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Significance of the Study – Examples and Writing Guide

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Significance of the Study

Significance of the Study


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

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

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

Types of Significance of the Study

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

Theoretical Significance

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

Practical Significance

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

Significance for Future Research

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

How to Write Significance of the Study

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

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

Significance of the Study in Research Paper

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

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

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

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

The significance of the study may be:

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

Significance of the Study In Thesis

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

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

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

Significance of the Study in Research Proposal

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

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

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

Significance Statement:

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

Examples of Significance of the Study

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

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

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

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

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

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

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

When to Write Significance of the Study

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

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

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

Advantages of Significance of the Study

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

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

Limitations of Significance of the Study

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

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

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What is the Significance of a Study? Examples and Guide

Significance of a study graphic, showing a female scientist reading a book

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.

Graphic showing the broadening significance of a study going from your study, the wider research field, business opportunities through to society as a whole.

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:

Figure from a paper showing the statistical significance of a study using asterisks

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

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What is the Significance of the Study?


  • By DiscoverPhDs
  • August 25, 2020

Significance of the Study

  • 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.

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The term monotonic relationship is a statistical definition that is used to describe the link between two variables.

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Scientific misconduct can be described as a deviation from the accepted standards of scientific research, study and publication ethics.

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This post explains where and how to write the list of figures in your thesis or dissertation.

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Rationale for Research

The term rationale of research means the reason for performing the research study in question.

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How To Write Significance of the Study (With Examples) 

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

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Jewel Kyle Fabula

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

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2.1 Why Is Research Important?

Learning objectives.

By the end of this section, you will be able to:

  • Explain how scientific research addresses questions about behavior
  • Discuss how scientific research guides public policy
  • Appreciate how scientific research can be important in making personal decisions

Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure 2.2 ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Use of Research Information

Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades. Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores (Shaw & Tan, 2015). Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills (Massimini & Peterson, 2009). Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student's acquisition of knowledge, study skills, and mental health.

In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.

We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children's development and position them for optimal levels of success in school and later in life (Blann, 2005). While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective (Neil & Christensen, 2009; Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants (Barnett, 2011). If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund.

Link to Learning

Watch this video about early childhood program effectiveness to learn how scientists evaluate effectiveness and how best to invest money into programs that are most effective.

Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine that your sister, Maria, expresses concern about her two-year-old child, Umberto. Umberto does not speak as much or as clearly as the other children in his daycare or others in the family. Umberto's pediatrician undertakes some screening and recommends an evaluation by a speech pathologist, but does not refer Maria to any other specialists. Maria is concerned that Umberto's speech delays are signs of a developmental disorder, but Umberto's pediatrician does not; she sees indications of differences in Umberto's jaw and facial muscles. Hearing this, you do some internet searches, but you are overwhelmed by the breadth of information and the wide array of sources. You see blog posts, top-ten lists, advertisements from healthcare providers, and recommendations from several advocacy organizations. Why are there so many sites? Which are based in research, and which are not?

In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.


Psychological research has a long history involving important figures from diverse backgrounds. While the introductory chapter discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work ( Figure 2.3 ). For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition (Margaret Floy Washburn, PhD, n.d.). Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States (Mary Whiton Calkins, n.d.).

Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University’s department of psychology, and because of his accomplishments, he is sometimes referred to as the “Father of Black Psychology.” Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser’s research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional (Ethnicity and Health in America Series: Featured Psychologists, n.d.).

Although the establishment of psychology’s scientific roots occurred first in Europe and the United States, it did not take much time until researchers from around the world began to establish their own laboratories and research programs. For example, some of the first experimental psychology laboratories in South America were founded by Horatio Piñero (1869–1919) at two institutions in Buenos Aires, Argentina (Godoy & Brussino, 2010). In India, Gunamudian David Boaz (1908–1965) and Narendra Nath Sen Gupta (1889–1944) established the first independent departments of psychology at the University of Madras and the University of Calcutta, respectively. These developments provided an opportunity for Indian researchers to make important contributions to the field (Gunamudian David Boaz, n.d.; Narendra Nath Sen Gupta, n.d.).

When the American Psychological Association (APA) was first founded in 1892, all of the members were White males (Women and Minorities in Psychology, n.d.). However, by 1905, Mary Whiton Calkins was elected as the first female president of the APA, and by 1946, nearly one-quarter of American psychologists were female. Psychology became a popular degree option for students enrolled in the nation’s historically Black higher education institutions, increasing the number of Black Americans who went on to become psychologists. Given demographic shifts occurring in the United States and increased access to higher educational opportunities among historically underrepresented populations, there is reason to hope that the diversity of the field will increasingly match the larger population, and that the research contributions made by the psychologists of the future will better serve people of all backgrounds (Women and Minorities in Psychology, n.d.).

The Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas ( Figure 2.4 ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.

For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.

We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure 2.5 .

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( Figure 2.6 ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.

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What Is Research, and Why Do People Do It?

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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.

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Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

An image of the book's description with the words like research, science, and inquiry and what the word research meant in the scientific world.

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.

An image represents the observation required in the scientific inquiry including planning and explaining.

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.

An image represents the data explanation as it is not limited and takes numerous non-quantitative forms including an interview, journal entries, etc.

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. ).

An image represents the scientific inquiry definition given by the editors of Britannica and also defines the hypothesis on the basis of the experiments.

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.

An image represents the rationale and the prediction for the scientific inquiry and different types of information provided by the terms.

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.

An image represents the cycle of events that take place before making predictions, developing the rationale, and studying the prediction and rationale multiple times.

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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How to Discuss the Significance of Your Research

How to Discuss the Significance of Your Research

  • 6-minute read
  • 10th April 2023


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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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

Understanding Research infographic

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Expaling How Research Works Infographic en español

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

  • Discoveries in Basic Science: A Perfectly Imperfect Process
  • When Clinical Research Is in the News
  • What is Basic Science and Why is it Important?
  • ​ What is a Research Organism?
  • What Are Clinical Trials and Studies?
  • Basic Research – Digital Media Kit
  • Decoding Science: How Does Science Know What It Knows? (NAS)
  • Can Science Help People Make Decisions ? (NAS)

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Six Reasons Why Research is Important

Importance of internet Research

Everyone conducts research in some form or another from a young age, whether news, books, or browsing the Internet. Internet users come across thoughts, ideas, or perspectives - the curiosity that drives the desire to explore. However, when research is essential to make practical decisions, the nature of the study alters - it all depends on its application and purpose. For instance, skilled research offered as a  research paper service  has a definite objective, and it is focused and organized. Professional research helps derive inferences and conclusions from solving problems. visit the HB tool services for the amazing research tools that will help to solve your problems regarding the research on any project.

What is the Importance of Research?

The primary goal of the research is to guide action, gather evidence for theories, and contribute to the growth of knowledge in data analysis. This article discusses the importance of research and the multiple reasons why it is beneficial to everyone, not just students and scientists.

On the other hand, research is important in business decision-making because it can assist in making better decisions when combined with their experience and intuition.

Reasons for the Importance of Research

  • Acquire Knowledge Effectively
  • Research helps in problem-solving
  • Provides the latest information
  • Builds credibility
  • Helps in business success
  • Discover and Seize opportunities

1-  Acquire Knowledge Efficiently through Research

The most apparent reason to conduct research is to understand more. Even if you think you know everything there is to know about a subject, there is always more to learn. Research helps you expand on any prior knowledge you have of the subject. The research process creates new opportunities for learning and progress.

2- Research Helps in Problem-solving

Problem-solving can be divided into several components, which require knowledge and analysis, for example,  identification of issues, cause identification,  identifying potential solutions, decision to take action, monitoring and evaluation of activity and outcomes.

You may just require additional knowledge to formulate an informed strategy and make an informed decision. When you know you've gathered reliable data, you'll be a lot more confident in your answer.

3- Research Provides the Latest Information

Research enables you to seek out the most up-to-date facts. There is always new knowledge and discoveries in various sectors, particularly scientific ones. Staying updated keeps you from falling behind and providing inaccurate or incomplete information. You'll be better prepared to discuss a topic and build on ideas if you have the most up-to-date information. With the help of tools and certifications such as CIRS , you may learn internet research skills quickly and easily. Internet research can provide instant, global access to information.

4- Research Builds Credibility

Research provides a solid basis for formulating thoughts and views. You can speak confidently about something you know to be true. It's much more difficult for someone to find flaws in your arguments after you've finished your tasks. In your study, you should prioritize the most reputable sources. Your research should focus on the most reliable sources. You won't be credible if your "research" comprises non-experts' opinions. People are more inclined to pay attention if your research is excellent.

5-  Research Helps in Business Success

R&D might also help you gain a competitive advantage. Finding ways to make things run more smoothly and differentiate a company's products from those of its competitors can help to increase a company's market worth.

6-  Research Discover and Seize Opportunities

People can maximize their potential and achieve their goals through various opportunities provided by research. These include getting jobs, scholarships, educational subsidies, projects, commercial collaboration, and budgeted travel. Research is essential for anyone looking for work or a change of environment. Unemployed people will have a better chance of finding potential employers through job advertisements or agencies. 

How to Improve Your Research Skills

Start with the big picture and work your way down.

It might be hard to figure out where to start when you start researching. There's nothing wrong with a simple internet search to get you started. Online resources like Google and Wikipedia are a great way to get a general idea of a subject, even though they aren't always correct. They usually give a basic overview with a short history and any important points.

Identify Reliable Source

Not every source is reliable, so it's critical that you can tell the difference between the good ones and the bad ones. To find a reliable source, use your analytical and critical thinking skills and ask yourself the following questions: Is this source consistent with other sources I've discovered? Is the author a subject matter expert? Is there a conflict of interest in the author's point of view on this topic?

Validate Information from Various Sources

Take in new information.

The purpose of research is to find answers to your questions, not back up what you already assume. Only looking for confirmation is a minimal way to research because it forces you to pick and choose what information you get and stops you from getting the most accurate picture of the subject. When you do research, keep an open mind to learn as much as possible.

Facilitates Learning Process

Learning new things and implementing them in daily life can be frustrating. Finding relevant and credible information requires specialized training and web search skills due to the sheer enormity of the Internet and the rapid growth of indexed web pages. On the other hand, short courses and Certifications like CIRS make the research process more accessible. CIRS Certification offers complete knowledge from beginner to expert level. You can become a Certified Professional Researcher and get a high-paying job, but you'll also be much more efficient and skilled at filtering out reliable data. You can learn more about becoming a Certified Professional Researcher.

Stay Organized

You'll see a lot of different material during the process of gathering data, from web pages to PDFs to videos. You must keep all of this information organized in some way so that you don't lose anything or forget to mention something properly. There are many ways to keep your research project organized, but here are a few of the most common:  Learning Management Software , Bookmarks in your browser, index cards, and a bibliography that you can add to as you go are all excellent tools for writing.

Make Use of the library's Resources

If you still have questions about researching, don't worry—even if you're not a student performing academic or course-related research, there are many resources available to assist you. Many high school and university libraries, in reality, provide resources not only for staff and students but also for the general public. Look for research guidelines or access to specific databases on the library's website. Association of Internet Research Specialists enjoys sharing informational content such as research-related articles , research papers , specialized search engines list compiled from various sources, and contributions from our members and in-house experts.

of Conducting Research

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  • Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

  • Debra L. Weiner 1 , 2 ,
  • Vivek Balasubramaniam 3 ,
  • Shetal I. Shah 4 &
  • Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Radcliffe, S. Here’s exactly where we are with vaccines and treatments for COVID-19. Health News . https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-for-covid-19 (2020).

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA

Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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The importance of scientific research in higher education

Scientific research is not only a tool for solving current problems, it is also an integral part of higher education today. Universities and academic institutions consider scientific research a fundamental focus of their educational programs, as they encourage students to think critically, analyze and explore.

This approach enhances students' ability to interact with the world around them and understand the complex challenges they face.

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Scientific research plays a crucial role in improving the quality of life, solving modern problems and providing distinguished higher education. Through innovation and discovery, scientific research opens new horizons for humanity and contributes to building a better and more sustainable future. Our investment in scientific research is an investment in our future and the future of future generations.

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Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

what is importance of the study in research

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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  • Innovative SPHHP research continues with announcement of UB's CTSI Translational Pilot Study Grants

Innovative SPHHP research continues with announcement of UB's CTSI Translational Pilot Study Grants

Photos of Jihnhee Yu, Gregory Wilding, Katia Noyes, Pauline Mendola, Jeffrey Miecznikowski, • Christopher Stavisky, and Jacob I. McPherson,.

Published January 30, 2024

News about innovative research at SPHHP continues with the announcement of UB’s Clinical and Translational Science Institute (CTSI) Translational  Pilot Studies Program  awards. Several SPHHP faculty are among recipients of this year’s seed money to assist them in developing promising new technologies and therapeutics from the conceptual stage to clinical studies.

“The projects awarded pilot studies grants for 2024 address valuable translational research and translational science questions,” says CTSI Pilot Studies Program Director  Brahm Segal, MD , chair, Department of Internal Medicine, chief, Division of Infectious Diseases, and professor of oncology, Roswell Park Comprehensive Cancer Center. “These are innovative projects that will advance new methods and technologies to impact health, foster cross-disciplinary collaborations, and lead to mentoring relationships.”

SPHHP projects awarded 2024 CTSI Translational Pilot Studies Program funding are:

“Feasibility of Aerobic Exercise for Recovery from Work-related Concussion”

Co-principal investigators.

  • Jacob I. McPherson , PT, DPT, PhD, clinical assistant professor, Department of Rehabilitation Sciences
  • Christopher Stavisky , PhD, OTR/L, clinical assistant professor, Department of Rehabilitation Science

Jeffrey Miecznikowski , PhD, professor, Department of Biostatistics is a co-investigator.

Work-related concussion is a common occupational injury that has received far less research attention compared to sport-related concussion. It results in prolonged lost time claims with negative impact on personal/family finances and perceived life roles and also affects workforce capacity in industries that are already facing worker shortages. This study will evaluate the feasibility of applying targeted heart rate aerobic exercise, a proven treatment approach to the management of sports-related concussion, to people diagnosed with work-related concussion. A secondary aim is to examine demographic and psychosocial factors that may be associated with recovery from work-related concussion.

“Feasibility of Reducing Postpartum Mortality Among Rural Patients: Use of Remote and Community-based Interventions”

  • Pauline Mendola , PhD, chair and professor, Department of Epidemiology and Environmental Health
  • Thaddeus P. Waters, MD, clinical professor and chief, Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Jacobs School of Medicine and Biomedical Sciences

The U.S. has among the highest maternal mortality rates compared to other high-income countries. Rural counties have a greater burden of poor outcomes. Evidence-based interventions to prevent maternal mortality have been developed, but limited evidence and significant barriers remain for implementation and scale-up of interventions in rural communities that lack obstetric providers and have elevated levels of social and economic deprivation. This study will engage rural care provider (hospitals, primary care and obstetric providers, visiting nursing services, etc.) patient advocacy groups into a partnership with the Regional Perinatal Center to adapt and assess approaches for reducing maternal mortality in rural obstetric care deserts.

SPHHP faculty are co-investigators and consultants on a number of other CTSI-awarded projects:

Katia Noyes , PhD, MPH, associate dean for translational and team sciences, Director, Division of Health Services Policy and Practice, professor and director, Department of Surgery, Surgical Outcomes and Research (SOAR) Center, Department of Epidemiology and Environmental Health: “Feasibility of Reducing Postpartum Mortality Among Rural Patients: Use of Remote and Community-based Interventions”; “Increasing Lung Cancer Screening: An Urban Primary Care Health Center Approach”; “Impact of the Increase in Availability of Ambulatory Surgery on Treatment Decision Making: A Mixed Methods Study”; “Impact of the Increase in Availability of Ambulatory Surgery on Treatment Decision Making: A Mixed Methods Study’

Gregory Wilding , PhD, professor, Department of Biostatistics: “Increasing Lung Cancer Screening: An Urban Primary Care Health Center Approach”; Impact of the Increase in Availability of Ambulatory Surgery on Treatment Decision Making: A Mixed Methods Study”

Jihnhee Yu , PhD, professor, Department of Biostatistics: “AHEAD—Acoustic Hemodynamic Examination Through AI Diagnosis”

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Greg rhee research – er visits for suicide attempts and self-harm, national trends in emergency department visits for suicide attempts and intentional self-harm.

Dr. Greg Rhee has gotten an important research article published in the Journal of Psychiatry. This study estimated national annual trends and characteristics of emergency department visits for suicide attempts and intentional self-harm in the United States from 2011 to 2020.

Article Excerpt:

Suicide continues to be a major public health problem in the United States, and suicide rates have risen by 35% since 2000 ( 1 ,   2 ). Suicide attempts are the single most important risk factor for suicide and the risk factor most likely to precipitate contact with the health care system ( 3 – 5 ). It is estimated that 1 in 25 individuals who present to the hospital for self-harm will die by suicide within 5 years ( 6 ). Since suicide prevention relies on identifying individuals at high risk for suicide, suicide attempts offer critical opportunities to intervene to prevent future suicide ( 7 – 9 ).

Since many patients are treated in emergency departments after a suicide attempt, emergency departments are an important setting for determining epidemiologic trends in suicidal behavior, especially because the United States does not have a national monitoring system for suicide attempts. Over the past three decades, several studies have raised concern about rising emergency department visits for suicide attempts ( 10 – 13 ). A U.S. national study of emergency departments found that the average annual number of emergency department visits for suicide attempts and intentional self-harm more than doubled between 1993 and 2008 ( 11 ). A more recent study found that rates were stable between 2006 and 2013 ( 14 ).

Yet, there have been no nationally representative U.S. estimates of annual trends in emergency department visits for suicide attempts and intentional self-harm since 2013, including trends stratified by sociodemographic groups. Given the important role of emergency departments in suicide prevention, examining national trends in visits for suicide attempts provides an opportunity to identify population-wide unmet mental health needs and inform suicide prevention strategies.

Learn more here: https://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.20230397

About Greg Rhee

Research Area(s) of Interest :  ​ Population-based clinical outcomes research and health care policy ​

Dr. Rhee is a psychiatric epidemiologist and mental health services researcher studying the access to care, quality of care,   and clinical and patient-reported outcomes in patients with psychiatric or substance use disorders. My current research   focuses on affective disorders (e.g., depression and bipolar disorder), suicidality, cannabis use disorder, and opioid use   disorder among other conditions. My expertise is grounded in population-based observational studies using   large   databases to characterize unmet needs and national practice  patterns in the assessment and management of behavioral   health conditions. I also investigate long-term comparative effectiveness and cost-effectiveness of pharmacological and   non-pharmacological interventions for psychiatric and substance use disorders. ​ ​ Dr. Rhee is an accredited professional statistician ( PStat ®) certified by the American Statistical Association with extensive   experience with administrative claims data, national health and healthcare survey data, and other data sources including   electronic health records, and mortality data. He has served as a senior statistician in 100+ peer-reviewed research   articles and has successfully served as Principal Investigator or Co-Investigator on multiple research projects funded by   major federal agencies, including Agency for Healthcare Research and Quality (AHRQ), Food and Drug Administration   (FDA), and National Institutes of Health (NIH).

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  • v.8(2); 2015 Oct

The Importance of Research—A Student Perspective

Rachel arena.

grid.252546.20000000122978753Department of Psychology, Auburn University, Magnolia Street and Duncan Drive and West Thatch Ave, Auburn, AL 36849 USA

Sheridan Chambers

Angelyn rhames, katherine donahoe.

As students, we will focus on the importance of an objective ranking system, research, and mentorship to an applicant. We will address points raised in the (Behavior Analysis In Practice 8(1):7–15, 2015) article as well as debate the usefulness of proposed standards of objective ranking.

A Student’s Perspective on Research

A little more than a year ago each of us was madly scrambling to negotiate the process of graduate program admissions. Like many people who go to graduate school, each of us had some history of viewing academic efforts through the lens of “too much is never enough,” and we applied our obsessive habits to the challenge of gathering information about graduate programs. We pored over Web sites and printed brochures. We stalked program faculty at conferences, via email and phone, and during campus visits. We talked to trusted mentors about the programs they respected. When in professional settings, we tried to find out where people who impressed us had attended graduate school, and we sometimes eavesdropped on strangers’ conversations for potentially valuable tidbits about the graduate programs they were considering.

Based on this chaotic and exhausting experience, we agree with Dixon et al. ( 2015 ) that consumers in our field need standardized information about the relative merits of graduate programs in applied behavior analysis (ABA). When we began the process of screening graduate programs, we knew that we were uninformed but we were less sure about what we needed to learn to become better consumers. We suspect that, like us, most college seniors find it difficult to know what aspects of a graduate program are crucial to the training of highly qualified ABA practitioners. To us, the most important contribution of Dixon et al. ( 2015 ) was to emphasize that our field should not abandon students to an uncertain process of self-education.

We agree with Dixon et al. ( 2015 ) that our field is better equipped than outside bodies (e.g., U.S. News & World Report ) to determine what constitutes top-quality graduate training. We were aware that the Behavior Analysis Certification Board publishes the rates at which graduates of various programs pass its certification exam, and we considered this information during our respective searches. Even as undergraduates, however, we knew that there is more to being a capable practitioner than simply passing the certification exam, and we would have appreciated much more guidance from our field than we received.

In the absence of standardized, objective information about graduate programs, prospective graduate students have to rely heavily on hearsay. As we gathered information on program reputations from mentors and colleagues, it occurred to us that this information sometimes says as much about the person providing it as about graduate programs themselves. We learned that some people are impressed by graduate programs that have a reputation for highly selective admissions, but we were not sure how or whether this predicted the quality of training that we could hope to receive. We learned that certain mentors thought highly of certain programs, but different people thought highly of different programs, and it was not always obvious how these opinions related to specific features of the training offered by the programs. We weren’t always sure whether the opinions were generic or had been offered with our individual needs and interests in mind.

Among the features of graduate programs that interested us was the type and degree of emphasis on research. Here, a few words of explanation will provide context for our perspective. As undergraduates, we learned to value evidence-based practices, data-based case management, and the science-based critical thinking that should guide clinical case management. But each of us decided to seek graduate training not just to apply current best practices; we also wanted to contribute to clinical innovation (e.g., Critchfield 2015 ). For various reasons, none of us wished to conduct research for a living, and we chose our program at Auburn University in part because its accelerated, 12-month, non-thesis curriculum would get us swiftly into the workplace where we knew, from past field experiences, our main reinforcers are to be found. Still, program research emphasis was important to us.

Unfortunately, far too much time and effort was required for us to understand that different programs have different types of research emphases. “Research training” comprises not a single repertoire but many. One involves conducting research. Another involves locating and consuming available research on a topic of interest. Yet, another involves translating from research findings in order to develop innovative interventions (Critchfield 2015 ; Critchfield & Reed, 2005 ). It is here that we would quibble with the position of Dixon et al. ( 2015 ), which suggests a one-size-fits-all approach to assessing the research climate at ABA graduate programs.

In order to gain insight about the research environment in graduate programs, undergraduates often compare their own research interests to those of faculty as described on program web sites and as illustrated in published articles. This comparison is most relevant to students who seek to become independent researchers. Our own goal is to become life-long consumers of research. It may not be the full-time job of Masters-level practitioners to conduct research, but in a field that is growing quickly it is pivotal that people like us not be limited to the state of our field’s knowledge at the time we take a certification exam. We need skills for tracking scholarly developments across the full breath of our careers.

We agree with Dixon et al. ( 2015 ) that it is helpful for ABA program faculty to maintain active research programs, but our concern is with what program graduates are able to do with the fruits of research, not how many articles a faculty member can publish. It has been suggested that the process of developing effective and transportable interventions from research findings requires a skill set that is independent of either conducting research or implementing existing interventions (e.g., Critchfield 2015 ; Critchfield and Reed, 2005 ). No skill set seems more relevant to our lifelong professional development.

Yes, we want to learn how to read and critically evaluate research, but we want to learn to do this from faculty who know how to translate and who care about helping us to become translators. Our ideal ABA program faculty member will have the time and inclination to focus on this. We want mentors who can conduct research, but more importantly who will discuss research with us on a regular basis and explore with us how research findings relate to the behavioral processes operating in practice settings. We want mentors whose skills and schedules allow them to provide on-site clinical supervision through which the connections between research and practice can be drawn explicitly.

While we applaud the efforts of Dixon et al. ( 2015 ) to rank ABA graduate programs in terms of program research climate, we stress that this climate has multiple facets. We represent a category of consumer who cares very much about our field’s research foundations, but we wish to harness rather than add to those foundations. Faculty publication counts may not be the best measure of a program’s ability to help us to this. Unfortunately, the program attributes that we particularly value are hard to quantify and thus will be difficult to incorporate into an objective system for ranking programs. Yet, if the purpose of rankings is to assist consumers (Dixon et al., 2015 ), then the needs of consumers like us should not be ignored.

Contributor Information

Rachel Arena, Email: ude.nrubua@0200azr .

Sheridan Chambers, Email: ude.nrubua@5400cms .

Angelyn Rhames, Email: ude.nrubua@7400rza .

Katherine Donahoe, Email: ude.nrubua@4200drk .

  • Critchfield TS. What counts as high-quality practitioner training in applied behavior analysis? Behavior Analysis In Practice. 2015; 8 (1):3–6. doi: 10.1007/s40617-015-0049-0. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Critchfield TS, Reed DD. Conduits of translation in behavior-science bridge research. In: Burgos JE, Ribes E, editors. Theory, basic and applied research, and technological applications in behavior science: Conceptual and methodological issues. Guadalajara, Mexico: University of Guadalajara Press; 2005. pp. 45–84. [ Google Scholar ]
  • Dixon MR, Reed DD, Smith T, Belisle J, Jackson RE. Research rankings of behavior analytic graduate training programs and their faculty. Behavior Analysis In Practice. 2015; 8 (1):7–15. doi: 10.1007/s40617-015-0057-0. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Americans Are Thinking About Immigration All Wrong

Population growth, economic growth, and income growth can be mutually reinforcing.

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Produced by ElevenLabs and News Over Audio (NOA) using AI narration.

W hat’s the United States’ most important problem? For the past three months, Americans have offered the same answer : immigration. More than inflation or political polarization, Americans are vexed by the influx of migrants. Republicans’ concerns spiked after the most recent southern-border crisis. But they’re not alone. In April, the number of independents who said immigration was the country’s biggest problem reached a high in Gallup polling dating back to 2014.

Scolding Americans for their alarm is pointless. The state of U.S. immigration policy is objectively chaotic. When Joe Biden became president, he rolled back some Trump-era restrictions , at the same time that migrants began to take greater advantage of loopholes in asylum law to stay in the country longer. Meanwhile, a sharp rise in crime in parts of Central and South America , combined with the strong U.S. economy, created the conditions for migration to surge. In 2022, illegal crossings hit a record high of 2.2 million. As asylum seekers made their way north, cities struggled to house them. In New York City, so many hotel rooms are taken up by migrants that it has created a historic shortage of tourist lodging .

In a perfect world, the brokenness of America’s immigration system would inspire Congress to swiftly pass new legislation convincing voters that the U.S. controls whom we let in and keep out of the country. The basic contours of this grand bargain have been fairly clear for decades. In exchange for expanded opportunities for legal immigration—more visas, more green cards, and targeted policies to increase immigration in technology and science—liberals would agree to stricter enforcement and control at the border. But major immigration reform is stuck. Changing the law requires Congress, and in the latest example of feckless delay, Donald Trump has instructed congressional Republicans to sandbag negotiations with the White House, to avoid giving the Biden administration an election-year win. What we’re left with is the perception of immigration chaos, anger about the chaos, and dithering in the face of it.

If American politicians are ever going to think about immigration policy through the lens of long-term opportunity planning rather than immediate crisis response, they first need to convince the American people that those long-term opportunities exist. This case is actually easy to make. Cheaper and more plentiful houses, higher average wages, more jobs, more innovation, more scientific breakthroughs in medicine, and more state government revenue without higher taxes—all while sticking it to our geopolitical adversary, China—require more immigration . Across economics, national security, fiscal sustainability, and geopolitical power, immigration is the opposite of America’s worst problem. It holds clear solutions to America’s most pressing issues.

I mmigration has for decades , even centuries, created a temporal paradox in American discourse: pride in the country’s history of immigration coming up against fears of its present and future. Benjamin Franklin, whose father was born in England, complained that migration from Central Europe would swarm the young nation’s Anglican culture with undue German influence. In the late 1800s, a more Germanic nation feared the influence of incoming Italians. A century later, a nation that had fully embraced Italian Americans bemoaned the influence of incoming Mexicans.

Ari Berman: The conservative who turned white anxiety into a movement

Although this brisk history of nativism might seem to make light of today’s anti-immigrant sentiment, ignoring the fears that people have about a sudden influx of migrants is counterproductive. The border crisis is not just a news-media illusion, or a platform for empty grandstanding. It really has endangered thousands of migrants and drained city and state resources, causing a liberal backlash even in deep-blue places. Last September, New York City Mayor Eric Adams predicted that the migrant crisis would “destroy New York.” As tens of thousands of migrants moved into Chicago, the city spent hundreds of millions of dollars to provide them with housing and education, building resentment among Black residents . What’s more, papering over anxieties about competition from foreign-born workers is not helpful. The Harvard economist Gordon Hanson asked me to think about the experience of a barber in an American city. If immigrants moving into his area open barber shops, they might reduce his ability to retain customers, raise prices, or make rent. The logic of fear is understandable: More competition within a given industry means less income for its incumbents.

Many Americans—and, really, many residents of every other nation—think about immigration through this lens of scarcity . If the economy includes a fixed number of jobs, then more foreign-born workers means less work left for Americans. If America contains a fixed number of houses, more immigrants mean less space for Americans to live.

But the truth is that no nation comprises a fixed amount of work or income. Population growth, economic growth, and income growth can be mutually reinforcing. “At the national level, immigration benefits from a more-is-more principle,” Hanson told me. “More people, and more density of people, leads to good things happening, like more specialization of labor.”

Specialization of labor might sound drab and technical. But it’s a key part of why immigration can help even low-income workers earn more money over time. Last month, the economists Alessandro Caiumi and Giovanni Peri published a new paper concluding that, from 2000 to 2019, immigration had a “positive and significant effect” on wage growth for less educated native workers. The key mechanism, they found, is that, over time, immigrants and natives specialize in different jobs that complement one another. As low-education immigrants cluster in fields such as construction, machine operation, and home-health-aid work, native-born workers upgrade to white-collar jobs with higher pay. To take the example of the American barber, let’s imagine that his son decides to go to a trade school or college to increase his skills in response to intense competition for barbers. He might be better off, making a higher wage than he would have had he remained in the profession. Although such specialization can be difficult for some people who switch out of their parents’ fields, it can lead to a more dynamic economy with higher wages for all.

For the past few years, I have been thinking and writing about an abundance agenda to identify win-win policies for Americans in housing, energy, health care, and beyond. Immigration is an essential ingredient in this agenda. The U.S. must contend with a national housing shortage that has contributed to record-high living costs and bone-dry inventory in some major metros. This is a story not merely about overregulation, zoning laws, and permitting requirements, but also about labor supply. The construction industry is short several hundred thousand jobs . In the largest states—such as California, Texas, and New York—two in five construction workers are foreign-born, according to estimates by the National Association of Home Builders. “The biggest challenge that the construction industry is facing [is] that people don’t want their babies to grow up to be construction workers,” Brian Turmail, the vice president of public affairs at the Associated General Contractors of America, has said . If Americans want more houses, we might very well need more foreign-born workers to build them. Achieving clean-energy abundance requires immigrants too. One in six solar and photovoltaic installers is an immigrant, according to the Bureau of Labor Statistics, and “23 percent of all green job workers are foreign born,” according to a report by the Mercatus Center at George Mason University .

T he debate over low-skill immigration and its effect on the economy can get a bit technical, if you’re an economist, and emotional, if you’re an anxious native worker. But even if Republicans and Democrats can’t agree on the complex macroeconomics of letting less educated migrants enter the U.S. in higher numbers, we cannot let that disagreement hold hostage the obvious benefits of expanding our recruitment of foreign-born talents into the U.S.

Immigration-as-recruitment is a particularly useful framework as the U.S. embraces a new kind of industrial policy to build more chips and clean-energy tech domestically. As The Wall Street Journal ’s Greg Ip wrote , America’s new economic strategy has three parts. The first is subsidies to build products in the U.S. that are crucial to our national security and energy independence, such as advanced semiconductor chips and electric vehicles. The second part is tariffs on cheap Chinese imports in these sectors. The third is explicit restrictions on Chinese technology that could be used to surveil or influence U.S. companies and people, such as Trump-era laws against Huawei equipment and the Biden-era law to force the sale of TikTok.

But this newly fashioned stool is missing an essential leg. If the U.S. is going to become more strategically selfish about protecting key industries such as computer-chip manufacturing from foreign competition, we need to revamp our high-skill-immigration policy too. In fact, the new American economic paradigm doesn’t make any sense otherwise. As a rich country, the U.S. will be at a disadvantage in semiconductor manufacturing because of our higher labor costs. If we can’t win on costs, we have to win on brains. That means staffing our semiconductor factories with the world’s most talented workers.

Jack Herrera: Is Texas about to turn Latinos into single-issue voters?

Semiconductor manufacturing requires a highly specialized workforce that is distributed around the world and concentrated in Asia. A large share of workers in advanced-chip manufacturing live in India and China . But green-card caps limit their ability to move to the U.S. As a result, we’re at risk of spending tens of billions of dollars on factories and products without a plan to staff them. “The talent shortage is the most critical issue confronting the semiconductor industry today,” Ajit Manocha, the president of the industry association for semiconductor equipment and materials manufacturers, said in 2022. This is a fixable problem. The Economic Innovation Group, a centrist think tank, has proposed a “Chipmaker’s Visa” that would annually authorize an accelerated path to a green card for 10,000 immigrants with specialized skills in semiconductor manufacturing.

What’s true for chipmaking is also true for AI development. According to the Federation of American Scientists , more “ top-tier ” AI researchers are born in China than in any other country in the world. But two-thirds of these elite researchers work in the U.S. The number could probably be even higher if the U.S. had a smarter, future-looking immigration policy regime. The administration has already taken small steps forward. In October, Biden issued an executive order that asked existing authorities to streamline visa criteria for immigrants with expertise in AI. More could be done with congressional help.

If the U.S. is in the early stages of a new cold war with the authoritarian axis of China, Russia, and Iran, we can’t logically pursue an industrial policy without an equally purposeful immigration policy. Immigration policy is industrial policy, because immigrants have for decades been a linchpin in our technological growth. As Jeremy Neufeld, a fellow at the Institute for Progress, has written , 30 percent of U.S. patents, almost 40 percent of U.S. Nobel Prizes in science, and more than 50 percent of billion-dollar U.S. start-ups belong to immigrants. And yet, we’ve allowed waiting times for green cards to grow, while the number of applicants stuck in immigration backlogs has gotten so large that some talented immigrants have stopped waiting and left the U.S. entirely. This is madness. Failing to solve the immigration-recruitment kludge as we spend hundreds of billions of dollars on technology subsidies is about as strategic as training to run a marathon while subsisting on a diet of donuts. When it comes to high-skill-immigration policy, we are getting in our own way.

I mmigration is central to America’s national security, industrial policy, abundance agenda, affordability crisis, and technological dominance. Without a higher number of foreign-born workers, the U.S. will have less of everything that makes us materially prosperous. But none of these advantages should distract immigration proponents from the fact that failure to secure the border is a gift to immigration restrictionists. Border chaos is horrendous branding for the pro-immigration cause.

“Immigration is too important to be chaotic,” Hanson, the economist, told me. “Chaos leads to short-term policy fixes. But you don’t want a 10-month immigration policy for the U.S. You want a 100-year immigration policy.”

Taking that 100-year view leads to perhaps the most powerful case for expanding immigration. The Lancet recently published an analysis of global population trends through the end of the 21st century. By 2064, the worldwide human population will peak, researchers projected, at which point almost every rich country will have been shrinking for decades. Fertility is already below replacement level in almost every rich industrialized country in the world. In Japan and South Korea, there are already fewer working-age adults with every passing year. China’s birth rate has fallen by 50 percent in just the past decade. Within a few years, immigration will be the only dependable lever of population growth for every rich industrialized nation.

The U.S. faces a stark choice. Politicians can squander the fact that the U.S. is the world’s most popular destination for people on the move . They can frame immigration as a persistent threat to U.S. national security, U.S. workers, and the solidity of U.S. culture. Or they can take the century-long view and recognize that America’s national security, the growth of the U.S. labor force, and the project of American greatness all depend on a plan to demonstrate enough control over the border that we can continue to expand immigration without incurring the wrath of restrictionists.


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  1. Significance of the Study

    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 ...

  2. What is the Significance of a Study? Examples and Guide

    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.

  3. What is the Significance of the Study?

    The significance of the study is a section in the introduction of your thesis or paper. It's purpose is to make clear why your study was needed and the specific contribution your research made to furthering academic knowledge in your field. In this guide you'll learn: what the significance of the study means, why it's important to include ...

  4. How To Write Significance of the Study (With Examples)

    4. Mention the Specific Persons or Institutions Who Will Benefit From Your Study. 5. Indicate How Your Study May Help Future Studies in the Field. Tips and Warnings. Significance of the Study Examples. Example 1: STEM-Related Research. Example 2: Business and Management-Related Research.

  5. What is the significance of a study and how is it stated in a research

    Answer: In simple terms, the significance of the study is basically the importance of your research. The significance of a study must be stated in the Introduction section of your research paper. While stating the significance, you must highlight how your research will be beneficial to the development of science and the society in general.

  6. A Practical Guide to Writing Quantitative and Qualitative Research

    Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. ... To construct effective research questions and hypotheses, it is very important to 1) ...

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    An important consequence of interpreting significance as a carefully developed argument for the importance of your research study within a larger domain is that it reveals the advantage of conducting a series of connected studies rather than single, disconnected studies. Building the significance of a research study requires time and effort.

  8. PDF Why research is important

    in a research study • your own barriers and motivators around learning more about research. A pragmatic definition of research A useful working definition of research is: a systematic process of critical inquiry leading to valid propositions and conclusions that are communicated to interested others. Breaking this definition down into its ...

  9. 2.1 Why Is Research Important?

    Appreciate how scientific research can be important in making personal decisions; Scientific research is a critical tool for successfully navigating our complex world. ... Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills ...

  10. Why does research matter?

    Abstract. A working knowledge of research - both how it is done, and how it can be used - is important for everyone involved in direct patient care and the planning & delivery of eye programmes. A research coordinator collecting data from a health extension worker. ethiopia. The mention of 'research' can be off-putting and may seem ...

  11. Study designs: Part 1

    The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

  12. What Is Research, and Why Do People 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 ...

  13. How to Discuss the Significance of 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 ...

  14. 7 Reasons Why Research Is Important

    Why Research Is Necessary and Valuable in Our Daily Lives. It's a tool for building knowledge and facilitating learning. It's a means to understand issues and increase public awareness. It helps us succeed in business. It allows us to disprove lies and support truths. It is a means to find, gauge, and seize opportunities.

  15. Explaining How Research Works

    Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle. Questions about how the world works are often investigated on many different levels.

  16. Six Reasons Why Research Is Important

    2- Research Helps in Problem-solving. The goal of the research is to broaden our understanding. Research gives us the information and knowledge to solve problems and make decisions. To differentiate between research that attempts to advance our knowledge and research that seeks to apply pre-existing information to real-world situations.

  17. COVID-19 impact on research, lessons learned from COVID-19 research

    The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research ...

  18. What is the importance of scientific research in daily life?

    The importance of scientific research in higher education. Scientific research provides us with knowledge. Scientific research drives progress forward. Improving health care. The development of technology. The role of academics throughout history. Our future depends on scientific research. According For Canadian universities Basic research has ...

  19. Current status and ongoing needs for the teaching and assessment of

    Background Clinical reasoning (CR) is a crucial ability that can prevent errors in patient care. Despite its important role, CR is often not taught explicitly and, even when it is taught, typically not all aspects of this ability are addressed in health professions education. Recent research has shown the need for explicit teaching of CR for both students and teachers. To further develop the ...

  20. Social Media Fact Sheet

    ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions.

  21. The critical steps for successful research: The research proposal and

    INTRODUCTION. Creativity and critical thinking are of particular importance in scientific research. Basically, research is original investigation undertaken to gain knowledge and understand concepts in major subject areas of specialization, and includes the generation of ideas and information leading to new or substantially improved scientific insights with relevance to the needs of society.

  22. Internet addiction may harm the teen brain, MRI study finds

    A new study has possibly captured that objectively, finding that for teens diagnosed with internet addiction, signaling between brain regions important for controlling attention, working memory ...

  23. The state of AI in early 2024: Gen AI adoption spikes and starts to

    The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 "The economic potential of generative AI: The next productivity frontier," McKinsey, June 14 ...

  24. Innovative SPHHP research continues with ...

    News about innovative research at SPHHP continues with the announcement of UB's Clinical and Translational Science Institute (CTSI) Translational Pilot Studies Program awards. Several SPHHP faculty are among recipients of this year's seed money to assist them in developing promising new technologies and therapeutics from the conceptual stage to clinical studies.

  25. Greg Rhee Research

    Research Area(s) of Interest: Population-based clinical outcomes research and health care policy . Dr. Rhee is a psychiatric epidemiologist and mental health services researcher studying the access to care, quality of care, and clinical and patient-reported outcomes in patients with psychiatric or substance use disorders. My current research focuses on affective disorders (e.g., depression and ...

  26. The Importance of Research—A Student Perspective

    Abstract. As students, we will focus on the importance of an objective ranking system, research, and mentorship to an applicant. We will address points raised in the (Behavior Analysis In Practice 8 (1):7-15, 2015) article as well as debate the usefulness of proposed standards of objective ranking. Keywords: Graduate school, Graduate training ...

  27. Living Near Violence Can Negatively Impact Academic Performance. A New

    And that's important because it's evidence in a really sort of fundamental way that schools matter in this equation." According to the report, between 2011 and 2019, about one in five CPS students lived within .2 miles of a homicide in any given year.

  28. Americans Are Thinking About Immigration All Wrong

    Population growth, economic growth, and income growth can be mutually reinforcing. "At the national level, immigration benefits from a more-is-more principle," Hanson told me. "More people ...