politically or tactically motivated [ 43 ]. Research may also create externalities or unintended effects. For example, general research activity might support an engaged learning environment, interactive research relationships, and additional research-related discourse that provides benefits that are not attributable to any specific research product or activity. This has important ramifications for how research is funded and the role that relevance can play in that assessment. Ultimately, RRA needs to go beyond a singular understanding of research use as instrumental use, to develop better methods for capturing and assessing the relevance of the many non-instrumental uses of research.

The temporal factor

Another closely related consideration for RRA is the temporal context. Almost all research is conducted in a temporally defined period. Yet, while the quality of research is typically characterised by its methodology, which is a static feature typically not subject to temporal variation (e.g. the assessed quality of a randomised controlled trial should be consistent over time), relevance of research can be considered at any time (e.g. prior to the initiation of a research study or at different points in time post-completion) and is therefore subject to dynamic perceptions as they pertain to evolving action or decision contexts. Cohen [ 15 ] suggests that “ …relevance, like reasoning, has a prospective dimension as well as a retrospective one. It helps prediction as well as explanation ” (p. 182). The important insight is that, in contrast to research quality, the relevance of a specific research product can change over time, making assessment of research relevance more challenging.

This requires RRA to acknowledge the temporal factor and its associated implications for research relevance. At minimum, RRA should specify the temporal context as either pre-research (e.g. proposal/funding stage) or post-research (e.g. after research results have been produced). RRA at the pre-research stage focuses on proposed inputs and hypothetical outputs and outcomes, and may be more likely to overestimate instrumental research use and underestimate non-instrumental use. RRA at the post-research stage focuses mainly on the importance and value of actual outputs and tangible results, and may capture more non-instrumental research use. The pre-research stage is clearly aligned with research funding/investment processes, while the post-research stage can contribute to retrospective return-on-investment calculations and more general research impact assessment. However, employing this simple temporal categorisation should not lead us to lose sight of the dynamic, iterative nature of research relevance and the opportunity to assess it at interim and ongoing stages that captures re-interpretations or re-applications of research findings over time.

Moving from a subjective to an intersubjective understanding of relevance

An underlying theme in our review of relevance is subjectivity. Consider the broad scientific paradigms of positivism and interpretivism that are typically respectively aligned with research quality and research relevance. Research quality can be viewed as relating to characteristics or features that are assessed objectively, while research relevance may be seen as subjectively adjudicated. The subjective focus emphasises the variability of different perspectives and contexts and the suggestion that anyone can have a different take on the relevance of a specific research product or activity. For RRA, this reinforces a user-centred orientation to relevance assessment that privileges the judgment of the interrogator and raises the key question regarding who is positioned as the main arbiter of research relevance.

However, while relevance may never be characterised as universal, it could be argued that it is not purely subjective either. Rather, relevance may be more consistent with an intersubjective understanding that emphasises the extent of agreement or shared understanding among individual subjective perspectives representing a way to bridge the personal and the universal. The intersubjective view, while not presenting an objective approach to measuring relevance, does provide a road towards a meaningful and structured assessment of research relevance. It also emphasises the importance of representation in forging the intersubjective judgments that guide the research enterprise.

This paper has unpacked research relevance from different perspectives and outlined key considerations for its assessment. Alongside research impact assessment, research relevance seems increasingly important in justifying research investments and guiding strategic research planning. Indeed, judgments of ‘relevance’ are becoming a key component of the health research enterprise. However, consideration of relevance has been largely tacit in the health research community, often depending on unexplained interpretations of value, fit and potential for impact. Reviewing the various uses of relevance in health research, the concept is sometimes used as a synonym for research impact or positioned as a reliable predictor of later consequence. In many ways, research relevance seems a necessary condition for impact – a process or component of efforts to make rigorous research usable. However, relevance is not a necessary or sufficient condition to achieve impact. We expect that research that is relevant, and thus accountable to specific and legitimate users, will be impactful, but this may not necessarily be the case where other factors intervene. Additionally, we may expect that research that is impactful will be appropriately accountable – but again, this is not necessarily the case. Ultimately, relevance stands apart from research impact. Like rigour, relevance is a complementary but distinctive dimension of what it is that ensures ‘the good’ in health research.

While ‘relevance’ is ever-present, understanding of the concept in terms of health research is emergent and not well codified. To improve our understanding, this paper outlines four key considerations, including how research relevance assessments (1) orientate to, capture and compare research versus non-research sources, (2) consider both instrumental versus non-instrumental uses of research, (3) accommodate dynamic temporal-shifting perspectives on research, and (4) align with an intersubjective understanding of relevance. We believe careful and explicit consideration of research relevance, guided by transparent principles and processes is vital to gauge the overall value and impact of a wide range of individual and collective research efforts and investments. We hope this paper generates more discussion and debate to facilitate progress.

Acknowledgements

We acknowledge and appreciate the contributions of participants of a roundtable discussion to gather feedback on an earlier version of this paper. Participants included Simon Denegri, National Director for Public Participation and Engagement in Research, National Institute for Health Research (NIHR) UK, and Chair of INVOLVE, UK; Lee Fairclough, Vice-President, Quality Improvement, Health Quality Ontario; Michael Hillmer, Director, Planning, Research and Analysis Branch, Ontario Ministry of Health and Long-Term Care; John McLaughlin, Chief Science Officer and Senior Scientist, Public Health Ontario; Allison Paprica, Director, Strategic Partnerships, ICES; Michael Schull, President and CEO, ICES; and Vasanthi Srinivasan, Executive Director, Ontario Strategy for Patient-Oriented Research (SPOR) SUPPORT Unit (OSSU). We also want to thank John Lavis of the McMaster Health Forum for his very helpful comments on an earlier draft. Though we owe these individuals and organisations many thanks for their insights and support, we alone are responsible for the final product.

This work was commissioned by the Ontario SPOR Support Unit (OSSU). The executive director of the OSSU was one of the participants in a roundtable discussion to gather feedback on an earlier version of this paper, but beyond that, the OSSU did not have any role in the design of the study, collection, analysis or interpretation of the data, or writing of the manuscript.

Availability of data and materials

Authors’ contributions.

ADB acquired funding for the study. MJD, FAM and ADB conceptualised the study. MJD, FAM, CF and ADB participated in the review and writing of the manuscript. MJD, FAM and ADB participated in the roundtable discussion. MJD, FAM and ADB reviewed and approved the final version of the manuscript (CF passed away prior to submission of the manuscript).

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abbreviations

This article is dedicated to the memory of Dr Cy Frank, our co-author and esteemed colleague, whose untimely death occurred midway through development of this work. Among his many interests, Dr Frank was a champion for improving understanding of research impact assessment and provided many insights on the concept of research relevance, some of which we expand upon in this article. His many contributions to the health sector will live on, but he will be greatly missed.

Contributor Information

Mark J. Dobrow, Email: [email protected] .

Fiona A. Miller, Email: [email protected] .

Adalsteinn D. Brown, Email: [email protected] .

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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.

Here’s another example to help demonstrate how a significance of the study can also be applied to non-technical fields:

The significance of this research lies in its potential to inform clinical practices and patient counseling. By understanding the psychological outcomes associated with non-surgical facial aesthetics, practitioners can better guide their patients in making informed decisions about their treatment plans. Additionally, this study contributes to the body of academic knowledge by providing empirical evidence on the effects of these cosmetic procedures, which have been largely anecdotal up to this point.

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.

There’s no doubt about it – writing can be difficult. Whether you’re writing the first sentence of a paper or a grant proposal, it’s easy

An academic transcript gives a breakdown of each module you studied for your degree and the mark that you were awarded.

When you should and shouldn’t capitalise the names of chemical compounds and their abbreviations is not always clear.

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Clara is in the first year of her PhD at the University of Castilla-La Mancha in Spain. Her research is based around understanding the reactivity of peroxynitrite with organic compounds such as commonly used drugs, food preservatives, or components of atmospheric aerosols.

Carlos is a third year PhD student at the Centre for Doctoral Training in Intelligent Games and Games Intelligence (IGGI), as part of the University of York and Goldsmiths, University of London.

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6-Evaluating Sources

2. Evaluating for Relevancy

Relevant sources are those that pertain to your research question. You’ll be able to identify them fairly quickly by reading or skimming particular parts of sources and maybe jotting down little tables that help you keep track. We’ll show you how below, including where to look in specific kinds of sources and what questions to ask yourself as you do.

One thing to consider early on as you make inferences about relevancy is the effect that timeliness– called a source’s currency–should have on deciding whether a source is relevant. Sometimes timeliness has a lot to do with relevancy; sometimes it is less important. Your research question and your discipline will determine that.

For instance, if your research question is about the life sciences, you probably should consider only the most recent sources relevant for citing because the life sciences are changing so quickly. There is a good chance that anything but the most recent sources may be out of date. So it’s a good idea to aim for life sciences sources no more than 5 years old. (An example of a discipline that calls for even newer sources is computer security.)

Sometimes emergencies change the schedule of what is recent enough. For instance, when the Covid-19 pandemic started, it was incredibility important for scientists to share their research information as quickly as possible. At that time, scientific information about Covid-19 could become outdated in weeks or months–before the peer review process was barely started.

Lives were at stake and for that reason, scientists started publishing their new research results on Covid-19 as preprints —publications of results that had not yet been peer-reviewed–in an attempt to have them be useful faster. Nonetheless, after preprint publication, the peer review process continued for much of that research.

But pre-prints didn’t start with the Covid pandemic. Around for more than 30 years and now at Cornell University, arXiv is a free distribution service and an open-access archive for more than two million scholarly articles first published as preprints in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on the site are not peer-reviewed by arXiv itself. (arXiv is pronounced archive.)

Before using preprints as sources, talk with your professor about whether she or he recommends their use in your situation.

Many sciences other than life sciences primarily use newer content under 10 years old. But not always. That’s because the history department is not alone in valuing older content. For instance, mathematics is a discipline that makes heavy use of older content. So how important the currency of your sources is will depend on your research question and your discipline. Your professor can guide you about your own situation.

In most cases, it’s best not to use a hard and fast rule about how current your sources have to be. Instead, consider your discipline and research question and do some critical thinking. For example, suppose your research question is about the Edo Period in Japan (1603-1868) or about Robert Falcon Scott, who explored the Antarctic from 1901-1913. In these cases, an item from 1918 might be just as useful as an item from 2018 (although new information may have been found in the 100-year gap). But something from 1899 about Antarctica or from 1597 about Japan would not be current enough for these research questions.

These examples also give you two more clues about how to treat the timeliness or currency of sources as you consider relevance:

• Because of how long ago they lived or occurred, it would be unusual for many sources on Robert Scott or the Edo Period to have been published very recently. So, unlike sources for the life sciences, whether a source is very recent should probably not determine its relevancy to research questions about Scott or the Edo Period.
• Primary sources might be considered especially relevant to many humanities and other non-science research questions. For disciplines in the humanities, the phrase primary sources refers to sources created at the same time as something under study—in this case, things such as Scott’s diaries and expedition photographs, as well as paintings, literature, clothing, and household items from the Edo Period. They go a long way to explain faraway people and times. (See Primary, Secondary, & Tertiary Sources .) On the other hand, when science disciplines use the phrase primary source, they usually mean where they primarily find the information they consider valid—in research journals.

EXAMPLE: TED Currency

Check out how currency is handled on TED . This site provides videos of speakers talking about new ideas in technology, entertainment, and design. (That’s what TED stands for.) Some videos are labeled “Newest Talks,” and TED tells when every video was recorded. That’s because currency matters with TED Talks.

For your own sources for which timeliness matters, see the section below called Where to Look, which includes where to look in websites, articles, and books for information about a source’s currency.

Time-Saving Tips

Instead of thinking you have to read all of every source in order to figure out whether it’s relevant, read or skim only parts of each source. If you’re looking at the right parts, that should give you enough information to make an educated guess about relevancy and currency.

But what should you be looking for as you do that reading and skimming? One way to figure that out is to first parse your research question so that you can figure out its main concepts . (This is like identifying main concepts in your research question in order to search precisely, as we advise in Chapter 4.)

For instance, suppose your research question is: How does having diverse members in a group increase the critical thinking of the group?

What are this question’s main concepts? Our answer is: group diversity and critical thinking.

So when trying to judge which sources are relevant to these main concepts, you would assess whether each source you’ve found pertains to at least one of these main concepts. We recommend you jot down a little table like the one in the example below to keep track of which sources address each main concept.

To be considered relevant to your research question, a source wouldn’t necessarily have to cover all of your main concepts. But finding sources that do is ideal. Otherwise, you just have to make do with what you’ve got. Don’t forget that each source would have to pass the currency test, too, if the currency is important to your research question. So it saves time to record your decisions about the sources’ currency on your tables, too.

EXAMPLE: Sources’ Main Concepts and Currency

Research question: How does having diverse members in a group increase the critical thinking of the group?

The table in this hypothetical example indicates that both Sources A and C are relevant because each pertains to at least one main concept from the research question. Currency doesn’t seem to matter much to our research question, so all three sources were marked current. But since currency is all that Source B has to offer, it is not relevant for this project.

If you do make little tables for relevance, it’s probably a good idea to hang on to them. You might find them helpful later in your research process.

Where to Look in Websites, Articles, and Books

The information below tells where to look and what questions to ask yourself to assess the relevancy of articles, books, and websites. The name of a source seldom tells you enough about its relevance, so whatever you do, don’t stop evaluating after looking only at a website’s name or the title of another source.

Save time by looking in particular places in sources for information that will help you figure out whether the source is relevant to your research project. Much of our advice below comes from “Speedy Reading” in The Craft of Research , second edition, by Wayne Booth, Gregory Colomb, and Joseph Williams, University of Chicago Press, 2003, pp. 108-109.

On a website , check the name of the website and its articles for clues that they contain material relevant to your research question. Consider whether time should have an impact on what information can be considered relevant to your research question. If so:

• Skim any dates, datelines, What’s New pages, and press releases to see whether any website content works with the time considerations you need.
• Check for page creation or revision dates that you find. What you’ve already learned from other sources can also help. For instance, you may know that the information covered by a particular website, which seems relevant, is no longer considered the latest thinking. In that case, you could mark it irrelevant on your little table.
• Skim any site map and index on the website for key words related to your research question.
• Try the key words of your research question in the search box. Do you see enough content about your keywords to make you think parts of the website could be helpful?

For a research journal article, magazine article, or newspaper article , think about the title. Does it have something to do with your research question? Consider whether time should have an impact on what sources can be considered relevant. If so:

• Is the publication date of any of these three kinds of articles within your parameters?
• Skim the abstract of a journal article to see whether the article works with the time considerations you need. For instance, if there is a time period in your research question, does the article address the same time period or was it created during that time period?
• Look at the abstract and section headings in a journal article or the early parts of a newspaper or magazine article to locate the problem or question that the article addresses, its solution, and the outline of the article’s argument for its main claim. Can those help answer your research question? Do they make it seem as if the article will give you information about what others have written about your research question? Do they offer a description of the situation surrounding your research question?
• Do the journal article’s introduction and conclusion sections help you answer your research question and/or offer a description of the situation surrounding your question so you can explain in your final product why the question is important?
• Check whether the journal article’s bibliography contains keywords related to your research question. Do the sources cited by the bibliography pertain to your research question? (Bibliographies are especially good places to look for sources.)
• If you decide the newspaper or magazine article is relevant, look at sources quoted or otherwise identified within it. Those may be additional sources for you.

For a book (perhaps in its library catalog listing) , check whether the title and/or subtitle indicates the book could be about your research question. You can find a lot of such information about the book from its listing in a library catalog. Consider whether time should have an impact on what sources can be considered relevant.

• Is the publication date or copyright date (usually listed in the library catalog or on the back of the book’s title page) too early or late for any time constraints in your research question? Maybe it’s just right.
• Skim some of the preface and introduction to see whether the book works with the time considerations you need.
• Check the bibliography to see whether the sources cited are about your research question.
• Skim the book’s table of contents and any summary chapters to locate the problem or question that the book addresses, its solution, and the broad outline of the book’s argument for its main claim. Will any of that be helpful in answering your research question?
• Do those sections give you information about what others have written about your research question?
• Do they offer a description of the situation surrounding your research question?
• Look for your key words in the bibliography. Do the sources cited pertain to your research question?
• Skim the index for topics with the most page references. Do the topics with the most page references pertain to your research question?

ACTIVITY: Follow a Title’s Clues for Relevance

Instructions: This quiz asks you to use logic, the titles of sources, and their publication dates, to identify the source most likely to be relevant to each research question. (Outside of this quiz, sources are not actually in competition with one another to be relevant. But this seemed like a good way to have you practice your skills at assessing relevance.) Many titles and dates below are fictitious, but that doesn’t affect their relevance within the quiz. Book, journal, website, and newspaper titles are italicized; chapter and article titles are in quotes.

• For each, read the information about the research question and each source.
• For each, record your judgments on a little table that you jot down like those illustrated earlier.
• For each, mark your answer, which should be the most relevant source according to the little table you completed for the question.
• Check your answers with our feedback.

ACTIVITY: Connecting the Dots Beyond the Title

Instructions: You always need to go beyond the title of a source when judging relevance. In the previous activity, you evaluated the titles of sources for currency and relevance. For this activity, you will investigate beyond the title to see whether one of the (hypothetical) articles named in the last activity is indeed relevant to meeting your information needs.

• Read the abstract of the article below, using your critical thinking skills to try to identify the information needs of your project it could help you meet.
• Then answer the questions about which information needs the source can help you meet. (Mark all that apply.)
• If there is at least one need it can help meet, you should judge the article relevant. Don’t forget to compare your answers with our feedback.

Your research question is: How does “prospect theory” in behavioral economics help explain medical doctors’ decisions to favor surgery or radiation to cure cancer in patients?

As usual, your information needs are:

• To learn more background information.
• To answer your research question.
• To convince your audience that your answer is correct or, at least, the most reasonable answer.
• To describe the situation surrounding your research question for your audience and explain why it’s important,
• To report what others have said about your question, including any different answers to your research question.

Abstract: “Cancer Treatment Prescription–Advancing Prospect Theory beyond Economics,” in Journal of The American Medical Association Oncology , June, 2022. (This article and abstract are fictitious but the journal and its form for abstracts are real.) Importance Cancer treatment is complex. We expect oncologists to make treatment decisions according to definitive standards of care. Finding out that prospect theory demonstrates that they react very much like most other people when deciding to recommend surgery or chemotherapy for their patients indicates that more self-reflection on oncologists’ part could help patients make better decisions. (Prospect theory describes how people choose between alternatives that have risk when the probability of different outcomes is unknown.) Objective To show whether prospect theory applies to how oncologists framed their recommendations for surgery or chemotherapy for patients in good condition and bad condition. Design, Settings, and Participants Records of 100 U.S. oncologists were examined for the years 2019 and 2020, which documented patient conditions and the way oncologists framed their recommendations regarding surgery or chemotherapy. Records of nine thousand patients were involved. Thus, a quasiexperimental ex post facto design was used for the study. Main Outcomes and Measures This study explored the relationship between the way in which the oncologists “framed” the choice of surgery or chemotherapy as they made recommendations to patients, the patients’ conditions, and the choice actually made. Those results were compared to what prospect theory would predict for this situation. Results Physicians seemed to present their recommendation of surgery or chemotherapy in a loss frame (e.g., “This is likely to happen to you if you don’t have this procedure”) when patients’ conditions were poor and in a gain frame (e.g., “By having this procedure, you can probably dramatically cut your chances of reoccurrence”) when their conditions were less poor. These results are what prospect theory would have predicted. Conclusions and Relevance This study opens up the possibility that, as described by prospect theory, a person’s choice of framing behavior is not limited to how we naturally act for ourselves but includes how we act for other people, as the oncologists were acting on behalf of their patients. More research is necessary to confirm this line of evidence and determine whether oncologists’ decision making and framing is the most effective and entirely according to the best standards of care.

Which information needs could this source help you meet if your research question was: How does “prospect theory” in behavioral economics help explain medical doctors’ decisions to favor surgery or radiation to cure cancer in patients?

A brief summary of what a journal article is about and a quick read in order to decide whether the article is likely to contain information relevant to your research project. The abstract may appear in research databases and, sometimes, in the article itself.

Choosing & Using Sources: A Guide to Academic Research Copyright © 2015 by Teaching & Learning, Ohio State University Libraries is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

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

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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.

You have full access to this open access chapter,  Download chapter PDF

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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Overview of Relevance in Research

Published 16 October, 2023

Relevance in research is an interconnection of one research topic with others. It is basically the level up to which you can apply research findings in real life. In simple words, the investigation which you are conducting is useful for others.

Meaning of Relevance in Research

The relevance in research is the understanding of how finding or studying one thing affects another. “Relevance” can also be seen as the extent to which a certain study or theory is significant.

Research is the pursuit of new knowledge. The relevancy in research means that study which you are performing should be useful for others as well- and high relevancies mean research which you are performing has great potential to fill a gap in knowledge, especially if it’s something people currently don’t know about or understand very well!

For instance:

• If you are conducting a market survey for an organization then the data which you are collecting through the survey research should be useful for your firm. High relevancy means the investigation which you are performing has a high potential of filling the knowledge gap.
• If you are selecting the unemployment issue as your research topic. You are performing a research process for identifying the root cause of unemployment and then information which you have gathered through research can be helpful for formulating effective policies for the nation.

Significance of relevance in research

Relevance has great significance in research as it helps you in maintaining the momentum. In addition to this, it is the relevant information that will help you in making your dissertation interesting to the reader. Relevancy is the factor in research that helps you and the reader in developing confidence about the findings and outcome of the investigation.

Maintaining a high level of relevance is also very crucial for getting the dissertation approved by the tutor. It is very much essential for you to make sure that the topic or field which you are selecting for performing the investigation has academic and social relevance. A high level of relevancy in research is very much crucial for eliminating risk and ethical issues.

Types of relevance in research        

The different types of relevance in research are:

1. Academic relevance

This basically means level up to which investigation performed on a particular topic has helped you in accomplishing your academic goals. Academic relevance is a measure of how much something helped you progress towards your academic goals. In order to be academically relevant, the information one has learned must have been able to assist in some way with achieving their own personal goal or objective.

Academic relevance is an important consideration for any student when deciding to study a subject. It can help you determine if the investigation performed on that topic will be helpful in achieving your academic goals.

2. Societal relevance  

It is referred to as the information gathered through investigation helps in developing the understanding of the society. Good research will help us understand society better by giving insight into how it functions- or more specifically what processes are occurring behind the scenes that we might not see otherwise due to our own biases as someone embedded in this culture.

3. Practical relevance  

It is basically an extent up to which the findings could be applied in real-life situations . Research that has practical relevance not only adds value but also can make a recommendation for particular industries or improve processes in an organization.

4. Scientific relevance

It is basically an extent up to which you can fill the knowledge gap thorough research on a specific topic. You have to make sure that the research you are doing will fill in a gap of knowledge for the scientific community. The best way to do this is by extensively researching your topic and finding what hasn’t been researched yet. It’s important not just because it makes an impact on science but also so that you find something stimulating enough for you as well!

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How to Discuss the Significance of Your Research

• 6-minute read
• 10th April 2023

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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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?

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/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

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

Browse all articles written by Jewel Kyle Fabula

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Relevance Of Research – Why Is It So Important?

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Research is a significant element in academia. It is a tool that helps us solve problems, make new discoveries, and understand the world better in general. During the research process , you can make a difference in people’s lives or in society. For this reason, students must complete research papers as part of any course in higher education. This article discusses the relevance of research in different fields of academic writing .

Inhaltsverzeichnis

• 1 In a nutshell: Relevance of research
• 2 Definition: Relevance of research
• 3 How to conduct research
• 4 Relevance of research in different courses
• 5 Types of relevance in research
• 6 Knowledge and learning
• 7 Issues and public awareness
• 8 A successful business
• 9 Lies and truths
• 10 Opportunities
• 11 Information
• 12 Relevance of research: Exercise for the mind

In a nutshell: Relevance of research

• Many academic fields require students to conduct academic research as part of their studies. Overall, research is also applied heavily by students in learning and the academic writing process.
• The key relevance of research in academia is that it allows students and researchers to find sources to make their arguments on a specific topic. Furthermore, most opinions are conceived through the research process.
• Besides students, trained professionals also recognize the relevance of research.

Definition: Relevance of research

Relevance of research refers to the importance of research in various fields. Here are a few reasons why research is relevant:

• It builds knowledge and promotes learning.
• It helps to increase public awareness.
• Research promotes success in business and other fields.
• It encourages the disapproval of lies and supports facts and truths.
• Research is a means for discovering opportunities and helps build credibility.
• It promotes confidence and passion in reading, sharing information, analyzing, and writing.
• Research nourishes and helps exercise the mind.

How to conduct research

The relevance of research is not a topic of debate. Therefore, students must learn how to research, so they can enjoy the benefits. The following steps explain how to conduct research.

• Choosing a topic and identifying a problem: Firstly, you must come up with ideas and find a general area of interest. Once you are settled on a topic, you must determine an issue that needs to be addressed in the area and why it matters.
• Formulating research questions and creating a research design: Next, you must create one or more research questions that target what you want to find out through your research. Additionally, create a practical framework for answering your research questions (research design).
• Writing a research proposal: Finally, create a research proposal that outlines the relevance of the research, context, purpose, and your plan. From there, you can start searching for sources and gathering information for your research.

Relevance of research in different courses

The relevance of research stands out in different courses. For this reason, most courses encourage their students to apply research in their studies and academic writing. Universities encourage and engage in research as part of their mission to promote learning and discovery.

Let us look at the relevance of research in different courses:

Types of relevance in research

There are different forms of the relevance of research. Let us look at some of the key ones.

Academic relevance

Societal relevance, practical relevance, scientific relevance.

The academic relevance of research is perhaps the most critical. Research is critical in the promotion of academic knowledge of a subject. Moreover, research helps individuals meet their academic goals. Academic relevance comes from learned information, which is obtained through research.

The purpose of research extends beyond academia and has a significant impact on society. Research generates knowledge that aids in addressing real-world problems and making informed decisions. Research provides a more profound understanding of society and its functions.

The relevance of research is also important in everyday life. Research findings apply in real-life situations to various extents. For instance, research allows entrepreneurs to discover problems and wants in society, and the findings help resolve these problems. Researchers make recommendations for particular industries and promote improved processes in critical organizations.

Research allows practitioners in various fields of science to bridge the knowledge gap in various subjects. Research also helps scientists make new and significant discoveries that help advance different fields. Scientists need research to come up with life-changing inventions.

Knowledge and learning

Research helps facilitate knowledge acquisition and learning. Students, academics, professionals, and non-professionals depend on research as a tool for learning and understanding a subject better. Research also equips individuals with information about the world and skills for survival and life improvement.

Issues and public awareness

Research is a tool for understanding issues and raising public awareness. It helps people understand each other and their world. People use research to understand current issues.

A successful business

Research is critical for business success. Successful companies and individuals rely on market and client research. It helps them understand their clients, their needs, and how to provide them with what they need. Therefore, research helps with targeted marketing. It also helps businesses understand their competition and establish ways to stand out.

Lies and truths

Background research and private investigations are critical in debunking lies and promoting truths. Researchers apply field-testing and peer reviews to validate facts. Therefore, research builds integrity and competence in facts. Fact-checking helps discover research bias, fake news, and propaganda.

Opportunities

Research helps people find, gauge, and seize opportunities. Therefore, it helps individuals nurture their potential and achieve goals by taking advantage of opportunities. People can use research to maximize career options and investments.

Information

Research promotes a passion and love for reading, writing, analyzing, and sharing information. It is a tool for critical thinking and comprehension. Sharing research promotes a wider understanding of a subject.

Relevance of research: Exercise for the mind

Research nourishes and exercises the mind. Critical thinking is a tool for promoting mental health. Students earn critical reasoning skills from research, which helps with their learning. Various studies have proven that mentally stimulating activities like research can promote brain health.

What is the meaning of relevance in research?

The relevance of research is the understanding of how studying one thing can affect another. It is the extent to which a specific study or theory is significant.

What are the different types of relevance of research?

The various forms of the relevance of research are:

How does research promote mental health?

Research nourishes and exercises the mind. Critical thinking is a tool for promoting mental health. Students earn critical reasoning skills from research, which helps with their learning.

What is the scientific relevance of research?

Research allows practitioners in various fields of science to bridge the knowledge gap in various subjects. It helps scientists make new and significant discoveries that help advance different fields.

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Reviewers: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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• Published: 08 May 2024

A meta-analysis on global change drivers and the risk of infectious disease

• Michael B. Mahon   ORCID: orcid.org/0000-0002-9436-2998 1 , 2   na1 ,
• Alexandra Sack 1 , 3   na1 ,
• O. Alejandro Aleuy 1 ,
• Carly Barbera 1 ,
• Ethan Brown   ORCID: orcid.org/0000-0003-0827-4906 1 ,
• Heather Buelow   ORCID: orcid.org/0000-0003-3535-4151 1 ,
• David J. Civitello 4 ,
• Jeremy M. Cohen   ORCID: orcid.org/0000-0001-9611-9150 5 ,
• Luz A. de Wit   ORCID: orcid.org/0000-0002-3045-4017 1 ,
• Meghan Forstchen 1 , 3 ,
• Fletcher W. Halliday 6 ,
• Patrick Heffernan 1 ,
• Sarah A. Knutie 7 ,
• Alexis Korotasz 1 ,
• Joanna G. Larson   ORCID: orcid.org/0000-0002-1401-7837 1 ,
• Samantha L. Rumschlag   ORCID: orcid.org/0000-0003-3125-8402 1 , 2 ,
• Emily Selland   ORCID: orcid.org/0000-0002-4527-297X 1 , 3 ,
• Alexander Shepack 1 ,
• Nitin Vincent   ORCID: orcid.org/0000-0002-8593-1116 1 &
• Jason R. Rohr   ORCID: orcid.org/0000-0001-8285-4912 1 , 2 , 3   na1  

Nature ( 2024 ) Cite this article

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• Infectious diseases

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors 1 . Studies have shown that infectious disease risk is modified by changes to biodiversity 2 , 3 , 4 , 5 , 6 , climate change 7 , 8 , 9 , 10 , 11 , chemical pollution 12 , 13 , 14 , landscape transformations 15 , 16 , 17 , 18 , 19 , 20 and species introductions 21 . However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host–parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

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Towards common ground in the biodiversity–disease debate

Biological invasions facilitate zoonotic disease emergences

Measuring the shape of the biodiversity-disease relationship across systems reveals new findings and key gaps

Data availability.

All the data for this Article have been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 .

Code availability

All the code for this Article has been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 . R markdown is provided in Supplementary Data 1 .

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Acknowledgements

We thank C. Mitchell for contributing data on enemy release; L. Albert and B. Shayhorn for assisting with data collection; J. Gurevitch, M. Lajeunesse and G. Stewart for providing comments on an earlier version of this manuscript; and C. Carlson and two anonymous reviewers for improving this paper. This research was supported by grants from the National Science Foundation (DEB-2109293, DEB-2017785, DEB-1518681, IOS-1754868), National Institutes of Health (R01TW010286) and US Department of Agriculture (2021-38420-34065) to J.R.R.; a US Geological Survey Powell grant to J.R.R. and S.L.R.; University of Connecticut Start-up funds to S.A.K.; grants from the National Science Foundation (IOS-1755002) and National Institutes of Health (R01 AI150774) to D.J.C.; and an Ambizione grant (PZ00P3_202027) from the Swiss National Science Foundation to F.W.H. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

These authors contributed equally: Michael B. Mahon, Alexandra Sack, Jason R. Rohr

Authors and Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Alexandra Sack, O. Alejandro Aleuy, Carly Barbera, Ethan Brown, Heather Buelow, Luz A. de Wit, Meghan Forstchen, Patrick Heffernan, Alexis Korotasz, Joanna G. Larson, Samantha L. Rumschlag, Emily Selland, Alexander Shepack, Nitin Vincent & Jason R. Rohr

Environmental Change Initiative, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Samantha L. Rumschlag & Jason R. Rohr

Eck Institute of Global Health, University of Notre Dame, Notre Dame, IN, USA

Alexandra Sack, Meghan Forstchen, Emily Selland & Jason R. Rohr

Department of Biology, Emory University, Atlanta, GA, USA

David J. Civitello

Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

Jeremy M. Cohen

Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA

Fletcher W. Halliday

Department of Ecology and Evolutionary Biology, Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA

Sarah A. Knutie

You can also search for this author in PubMed   Google Scholar

Contributions

J.R.R. conceptualized the study. All of the authors contributed to the methodology. All of the authors contributed to investigation. Visualization was performed by M.B.M. The initial study list and related information were compiled by D.J.C., J.M.C., F.W.H., S.A.K., S.L.R. and J.R.R. Data extraction was performed by M.B.M., A.S., O.A.A., C.B., E.B., H.B., L.A.d.W., M.F., P.H., A.K., J.G.L., E.S., A.S. and N.V. Data were checked for accuracy by M.B.M. and A.S. Analyses were performed by M.B.M. and J.R.R. Funding was acquired by D.J.C., J.R.R., S.A.K. and S.L.R. Project administration was done by J.R.R. J.R.R. supervised the study. J.R.R. and M.B.M. wrote the original draft. All of the authors reviewed and edited the manuscript. J.R.R. and M.B.M. responded to reviewers.

Corresponding author

Correspondence to Jason R. Rohr .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Colin Carlson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 prisma flowchart..

The PRISMA flow diagram of the search and selection of studies included in this meta-analysis. Note that 77 studies came from the Halliday et al. 3 database on biodiversity change.

Extended Data Fig. 2 Summary of the number of studies (A-F) and parasite taxa (G-L) in the infectious disease database across ecological contexts.

The contexts are global change driver ( A , G ), parasite taxa ( B , H ), host taxa ( C , I ), experimental venue ( D , J ), study habitat ( E , K ), and human parasite status ( F , L ).

Extended Data Fig. 3 Summary of the number of effect sizes (A-I), studies (J-R), and parasite taxa (S-a) in the infectious disease database for various parasite and host contexts.

Shown are parasite type ( A , J , S ), host thermy ( B , K , T ), vector status ( C , L , U ), vector-borne status ( D , M , V ), parasite transmission ( E , N , W ), free living stages ( F , O , X ), host (e.g. disease, host growth, host survival) or parasite (e.g. parasite abundance, prevalence, fecundity) endpoint ( G , P , Y ), micro- vs macroparasite ( H , Q , Z ), and zoonotic status ( I , R , a ).

Extended Data Fig. 4 The effects of global change drivers and subsequent subcategories on disease responses with Log Response Ratio instead of Hedge’s g.

Here, Log Response Ratio shows similar trends to that of Hedge’s g presented in the main text. The displayed points represent the mean predicted values (with 95% confidence intervals) from a meta-analytical model with separate random intercepts for study. Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. See Table S 3 for pairwise comparison results. Effects of the five common global change drivers ( A ) have the same directionality, similar magnitude, and significance as those presented in Fig. 2 . Global change driver effects are significant when confidence intervals do not overlap with zero and explicitly tested with two-tailed t-test (indicated by asterisks; t 80.62  = 2.16, p = 0.034 for CP; t 71.42  = 2.10, p = 0.039 for CC; t 131.79  = −3.52, p < 0.001 for HLC; t 61.9  = 2.10, p = 0.040 for IS). The subcategories ( B ) also show similar patterns as those presented in Fig. 3 . Subcategories are significant when confidence intervals do not overlap with zero and were explicitly tested with two-tailed one sample t-test (t 30.52  = 2.17, p = 0.038 for CO 2 ; t 40.03  = 4.64, p < 0.001 for Enemy Release; t 47.45  = 2.18, p = 0.034 for Mean Temperature; t 110.81  = −4.05, p < 0.001 for Urbanization); all other subcategories have p > 0.20. Note that effect size and study numbers are lower here than in Figs. 3 and 4 , because log response ratios cannot be calculated for studies that provide coefficients (e.g., odds ratio) rather than raw data; as such, all observations within BC did not have associated RR values. Despite strong differences in sample size, patterns are consistent across effect sizes, and therefore, we can be confident that the results presented in the main text are not biased because of effect size selection.

Extended Data Fig. 5 Average standard errors of the effect sizes (A) and sample sizes per effect size (B) for each of the five global change drivers.

The displayed points represent the mean predicted values (with 95% confidence intervals) from the generalized linear mixed effects models with separate random intercepts for study (Gaussian distribution for standard error model, A ; Poisson distribution for sample size model, B ). Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. Sample sizes (number of studies, n, and effect sizes, k) for each driver are as follows: n = 77, k = 392 for BC; n = 124, k = 364 for CP; n = 202, k = 380 for CC; n = 517, k = 1449 for HLC; n = 96, k = 355 for IS.

Extended Data Fig. 6 Forest plots of effect sizes, associated variances, and relative weights (A), Funnel plots (B), and Egger’s Test plots (C) for each of the five global change drivers and leave-one-out publication bias analyses (D).

In panel A , points are the individual effect sizes (Hedge’s G), error bars are standard errors of the effect size, and size of the points is the relative weight of the observation in the model, with larger points representing observations with higher weight in the model. Sample sizes are provided for each effect size in the meta-analytic database. Effect sizes were plotted in a random order. Egger’s tests indicated significant asymmetries (p < 0.05) in Biodiversity Change (worst asymmetry – likely not bias, just real effect of positive relationship between diversity and disease), Climate Change – (weak asymmetry, again likely not bias, climate change generally increases disease), and Introduced Species (relatively weak asymmetry – unclear whether this is a bias, may be driven by some outliers). No significant asymmetries (p > 0.05) were found in Chemical Pollution and Habitat Loss/Change, suggesting negligible publication bias in reported disease responses across these global change drivers ( B , C ). Egger’s test included publication year as moderator but found no significant relationship between Hedge’s g and publication year (p > 0.05) implying no temporal bias in effect size magnitude or direction. In panel D , the horizontal red lines denote the grand mean and SE of Hedge’s g and (g = 0.1009, SE = 0.0338). Grey points and error bars indicate the Hedge’s g and SEs, respectively, using the leave-one-out method (grand mean is recalculated after a given study is removed from dataset). While the removal of certain studies resulted in values that differed from the grand mean, all estimated Hedge’s g values fell well within the standard error of the grand mean. This sensitivity analysis indicates that our results were robust to the iterative exclusion of individual studies.

Extended Data Fig. 7 The effects of habitat loss/change on disease depend on parasite taxa and land use conversion contexts.

A) Enemy type influences the magnitude of the effect of urbanization on disease: helminths, protists, and arthropods were all negatively associated with urbanization, whereas viruses were non-significantly positively associated with urbanization. B) Reference (control) land use type influences the magnitude of the effect of urbanization on disease: disease was reduced in urban settings compared to rural and peri-urban settings, whereas there were no differences in disease along urbanization gradients or between urban and natural settings. C) The effect of forest fragmentation depends on whether a large/continuous habitat patch is compared to a small patch or whether disease it is measured along an increasing fragmentation gradient (Z = −2.828, p = 0.005). Conversely, the effect of deforestation on disease does not depend on whether the habitat has been destroyed and allowed to regrow (e.g., clearcutting, second growth forests, etc.) or whether it has been replaced with agriculture (e.g., row crop, agroforestry, livestock grazing; Z = 1.809, p = 0.0705). The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included enemy type (A), reference land use type (B), or land use conversion type (C). Data for (A) and (B) were only those studies that were within the “urbanization” subcategory; data for (C) were only those studies that were within the “deforestation” and “forest fragmentation” subcategories. Sample sizes (number of studies, n, and effect sizes, k) in (A) for each enemy are n = 48, k = 98 for Virus; n = 193, k = 343 for Protist; n = 159, k = 490 for Helminth; n = 10, k = 24 for Fungi; n = 103, k = 223 for Bacteria; and n = 30, k = 73 for Arthropod. Sample sizes in (B) for each reference land use type are n = 391, k = 1073 for Rural; n = 29, k = 74 for Peri-urban; n = 33, k = 83 for Natural; and n = 24, k = 58 for Urban Gradient. Sample sizes in (C) for each land use conversion type are n = 7, k = 47 for Continuous Gradient; n = 16, k = 44 for High/Low Fragmentation; n = 11, k = 27 for Clearcut/Regrowth; and n = 21, k = 43 for Agriculture.

Extended Data Fig. 8 The effects of common global change drivers on mean infectious disease responses in the literature depends on whether the endpoint is the host or parasite; whether the parasite is a vector, is vector-borne, has a complex or direct life cycle, or is a macroparasite; whether the host is an ectotherm or endotherm; or the venue and habitat in which the study was conducted.

A ) Parasite endpoints. B ) Vector-borne status. C ) Parasite transmission route. D ) Parasite size. E ) Venue. F ) Habitat. G ) Host thermy. H ) Parasite type (ecto- or endoparasite). See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and the focal independent variable (whether the endpoint measured was a host or parasite, whether the parasite is vector-borne, has a complex or direct life cycle, is a macroparasite, whether the study was conducted in the field or lab, habitat, the host is ectothermic, or the parasite is an ectoparasite).

Extended Data Fig. 9 The effects of five common global change drivers on mean infectious disease responses in the literature only occasionally depend on location, host taxon, and parasite taxon.

A ) Continent in which the field study occurred. Lack of replication in chemical pollution precluded us from including South America, Australia, and Africa in this analysis. B ) Host taxa. C ) Enemy taxa. See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and continent, host taxon, and enemy taxon.

Extended Data Fig. 10 The effects of human vs. non-human endpoints for the zoonotic disease subset of database and wild vs. domesticated animal endpoints for the non-human animal subset of database are consistent across global change drivers.

(A) Zoonotic disease responses measured on human hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on non-human (animal) hosts (Z = 2.306, p = 0.021). Note, IS studies were removed because of missing cells. (B) Disease responses measured on domestic animal hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on wild animal hosts (Z = 2.636, p = 0.008). These results were consistent across global change drivers (i.e., no significant interaction between endpoint and global change driver). As many of the global change drivers increase zoonotic parasites in non-human animals and all parasites in wild animals, this may suggest that anthropogenic change might increase the occurrence of parasite spillover from animals to humans and thus also pandemic risk. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variable of global change driver and human/non-human hosts. Data for (A) were only those diseases that are considered “zoonotic”; data for (B) were only those endpoints that were measured on non-human animals. Sample sizes in (A) for zoonotic disease measured on human endpoints across global change drivers are n = 3, k = 17 for BC; n = 2, k = 6 for CP; n = 25, k = 39 for CC; and n = 175, k = 331 for HLC. Sample sizes in (A) for zoonotic disease measured on non-human endpoints across global change drivers are n = 25, k = 52 for BC; n = 2, k = 3 for CP; n = 18, k = 29 for CC; n = 126, k = 289 for HLC. Sample sizes in (B) for wild animal endpoints across global change drivers are n = 28, k = 69 for BC; n = 21, k = 44 for CP; n = 50, k = 89 for CC; n = 121, k = 360 for HLC; and n = 29, k = 45 for IS. Sample sizes in (B) for domesticated animal endpoints across global change drivers are n = 2, k = 4 for BC; n = 4, k = 11 for CP; n = 7, k = 20 for CC; n = 78, k = 197 for HLC; and n = 1, k = 2 for IS.

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Mahon, M.B., Sack, A., Aleuy, O.A. et al. A meta-analysis on global change drivers and the risk of infectious disease. Nature (2024). https://doi.org/10.1038/s41586-024-07380-6

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When Should You Get Your Next Colonoscopy? Study Finds Waiting 15 Years May Be Safe for Some

• Most U.S. health experts recommend people get colonoscopies every 10 years.
• Researchers in a new study suggest people with a negative colonoscopy and no family history of colon cancer may be able to wait as long as 15 years between colonoscopies.
• Changing guidelines in the U.S. from 10 years to 15 years would likely require additional research and further discussions on the benefits and risks.

In the U.S., most screening guidelines recommend people get a colonoscopy every 10 years to check for colorectal cancer. But a new study suggests that certain groups may be able to wait even longer between colonoscopy appointments.

The new research—published in JAMA Oncology on May 2— looked at a large cohort of people who tested negative on their first colonoscopy, and who had no family history of the disease. They found that this group may be able to wait 15 years, rather than 10 years, to get their second colonoscopy done.

Colonoscopies are recommended for people ages 45 to 75, so under current guidelines, people would need four over the course of their lifetime if they’re at average risk for colorectal cancer.

Like other screening tests, colonoscopies help doctors detect cancer or any growths that may need to be removed. However, colonoscopies are invasive procedures, require a fair amount of preparation , and come with risks.

“There was limited real-world evidence to support this specific [10-year] timeframe,” Mahdi Fallah, MD, PhD , senior study author and leader of the Risk Adapted Prevention group at the German Cancer Research Center (DKFZ), told Health . “This study aimed to investigate whether this interval could be safely extended.”

Here’s what experts had to say about the new research, and how to determine the best colorectal cancer screening for you.

MoMo Productions / Getty Images

New Study Provides an Argument for Less Frequent Colonoscopies

For this study, Fallah and his colleagues looked at a group of over 110,000 Swedish adults, 59% of whom were assigned female at birth. Participants were between ages 45 and 69 when they had their first colonoscopy—all tested negative. Researchers compared this group to a control population, who either tested positive on their colonoscopy or never returned for another screening during the study period.

After tracking both groups’ incidence of colorectal cancer diagnosis and death over 29 years, study authors found that for the group that originally tested negative, “a repeat colonoscopy 15 years later could be just as effective as the current 10-year recommendation with minimal toll,” Fallah said.

Extending this colonoscopy interval to 15 years would lead to only a small number of additional deadly colorectal cancer cases (about 1 per 1,000 people) while avoiding a high number of screenings.

“This minimal increase in missed fatal cancers may be outweighed by the benefit of avoiding unnecessary colonoscopies,” Fallah explained.

Though this wasn’t included in the scope of the study, Fallah said potentially using cheaper, non-invasive screening methods—such as at-home stool test kits —during years 10 to 15 after a person’s first colonoscopy could “significantly reduce or even eliminate this small number of missed cases.”

Colonoscopy is an extremely helpful tool to prevent colorectal cancer. But it’s also considered an invasive procedure, and it does carry some risks, Fallah said.

“There’s also a small chance of complications like infection or bleeding or perforation (a tear in the large bowel),” he said. “Waiting longer for a repeat colonoscopy in individuals with a low likelihood of colorectal cancer can potentially reduce these unnecessary risks.”

This move could even make colonoscopies more appealing to some people, since they’d have to do them less frequently, added Rohan Jeyarajah, MD , chair of surgery at the Burnett School of Medicine at Texas Christian University. It could also ease some logistical issues.

“[Longer intervals] would really have an impact on resources that are available for colonoscopies,” he told Health . “At this point, we are struggling to get people in for the 10-year colonoscopy mark based on physician availability. This is especially true in rural communities where there is a lack of gastroenterologists or surgeons who are able to perform screenings for colonoscopies.”

Many Experts Still Think the 10-Year Timeframe Is Best

Despite the study’s results, other colorectal cancer experts are more skeptical about extending the time between certain people’s first and second colonoscopies.

Colorectal cancer is usually a slow-growing cancer, but delaying screening could lead to negative consequences, Misagh Karimi, MD , medical oncologist at City of Hope Orange County Lennar Foundation Cancer Center, told Health .

“It’s estimated that it generally takes a polyp around 10 years to develop into cancer,” Karimi explained.

Additionally, colorectal cancer isn’t hereditary and has few symptoms , said Christina Seo, MD , a colon and rectal surgeon at Holy Name Medical Center in New Jersey. This means “there is not a good way to know who would benefit from a longer interval in colonoscopies,” she told Health.

Though this 15-year gap between colonoscopies would largely be affecting people over 50, experts also noted that rates of cancer , including colorectal cancer, in young people are on the rise. In his clinic, Karimi said he’s seeing some young people who developed colorectal cancer despite having no known risk factors or noticeable symptoms.

“We have actually lowered the starting screening age from 50 to 45 because of the uptick in cancers seen in younger patients,” Seo said.

The current focus seems to be on more screening for colorectal cancer, not less.

A final caveat to the new JAMA Oncology study is that the data came out of Sweden, not the U.S., Jeyarajah said.

“You have to take into consideration that this paper was published in another part of the world,” he explained. “We do know from incidences of gastric cancer in the Far East and colorectal cancer in the U.S. that there are nutritional and environmental factors that affect the outcome.”

The Importance of Screening for Colorectal Cancer

At this point, it’s unlikely that the U.S. will see any concrete guidelines changes due to this research. There would probably need to be long-term data and new regulations adopted in Europe before the U.S. would adopt a longer interval between colonoscopies, said Seo.

“This research offers valuable insights but doesn’t necessarily mean immediate changes to screening guidelines,” Fallah said. “It paves the way for further discussion and potentially revised recommendations in the future.”

Logistics aside, the most important thing is simply that people are getting screened for colorectal cancer in general, experts stressed.

“While colorectal cancer is one of the most curable cancers, research shows that tens of millions of people are skipping out on these lifesaving screenings due to fear of the preparation, test, and results,” Karimi said.

Since the prognosis is good for colorectal cancer when it’s caught early, screenings are of the utmost importance, he added.

Screening should start at age 45 for people who are at average risk—there are a number of different screening tests in addition to colonoscopy, so it’s best to have a conversation with your healthcare provider about the best colorectal cancer screening schedule for you.

People with a higher risk—including people with a family history of the disease or inflammatory bowel disease —may need to start colorectal cancer screening earlier or have tests done more frequently.

“The best way to stop cancer is to prevent it in the first place,” said Karimi. “Preventive screenings assist with early detection, and the earlier cancer is detected, the better the outcome.”

Centers for Disease Control and Prevention. Screening for colorectal cancer .

American Cancer Society. American Cancer Society Guideline for Colorectal Cancer Screening .

Liang Q, Mukama T, Sundquist K, et al. Longer interval between first colonoscopy with negative findings for colorectal cancer and repeat colonoscopy .  JAMA Oncol . Published online May 2, 2024. doi:10.1001/jamaoncol.2024.0827

National Institute of Diabetes and Digestive and Kidney Diseases. Colonoscopy .

American Cancer Society. If you have colon or rectal cancer .

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Consumer goods and services now

of consumer goods executives aspire to set a new standard for the industry – or even outside of it

of executives believe consumers are changing faster than they can change their businesses

of consumer goods companies identify the omni-connected consumer as a top priority

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Deliver winning consumer and customer experiences.

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Improve accuracy

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Step and time-based exercise targets are equally beneficial for health – study

Step-based and time-based exercise are equally beneficial for heart health and living longer, research suggests.

The findings suggest that whether someone chooses a time or step goal may not be as important as choosing a goal that suits them.

Researchers argue that the study highlights the importance of step-based targets being added to guidelines.

Movement looks different for everyone, and nearly all forms of movement are beneficial to our health

Physical activity reduces the risk of illness and infection, and promotes long life, and the NHS recommends that adults should do some type of physical activity every day.

It suggests that adults aged 19 to 65 should do at least 150 minutes of moderate intensity activity a week or 75 minutes of vigorous intensity activity a week.

But with more and more people able to track the number of steps they take each day, through a mobile phone, smart watch or similar device, researchers from the Brigham and Women’s Hospital (BWH) in Massachusetts , US, wanted to look at how time-based goals stack up against step-based ones.

They found that higher levels of physical activity – whether assessed as step counts or time in moderate-to-vigorous activity – were associated with large risk reductions in death or cardiovascular disease.

The most active quarter of women in the study had 30% to 40% risk reductions compared with the least active quarter.

People in the top three most active groups outlived those in the bottom group by an average of 2.22 and 2.36 months respectively, based on time and step-based measurements.

According to the findings, this was the case regardless of differences in body mass index (BMI).

Lead author Rikuta Hamaya, a researcher in the Division of Preventive Medicine at BWH, explained that both methods for measuring activity have advantages and disadvantages.

For example, step counts may not account for differences in fitness levels – if a 20-year-old and 80-year-old both walk for 30 minutes at moderate intensity, their step counts may differ.

However, steps are straightforward to measure and less subject to interpretation compared with exercise intensity.

Additionally, steps pick up on the movements of everyday life, not just exercise, and these kinds of daily life activities likely are those carried out by older individuals.

Dr Hamaya said: “For some, especially for younger individuals, exercise may involve activities like tennis, soccer, walking, or jogging, all of which can be easily tracked with steps.

“However, for others, it may consist of bike rides or swimming, where monitoring the duration of exercise is simpler.

“That’s why it’s important for physical activity guidelines to offer multiple ways to reach goals.

Our findings further establish the importance of adding step-based targets, in order to accommodate flexibility of goals that work for individuals with differing preferences, abilities and lifestyles

“Movement looks different for everyone, and nearly all forms of movement are beneficial to our health.”

For the new study, published in Jama Internal Medicine, researchers collected data from 14,399 healthy women who took part in the Women’s Health Study.

Between 2011 and 2015, women aged 62 years and older were asked to wear devices for seven consecutive days to record their physical activity levels.

While wearing the devices, the women in the study took part in an average of 62 minutes of moderate-to-vigorous intensity physical activity per week and accumulated an average of 5,183 steps per day.

Over an average follow-up of nine years, some 9% had died and around 4% had developed heart disease.

Those who did the most physical activity had the greatest reduction in risk of death or cardiovascular disease.

Senior author I-Min Lee, MBBS, ScD, an epidemiologist in the Division of Preventive Medicine at BWH, said: “The next federal physical activity guidelines are planned for 2028.

“Our findings further establish the importance of adding step-based targets, in order to accommodate flexibility of goals that work for individuals with differing preferences, abilities and lifestyles.”

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