the importance of research articles

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How Should We Determine the Importance of Research?

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D U Jette, How Should We Determine the Importance of Research?, Physical Therapy , Volume 98, Issue 3, March 2018, Pages 149–152, https://doi.org/10.1093/ptj/pzx119

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For scientific and professional journals, the publication of research papers that are important and have potentially significant impact is a multifactorial quest. The first step is attracting authors/researchers who are publishing work that is impactful. Editorial boards, such as that of PTJ , have continual conversations about how to attract excellent researchers to publish in their journals. At the same time, higher education institutions in which many authors are employed continue to emphasize research productivity when evaluating faculty performance. 1 , 2 This fact motivates authors to submit manuscripts to what they perceive to be the highest-quality journals. 3 Evaluation of research quality influences who has a career in academia as well as where researchers publish and which journals succeed. 4 Assessing the situation, Altbach remarked, “Universities are engaged in a global arms race of publication: and the academics are the shock troops of the struggle.” 5 (p6) One reason for the added emphasis on research is that research increases visibility of an institution and, therefore, its prestige. 6 More funding flows to universities with prestigious, top-ranked research profiles. 2 , 5 Institutions also emphasize research prestige to attract better students and faculty, thus further bolstering their reputations.

The quest to hire, promote, and retain faculty who bring prestige to the institution in the form of research encourages institutions to measure research recognition. Terms describing the necessary attributes of research contributions for institutional promotion and tenure decisions include excellence, importance and significance, 2 substantiveness, 7 and impact. 8 Assessment of these attributes by hiring and promotion committees is based to some extent on the number of scholarly “products” and their rate of production, whether a product is peer reviewed, the number of citations of the work, numeric ratings of the journals in which work appears, and the general reputation of the journal or publisher among professional peers. 9 Review for promotion and tenure also often includes evaluation of candidates’ research by peers outside of their home institution with like expertise. Peers are asked to comment on the quality of the candidate's research and its impact on the field. 10 Although they may have a better perspective on whether a candidate's work has had an impact on their field, external reviewers are likely to assess the quality of publications by using metrics similar to the institutional review committee, perhaps with more knowledge of the best-known journals in their field.

One common indicator used by institutional review committees to determine qualities such as excellence, impact, or importance is the journal impact factor (JIF). 9 The JIF relies on citation numbers over a relatively short period of time and has well-known limitations. 11 The JIF was originally designed to help librarians decide which journals to buy and has subsequently, and perhaps inappropriately, been used as a surrogate for the quality of individual papers and individual researchers’ scholarship. 12 Other indices such as the h-index are also commonly, and more appropriately, applied at the individual level; however the h-index also is a measure based on numbers of citations. Because authors are driven by the performance expectations and the reward systems at their institutions, 8 they are likely to seek journals with reputations for publishing high-value and frequently cited work, that is, journals with a high JIF. Alberts, 13 editor-in-chief of Science in 2013, noted that this tendency has led to researchers submitting inappropriate papers to highly cited journals so as to “gain points” when being evaluated, and also to journal bias against accepting papers that might not be highly cited. In addition, traditional metrics such as JIF may deter faculty from pursuing anything but the scholarship of discovery 9 and may be detrimental to review of junior faculty because of time lags in publication and citation. 14

Alberts’ statements lead to the question of how to align the desires of authors and journals. Authors must demonstrate the importance of their research; journals want to solicit authors doing important and useful work. A systematic review of literature to identify measures of health care research significance or importance suggested 6 areas of consideration: research activity, scientific production and impact, collaboration, dissemination, industrial production, and health services impact. 15 The authors identified 57 indicators across the 6 categories, the most common (24) categorized as indicators of scientific production. The most frequently identified indicators among the 76 articles included in the review were h-index, number of publications, number of citations, and JIF. Indices that rely on citations, such as JIF, however, are a somewhat narrow reflection of “scholarly” impact, 16 that is, they reflect usefulness of published papers to the scientific community.

Citation indices have several limitations. They are biased toward English language, peer reviewed journals; fail to account for applications of publications by researchers, clinicians, or educators who are not cited in the peer reviewed literature; and do not represent conference presentations and books very well. 17 Moreover, these indicators do not necessarily reflect the quality of the science, how widely read and discussed published work might be, 18 or whether the work has an impact on a broader audience, such as patients or policy makers. The editor of JAMA Facial Plastic Surgery noted that the journal desires “high-impact” articles and identified 3 key themes that characterize this type of article: number of citations, number of downloads, and altmetric score. 19 Similarly, PTJ wishes to publish “innovative and highly relevant content for both clinicians and scientists,” 20 and editorial board members consider the potential importance and significance of the work represented by newly submitted manuscripts when recommending them for full review. The importance of scientific findings to multiple audiences, as well as the significant changes in the way knowledge is disseminated and accessed, suggest the need for revised thinking on how research quality, importance, and impact are evaluated.

Recently, new types of indicators of research impact have become available through the application of technology ( Figure ). They include mentions in public policy documents, mainstream media, blogs, Wikipedia, social media, course syllabi, open and post peer review forums, and downloads into citation managers. The definitions of impact and importance are thereby expanded beyond the benefits to research scientists and include contributions to public health and professional practice and education. 21 These indicators can be gathered from multiple sources in an automated manner by data-aggregating platforms such as Public Library of Science (PLoS) metrics, Webometric Analyst through Bing, Altmetric.com , ImpactStory, and PlumX.

Examples of types of impact metrics tracking how research has been used. Source: Wilsdon J, Allen L, Belfiore E, et al. The Metric Tide: Report of the Independent Review of the Role of Metrics in Research Assessment and Management . July 2015. HEFCE. DOI: 10.13140/RG.2.1.4929.1363. This information is licensed under the Open Government License v3.0. To view this license, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/ 12 .

Although commonly referred to as “altmetrics,” these indicators of research impact are not alternatives to citation indicators, but rather complementary to them (“addmetrics”). 12 , 22 In fact, these indicators may not provide a measure of impact as it's traditionally defined, but rather a measure of amount of attention 21 or breadth of dissemination. 16 Attention could be due to negative press; reactions to poorly conducted, invalid research; or even research fraud. At the same time, although traditional metrics indicate the “scholarly impact” of an article, 16 they cannot shed light on whether the findings of the article have had an impact on practice. 21 Studies describing the relationship between traditional indicators and altmetrics suggest that they are related but distinct. 23 , 24 Thelwell et al 24 found that tweets, Facebook wall posts, and blogs were associated with journal-level citations for documents in PubMed between 2010 and 2012. In addition to these 3 indicators, research highlights identified from Nature Publishing Group journals, mainstream media citations, and forum posts were related to individual article citations. The tools used to determine these complementary indicators of impact have some of the same challenges as more traditional indicators in terms of determining how and when they may be useful. Derivation of some of the measurements or calculations may be somewhat opaque; they may be susceptible to “gaming” by authors, and the reliability of the measures may be affected by inaccuracies in the data on which they rely. 25

Despite the limitations, it is worth examining how application of the range of indicators of scholarly, educational, policy and practice impact, dissemination, and attention might support PTJ and other rehabilitation-focused journals in attracting the best authors and papers with potential to significantly affect patient care and health policy, as well as advance rehabilitation science. Suggestions include:

Provide information on individual articles, affording readership and authors information on the attention or dissemination of the article across various platforms. 11

Convert to open-access status, making published work more broadly available, more quickly.

Promote the JIF only in the context of other relevant data, such as Eigenfactor score, SCImago journal rankings, h-index, and publication times 11 —although these measures, too, have limitations.

Eliminate any restrictions on the number of references in papers, and ensure that original works, not secondary sources, are cited. 11

Journal editors and editorial boards should encourage more discussion among authors, reviewers, and readers to raise awareness about the usefulness and meaning of the various types of indicators. By expanding the ways in which the influence of a journal—as well as of the works it contains—is reported and viewed, authors may be encouraged to submit manuscripts to a particular journal, and institutions of higher education would be afforded a broader lens through which to evaluate faculty research that appears in the journal. At the same time, it is important to recognize that the indicators themselves may “change the system through the incentives they establish,” 26 (pg 431) so their application and influence should be evaluated over time.

Journals could benefit from publicizing a variety of indicators of impact to entice authors as they identify potential journals to which to submit their work. Suggested indicators include number of downloads; number of citations, perhaps from more than one platform, such as Web of Science, Scopus, or Google Scholar; and indicators of attention reported from data aggregators such as those mentioned above. Citations take time to accrue, whereas downloads and indicators of attention are more immediate. Making this type of information available to authors immediately and over the long term provides them with valuable feedback about the influence of their work. 27 Although some may argue that using a wide variety of article-level metrics may confound the definition of importance of a research article or body of work, consideration of diverse metrics would afford an editorial board a multifaceted perspective of its meaning. Editorial boards could gain a better understanding of which content has meaning for their audience. 27 Editorial boards might also use information to determine content and methodological areas in which manuscripts should be solicited and areas for special topic issues. 27 Additionally, the geographical information from the indicators may help to direct marketing and outreach. 27 In the long run, a broad perspective of impact may facilitate discussions within editorial boards that lead to greater clarity about the meaning of impact for their journal.

In summary, using an array of indicators of the importance and significance of published research to assess impact of published work—and, by extension, author and journal performance—has the potential to improve success for authors and journals in the milieus in which they must compete and thrive.

Concept/idea/research design: D. U. Jette

Writing: D. U. Jette

There are no funders to report for this submission

The author completed the ICJME Form for Disclosure of Potential Conflicts of Interest and reported no conflicts of interest.

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• Editorial Notes
• Open access
• Published: 07 January 2020

What is useful research? The good, the bad, and the stable

• David M. Ozonoff 1 &
• Philippe Grandjean 2 , 3  

Environmental Health volume  19 , Article number:  2 ( 2020 ) Cite this article

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A scientific journal like Environmental Health strives to publish research that is useful within the field covered by the journal’s scope, in this case, public health. Useful research is more likely to make a difference. However, in many, if not most cases, the usefulness of an article can be difficult to ascertain until after its publication. Although replication is often thought of as a requirement for research to be considered valid, this criterion is retrospective and has resulted in a tendency toward inertia in environmental health research. An alternative viewpoint is that useful work is “stable”, i.e., not likely to be soon contradicted. We present this alternative view, which still relies on science being consensual, although pointing out that it is not the same as replicability, while not in contradiction. We believe that viewing potential usefulness of research reports through the lens of stability is a valuable perspective.

Good science as a purpose

Any scientific journal wishes to add to the general store of knowledge. For Environmental Health, an additional important goal is also to publish research that is useful for public health. While maximizing scientific validity is an irreducible minimum for any research journal, it does not guarantee that the outcome of a “good” article is useful. Most writing on this subject concerns efficiencies and criteria for generating new and useful research results while avoiding “research waste” [ 1 ]. In this regard, the role of journals is hard to define. Indeed, a usefulness objective depends upon what happens after publication, thus to some extent being out of our control. That said, because of the importance of this issue the Editors have set out to clarify our thinking about what makes published research useful.

First the obvious: properly conducted scientific research may not be useful, or worse, may potentially mislead, confuse or be erroneously interpreted. Journal editors and reviewers can mitigate such regrettable outcomes by being attentive to faulty over- or under-interpretation of properly generated data, and vice versa, ensuring that unrealistic standards don’t prevent publication of a “good” manuscript. In regard to the latter, we believe our journal should not shy away from alternative or novel interpretations that may be counter to established paradigms and have consciously adopted a precautionary orientation [ 2 ]: We believe that it is reasonable to feature risks that may seem remote at the moment because the history of environmental and occupational health is replete with instances of red flags ignored, resulting in horrific later harms that could no longer be mitigated [ 3 , 4 ].

Nonetheless, it has happened that researchers publishing results at odds with vested interests have become targets of unreasonable criticism and intimidation whose aim is to suppress or throw suspicion on unwelcome research information, as in the case of lead [ 3 , 5 ] and many other environmental chemicals [ 6 ]. An alternative counter strategy is generating new results favorable to a preferred view [ 7 , 8 ], with the objective of casting doubt on the uncomfortable research results. Indeed, one trade association involved in supporting such science once described its activities with the slogan, “Doubt is our product” [ 9 ]. Thus, for better or for worse, many people do not separate science, whether good or bad, from its implications [ 10 ].

Further, even without nefarious reasons, it is not uncommon for newly published research to be contradicted by additional results from other scientists. Not surprisingly, the public has become all too aware of findings whose apparent import is later found to be negligible, wrong, or cast into serious doubt, legitimately or otherwise [ 11 ]. This has been damaging to the discipline and its reputation [ 12 ].

Replication as a criterion

A principal reaction to this dilemma has been to demand that results be “replicated” before being put to use. As a result, both funding agencies [ 13 ] and journals [ 14 ] have announced their intention of emphasizing the reproducibility of research, thereby also facilitating replication [ 15 ]. On its face this sounds reasonable, but usual experimental or observational protocols are already based on internal replication. If some form of replication of a study is desired, attempts to duplicate an experimental set-up can easily produce non-identical measurements on repeated samples, and seemingly similar people in a population may yield somewhat different observations. Given an expected variability within and between studies, we need to define more precisely what is to be replicated and how it is to be judged.

That said, in most instances, it seems that what we are really asking for is interpretive replication (i.e., do we think two or more studies mean the same thing), not observational or measurement replication. Uninterpreted evidence is just raw data. The main product of scientific journals like Environmental Health is interpreted evidence. It is interpreted evidence that is actionable and likely to affect practice and policy.

Research stability

This brings us back to the question of what kind of evidence and its accompanying interpretation is likely to be of use? The philosopher Alex Broadbent distinguishes between how results get used and the decision about which results are likely to be used [ 16 ]. Discussions of research translation tend to focus on the former question, while the latter is rarely discussed. Broadbent introduces a new concept into the conversation, the stability of the research results.

He begins by identifying which results are not likely to be used. Broadbent observes that if a practitioner or policy-maker thinks a result might soon be overturned she is unlikely to use it. Since continual revision is a hallmark of science, this presents a dilemma. All results are open to revision as science progresses, so what users and policy makers really want are stable results, ones whose meaning is unlikely to change in ways that make a potential practice or policy quickly obsolete or wrong. What are the features of a stable result?

This is a trickier problem than it first appears. As Broadbent observes it does not seem sufficient to say that a stable a result is one that is not contradicted by subsequent work, an idea closely related to replication. Failure to contradict, like lack of replication, may have many reasons, including lack of interest, lack of funding, active suppression of research in a subject, or external events like social conflict or recession. Moreover, there are many examples of clinical practice, broadly accepted as stable in the non-contradiction sense, that have not been tested for one reason or another. Contrariwise, contradictory results may also be specious or fraudulent, e.g., due to attempts to make an unwelcome result appear unstable and hence unusable [ 6 , 9 ]. In sum, lack of contradiction doesn’t automatically make a result stable, nor does its presence annul the result.

One might plausibly think that the apparent truth of a scientific result would be sufficient to make a result stable. This is also in accordance with Naomi Oreskes’ emphasis of scientific knowledge being fundamentally consensual [ 10 ] and relies on the findings being generalizable [ 15 ]. Our journal, like most, employs conventional techniques like pre-publication peer review and editorial judgment, to maximize scientific validity of published articles; and we require Conflict of Interest declarations to maximize scientific integrity [ 6 , 17 ]. Still, a result may be true but not useful, and science that isn’t true may be very useful. Broadbent’s example of the latter is the most spectacular. Newtonian physics continues to be a paragon of usefulness despite the fact that in the age of Relativity Theory we know it to be false. Examples are also prevalent in environmental health. When John Snow identified contaminated water as a source of epidemic cholera in the mid-nineteenth Century he believed a toxin was the cause, as the germ theory of disease had not yet found purchase. This lack of understanding did not stop practitioners from advocating limiting exposure to sewage-contaminated water. Nonetheless, demands for modes of action or adverse outcome pathways are often used to block the use of new evidence on environmental hazards [ 18 ].

Criteria for stability

Broadbent’s suggestion is that a result likely to be seen as stable by practitioners and policy makers is one that (a) is not contradicted by good scientific evidence; and (b) would not likely be soon contradicted by further good research [ 16 ] (p. 63).

The first requirement, (a), simply says that any research that produces contradictory evidence be methodologically sound and free from bias, i.e., “good scientific evidence.” What constitutes “good” scientific evidence is a well discussed topic, of course, and not a novel requirement [ 1 ], but the stability frame puts existing quality criteria, in a different, perhaps more organized, structure, situating the evidence and its interpretation in relation to stability as a criterion for usefulness.

More novel is requirement (b), the belief that if further research were done it would not likely result in a contradiction. The if clause focuses our attention on examining instances where the indicated research has not yet been done. The criterion is therefore prospective, where the replication demand can only be used in retrospect.

This criterion could usefully be applied to inconclusive or underpowered studies that are often incorrectly labeled “negative” and interpreted to indicate “no risk” [ 18 ]. A U.S. National Research Council committee called attention to the erroneous inference that chemicals are regarded inert or safe, unless proven otherwise [ 19 ]. This “untested-chemical assumption” has resulted in exposure limits for only a small proportion of environmental chemicals, limits often later found to be much too high to adequately protect against adverse health effects [ 20 , 21 ]. For example, some current limits for perfluorinated compounds in drinking water do not protect against the immunotoxic effects in children and may be up to 100-fold too high [ 22 ].

Inertia as a consequence

Journals play an unfortunate part in the dearth of critical information on emerging contaminants, as published articles primarily address chemicals that have already been well studied [ 23 ]. This means that environmental health research suffers from an impoverishing inertia, which may in part be due to desired replications that may be superfluous or worse. The bottom line is that longstanding acceptance in the face of longstanding failure to test a proposition should not be used as a criterion of stability or of usefulness, although this is routinely done.

If non-contradiction, replication or truth are not reliable hallmarks of a potentially useful research result, then what is? Broadbent makes the tentative proposal that a stable interpretation is one which has a satisfactory answer to the question, “Why this interpretation rather than another?” Said another way, are there more likely, almost or equally as likely, or other possible explanations (including methodological error in the work in question)? Sometimes the answer is patently obvious. Such an evaluation is superfluous in instances where the outcomes have such forceful explanations that this exercise would be a waste of time, for example a construction worker falling from the staging. We only need one instance and (hopefully no repetitions) to make the case.

Consensus and stability

Having made the argument for perspicuous interpretation, we must also issue a note of caution. It is quite common to err in the other direction by downplaying conclusions and implications. Researchers frequently choose to hedge their conclusions by repeated use of words such as ‘maybe’, ‘perhaps’, ‘in theory’ and similar terms [ 24 ]. Indeed, we might call the hedge the official flower of epidemiology. To a policy maker, journalist or member of the public not familiar with the traditions of scientific writing, the caveats and reservations may sound like the new results are irredeemably tentative, leaving us with no justification for any intervention. To those with a vested interest, the soft wording can be exploited through selective quotation and by emphasizing real or alleged weaknesses [ 25 ]. This tendency goes beyond one’s own writings and affects peer review and evaluations of manuscripts and applications. Although skepticism is in the nature of science, a malignant form is the one that is veiled and expressed in terms of need for further replication or emphasizing limitations of otherwise stable observations [ 9 ]. By softening the conclusions and avoiding attribution of specific causality and the possible policy implications, researchers protect themselves against critique by appearing well-balanced, unassuming, or even skeptical toward one’s own findings. In seeking consensus, researchers often moderate or underestimate their findings, a tendency that is not in accordance with public health interests.

These are difficult issues, requiring a balancing act. The Editors continue to ponder the question how to inspire, improve and support the best research and its translation. We believe Broadbent’s stability idea is worth considering as an alternative perspective to the replication and research translation paradigms prevalent in discussions of this topic. We also believe in Oreskes’ vision of consensus, though not to a degree that will preclude new interpretations. Meanwhile, we will endeavor to keep the Journal’s standards high while encouraging work that will make a difference.

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Reliability and validity: Importance in Medical Research

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• 1 Al-Nafees Medical College,Isra University, Islamabad, Pakistan.
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Reliability and validity are among the most important and fundamental domains in the assessment of any measuring methodology for data-collection in a good research. Validity is about what an instrument measures and how well it does so, whereas reliability concerns the truthfulness in the data obtained and the degree to which any measuring tool controls random error. The current narrative review was planned to discuss the importance of reliability and validity of data-collection or measurement techniques used in research. It describes and explores comprehensively the reliability and validity of research instruments and also discusses different forms of reliability and validity with concise examples. An attempt has been taken to give a brief literature review regarding the significance of reliability and validity in medical sciences.

Keywords: Validity, Reliability, Medical research, Methodology, Assessment, Research tools..

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What Is the Importance of Research? 5 Reasons Why Research is Critical

by Logan Bessant | Nov 16, 2021 | Science

Most of us appreciate that research is a crucial part of medical advancement. But what exactly is the importance of research? In short, it is critical in the development of new medicines as well as ensuring that existing treatments are used to their full potential. 

Research can bridge knowledge gaps and change the way healthcare practitioners work by providing solutions to previously unknown questions.

In this post, we’ll discuss the importance of research and its impact on medical breakthroughs.  

The Importance Of Health Research

The purpose of studying is to gather information and evidence, inform actions, and contribute to the overall knowledge of a certain field. None of this is possible without research. 

Understanding how to conduct research and the importance of it may seem like a very simple idea to some, but in reality, it’s more than conducting a quick browser search and reading a few chapters in a textbook. 

No matter what career field you are in, there is always more to learn. Even for people who hold a Doctor of Philosophy (PhD) in their field of study, there is always some sort of unknown that can be researched. Delving into this unlocks the unknowns, letting you explore the world from different perspectives and fueling a deeper understanding of how the universe works.

To make things a little more specific, this concept can be clearly applied in any healthcare scenario. Health research has an incredibly high value to society as it provides important information about disease trends and risk factors, outcomes of treatments, patterns of care, and health care costs and use. All of these factors as well as many more are usually researched through a clinical trial. 

What Is The Importance Of Clinical Research?

Clinical trials are a type of research that provides information about a new test or treatment. They are usually carried out to find out what, or if, there are any effects of these procedures or drugs on the human body. 

All legitimate clinical trials are carefully designed, reviewed and completed, and need to be approved by professionals before they can begin. They also play a vital part in the advancement of medical research including:

• Providing new and good information on which types of drugs are more effective.  
• Bringing new treatments such as medicines, vaccines and devices into the field. 
• Testing the safety and efficacy of a new drug before it is brought to market and used in clinical practice.
• Giving the opportunity for more effective treatments to benefit millions of lives both now and in the future. 
• Enhancing health, lengthening life, and reducing the burdens of illness and disability. 

This all plays back to clinical research as it opens doors to advancing prevention, as well as providing treatments and cures for diseases and disabilities. Clinical trial volunteer participants are essential to this progress which further supports the need for the importance of research to be well-known amongst healthcare professionals, students and the general public. 

Five Reasons Why Research is Critical

Research is vital for almost everyone irrespective of their career field. From doctors to lawyers to students to scientists, research is the key to better work. 

• Increases quality of life

 Research is the backbone of any major scientific or medical breakthrough. None of the advanced treatments or life-saving discoveries used to treat patients today would be available if it wasn’t for the detailed and intricate work carried out by scientists, doctors and healthcare professionals over the past decade. 

This improves quality of life because it can help us find out important facts connected to the researched subject. For example, universities across the globe are now studying a wide variety of things from how technology can help breed healthier livestock, to how dance can provide long-term benefits to people living with Parkinson’s. 

For both of these studies, quality of life is improved. Farmers can use technology to breed healthier livestock which in turn provides them with a better turnover, and people who suffer from Parkinson’s disease can find a way to reduce their symptoms and ease their stress. 

Research is a catalyst for solving the world’s most pressing issues. Even though the complexity of these issues evolves over time, they always provide a glimmer of hope to improving lives and making processes simpler. 

• Builds up credibility 

People are willing to listen and trust someone with new information on one condition – it’s backed up. And that’s exactly where research comes in. Conducting studies on new and unfamiliar subjects, and achieving the desired or expected outcome, can help people accept the unknown.

However, this goes without saying that your research should be focused on the best sources. It is easy for people to poke holes in your findings if your studies have not been carried out correctly, or there is no reliable data to back them up. 

This way once you have done completed your research, you can speak with confidence about your findings within your field of study. 

• Drives progress forward 

It is with thanks to scientific research that many diseases once thought incurable, now have treatments. For example, before the 1930s, anyone who contracted a bacterial infection had a high probability of death. There simply was no treatment for even the mildest of infections as, at the time, it was thought that nothing could kill bacteria in the gut.

When antibiotics were discovered and researched in 1928, it was considered one of the biggest breakthroughs in the medical field. This goes to show how much research drives progress forward, and how it is also responsible for the evolution of technology . 

Today vaccines, diagnoses and treatments can all be simplified with the progression of medical research, making us question just what research can achieve in the future. 

• Engages curiosity 

The acts of searching for information and thinking critically serve as food for the brain, allowing our inherent creativity and logic to remain active. Aside from the fact that this curiosity plays such a huge part within research, it is also proven that exercising our minds can reduce anxiety and our chances of developing mental illnesses in the future. 

Without our natural thirst and our constant need to ask ‘why?’ and ‘how?’ many important theories would not have been put forward and life-changing discoveries would not have been made. The best part is that the research process itself rewards this curiosity. 

Research opens you up to different opinions and new ideas which can take a proposed question and turn into a real-life concept. It also builds discerning and analytical skills which are always beneficial in many career fields – not just scientific ones. 

• Increases awareness 

The main goal of any research study is to increase awareness, whether it’s contemplating new concepts with peers from work or attracting the attention of the general public surrounding a certain issue. 

Around the globe, research is used to help raise awareness of issues like climate change, racial discrimination, and gender inequality. Without consistent and reliable studies to back up these issues, it would be hard to convenience people that there is a problem that needs to be solved in the first place. 

The problem is that social media has become a place where fake news spreads like a wildfire, and with so many incorrect facts out there it can be hard to know who to trust. Assessing the integrity of the news source and checking for similar news on legitimate media outlets can help prove right from wrong. 

This can pinpoint fake research articles and raises awareness of just how important fact-checking can be. 

The Importance Of Research To Students

It is not a hidden fact that research can be mentally draining, which is why most students avoid it like the plague. But the matter of fact is that no matter which career path you choose to go down, research will inevitably be a part of it. 

But why is research so important to students ? The truth is without research, any intellectual growth is pretty much impossible. It acts as a knowledge-building tool that can guide you up to the different levels of learning. Even if you are an expert in your field, there is always more to uncover, or if you are studying an entirely new topic, research can help you build a unique perspective about it.

For example, if you are looking into a topic for the first time, it might be confusing knowing where to begin. Most of the time you have an overwhelming amount of information to sort through whether that be reading through scientific journals online or getting through a pile of textbooks. Research helps to narrow down to the most important points you need so you are able to find what you need to succeed quickly and easily. 

It can also open up great doors in the working world. Employers, especially those in the scientific and medical fields, are always looking for skilled people to hire. Undertaking research and completing studies within your academic phase can show just how multi-skilled you are and give you the resources to tackle any tasks given to you in the workplace. 

The Importance Of Research Methodology

There are many different types of research that can be done, each one with its unique methodology and features that have been designed to use in specific settings. 

When showing your research to others, they will want to be guaranteed that your proposed inquiry needs asking, and that your methodology is equipt to answer your inquiry and will convey the results you’re looking for.

That’s why it’s so important to choose the right methodology for your study. Knowing what the different types of research are and what each of them focuses on can allow you to plan your project to better utilise the most appropriate methodologies and techniques available. Here are some of the most common types:

• Theoretical Research: This attempts to answer a question based on the unknown. This could include studying phenomena or ideas whose conclusions may not have any immediate real-world application. Commonly used in physics and astronomy applications.
• Applied Research: Mainly for development purposes, this seeks to solve a practical problem that draws on theory to generate practical scientific knowledge. Commonly used in STEM and medical fields. 
• Exploratory Research: Used to investigate a problem that is not clearly defined, this type of research can be used to establish cause-and-effect relationships. It can be applied in a wide range of fields from business to literature. 
• Correlational Research: This identifies the relationship between two or more variables to see if and how they interact with each other. Very commonly used in psychological and statistical applications. 

The Importance Of Qualitative Research

This type of research is most commonly used in scientific and social applications. It collects, compares and interprets information to specifically address the “how” and “why” research questions. 

Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience because you’re not limited by survey instruments with a fixed set of possible responses.

Information can be gathered in numerous ways including interviews, focus groups and ethnographic research which is then all reported in the language of the informant instead of statistical analyses. 

This type of research is important because they do not usually require a hypothesis to be carried out. Instead, it is an open-ended research approach that can be adapted and changed while the study is ongoing. This enhances the quality of the data and insights generated and creates a much more unique set of data to analyse. 

The Process Of Scientific Research

No matter the type of research completed, it will be shared and read by others. Whether this is with colleagues at work, peers at university, or whilst it’s being reviewed and repeated during secondary analysis.

A reliable procedure is necessary in order to obtain the best information which is why it’s important to have a plan. Here are the six basic steps that apply in any research process. 

• Observation and asking questions: Seeing a phenomenon and asking yourself ‘How, What, When, Who, Which, Why, or Where?’. It is best that these questions are measurable and answerable through experimentation. 
• Gathering information: Doing some background research to learn what is already known about the topic, and what you need to find out. 
• Forming a hypothesis: Constructing a tentative statement to study.
• Testing the hypothesis: Conducting an experiment to test the accuracy of your statement. This is a way to gather data about your predictions and should be easy to repeat. 
• Making conclusions: Analysing the data from the experiment(s) and drawing conclusions about whether they support or contradict your hypothesis. 
• Reporting: Presenting your findings in a clear way to communicate with others. This could include making a video, writing a report or giving a presentation to illustrate your findings. 

Although most scientists and researchers use this method, it may be tweaked between one study and another. Skipping or repeating steps is common within, however the core principles of the research process still apply.

By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. This is especially beneficial for peer reviews that try to replicate the results to ensure that the study is sound. 

What Is The Importance Of Research In Everyday Life?

Conducting a research study and comparing it to how important it is in everyday life are two very different things.

Carrying out research allows you to gain a deeper understanding of science and medicine by developing research questions and letting your curiosity blossom. You can experience what it is like to work in a lab and learn about the whole reasoning behind the scientific process. But how does that impact everyday life? 

Simply put, it allows us to disprove lies and support truths. This can help society to develop a confident attitude and not believe everything as easily, especially with the rise of fake news.

Research is the best and reliable way to understand and act on the complexities of various issues that we as humans are facing. From technology to healthcare to defence to climate change, carrying out studies is the only safe and reliable way to face our future.

Not only does research sharpen our brains, but also helps us to understand various issues of life in a much larger manner, always leaving us questioning everything and fuelling our need for answers. 

Logan Bessant is a dedicated science educator and the founder of Science Resource Online, launched in 2020. With a background in science education and a passion for accessible learning, Logan has built a platform that offers free, high-quality educational resources to learners of all ages and backgrounds.

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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.

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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Why Research is Important: Understanding Research Articles Part I

The medical care we enjoy today is built upon decades  of effort by physicians, researchers, and other medical professionals investigating the causes of, and potential treatments for disease. Insights provided by past and current medical research promise to lessen the impact of aplastic anemia , MDS, and PNH. As science continues to unveil the molecular workings that underpin disease, we will see profound changes in the approach to treating these rare diseases. In last month’s article “Why Research is Important | Bench-to-Bedside, we learned about the benefits of scientific research and also the timeline associated with moving things through the researchprocess. This research is critical to everything else that follows in medicine. Once it is known how diseases are caused, ways to prevent them can be discovered. Translational medical research seeks to take the medical discoveries that have been made in a laboratory setting and translate them into medicalprocedures used by physicians. These  articles will help you understand more about reading—research articles.  Reading a scientific paper is a completely different process from reading an article in a blog or newspaper. Not only do you read the sections in a different order than they're presented, but you also have to take notes, read it multiple times, and probably go look up other papers in order to understand some of the details. Reading a single paper may take you a very long time at first, but this will go much faster as you gain experience. First we want to familiarize you with the sections commonly found in research articles.  Abstract The abstract is a summary of the paper. It usually highlights the main objective of the author(s)’ research, provides the key results of their experiments, and gives an overview of the  their conclusions. Reading the abstract will help you decide if the article was what you were looking for, without spending a long time reading the entire  paper. Abstracts are usually accessible for free either online at journal websites or in scientific literature databases (name a few?) Introduction The introduction gives background information about the paper’s topic, and sets out the specific questions to be addressed by the authors. Reading the introduction lets you know if you are ready to read the rest of the paper; if the introduction doesn't make sense to you or is hard to understand then the rest of the paper won't either. If you find yourself confused by the introduction, try going to other sources for information about the topic before you tackle the rest of the paper. Good sources can include  textbooks, patient guides, online tutorials, reviews, or other explanations.  If after trying all these sources you're still confused, it may be worth asking your health care provider, advocacy organization, or someone with a background in the research for help. Materials and Methods The materials and methods section gives the technical details of how the experiments were conducted.  Reading the methods section is helpful in understanding exactly what the authors did. This section also serves as a "how-to" manual if you're interested in carrying out similar experiments, or even in repeating the same experiments as the authors did. The materials and methods section is most commonly placed directly after the introduction. Results The results section is  an important part of a primary research article.  This section contains all the data from the experiments and the  figures contain the majority of the data. The accompanying text contains written descriptions of the parts  of data the authors feel were most critical. So to get the most out of the results section, make sure to spend ample time thoroughly looking at all the graphs, pictures, and tables, and reading their accompanying legends. Discussion The discussion section is the authors' opportunity to give you their opinions. It is where they draw conclusions about the results. They may choose to put their results in the context of previous findings and offer theories or new hypotheses. Or the authors may comment on new questions and avenues of exploration that their results give rise to. The purpose of discussion sections in papers is to allow the exchange of ideas between scientists. References Throughout the article, the authors will refer to information from other papers. These citations are all listed in the references section, sometimes referred to as the bibliography. This makes the reference section useful for broadening your own literature search. If you're reading a paragraph in the current paper and want more information on the content, you should always try to find and read the articles cited in that paragraph. Part II will provide you with some tips on how to read research articles.

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Six Reasons Why Research is Important

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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The role of research at universities: why it matters.

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Teaching and learning, research and discovery, synthesis and creativity, understanding and engagement, service and outreach. There are many “core elements” to the mission of a great university. Teaching would seem the most obvious, but for those outside of the university, “research” (taken to include scientific research, scholarship more broadly, as well as creative activity) may be the least well understood. This creates misunderstanding of how universities invest resources, especially those deriving from undergraduate tuition and state (or other public) support, and the misperception that those resources are being diverted away from what is believed should be the core (and sole) focus, teaching. This has led to a loss of trust, confidence, and willingness to continue to invest or otherwise support (especially our public) universities.

Why are universities engaged in the conduct of research? Who pays? Who benefits? And why does it all matter? Good questions. Let’s get to some straightforward answers. Because the academic research enterprise really is not that difficult to explain, and its impacts are profound.

So let’s demystify university-based research. And in doing so, hopefully we can begin building both better understanding and a better relationship between the public and higher education, both of which are essential to the future of US higher education.   

Why are universities engaged in the conduct of research?

Universities engage in research as part of their missions around learning and discovery. This, in turn, contributes directly and indirectly to their primary mission of teaching. Universities and many colleges (the exception being those dedicated exclusively to undergraduate teaching) have as part of their mission the pursuit of scholarship. This can come in the form of fundamental or applied research (both are most common in the STEM fields, broadly defined), research-based scholarship or what often is called “scholarly activity” (most common in the social sciences and humanities), or creative activity (most common in the arts). Increasingly, these simple categorizations are being blurred, for all good reasons and to the good of the discovery of new knowledge and greater understanding of complex (transdisciplinary) challenges and the creation of increasingly interrelated fields needed to address them.

It goes without saying that the advancement of knowledge (discovery, innovation, creation) is essential to any civilization. Our nation’s research universities represent some of the most concentrated communities of scholars, facilities, and collective expertise engaged in these activities. But more importantly, this is where higher education is delivered, where students develop breadth and depth of knowledge in foundational and advanced subjects, where the skills for knowledge acquisition and understanding (including contextualization, interpretation, and inference) are honed, and where students are educated, trained, and otherwise prepared for successful careers. Part of that training and preparation derives from exposure to faculty who are engaged at the leading-edge of their fields, through their research and scholarly work. The best faculty, the teacher-scholars, seamlessly weave their teaching and research efforts together, to their mutual benefit, and in a way that excites and engages their students. In this way, the next generation of scholars (academic or otherwise) is trained, research and discovery continue to advance inter-generationally, and the cycle is perpetuated.

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University research can be expensive, particularly in laboratory-intensive fields. But the responsibility for much (indeed most) of the cost of conducting research falls to the faculty member. Faculty who are engaged in research write grants for funding (e.g., from federal and state agencies, foundations, and private companies) to support their work and the work of their students and staff. In some cases, the universities do need to invest heavily in equipment, facilities, and personnel to support select research activities. But they do so judiciously, with an eye toward both their mission, their strategic priorities, and their available resources.

Medical research, and medical education more broadly, is expensive and often requires substantial institutional investment beyond what can be covered by clinical operations or externally funded research. But universities with medical schools/medical centers have determined that the value to their educational and training missions as well as to their communities justifies the investment. And most would agree that university-based medical centers are of significant value to their communities, often providing best-in-class treatment and care in midsize and smaller communities at a level more often seen in larger metropolitan areas.

Research in the STEM fields (broadly defined) can also be expensive. Scientific (including medical) and engineering research often involves specialized facilities or pieces of equipment, advanced computing capabilities, materials requiring controlled handling and storage, and so forth. But much of this work is funded, in large part, by federal agencies such as the National Science Foundation, National Institutes of Health, US Department of Energy, US Department of Agriculture, and many others.

Research in the social sciences is often (not always) less expensive, requiring smaller amount of grant funding. As mentioned previously, however, it is now becoming common to have physical, natural, and social scientist teams pursuing large grant funding. This is an exciting and very promising trend for many reasons, not the least of which is the nature of the complex problems being studied.

Research in the arts and humanities typically requires the least amount of funding as it rarely requires the expensive items listed previously. Funding from such organizations as the National Endowment for the Arts, National Endowment for the Humanities, and private foundations may be able to support significant scholarship and creation of new knowledge or works through much more modest grants than would be required in the natural or physical sciences, for example.

Philanthropy may also be directed toward the support of research and scholarly activity at universities. Support from individual donors, family foundations, private or corporate foundations may be directed to support students, faculty, labs or other facilities, research programs, galleries, centers, and institutes.

Who benefits?

Students, both undergraduate and graduate, benefit from studying in an environment rich with research and discovery. Besides what the faculty can bring back to the classroom, there are opportunities to engage with faculty as part of their research teams and even conduct independent research under their supervision, often for credit. There are opportunities to learn about and learn on state-of-the-art equipment, in state-of-the-art laboratories, and from those working on the leading edge in a discipline. There are opportunities to co-author, present at conferences, make important connections, and explore post-graduate pathways.

The broader university benefits from active research programs. Research on timely and important topics attracts attention, which in turn leads to greater institutional visibility and reputation. As a university becomes known for its research in certain fields, they become magnets for students, faculty, grants, media coverage, and even philanthropy. Strength in research helps to define a university’s “brand” in the national and international marketplace, impacting everything from student recruitment, to faculty retention, to attracting new investments.

The community, region, and state benefits from the research activity of the university. This is especially true for public research universities. Research also contributes directly to economic development, clinical, commercial, and business opportunities. Resources brought into the university through grants and contracts support faculty, staff, and student salaries, often adding additional jobs, contributing directly to the tax base. Research universities, through their expertise, reputation, and facilities, can attract new businesses into their communities or states. They can also launch and incubate startup companies, or license and sell their technologies to other companies. Research universities often host meeting and conferences which creates revenue for local hotels, restaurants, event centers, and more. And as mentioned previously, university medical centers provide high-quality medical care, often in midsize communities that wouldn’t otherwise have such outstanding services and state-of-the-art facilities.

(Photo by Justin Sullivan/Getty Images)

And finally, why does this all matter?

Research is essential to advancing society, strengthening the economy, driving innovation, and addressing the vexing and challenging problems we face as a people, place, and planet. It’s through research, scholarship, and discovery that we learn about our history and ourselves, understand the present context in which we live, and plan for and secure our future.

Research universities are vibrant, exciting, and inspiring places to learn and to work. They offer opportunities for students that few other institutions can match – whether small liberal arts colleges, mid-size teaching universities, or community colleges – and while not right for every learner or every educator, they are right for many, if not most. The advantages simply cannot be ignored. Neither can the importance or the need for these institutions. They need not be for everyone, and everyone need not find their way to study or work at our research universities, and we stipulate that there are many outstanding options to meet and support different learning styles and provide different environments for teaching and learning. But it’s critically important that we continue to support, protect, and respect research universities for all they do for their students, their communities and states, our standing in the global scientific community, our economy, and our nation.

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• > Journals
• > BJPsych Bulletin
• > The Psychiatrist
• > Volume 37 Issue 6
• > Qualitative research: its value and applicability

Article contents

What questions are best answered using qualitative research, countering some misconceptions, in conclusion, qualitative research: its value and applicability.

Published online by Cambridge University Press:  02 January 2018

Qualitative research has a rich tradition in the study of human social behaviour and cultures. Its general aim is to develop concepts which help us to understand social phenomena in, wherever possible, natural rather than experimental settings, to gain an understanding of the experiences, perceptions and/or behaviours of individuals, and the meanings attached to them. The effective application of qualitative methods to other disciplines, including clinical, health service and education research, has a rapidly expanding and robust evidence base. Qualitative approaches have particular potential in psychiatry research, singularly and in combination with quantitative methods. This article outlines the nature and potential application of qualitative research as well as attempting to counter a number of misconceptions.

Qualitative research has a rich tradition in the social sciences. Since the late 19th century, researchers interested in studying the social behaviour and cultures of humankind have perceived limitations in trying to explain the phenomena they encounter in purely quantifiable, measurable terms. Anthropology, in its social and cultural forms, was one of the foremost disciplines in developing what would later be termed a qualitative approach, founded as it was on ethnographic studies which sought an understanding of the culture of people from other societies, often hitherto unknown and far removed in geography. Reference Bernard 1 Early researchers would spend extended periods of time living in societies, observing, noting and photographing the minutia of daily life, with the most committed often learning the language of peoples they observed, in the hope of gaining greater acceptance by them and a more detailed understanding of the cultural norms at play. All academic disciplines concerned with human and social behaviour, including anthropology, sociology and psychology, now make extensive use of qualitative research methods whose systematic application was first developed by these colonial-era social scientists.

Their methods, involving observation, participation and discussion of the individuals and groups being studied, as well as reading related textual and visual media and artefacts, form the bedrock of all qualitative social scientific inquiry. The general aim of qualitative research is thus to develop concepts which help us to understand social phenomena in, wherever possible, natural rather than experimental settings, to gain an understanding of the experiences, perceptions and/or behaviours of those studied, and the meanings attached to them. Reference Bryman 2 Researchers interested in finding out why people behave the way they do; how people are affected by events, how attitudes and opinions are formed; how and why cultures and practices have developed in the way they have, might well consider qualitative methods to answer their questions.

It is fair to say that clinical and health-related research is still dominated by quantitative methods, of which the randomised controlled trial, focused on hypothesis-testing through experiment controlled by randomisation, is perhaps the quintessential method. Qualitative approaches may seem obscure to the uninitiated when directly compared with the experimental, quantitative methods used in clinical research. There is increasing recognition among researchers in these fields, however, that qualitative methods such as observation, in-depth interviews, focus groups, consensus methods, case studies and the interpretation of texts can be more effective than quantitative approaches in exploring complex phenomena and as such are valuable additions to the methodological armoury available to them. Reference Denzin and Lincoln 3

In considering what kind of research questions are best answered using a qualitative approach, it is important to remember that, first and foremost, unlike quantitative research, inquiry conducted in the qualitative tradition seeks to answer the question ‘What?’ as opposed to ‘How often?’. Qualitative methods are designed to reveal what is going on by describing and interpreting phenomena; they do not attempt to measure how often an event or association occurs. Research conducted using qualitative methods is normally done with an intent to preserve the inherent complexities of human behaviour as opposed to assuming a reductive view of the subject in order to count and measure the occurrence of phenomena. Qualitative research normally takes an inductive approach, moving from observation to hypothesis rather than hypothesis-testing or deduction, although the latter is perfectly possible.

When conducting research in this tradition, the researcher should, if possible, avoid separating the stages of study design, data collection and analysis, but instead weave backwards and forwards between the raw data and the process of conceptualisation, thereby making sense of the data throughout the period of data collection. Although there are inevitable tensions among methodologists concerned with qualitative practice, there is broad consensus that a priori categories and concepts reflecting a researcher's own preconceptions should not be imposed on the process of data collection and analysis. The emphasis should be on capturing and interpreting research participants' true perceptions and/or behaviours.

Using combined approaches

The polarity between qualitative and quantitative research has been largely assuaged, to the benefit of all disciplines which now recognise the value, and compatibility, of both approaches. Indeed, there can be particular value in using quantitative methods in combination with qualitative methods. Reference Barbour 4 In the exploratory stages of a research project, qualitative methodology can be used to clarify or refine the research question, to aid conceptualisation and to generate a hypothesis. It can also help to identify the correct variables to be measured, as researchers have been known to measure before they fully understand the underlying issues pertaining to a study and, as a consequence, may not always target the most appropriate factors. Qualitative work can be valuable in the interpretation, qualification or illumination of quantitative research findings. This is particularly helpful when focusing on anomalous results, as they test the main hypothesis formulated. Qualitative methods can also be used in combination with quantitative methods to triangulate findings and support the validation process, for example, where three or more methods are used and the results compared for similarity (e.g. a survey, interviews and a period of observation in situ ).

‘There is little value in qualitative research findings because we cannot generalise from them’

Generalisability refers to the extent that the account can be applied to other people, times and settings other than those actually studied. A common criticism of qualitative research is that the results of a study are rarely, if ever, generalisable to a larger population because the sample groups are small and the participants are not chosen randomly. Such criticism fails to recognise the distinctiveness of qualitative research where sampling is concerned. In quantitative research, the intent is to secure a large random sample that is representative of the general population, with the purpose of eliminating individual variations, focusing on generalisations and thereby allowing for statistical inference of results that are applicable across an entire population. In qualitative research, generalisability is based on the assumption that it is valuable to begin to understand similar situations or people, rather than being representative of the target population. Qualitative research is rarely based on the use of random samples, so the kinds of reference to wider populations made on the basis of surveys cannot be used in qualitative analysis.

Qualitative researchers utilise purposive sampling, whereby research participants are selected deliberately to test a particular theoretical premise. The purpose of sampling here is not to identify a random subgroup of the general population from which statistically significant results can be extrapolated, but rather to identify, in a systematic way, individuals that possess relevant characteristics for the question being considered. Reference Strauss and Corbin 5 The researchers must instead ensure that any reference to people and settings beyond those in the study are justified, which is normally achieved by defining, in detail, the type of settings and people to whom the explanation or theory applies based on the identification of similar settings and people in the study. The intent is to permit a detailed examination of the phenomenon, resulting in a text-rich interpretation that can deepen our understanding and produce a plausible explanation of the phenomenon under study. The results are not intended to be statistically generalisable, although any theory they generate might well be.

‘Qualitative research cannot really claim reliability or validity’

In quantitative research, reliability is the extent to which different observers, or the same observers on different occasions, make the same observations or collect the same data about the same object of study. The changing nature of social phenomena scrutinised by qualitative researchers inevitably makes the possibility of the same kind of reliability problematic in their work. A number of alternative concepts to reliability have been developed by qualitative methodologists, however, known collectively as forms of trustworthiness. Reference Guba 6

One way to demonstrate trustworthiness is to present detailed evidence in the form of quotations from interviews and field notes, along with thick textual descriptions of episodes, events and settings. To be trustworthy, qualitative analysis should also be auditable, making it possible to retrace the steps leading to a certain interpretation or theory to check that no alternatives were left unexamined and that no researcher biases had any avoidable influence on the results. Usually, this involves the recording of information about who did what with the data and in what order so that the origin of interpretations can be retraced.

In general, within the research traditions of the natural sciences, findings are validated by their repeated replication, and if a second investigator cannot replicate the findings when they repeat the experiment then the original results are questioned. If no one else can replicate the original results then they are rejected as fatally flawed and therefore invalid. Natural scientists have developed a broad spectrum of procedures and study designs to ensure that experiments are dependable and that replication is possible. In the social sciences, particularly when using qualitative research methods, replication is rarely possible given that, when observed or questioned again, respondents will almost never say or do precisely the same things. Whether results have been successfully replicated is always a matter of interpretation. There are, however, procedures that, if followed, can significantly reduce the possibility of producing analyses that are partial or biased. Reference Altheide, Johnson, Denzin and Lincoln 7

Triangulation is one way of doing this. It essentially means combining multiple views, approaches or methods in an investigation to obtain a more accurate interpretation of the phenomena, thereby creating an analysis of greater depth and richness. As the process of analysing qualitative data normally involves some form of coding, whereby data are broken down into units of analysis, constant comparison can also be used. Constant comparison involves checking the consistency and accuracy of interpretations and especially the application of codes by constantly comparing one interpretation or code with others both of a similar sort and in other cases and settings. This in effect is a form of interrater reliability, involving multiple researchers or teams in the coding process so that it is possible to compare how they have coded the same passages and where there are areas of agreement and disagreement so that consensus can be reached about a code's definition, improving consistency and rigour. It is also good practice in qualitative analysis to look constantly for outliers – results that are out of line with your main findings or any which directly contradict what your explanations might predict, re-examining the data to try to find a way of explaining the atypical finding to produce a modified and more complex theory and explanation.

Qualitative research has been established for many decades in the social sciences and encompasses a valuable set of methodological tools for data collection, analysis and interpretation. Their effective application to other disciplines, including clinical, health service and education research, has a rapidly expanding and robust evidence base. The use of qualitative approaches to research in psychiatry has particular potential, singularly and in combination with quantitative methods. Reference Crabb and Chur-Hansen 8 When devising research questions in the specialty, careful thought should always be given to the most appropriate methodology, and consideration given to the great depth and richness of empirical evidence which a robust qualitative approach is able to provide.

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• Volume 37, Issue 6
• Steven J. Agius (a1)
• DOI: https://doi.org/10.1192/pb.bp.113.042770

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CoB Research Experience for Undergraduates (REU)

• Learn More About a Topic
• Find Scholarly Articles
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Question? Ask me!

What is a Scholarly Article?

• Defining scholarly articles
• Peer-review
• Other article types

Scholarly articles are articles written by experts and researchers in a field of study to educate or share new discoveries and research. You can use scholarly articles to find out about new innovations, research methodologies, and to dive more deeply into understanding the themes and subtopics of your field of research.

Other names: Scholarly articles are sometimes also called peer-reviewed articles or academic articles. However, not all scholarly articles are peer reviewed.

How to identify a scholarly article:

• Investigate the publisher. Most scholarly articles are published in peer-reviewed journals, e.g.  Strategic Management Journal  or  Journal of Labor Economics
• Browse the structure of the article. Scholarly articles often contain multiples sections and have headers such as introduction, literature review, methodology, discussion, conclusion, etc.
• Check for in-text citations and a large bibliography at the end of the article
• Take note of the language an author uses to write their article. Scholarly articles often use jargon - or specific terms that are used and understood by other professionals in the field. This can make them tricky to understand by others without deeper expertise of the topic.

If you are using a Libraries' database, use a filter to narrow your search to just scholarly articles. This is helpful if you do not want news or magazine articles to show up in your search results.

What is Peer Review?

Some journals require articles to go through a peer review process before accepting it for publication. This means an article is reviewed by other experts in the field to check for accuracy and relevancy before a journal will publish it. 

Why Does This Matter?

Many researchers consider peer review articles as a gold standard in research as they have already been evaluated by a panel of experts. Not all scholarly articles are peer reviewed and while both can be helpful to use in your research, you may need to evaluate scholarly articles for quality just like you would a news article before using it in your research project.

How Can I Tell a Scholarly Article from a Peer Reviewed Journal?

Try googling a journal's name. Many times the journal will mention if they require peer review in their about us, but you can also look at their submission criteria or article requirements to find out what process they use.

For example, this is on the about page for the Journal of Business :

"The  Journal of Business   (JoB)  is a peer-reviewed journal with the focus on research articles and case studies in all academic fields of business discipline. The scope of the journal covers the broad range of areas related to business studies including interdisciplinary topics and newly developing areas of business. Submissions comprise research articles – both theoretical and empirical, case studies and reviews of the literature."

When you are searching in a database there may be other articles that you may come across that are not scholarly, but that can be helpful to you in other ways.

Grey Literature

As the name implies, these articles fall into a sort of grey area, they aren't quite scholarly, but they also don't fit anywhere else. The types of articles you might find that fit into this category are:

• Government documents
• Reports, policies, and white papers either produced by an organization, company, or government agency
• Thesis and dissertations, that graduate students produce as part of their degree requirements
• Conference proceedings that researchers produce to share their research with others in more informal ways than through publication

Trade Journals and Magazines

Trade journals are also not the same as a scholarly journal, though they might be easy to confuse. Trade journals are written for professionals in a trade or industry and cover practical topics that impact their career. These articles are written more like news or magazine articles and are meant for working professionals to learn more about innovative technology, relevant news, and current events that impact their industry.

For example, say you decide to pursue a career in managing a fitness center or gym. A relevant trade journal for you would be National Fitness.

How to Get the Most Out of a Scholarly Article

Because of the technical content and level of prior knowledge the author(s) expect their readers to have; being able to get the most out of a scholarly article is a skill that takes time and practice to get good at.

When you are looking for scholarly articles that fulfill your research needs it can be time consuming to read each one top to bottom to determine whether or not it is relevant to your project. Instead try reading the article out of order to determine whether or not a scholarly article is relevant to your project.

• First, read the abstract - if RELEVANT then read the... 
• Discussion and/or Conclusion - if still RELEVANT read the...
• Methodology and/or Literature Review - if SOUND, examine the...
• Argument - if BIAS is limited, check the...
• References or bibliography - for other sources you can use in your research

Start by reading a scholarly article in this order. If what you read sounds relevant, then move on to reading the next section. At any time if they article no longer meets your needs, stop reading and move on. Once you complete reading an article out of order, and you determine that it is relevant to your project, it can be helpful to read the article again from top to bottom and annotate your thoughts as you go.

How to Evaluate Scholarly Articles for Quality

Since many scholarly articles go through a peer review process, it can be quicker and easier to evaluate. However, there are still a few things to investigate before using a scholarly article in your research.

Examine the Article

• How current is the article? Don't just look at when the article was published, but also scan the references to make sure the author(s) used current sources.
• Is the methodology used sound? This may require deeper expertise of the methods used in your field to evaluate. Talk with your faculty mentor about the articles you found to help determine what is appropriate for your field.
• Is this relevant to my research topic? Consider if and how the article you found is relevant to your research topic. Does it provide background on your topic or relate to your research in other ways? Even if an article is well cited, it won't be helpful to you if it doesn't connect to your topic.

Investigate Beyond the Article

• Does the journal require peer review?
• Is this journal well regarded by other experts?
• What expertise do the authors have on this topic?
• What other works have these authors published?
• What is it - https://www.nature.com/articles/d41586-023-03974-8
• Tools like Retraction Watch can be helpful

It can be helpful to google the author and publisher to see what you can find out about them.

Evaluating a source is to explore the source. You do not need to answer all the questions above each time you evaluate a source. Over time you will become familiar with well regarded journals and authors in your field. All research skills take practice, the more you use this skill the faster and better you will become at it.

• Retraction Watch A database that tracks retractions of scholarly articles
• Retraction - Example This article was retracted from the journal Environmental Science and Pollution research due to a number of concerns including including but not limited to a compromised peer review process, inappropriate or irrelevant references and citation behavior.
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Research: Competent Leaders Know The Limits of Their Expertise

• David Dunning

How to spot the difference between confidence and competence.

It is very important as a manager to accurately gauge one’s competence; overconfidence can lead to significant business failures. Self-perceived expertise can cause individuals to overclaim knowledge, often mistaking confidence for actual competence. Genuine expertise, however, is marked by an accurate understanding of one’s limitations. The article advises leaders to rely on proven track records and data when evaluating their own abilities and those of others, underscoring Warren Buffet’s philosophy: success hinges on knowing the boundaries of your circle of competence.

Accurately gauging what you know — and more importantly, what you don’t — can mean the difference between success and failure as a manager.

• SA Stav Atir is an assistant professor of management at the University of Wisconsin-Madison’s Wisconsin School of Business. Her research focuses on the psychological processes that underlie knowledge judgments and learning decisions. She also studies topics related to diversity, equity, and inclusion.
• DD David Dunning is Mary Ann and Charles R. Walgreen, Jr., Professor of the Study of Human Understanding, as well as Professor of Psychology at the University of Michigan. A social psychologist, his work focuses on misbeliefs about the self and misunderstandings between people.

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Study shows AI investment plus connected, skilled workforce a winning combination

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By KEVIN MANNE

Published September 27, 2024

Countries that invest in artificial intelligence see a significant impact on their productivity and growth, but they should take a strategic approach, according to new School of Management research.

Published in Decisions Analytics Journal , the study found that AI innovation — as measured by the number of AI-related patents and capital investment — works best with the presence of a highly skilled labor force and the proper internet infrastructure to harness its full potential.

“AI innovation has the potential to transform economies, but our study shows that more patents and investments do not automatically translate into higher production efficiency,” says co-author Raj Sharman, professor of management science and systems. “A strategic approach that includes high-speed internet access and skilled labor is key to realizing AI’s full benefits.”

To study the impact of AI innovation, the researchers analyzed data for AI patents, capital and labor from 10 countries over an 11-year period. They used the stochastic production frontier model, employing both the Cobb-Douglas function and the Constant Elastic Substitution model, to evaluate the relationship between traditional economic inputs, such as capital and labor, and AI inputs to determine production efficiency.

The researchers found that while the U.S. leads in AI innovation with the highest number of patents, the U.K. has the highest production efficiency. Meanwhile, China ranks fourth in AI innovation, but has the lowest production efficiency among the countries studied.

“The U.S., with its long history and vast resources in AI research, does not show the best efficiency,” says Sharman. “The U.K. has performed better despite its lower investment in AI because they’ve used their resources better and have more effectively integrated AI into their work.”

Looking ahead, the researchers say that countries and businesses need to shift their focus from just increasing AI innovation to improving how efficiently they implement and use the technology. This means investing in internet bandwidth, upskilling the workforce and strategically optimizing resources to achieve the best results.

Sharman collaborated on the study with Ying-Chih Sun, assistant professor in the East Central University School of Business; Ozlem Cosgun, associate professor of information management and business in the Montclair State University Feliciano School of Business; UB School of Management graduate Pavankumar Mulgund, assistant professor of management information systems at the University of Memphis Fogelman College of Business and Economics; and Dursun Delen, the Regents Professor of Management Science and Information Systems in the Oklahoma State University Spears School of Business.

JCU Online Blog

What is the importance of research in nursing.

The future for Australian nurses is rich with opportunity, with demand for skilled professionals running high and projected to increase.

According to the Australian Government’s Nursing Supply and Demand Study , an additional 80,000 nurses will be needed by 2035. As healthcare evolves, research is an important and often overlooked tool for nurses who want to make a lasting impact on patient care and improve the future of nursing .

Research in nursing isn’t just about academic inquiry; it directly improves the quality of care, informs healthcare policies and enhances clinical practices. The ability to understand and conduct research in the realm of healthcare empowers nurses to step into leadership roles and drive meaningful, transformative change across the industry.

In this article, we’ll explore the importance of research in nursing and outline its practical applications. We’ll also discuss how developing research skills can help nurses differentiate their careers and influence transformation in a healthcare system that needs it.

What is research in nursing?

Research in nursing refers to the systemic investigation of clinical practices, patient care, and healthcare outcomes to inform practice. It involves developing hypotheses, gathering evidence, synthesising and analysing qualitative and quantitative data, and applying the findings to improve individual patient care and broader healthcare policies.

Research can be applied in various ways across different nursing roles . An intensive care unit (ICU) nurse might conduct research on the effectiveness of different pain management strategies for critical patients, and a nurse manager in a surgical ward may use research practices to investigate infection control measures.

Crucially, research allows nurses to move beyond anecdotal experience and apply scientifically supported methods in their practice to improve care and overall patient outcomes. As leadership in nursing becomes increasingly important and data-led, the role of research in this sector stands to gain even more value.

Research is critical in nursing for a number of key reasons. It underpins the continuous improvement of patient care, supports nurses in staying up-to-date with new healthcare developments, and enables them to make informed and effective decisions in their practice.

Below are reasons why research in nursing is important. 

Shape and improve care practices

As patient expectations rise and healthcare delivery becomes more complex, research empowers nurses to meet demand with evidence-backed solutions. Best practices in patient care are heavily informed by research.

By conducting and applying qualitative and quantitative research methodologies, nurses can identify the most effective treatment methods, improve patient recovery rates and refine care processes. Clinical research, for example, has been instrumental in areas like pain management, infection control and patient safety — enabling nurses to provide care based on proven outcomes, rather than relying on intuition alone.

Stay up to date with emerging trends

Advancements in tech have been outpacing most industries for decades, and healthcare is no exception. With the rapid adoption of artificial intelligence (AI) and other cutting-edge technologies, the pace of change continues to be rapid. Nursing has the potential to benefit enormously from these innovations, but skilled professionals are needed to guide their proper integration.

Emerging trends extend beyond technology. Shifts in management expectations, evolving employee needs and advancements in nursing knowledge all play a role in shaping modern healthcare. Research is essential for nurses to stay ahead of these changes and remain informed about new treatments, care strategies and patient management techniques.

Whether it’s integrating digital health technologies or adopting innovative care models, strong research skills enable nurses to critically evaluate new trends and incorporate them effectively into their practice. This adaptability is crucial as healthcare becomes more reliant on data and technology.

Improved critical thinking and decision-making

Research sharpens a nurse’s ability to think critically and make informed decisions. Whether in roles across medical, clinical or management settings, decisions in healthcare often need to be made quickly. Having a strong understanding of research methods allows nurses to assess the available evidence and make the best possible choice for their patients.

This enhanced and data-driven decision-making is critical for providing high-quality care to patients, particularly in high-pressure or emergency situations.

For nurses pursuing leadership or advanced practice career paths , the ability to analyse data, synthesise evidence and apply research findings is important. Subjects like JCU Online’s Synthesising Evidence for Healthcare in the Master of Nursing degree provide nurses with the tools to critically appraise research and make evidence-based decisions that improve patient outcomes. 

Applicable to a range of nursing roles

Research skills can open doors to a wide range of career paths for nurses. They may pursue roles in areas such as academia, clinical research, healthcare administration or leadership.

For those aiming to take on leadership roles, research provides the foundation for making informed decisions that impact practice. Likewise, nurses with an interest in teaching can use their research knowledge to contribute to the education and training of future nurses, helping to shape the next generation of healthcare professionals.

JCU Online’s Master of Nursing offers three in-demand specialisations: Leadership and Management, Advanced Practice, and Education. Each major allows nurses to cultivate research expertise, equipping them to step into senior roles where they can influence healthcare policies and practices. 

How can nurses build their research skills?

Cultivating strong research skills is one of the best ways future-focused nurses can establish a competitive advantage in the industry and equip themselves to help shape it. 

There are a number of practical ways nurses can develop these skills, including:

• Engage in evidence-based practice: Review and integrate the latest nursing research wherever possible in day-to-day work. This helps to stay current and understand how evidence-based practices are applied in clinical settings. Reading journals and case studies is also a great way for nurses to familiarise and recognise high-quality research and how it can inform patient care.
• Pursue formal education: Enrolling a postgraduate degree like JCU Online’s Master of Nursing is an effective way to hone the type of research skills that will set you apart. Core subjects like Qualitative Research in Healthcare and Quantitative Research in Healthcare equip students with the skills to conduct evidence-based research, from formulating questions to developing hypotheses, interpreting data, evaluating results and integrating learnings in a practical setting.
• Participate in clinical research projects: Many healthcare organisations encourage staff to participate in research projects. Getting involved provides hands-on experience in designing studies, collecting data and evaluating outcomes – all of which are invaluable skills for nurses interested in advancing their careers.
• Collaborate with research teams: Working with interdisciplinary research teams can broaden your perspective as a nurse and deepen your understanding of healthcare challenges. It also provides the opportunity to contribute to larger-scale studies, and learn from more experienced researchers to further develop your own expertise. 

Take your nursing career to the next level

With the increasing complexity and rapid evolution of healthcare, the need for nurses with strong research skills is increasing, too.

Ranked among the top 25 universities in Australia, JCU Online offers students the opportunity to gain a highly regarded qualification while connecting with industry leaders, experienced nurse academics and specialists. 

JCU Online’s Master of Nursing gives you the qualifications and skills you need to evolve your career as fast as healthcare itself is changing. The course - delivered 100% online with personalised support - will allow you to take on more complex roles and deliver better patient care.

As a student of the Master of Nursing, you will benefit from the course content informed by the latest approaches to high-quality, innovative and cost-effective nurse leadership and care, supported by our strong partnerships with hospitals and healthcare providers throughout Australia. 

Find out how JCU Online's Master of Nursing can help create a rewarding career pathway and stay ahead. Speak to one of our Student Enrolment Advisors today on 1300 535 919.

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Majority of Americans continue to favor moving away from Electoral College

As has been the case for over 200 years, the Electoral College will determine the outcome of the U.S. presidential race this fall. Yet most Americans have long supported moving away from this system.

In 2000 and 2016, the winners of the popular vote lost their bids for U.S. president after receiving fewer Electoral College votes than their opponents. To continue tracking how the public views the U.S. system for presidential elections, we surveyed 9,720 U.S. adults from Aug. 26 to Sept. 2, 2024.

Everyone who took part in the current survey is a member of Pew Research Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. 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, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

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

The Electoral College allocates a number of electors based on how many senators and representatives each state has in Congress (plus three electors for the District of Columbia, for a total of 538). Most states award all of their electoral votes to the candidate who wins that state.

More than six-in-ten Americans (63%) would instead prefer to see the winner of the presidential election be the person who wins the most votes nationally. Roughly a third (35%) favor retaining the Electoral College system, according to a Pew Research Center survey of 9,720 adults conducted Aug. 26-Sept. 2, 2024.

The Electoral College is always in focus during presidential elections. But a recent – as yet unsuccessful – effort to change how Nebraska awards its electoral votes has highlighted the prospect of a narrow Electoral College victory for either candidate in an extremely close race.

Related: In Tied Presidential Race, Harris and Trump Have Contrasting Strengths, Weaknesses

As has been the case for more than two decades, there are wide partisan differences in attitudes about the Electoral College:

• Eight-in-ten Democrats and Democratic-leaning independents favor replacing the Electoral College with a popular vote system.
• Republicans and Republican leaners are more evenly divided: 53% favor keeping the Electoral College, while 46% would prefer to replace it.

Focus on the Electoral College

In 48 states and D.C., the candidate who receives the most votes in that state is awarded all of its electoral votes.

Nebraska and Maine have a different approach, allocating two electoral votes to the candidate who wins the most votes statewide and one to the winner of each congressional district. Some Republicans have been pressing to change Nebraska’s rules so that the statewide winner gets all five of its electoral votes. This would likely work to former President Donald Trump’s advantage, given Nebraska’s consistent support of GOP presidential candidates .

A candidate must win a majority of the 538 electoral votes to become president. If no candidate wins a majority , the election outcome is decided by the U.S. House of Representatives, with each state’s delegation casting one vote.

Under the current electoral system in the United States, the winner of the popular vote may not secure enough Electoral College votes to win the presidency.

• This occurred in both the 2000 and 2016 elections. George W. Bush and Donald Trump, respectively, won these elections with clear Electoral College victories, but they did not win the most votes nationwide.
• In 2020, while President Joe Biden won the popular vote by more than 7 million votes, his Electoral College victory was decided by fewer than 50,000 votes in a few close states.
• As a result of the Electoral College system, there is outsize attention to outcomes in a handful of battleground states . This year, those battlegrounds include Arizona, Georgia, Michigan, Nevada, North Carolina, Pennsylvania and Wisconsin.

Partisan views of the Electoral College over time

Since the 2000 election, two-thirds or more Democrats have backed moving to a popular vote system.

Republicans remain fairly divided today, with 46% in favor of moving to a popular vote system.

Republicans were less supportive of this change following Trump’s 2016 win. In November of that year, in the wake of Trump’s Electoral College victory and popular vote loss, just 27% of Republicans supported a popular vote system.

Party and ideology

There are only modest differences by ideology on this question among Democrats:

• 87% of liberal Democrats and 74% of conservative and moderate Democrats say they would prefer presidents to be elected by popular vote.

Ideological differences are wider among Republicans:

• 63% of conservative Republicans prefer keeping the current system.
• In contrast, 61% of moderate and liberal Republicans (who are a much smaller share of the Republican coalition) say they support a popular vote for president.

Majorities across all age groups support changing the system. However, adults younger than 50 are somewhat more supportive of this than those ages 50 and older (66% vs. 59%).

Note: This is an update of a post previously published in 2021, 2022 and 2023.

In January 2020, Pew Research Center ran a survey experiment that asked this question in two slightly different ways. One used the language that we and other organizations had used in prior years, with the reform option asking about “amending the Constitution so the candidate who receives the most votes nationwide wins the election.” The other version asked about “changing the system so the candidate who receives the most votes nationwide wins the election.” The January 2020 survey revealed no substantive differences between asking about “amending the Constitution” and “changing the system.”

We conducted this experiment in large part because reforming the way presidents are selected does not technically require amending the Constitution. The National Popular Vote Interstate Compact , for example, could theoretically accomplish it without a constitutional amendment. Since there was no substantive difference in the survey results between the two question wordings, we have adopted the revised wording, which references “changing the system.”

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Jocelyn Kiley is a senior associate director of research at Pew Research Center .

Americans see many federal agencies favorably, but Republicans grow more critical of Justice Department

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The human side of generative AI: Creating a path to productivity

Ever since OpenAI’s ChatGPT exploded into public view in late 2022, the possibilities of generative AI (gen AI) have captured imaginations throughout the business world.

When it comes to crafting an effective talent strategy, organizations have focused mostly on how gen AI can increase productivity levels. This is understandable, given the trillions in value at stake . However, it may not be the most strategic approach. To match the right talent to jobs, leaders first must understand how gen AI is changing the way employees view their work experience. 1 Generative AI is a form of AI that can generate text, images, and other content in response to user prompts. The technology differs from previous versions of AI, in part, because of the scope of outputs it can create.

McKinsey recently surveyed a cross-section of employees as part of our continuing research into how organizations can improve workforce engagement, retention, and attraction (see sidebar, “About the research”). Respondents provided several intriguing insights that can help organizations as they build gen AI talent capabilities.

• In any given organization, the pool of gen AI talent  is likely broader than many leaders realize—and it’s poised to grow rapidly. This cohort isn’t limited to technical talent such as data scientists, software engineers, and machine learning specialists, important as those roles are. In fact, just 12 percent of our respondents fall into this tech-heavy category of traditional gen AI talent. The vast remainder of respondents, or 88 percent, are in nontechnical jobs that use gen AI for help with rote tasks. These jobs include middle managers, healthcare workers, educators, and administrators, among others (Exhibit 1).
• Fifty-one percent of respondents in technical and nontechnical roles who identify as gen AI creators and heavy users of the technology say they plan to quit their jobs over the next three to six months. This is sobering news for those executive respondents in the survey who say they want to build gen AI talent in-house; it’s hard to reskill and upskill people when they are looking to leave.
• Although those who self-identify as heavy users and creators of gen AI represent an in-demand employee group, these workers aren’t staying in jobs or attracted to them because of compensation. In fact, the survey shows that this group strongly emphasizes flexibility and relational factors such as meaningful work, caring leaders, and health and well-being  over pay.
• Finally, and perhaps most surprising: heavy users and creators of gen AI overwhelmingly feel they need higher-level cognitive and social-emotional skills 2 Higher cognitive skills involve more complex thinking processes; social-emotional skills include effectively managing emotions, interpersonal relations, and personal responsibilities. to do their jobs, more than they need to build technological skills. As workers increasingly use gen AI to tackle more repetitive tasks, the human-centric skills of critical thinking and decision making will become ever more important.

These revelations have broad implications for employers as they try to attract and engage their workforces. Organizations are on the cusp of gen AI pushing either positive or negative change when it comes to the nature of work. Leaders have an opportunity to humanize that work  by deciding where, when, and how their teams use gen AI so that people are freed up from routine tasks to do more creative, collaborative, and innovative thinking. Gen AI talent agrees.

About the research

To continue to understand labor market trends related to employee retention, engagement, and attraction, we surveyed 12,802 workers—9,684 employees and 3,118 employers—across 16 industries in Canada (n = 3,183), the United Kingdom (n = 3,227), and the United States (n = 6,392). We focused on two key subpopulations of interest for leaders; these subpopulations represented 9.93 percent of the overall employee sample: generative AI (gen AI) creators at 1.75 percent (n = 169) and heavy users at 8.19 percent (n = 793). The other category—self-identified gen AI light users—comprised 18.18 percent (n = 1,761) of the sample, leading to a total of 28.12 percent (n = 2,723) of workers who self-identified as creators, heavy users, or light users. Nonusers were 71.88 percent (n = 6,961) of workers. We also surveyed more than 3,000 executives in companies across industries to find out how they expect to close their organization’s gen AI skills gaps over the next two years. The survey was conducted from July 28, 2023, to August 15, 2023.

In this article, we break down crucial segments of workers who are at the forefront of gen AI usage or creation and dig deeper into the job factors and skills they say they need. We then discuss how organizations can enhance productivity by crafting jobs that put people before tech—not the other way around. Companies that set a people-centric talent strategy will give themselves a competitive edge as more workers and jobs are affected by the changes gen AI brings.

The workforce: Who is in the gen AI mix?

If companies are to take advantage of the productivity gains from gen AI, they first must consider the broad range of skills required for its successful deployment across the enterprise .

While there are many categories of workers who can be described as gen AI talent, we focus on four distinct archetypes in our survey based on gen AI use:

Creators: These employees help build the gen AI models for their organizations and develop the tools and interfaces most of us use to interact with these models. Creators (2 percent of employees surveyed) tend to be predominantly software engineers, programmers, and machine learning scientists who develop the tools and interfaces most of us use to interact with gen AI.

Heavy users: These employees use gen AI to help them perform most of their core tasks or to enhance their work functions. Heavy users (8 percent of our sample) include a wide range of workers, from designers who use gen AI to expedite 3D modeling to data scientists who use gen AI to verify the accuracy of their coding language semantics.

Light users: Workers in this category use gen AI to perform less than 50 percent of their primary tasks. Representing about 18 percent of the sample, they include middle managers, educators, and communications professionals. For example, a manager might use gen AI to create meeting notes or to help delegate tasks, while a teacher may use it to innovate classroom activities. Journalists and writers researching topics might use gen AI to give them a baseline of facts or to help write a first draft.

Nonusers: These are individuals who are either unaffected by or unaware of the impact of gen AI on their jobs. Examples in our sample include nurse practitioners and healthcare workers engaged in direct patient care, as well as retail associates whose primary role is face-to-face interactions with customers. Although these employees currently represent about 70 percent of the survey, our expectation is that a majority of nonusers will become light or heavy users as the scope and usage of gen AI changes.

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People over pay: The job factors that workers value most

The COVID-19 pandemic revealed that for many workers, what they want most from their work experience has fundamentally changed . Employees increasingly value relational elements such as caring leaders and coworkers, as well as support for health and well-being, more than compensation (though pay is always important). In 2021, we saw workers quitting in droves—in fact, 40 percent of respondents across jobs, industries, and geographies  said they planned to quit their jobs in the next three to six months. That figure has since dropped to 34 percent.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Certain worker segments, however, remain a greater flight risk. Of self-identified gen AI creators and heavy users, 51 percent of respondents to our latest survey say they plan to leave in the next three to six months.

Early creators and heavy adopters, in particular, wield power when it comes to job choice and shaping their careers. Many company leaders believe that workers in these groups are leaving at higher rates because they can find better compensation elsewhere. Yet an examination of the employee-value-proposition (EVP) factors that resonate most with these segments busts the myth, once again, that compensation is a primary motivator.

Our survey shows that creators and heavy users prioritize workplace flexibility more than total compensation, and that they are seeking a sense of belonging, care, and reliability within their work community. They stay in their jobs when they are given flexibility, and they leave when they aren’t. The other factors that make them stay are meaningful work, support for health and well-being, reliable and supportive coworkers, and a safe workplace environment. This experience is similar to what most workers want, with one glaring exception: compensation appears much further down the list (Exhibit 2).

McKinsey analysis shows that high disengagement and dissatisfaction rates can cost companies millions of dollars a year . Broadly speaking, addressing why workers stay or go is therefore paramount for companies as the use of gen AI grows.

When we dig deeper into self-identified heavy users and creators who are staying in their jobs, we find that a healthy 72 percent report feeling engaged at work, compared with 63 percent in our total survey sample. However, a worrying 55 percent report clinical levels of burnout, a much higher rate than the global sample of 32 percent. In other words, companies may not be getting the productivity and engagement  they expect from these workers.

These EVP elements also play a big part in steering workers into new positions. For the broader workforce, the top four factors for why people take a job are similar to why they stay. However, for workers who identify as heavy users and creators of gen AI, there is a stronger emphasis on relationships with managers and peers, and on a sense of community more broadly.

Specifically, half say that reliable and supportive people are crucial, and nearly half emphasize the importance of caring and inspiring leaders. Roughly two in five say that meaningful work and an inclusive community are core motivators, even above flexibility, which registered as of primary importance to those staying in their jobs. In contrast to the broader set of workers where compensation is the third most important attractor, for this subpopulation it again ranks seventh as a motivating factor. People won’t come just for the money, and they certainly won’t stay for it (Exhibit 3).

Most wanted: Cognitive and social-emotional skills

As gen AI interaction deepens (moving from nonuse to light use to heavy use), we see a consistent trend among both technical and nontechnical workers: they rate higher cognitive skills as more important than technological skills. Even among the technical workers who identify as gen AI creators, higher cognitive skills, at 59 percent, are rated as more important than technological skills, at 55 percent (Exhibit 4).

Regarding social-emotional skills, two interesting trends emerge. First, most technical talent sees social-emotional skills rise in importance as this group increases its usage of gen AI, while nontechnical talent reports the opposite trend. Second, creators who identify as technical talent report lower importance for social-emotional skills at a similar level to nonusers.

Taken together, it appears that as workers become more heavily involved with gen AI, their focus shifts away from social-emotional skills, unless they are in technical positions. It may be that workers are unaware of how their jobs will change in relation to managing and interacting with other people, particularly regarding the importance of developing crucial social-emotional skills.

The disconnect: Employers want to build gen AI talent mostly in-house

Many companies are striving for the most effective way to solve the supply–demand issue when it comes to gen AI talent. Our survey of executives found that most organizations plan to build their gen AI capabilities internally, through upskilling, reskilling, and redeploying talent, more than by external hiring and contracting (Exhibit 5). Naturally, given the spread of worker archetypes  in organizations and the workforce more broadly, some subpopulations, such as programmers and software engineers, may be best brought in through hiring while other types of workers, such as associates and customer experience specialists, will benefit more from upskilling and reskilling to bridge the gap.

The problem is that if companies want to build gen AI skills with the employees they already have, they need to retain the very people who, according to the survey, have indicated that they plan to leave in the next three to six months.

This gap between what employees say they want in a job and what employers are willing to offer them has been central to the workplace experience since the pandemic erupted. Our talent trends research has found that employees consistently want flexibility and meaningful work , and they want to feel valued and engaged.

When mapping self-identified heavy users and creators of gen AI onto which EVP factors matter most, we see that their emphasis on relational factors is largely the same as our broader survey sample. The need to care for family shows the largest increase in importance, while compensation registers the largest decrease.

Additionally, feeling valued by a manager, having access to development opportunities, and doing meaningful work also show a notable increase in importance. Advancement opportunities, on the other hand, are not as highly valued, suggesting that there are some unique conditions to being in a highly technical job, either through the creation of gen AI or through its heavy use (Exhibit 6).

There is little doubt that gen AI can help increase individual and workforce productivity; it may automate up to 30 percent of business activities across occupations by 2030.

How leaders can close the gap

There is little doubt that gen AI can help increase individual and workforce productivity; McKinsey research suggests it may well automate up to 30 percent of business activities across occupations by 2030 .

Leaders should explore answers to three fundamental questions about their workforces in light of the impact of gen AI:

How can we reimagine jobs to be more human centric? Begin by defining which tasks people should do, which tasks gen AI can do, and how humans should manage other people as well as gen AI usage itself. Technological skills such as coding will be the baseline for many jobs, but social-emotional skills and higher cognitive skills will be the differentiators for creative, collaborative work in the future. Perhaps this means more in-office meetings or other ways for people to engage in the most productive ways they can.

Workers who perform at high levels and inspire others—we call them “thriving stars”—help spur collaboration, innovation, and better decision making . However, they make up as little as 4 percent of organizations. Their scarcity makes it particularly important to place these employees in positions that will boost overall performance.

How can we redefine flexibility? As jobs change, companies will need to look at worker outcomes according to the results achieved, not by hours spent. The benchmark for output will have to shift. For instance, some written code may be longer, but it may not necessarily be better or more user friendly.

With the potential for gen AI to help make jobs more efficient, could an employee’s meaningful work in a given week be completed in as little as 20 hours ? And if that’s the case, is the 40-hour workweek still the benchmark? Rather than filling hours with tasks to get to a specific number in a given week, companies can focus on ways to emphasize the distinctive, creative part of a job that makes it meaningful. Jobs that create the space for the human touch can also help facilitate a more engaged and more productive workforce.

How do we emphasize the right kind of listening? This is a basic concept that many organizations seem to have trouble embracing: talking with employees rather than leading by assumption. Creating a constantly evolving dialogue can help with both problem solving and morale. This is particularly relevant as the gen AI talent pool expands.

Survey respondents overwhelmingly express enthusiasm about the integration of gen AI into their workplaces, though approximately 4 percent say they are concerned about job displacement (rising to 7 percent for workers aged 18 to 24). This undercurrent of worry presents an opportunity for leaders to engage workers about the potential changes gen AI will bring.

To illustrate how these shifts apply to the workforce today, we offer two examples of nontechnical gen AI talent: a communications specialist and a middle manager.

More time for innovation and collaboration

A communications specialist in a large corporation is currently a heavy user of gen AI. Her job has involved interviewing C-suite executives and synthesizing their ideas to create speeches, talking points, emails, and other communications for both internal and external audiences. Her performance has been measured by how many discrete communications she facilitates and the quality of the copy that is produced.

She used to send questions to executives ahead of time and then schedule a series of interviews, which would take several weeks to complete. Now, she can feed their recorded interviews into a gen AI chatbot and get a synthesis of their remarks in seconds.

The communications expert will still review and edit that text, but the overall process is much faster. Whereas before she spent 60 percent of her time synthesizing material, that task now takes only 10 percent of her time, freeing up bandwidth to think strategically about the message the speech is intended to convey and what form of communication would be most effective. She may also have more time to deepen relationships with industry reporters, which could benefit coverage of the company, and to help the chief human resources officer write that book she has been eager to start.

This gen-AI-related efficiency gain leads to increased productivity, more innovative thinking, and welcome face time with key constituencies—good for the employee, her team, and the organization. The value she adds to the job is now fundamentally different.

Managing people, managing gen AI

Now, consider a middle manager at a technology company who identifies as a nontechnical creator of gen AI. Currently, middle managers report spending  almost half of their time on individual-contributor and administrative tasks and only about a quarter of their time on people-related activities. In a gen-AI-enabled world, they could significantly reduce the number of hours spent on non-people-related activities and reallocate that time toward supporting direct reports and engaging in broader strategy concerns.

As teams start using gen AI to help free up their capacity, the middle manager’s job will evolve  to managing both people and the use of this technology to enhance their output. In other words, gen AI will become another member of the team to be managed. And just like a direct report who needs some intensive coaching to get up to speed, gen AI may need more guidance and involvement from managers—at least initially and perhaps for much longer.

Lastly, a core part of the manager’s role will be to ensure the humanization of work. As the nature of tasks and time spent change, and the focus shifts from process oriented to results oriented, managers will be a decisive factor in whether an organization allows gen AI to elevate people’s work. Keeping a finger on the pulse of their teams raises the likelihood that managers will do their part to create jobs that are less abstract and disconnected and more fulfilling and collaborative. To prepare people, managers can encourage employees to recognize the centrality of their insights and creative contributions with respect to the broader organization as gen AI use evolves.

The employer–employee disconnect has led to high levels of workforce discontent, which is affecting workers at the forefront of the gen AI push even more dramatically when it comes to burnout and attrition. Companies that want to capitalize on gen-AI-fueled productivity gains have an opportunity to address this rapidly expanding group’s concerns about the nature of work. Those that emphasize the importance of human skills over a simple race for increased output are likely to earn the loyalty of their workforces and higher performance over the long term.

Aaron De Smet is a senior partner in McKinsey’s New Jersey office, Sandra Durth is a partner in the Cologne office, Bryan Hancock is a partner in the Washington, DC, office, Marino Mugayar-Baldocchi is a research science expert in the New York office, and Angelika Reich is an alumna of the Vienna office.

The authors wish to thank Yueyang Chen and James Paguay for their contributions to this article.

This article was edited by Barbara Tierney, a senior editor in the New York office.

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Advancing collaborative research for health: why does collaboration matter?

Carla saenz.

1 Pan American Health Organization, Washington, District of Columbia, USA

Timothy M Krahn

2 Dalhousie University, Halifax, Nova Scotia, Canada

Maxwell J Smith

3 Faculty of Health Sciences, University of Western Ontario, London, Ontario, Canada

Michelle M Haby

Sarah carracedo, ludovic reveiz, associated data.

No data are available.

The calls for health research to be collaborative are ubiquitous—even as part of a recent World Health Assembly resolution on clinical trials—yet the arguments in support of collaborative research have been taken for granted and are absent in the literature. This article provides three arguments to justify why health research ought to be collaborative and discusses trade-offs to be considered among the ethical values guiding each argument.

SUMMARY BOX

• Health research ought to be collaborative in emergency and non-emergency situations.
• Arguments for collaborations in health research are grounded in the values of efficiency, benefit maximisation and equity.
• Health research collaboration can encompass many differences and take place in very diverse settings. The values of efficiency, benefit maximisation and equity do not dictate a formula for research collaborations in specific circumstances.
• It may be necessary to consider trade-offs between these values. One may be justified to depart from (more robust) collaboration in specific circumstances. However, it is never acceptable to compromise respect and fairness to advance research collaboration.

The COVID-19 pandemic has made it clear that the impact of research is not limited to advancing people’s health in the future. Research can also be impactful for current health threats. 1 Research that was conducted very quickly led to the timely development of effective COVID-19 diagnostic tests, vaccines, therapeutics and public health interventions. 2 Yet this success story should not obscure challenges in the conduct of COVID-19 research. 3 For example, multiple repetitive, small trials have consumed an important share of research resources while not being able to yield much-needed knowledge about the efficacy of the interventions under study. 4 , 7 These challenges have been acknowledged to the extent that there have been various calls for increased collaboration in research, 38 , 10 along with a World Health Assembly resolution calling for increased coordination of clinical trials. 11 Furthermore, as part of the response to the mpox emergency, WHO urged for ‘collaborative research’. 12

The call for research to be collaborative has been ubiquitous, even before the pandemic, 13 , 17 yet the justification to proceed collaboratively when conducting research is not obvious. What do collaborations add to research? Why should we advance research collaborations, instead of just ensuring that research needs are met? Why are research collaborations described as an ethical imperative, particularly in the context of health emergencies?

In this article, we explain why research collaborations are ethically valuable, provide three arguments to justify why health research ought to be collaborative and discuss trade-offs to be considered among the ethical values guiding each argument.

An ethical framework for collaborative health research

Health is recognised as a common good 18 19 that critically affects our life prospects and welfare, even our very survival. Health research is vital for advancing health; it is through the conduct of research that we find cures for diseases and ways to prevent and alleviate suffering. To the extent that we ought to promote health, we ought to promote health research. 20 The ethical value of health confers ethical value to health research.

Therefore, health research is not an ethically neutral activity, that is, one that is impartial to what is ethically valuable and as such optional. Research is an ethically loaded undertaking because it is crucial to advance our common good. 21 While the connection between research and our health and well-being has been globally palpable during the pandemic, the impact of health research on our health and well-being transcends emergency circumstances because our health and well-being are also threatened by numerous other diseases and health conditions.

This understanding of health research as an ethical endeavour frames the discussion about collaborations in health research. Specifically, the ethical character of health research dictates the ways in which it should be conducted. Whether we conduct this research faster or slower, with greater impact or lesser impact, reaching everybody or only a few with its benefits is not ethically neutral, because something as important as our health and well-being is at stake. That is, since conducting health research constitutes an ethical duty, the way in which that duty is discharged is ethically relevant; it can be done in a way that is more right or wrong, more good or bad. 22 23

Arguments for collaboration in health research

Health research is a complex enterprise that is conducted in vastly diverse settings. Accordingly, in emergency and non-emergency situations, it can encompass collaborations among very different parties, including governmental entities, academia, pharmaceutical companies, international organisations and non-governmental organizations (NGOs). Collaborating institutions may be in the same jurisdiction or in distant countries. They may also be very differently resourced, even when they are located within the same country.

The collaborative conduct of health research can entail the following: (1) seeking the involvement of researchers in the locations where research is going to be conducted, (2) seeking the involvement of researchers in the locations where the research results are expected to be beneficial, (3)seeking the involvement of researchers conducting similar studies to avoid duplication and (4) seeking the involvement of researchers with relevant expertise, whether or not they are in the locations where research is going to be conducted or where the research results are expected to be beneficial. 24 25

Ideally, collaborative research should entail all those four types of collaboration, and more robust collaboration, understood as collaboration that entails more of these types, is in general preferable over less collaborative research. A common path for health research involves a lengthy process that starts in a laboratory, evolves to trials with human participants, which ultimately prove if an intervention is safe and efficacious and moves forward to studies in real-life scenarios to learn about its effectiveness or the challenges posed by its implementation. The further researchers are in the process of putting into practice research results, the stronger the argument for proceeding collaboratively and pursuing all types of collaboration. However, it may be justifiable for researchers to depart from the ideal scenario that includes the four types of collaborations outlined above, provided there are good reasons to do so.

Nonetheless, it is not obvious why collaborations in health research are not just a matter of researcher preference, convenience or standard practice but an ethical imperative. That health research is ethically valuable does not explain why it ought to be conducted collaboratively. One may think that it is only justified to team up with those with the highest knowledge and expertise on the research topic, which tend to be in HICs or high-income settings, thus restricting collaborations to those in geographical proximity or those with certain status and reputation. 26 While the Nuffield Council on Bioethics’ Research in Global Health Emergencies report has made a key contribution stressing that research collaboration is inherently ethical in emergency situations, specific ethical arguments that apply to emergency and non-emergency settings, along with clarification about the implications of these arguments in particular circumstances are still needed. 17

There are at least three important reasons why collaboration in health research is ethically valuable and ought to be advanced (see table 1 ). First, collaborations can help research to be conducted faster. Aggregating data from different research sites can yield statistically meaningful results faster. The urge for speed attracts support in emergencies, although there is no prima facie reason to justify delaying the production of research results for non-emergency situations, for example, to treat cancer or chronic conditions. Indeed, speed in non-emergency times is also important to speed up the attainment of the benefit. Furthermore, teaming up with local researchers may be necessary to be able to conduct studies (eg, to have access to the affected patient population or existing samples) or to be able to navigate ethical and regulatory requirements to initiate and adequately oversee them within reasonable time frames. Collaboration can help reduce unnecessary duplication of efforts. 7 We refer to these reasons as the efficiency argument for collaboration in research. 927 , 29

Second, collaborations can facilitate the implementation of research results. Health research seeks to improve the health and well-being of populations, which are the potential benefits of research, that is, its social value. Yet, this value is realised only when research results are implemented. Teaming up with researchers based in the area where research results are meant to be beneficial establishes relationships and builds trust that can expedite the implementation of research results. Moreover, working closely with local researchers helps in the adjustment of research protocols so they are responsive to the specific needs and priorities of affected communities. This enhances the social value of the study, thus facilitating the uptake of its results. This is the benefit maximisation argument for collaboration in research.

Third, collaborations can help to build research capacity. 25 There is significantly more research capacity in high-income countries (HICs) than in low- and middle-income countries (LMICs), 30 where many studies are being conducted (often because they host populations with the conditions being researched) or where research results are meant to be beneficial. This situation is often reproduced within countries because there tends to be a research capacity gap between high-income and low-income settings. Teaming up with researchers from LMICs and low-income settings can enhance their research capacity, which in turn can further the prospects for equity in health. Local research capacity is necessary to lead research that addresses the specific health needs of LMICs and low-income settings, and thus effectively address issues that cause health inequities. 24 31 This is the equity argument for advancing collaborative research.

Acceptable and unacceptable trade-offs

Concerns for efficiency, benefit maximisation and equity do not dictate a formula for health research collaborations, that is, for optimally dividing roles and responsibilities among collaborating parties. Research collaboration encompasses a wide set of arrangements ranging from close partnerships to informal cooperative interactions. 32 In general, the stronger the collaborative ties, the better the collaboration may realise the values of efficiency, benefit maximisation and equity that justify and guide research collaborations. However, these values are realised in specific circumstances that may involve various challenges. Moreover, the values are realised in different time frames, for example, equity may only be realised after a long period of collaboration.

Therefore, these values may need to be balanced against each other in particular circumstances to find the optimal approach to a specific collaboration. This is in general how ethical values direct action in every facet of life: instead of dictating a univocal recipe for action, they guide an analysis that considers specific circumstances. Furthermore, research collaborations are not the only way to realise these values of efficiency, benefit maximisation and equity in global health. Similarly, there may be practical obstacles to realise these values through research collaborations that must be taken into account when assessing the best course of action, for example, administrative hurdles, language barriers or even lack of access to knowledge about local expertise. 33

In situations like outbreaks or health emergencies that are characterised by the absence of effective countermeasures, it may be justified to give higher priority to efficiency and benefit maximisation, which in turn may call for a less robust collaboration with researchers in affected areas if no prior collaboration has been established. While discussions about research plans and methodology are key components of collaborative research and crucial to build capacity and advance equity, they take time that in these circumstances may be better used expediting the initiation of the study. However, a higher priority should not be confused with absolute priority to any value, for example, a higher priority to efficiency and benefit maximisation in specific circumstances should not preclude equity.

Trade-offs between the values of efficiency, benefit maximisation and equity may be acceptable when discharging the duty to conduct health research collaboratively. 23 34 Yet it is often the case that the achievement of any one of them is dependent on the others. For instance, benefit maximisation can be threatened if research is not efficient. Equity can be threatened when there is inefficiency (ie, efficiency leads to greater benefits and thus greater equity). Benefit maximisation can be threatened if research is inequitable.

Other trade-offs are, however, ethically unacceptable. Research collaboration must always adhere to the bedrock principles of respect and fairness, which are embedded in the standards of research integrity. 17 35 For example, it would be unethical to exploit collaborators or fail to give credit when credit is due in order to advance efficiency in the publication of research results. Respect and fairness must never be compromised to advance efficiency, benefit maximisation or equity in research collaborations.

The way forward

Research is a powerful tool to advance people’s health. Collaboration with researchers from affected areas or areas expected to implement research results is not optional but ethically required to advance the values of efficiency, benefit maximisation and equity. Clarifying the reasons why health research ought to be conducted collaboratively and the values that guide research collaborations is, however, only a first step to foster collaboration in health research. Additional practical guidance is necessary to identify research collaborators and establish effective collaborations that advance the best balance of the ethical values at stake.

As with other aspects of research, no algorithm but actual ethics analysis is needed to elucidate the best course of action, for example, when and why departures from more robust collaborations and trade-offs between the values are justified. Robust collaborative research that realises the values of efficiency, benefit maximisation and equity should be advanced as the moral default, in the way we currently advance research that includes populations historically excluded from research as the default and exclude groups only if justified. Proceeding collaboratively when conducting health research is an ethical duty, even if one may be justified in specific circumstances to depart from it or to adopt limited collaborative arrangements.

In order to advance research collaboration across the globe, it is critical for researchers to know what research is underway and who has relevant expertise and capacities. Transparency in research, which includes registration that feeds into WHO’s International Clinical Trials Registry Platform 36 and publication of research results in indexed journals, is, therefore, essential. But it does not suffice. Strategies to promote fair, respectful health research collaborations must be developed as part of our global health agenda. While frameworks and models to advance collaborations in health research have been proposed (eg, the Bergen Model of Collaborative Functioning 37 ), they have not specified why collaborations in health research are not just an option but ought to be pursued. As we move forward, the reasons why health research ought to be collaborative, as shown in this article, should meaningfully guide the strategies to advance such collaborations and address practical challenges of implementing research collaborations, for example, overcoming the various barriers that hamper collaborations.

The authors alone are responsible for the views expressed in this article and they do not necessarily represent the official positions of the Pan American Health Organization.

Funding: Many of the authors work for the Pan American Health Organization, which serves the Regional Office for the Americas of the World Health Organization, and have developed this manuscript as part of their work. The Pan American Health Organization owns the copyright of this work, as per the Organization’s rules.

Handling editor: Helen J Surana

Patient consent for publication: Not applicable.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement