what is a research value

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How to demonstrate the value of your research

A tool to help you master the four Cs: citations, communication, coverage, and collaboration.

Carsten Lund Pedersen

Credit: sorbetto/Getty

20 March 2020

sorbetto/Getty

Successfully publishing a paper in a peer-reviewed journal is only part of the journey – once your research is in the public sphere, you’re expected to demonstrate its value .

This goes beyond simply explaining your work to a broad audience. Future career and funding opportunities depend on you being able to show why it’s valuable, and to whom.

Scientists are rarely taught how to promote their research. Studies have found that 93% of humanities research and 45% of social sciences research remains uncited within five years of publication. There is also a gender gap in academic self-promotion , with studies finding that male researchers are more likely to promote their research.

Science that has no life beyond a published paper leads to many lost opportunities. With this in mind, I’ve created a tool that can help you exhibit the value of your research using “the four Cs”: citations, communication, coverage, and collaboration:

The first thing to consider is who your primary target audience is, whether it’s scholars and scientists or practitioners and policy-makers. Next, you need to define the value of your research through facts and figures or experiences and engagement.

Put together, four different value demonstration opportunities emerge.

1. Citations: If your primary audience is scholars and scientists, and value is best demonstrated through facts and figures, the value demonstration mode is citations. While researchers are well-aware of these metrics, they can be overlooked when demonstrating the value of your research.

Google Scholar is a great tool for this, as it aggregates and disseminates citations information and displays it under individual researcher profiles.

2. Communication: If the primary audience is scholars and scientists, and value is best demonstrated through experiences and engagement, the value demonstration mode is communication.

Here, the researcher demonstrates value in live settings, for example, at a conference. By engaging with other academics in an interactive manner, you can demonstrate the value of your research and your knowledge in the field.

3. Coverage: If your primary audience is practitioners and policy-makers, and value is best demonstrated through facts and figures, the value demonstration mode is coverage.

In this scenario, the researcher demonstrates value to broader societal stakeholders. The proficiency of this can be gauged through measures such as the “ Twi-Li index ”, which is a proxy to assess scientists’ relative social media impact, and the Altmetric Attention Score , which helps to identify how much attention a research output is receiving online.

You can use these measures to demonstrate to societal stakeholders that your research is having an impact beyond the walls of academia.

4. Collaboration: If your primary audience is practitioners and policy-makers, and value is best demonstrated through experiences and engagement, the value demonstration mode is collaboration.

Researchers can collaborate with societal actors in different ways, such as consulting with decision-makers or engaging in committees. By doing so, you are ensuring that stakeholders can experience first-hand the value of your research, as it is demonstrated in the context of their specific project or problem.

How can you use the four Cs?

First, when your paper is published, you can plan relevant activities in each of the four Cs. For instance, how many citations does it have? What’s its Altmetric Attention Score? Which conferences or seminars has it been presented in? Do you intend to collaborate with any practitioners or policy-makers?

Second, what do you want to achieve with your value demonstration? Is it tenure or new funding opportunities? Who is your most important target audience? Your objectives will determine which of the four Cs are most important to you.

Third, which activities are you going to prioritize, and when will you perform them? While it’s beneficial to demonstrate value immediately upon publication, it may also be useful to demonstrate it further down the track, if you can see that the topic gains traction and is becoming more timely.

While you might find it uncomfortable or outside your field of expertise, demonstrating the value of your research is essential. As the saying goes, “If you’ve got it, flaunt it.”

Carsten Lund Pedersen is an assistant professor in the Department of Marketing at the Copenhagen Business School, Denmark.

Fostering Integrity in Research (2017)

Chapter: 2 foundations of integrity in research: core values and guiding norms, 2 foundations of integrity in research: core values and guiding norms.

Problems of scientific freedom and responsibility are not new; one need only consider, as examples, the passionate controversies that were stirred by the work of Galileo and Darwin. In our time, however, such problems have changed in character, and have become far more numerous, more urgent and more complex. Science and its applications have become entwined with the whole fabric of our lives and thoughts. . . . Scientific freedom, like academic freedom, is an acquired right, generally accepted by society as necessary for the advancement of knowledge from which society may benefit. Scientists possess no rights beyond those of other citizens except those necessary to fulfill the responsibility arising from their special knowledge, and from the insight arising from that knowledge.

— John Edsall (1975)

Synopsis: The integrity of research is based on adherence to core values—objectivity, honesty, openness, fairness, accountability, and stewardship. These core values help to ensure that the research enterprise advances knowledge. Integrity in science means planning, proposing, performing, reporting, and reviewing research in accordance with these values. Participants in the research enterprise stray from the norms and appropriate practices of science when they commit research misconduct or other misconduct or engage in detrimental research practices.

TRANSMITTING VALUES AND NORMS IN RESEARCH

The core values and guiding norms of science have been studied and written about extensively, with the work of Robert Merton providing a foundation for subsequent work on the sociology of science ( Merton, 1973 ). Merton posited a set of norms that govern good science: (1) Communalism (common ownership of scientific knowledge), (2) Universalism (all scientists can contribute to the advance of knowledge), (3) Disinterestedness (scientists should work for the good of the scientific enterprise as opposed to personal gain), and (4) Organized Skepticism (results should be examined critically before they are accepted). Research on scientists and scientific organizations has also led to a better understanding of

counternorms that appear to conflict with the dominant Mertonian norms but that are recognized as playing an inherent part in the actual practice of science, such as the personal commitment that a scientist may have to a particular hypothesis or theory ( Mitroff, 1974 ).

More recent work on the effectiveness of responsible conduct of research education, covered in more detail in Chapter 9 , explores evidence that at least some scientists may not understand and reflect upon the ethical dimensions of their work ( McCormick et al., 2012 ). Several causes are identified, including a lack of awareness on the part of researchers of the ethical issues that can arise, confidence that they can identify and address these issues without any special training or help, or apprehension that a focus on ethical issues might hinder their progress. An additional challenge arises from the apparent gap “between the normative ideals of science and science’s institutional reward system” ( Devereaux, 2014 ). Chapter 6 covers this issue in more detail. Here, it is important to note that identifying and understanding the values and norms of science do not automatically mean that they will be followed in practice. The context in which values and norms are communicated and transmitted in the professional development of scientists is critically important.

Scientists are privileged to have careers in which they explore the frontiers of knowledge. They have greater autonomy than do many other professionals and are usually respected by other members of society. They often are able to choose the questions they want to pursue and the methods used to derive answers. They have rich networks of social relationships that, for the most part, reinforce and further their work. Whether actively involved in research or employed in some other capacity within the research enterprise, scientists are able to engage in an activity about which they are passionate: learning more about the world and how it functions.

In the United States, scientific research in academia emerged during the late 19th century as an “informal, intimate, and paternalistic endeavor” ( NAS-NAE-IOM, 1992 ). Multipurpose universities emphasized teaching, and research was more of an avocation than a profession. Even today, being a scientist and engaging in research does not necessarily entail a career with characteristics traditionally associated with professions such as law, medicine, architecture, some subfields of engineering, and accounting. For example, working as a researcher does not involve state certification of the practitioner’s expertise as a requirement to practice, nor does it generally involve direct relationships with fee-paying clients. Many professions also maintain an explicit expectation that practitioners will adhere to a distinctive ethical code ( Wickenden, 1949 ). In contrast, scientists do not have a formal, overarching code of ethics and professional conduct.

However, the nature of professional practice even in the traditional professions continues to evolve ( Evetts, 2013 ). Some scholars assert that the concept of professional work should include all occupations characterized by “expert knowledge, autonomy, a normative orientation grounded in community, and

high status, income, and other rewards” ( Gorman and Sandefur, 2011 ). Scientific research certainly shares these characteristics. In this respect, efforts to formalize responsible conduct of research training in the education of researchers often have assumed that this training should be part of the professional development of researchers ( IOM-NRC, 2002 ; NAS-NAE-IOM, 1992 ). However, the training of researchers (and research itself) has retained some “informal, intimate, and paternalistic” features. Attempts to formalize professional development training sometimes have generated resistance in favor of essentially an apprenticeship model with informal, ad hoc approaches to how graduate students and postdoctoral fellows learn how to become professional scientists.

One challenge facing the research enterprise is that informal, ad hoc approaches to scientific professionalism do not ensure that the core values and guiding norms of science are adequately inculcated and sustained. This has become increasingly clear as the changes in the research environment described in Chapter 3 have emerged and taken hold. Indeed, the apparent inadequacy of these older forms of training to the task of socializing and training individuals into responsible research practices is a recurring theme of this report.

Individual scientists work within a much broader system that profoundly influences the integrity of research results. This system, described briefly in Chapter 1 , is characterized by a massive, interconnected web of relationships among researchers, employing institutions, public and private funders, and journals and professional societies. This web comprises unidirectional and bidirectional obligations and responsibilities between the parts of the system. The system is driven by public and private investments and results in various outcomes or products, including research results, various uses of those results, and trained students. However, the system itself has a dynamic that shapes the actions of everyone involved and produces results that reflect the functioning of the system. Because of the large number of relationships between the many players in the web of responsibility, features of one set of relationships may affect other parts of the web. These interdependencies complicate the task of devising interventions and structures that support and encourage the responsible conduct of research.

THE CORE VALUES OF RESEARCH

The integrity of research is based on the foundational core values of science. The research system could not operate without these shared values that shape the behaviors of all who are involved with the system. Out of these values arise the web of responsibilities that make the system cohere and make scientific knowledge reliable. Many previous guides to responsible conduct in research have identified and described these values ( CCA, 2010 ; ESF-ALLEA, 2011 ; IAC-IAP, 2012 ; ICB, 2010 ; IOM-NRC, 2002 ). This report emphasizes six values that are most influential in shaping the norms that constitute research practices and relationships and the integrity of science:

Objectivity

Accountability, stewardship.

This chapter examines each of these six values in turn to consider how they shape, and are realized in, research practices.

The first of the six values discussed in this report—objectivity—describes the attitude of impartiality with which researchers should strive to approach their work. The next four values—honesty, openness, accountability, and fairness—describe relationships among those involved in the research enterprise. The final value—stewardship—involves the relationship between members of the research enterprise, the enterprise as a whole, and the broader society within which the enterprise is situated. Although we discuss stewardship last, it is an essential value that perpetuates the other values.

The hallmark of scientific thinking that differentiates it from other modes of human inquiry and expression such as literature and art is its dedication to rational and empirical inquiry. In this context, objectivity is central to the scientific worldview. Karl Popper (1999) viewed scientific objectivity as consisting of the freedom and responsibility of the researcher to (1) pose refutable hypotheses, (2) test the hypotheses with the relevant evidence, and (3) state the results clearly and unambiguously to any interested person. The goal is reproducibility, which is essential to advancing knowledge through experimental science. If these steps are followed diligently, Popper suggested, any reasonable second researcher should be able to follow the same steps to replicate the work.

Objectivity means that certain kinds of motivations should not influence a researcher’s action, even though others will. For example, if a researcher in an experimental field believes in a particular hypothesis or explanation of a phenomenon, he or she is expected to design experiments that will test the hypothesis. The experiment should be designed in a way that allows the possibility for the hypothesis to be disconfirmed. Scientific objectivity is intended to ensure that scientists’ personal beliefs and qualities—motivations, position, material interests, field of specialty, prominence, or other factors—do not introduce biases into their work.

As will be explored in later chapters, in practice it is not that simple. Human judgment and decisions are prone to a variety of cognitive biases and systematic errors in reasoning. Even the best scientific intentions are not always sufficient to ensure scientific objectivity. Scientific objectivity can be compromised acci-

dentally or without recognition by individuals. In addition, broader biases of the reigning scientific paradigm influence the theory and practice of science ( Kuhn, 1962 ). A primary purpose of scientific replication is to minimize the extent to which experimental findings are distorted by biases and errors. Researchers have a responsibility to design experiments in ways that any other person with different motivations, interests, and knowledge could trust the results. Modern problems related to reproducibility are explored later in the report.

In addition, objectivity does not imply or require that researchers can or should be completely neutral or disinterested in pursuing their work. The research enterprise does not function properly without the organized efforts of researchers to convince their scientific audiences. Sometimes researchers are proven correct when they persist in trying to prove theories in the face of evidence that appears to contradict them.

It is important to note, in addition, Popper’s suggestion that scientific objectivity consists of not only responsibility but freedom . The scientist must be free from pressures and influences that can bias research results. Objectivity can be compromised when institutional expectations, laboratory culture, the regulatory environment, or funding needs put pressure on the scientist to produce positive results or to produce them under time pressure. Scientists and researchers operate in social contexts, and the incentives and pressures of those contexts can have a profound effect on the exercise of scientific methodology and a researcher’s commitment to scientific objectivity.

Scientific objectivity also must coexist with other human motivations that challenge it. As an example of such a challenge, a researcher might become biased in desiring definitive results evaluating the validity of high-profile theories or hypotheses that their experiments were designed to support or refute. Both personal desire to obtain a definitive answer and institutional pressures to produce “significant” conclusions can provide strong motivation to find definitive results in experimental situations. Dedication to scientific objectivity in those settings represents the best guard against scientists finding what they desire instead of what exists. Institutional support of objectivity at every level—from mentors, to research supervisors, to administrators, and to funders—is crucial in counterbalancing the very human tendency to desire definitive outcomes of research.

A researcher’s freedom to advance knowledge is tied to his or her responsibility to be honest . Science as an enterprise producing reliable knowledge is based on the assumption of honesty. Science is predicated on agreed-upon systematic procedures for determining the empirical or theoretical basis of a proposition. Dishonest science violates that agreement and therefore violates a defining characteristic of science.

Honesty is the principal value that underlies all of the other relationship val-

ues. For example, without an honest foundation, realizing the values of openness, accountability, and fairness would be impossible.

Scientific institutions and stakeholders start with the assumption of honesty. Peer reviewers, granting agencies, journal editors, commercial research and development managers, policy makers, and other players in the scientific enterprise all start with an assumption of the trustworthiness of the reporting scientist and research team. Dishonesty undermines not only the results of the specific research but also the entire scientific enterprise itself, because it threatens the trustworthiness of the scientific endeavor.

Being honest is not always straightforward. It may not be easy to decide what to do with outlier data, for example, or when one suspects fraud in published research. A single outlier data point may be legitimately interpreted as a malfunctioning instrument or a contaminated sample. However, true scientific integrity requires the disclosure of the exclusion of a data point and the effect of that exclusion unless the contamination or malfunction is documented, not merely conjectured. There are accepted statistical methods and standards for dealing with outlier data, although questions are being raised about how often these are followed in certain fields ( Thiese et al., 2015 ).

Dishonesty can take many forms. It may refer to out-and-out fabrication or falsification of data or reporting of results or plagiarism. It includes such things as misrepresentation (e.g., avoiding blame, claiming that protocol requirements have been followed when they have not, or producing significant results by altering experiments that have been previously conducted), nonreporting of phenomena, cherry-picking of data, or overenhancing pictorial representations of data. Honest work includes accurate reporting of what was done, including the methods used to do that work. Thus, dishonesty can encompass lying by omission, as in leaving out data that change the overall conclusions or systematically publishing only trials that yield positive results. The “file drawer” effect was first discussed almost 40 years ago; Robert Rosenthal (1979) presented the extreme view that “journals are filled with the 5 percent of the studies that show Type I errors, while the file drawers are filled with the 95 percent of the studies that show non-significant results.” This hides the possibility of results being published from 1 significant trial in an experiment of 100 trials, as well as experiments that were conducted and then altered in order to produce the desired results. The file drawer effect is a result of publication bias and selective reporting, the probability that a study will be published depending on the significance of its results ( Scargle, 2000 ). As the incentives for researchers to publish in top journals increase, so too do these biases and the file drawer effect.

Another example of dishonesty by omission is failing to report all funding sources where that information is relevant to assessing potential biases that might influence the integrity of the work. Conversely, dishonesty can also include reporting of nonexistent funding sources, giving the impression that the research

was conducted with more support and so may have been more thorough than in actuality.

Beyond the individual researcher, those engaged in assessing research, whether those who are funding it or participating in any level of the peer review process, also have fundamental responsibilities of honesty. Most centrally, those assessing the quality of science must be honest in their assessments and aware of and honest in reporting their own conflicts of interest or any cognitive biases that may skew their judgment in self-serving ways. There is also a need to guard against unconscious bias, sometimes by refusing to assess work even when a potential reviewer is convinced that he or she can be objective. Efforts to protect honesty should be reinforced by the organizations and systems within which those assessors function. Universities, research organizations, journals, funding agencies, and professional societies must all work to hold each other to honest interactions without favoritism and with potentially biasing factors disclosed.

Openness is not the same as honesty, but it is predicated on honesty. In the scientific enterprise, openness refers to the value of being transparent and presenting all the information relevant to a decision or conclusion. This is essential so that others in the web of the research enterprise can understand why a decision or conclusion was reached. Openness also means making the data on which a result is based available to others so that they may reproduce and verify results or build on them. In some contexts, openness means listening to conflicting ideas or negative results without allowing preexisting biases or expectations to cloud one’s judgment. In this respect, openness reinforces objectivity and the achievement of reliable observations and results.

Openness is an ideal toward which to strive in the research enterprise. It almost always enhances the advance of knowledge and facilitates others in meeting their responsibilities, be it journal editors, reviewers, or those who use the research to build products or as an input to policy making. Researchers have to be especially conscientious about being open, since the incentive structure within science does not always explicitly reward openness and sometimes discourages it. An investigator may desire to keep data private to monopolize the conclusions that can be drawn from those data without fear of competition. Researchers may be tempted to withhold data that do not fit with their hypotheses or conclusions. In the worst cases, investigators may fail to disclose data, code, or other information underlying their published results to prevent the detection of fabrication or falsification.

Openness is an ideal that may not always be possible to achieve within the research enterprise. In research involving classified military applications, sensitive personal information, or trade secrets, researchers may have an obligation not to disseminate data and the results derived from those data. Disclosure of results

and underlying data may be delayed to allow time for filing a patent application. These sorts of restrictions are more common in certain research settings—such as commercial enterprises and government laboratories—than they are in academic research institutions performing primarily fundamental work. In the latter, openness in research is a long-held principle shared by the community, and it is a requirement in the United States to avoid privileged access that would undermine the institution’s nonprofit status and to maintain the fundamental research exclusion from national security-based restrictions.

As the nature of data changes, so do the demands of achieving openness. For example, modern science is often based on very large datasets and computational implementations that cannot be included in a written manuscript. However, publications describing such results could not exist without the data and code underlying the results. Therefore, as part of the publication process, the authors have an obligation to have the available data and commented code or pseudocode (a high-level description of a program’s operating principle) necessary and sufficient to re-create the results listed in the manuscript. Again, in some situations where a code implementation is patentable, a brief delay in releasing the code in order to secure intellectual property protection may be acceptable. When the resources needed to make data and code available are insufficient, authors should openly provide them upon request. Similar considerations apply to such varied forms of data as websites, videos, and still images with associated text or voiceovers.

Central to the functioning of the research enterprise is the fundamental value that members of the community are responsible for and stand behind their work, statements, actions, and roles in the conduct of their work. At its core, accountability implies an obligation to explain and/or justify one’s behavior. Accountability requires that individuals be willing and able to demonstrate the validity of their work or the reasons for their actions. Accountability goes hand in hand with the credit researchers receive for their contributions to science and how this credit builds their reputations as members of the research enterprise. Accountability also enables those in the web of relationships to rely on work presented by others as a foundation for additional advances.

Individual accountability builds the trustworthiness of the research enterprise as a whole. Each participant in the research system, including researchers, institutional administrators, sponsors, and scholarly publishers, has obligations to others in the web of science and in return should be able to expect consistent and honest actions by others in the system. Mutual accountability therefore builds trust, which is a consequence of the application of the values described in this report.

The purpose of scientific publishing is to advance the state of knowledge through examination by peers who can assess, test, replicate where appropriate, and build on the work being described. Investigators reporting on their work thus

must be accountable for the accuracy of their work. Through this accountability, they form a compact with the users of their work. Readers should be able to trust that the work was performed by the authors as described, with honest and accurate reporting of results. Accountability means that any deviations from the compact would be flagged and explained. Readers then could use these explanations in interpreting and evaluating the work.

Investigators are accountable to colleagues in their discipline or field of research, to the employer and institution at which the work is done, to the funders or other sponsors of the research, to the editors and institutions that disseminate their findings, and to the public, which supports research in the expectation that it will produce widespread benefits. Other participants in the research system have other forms of accountability. Journals are accountable to authors, reviewers, readers, the institutions they represent, and other journals (for the reuse of material, violation of copyright, or other issues of mutual concern). Institutions are accountable to their employees, to students, to the funders of both research and education, and to the communities in which they are located. Organizations that sponsor research are accountable to the researchers whose work they support and to their governing bodies or other sources of support, including the public. These networks of accountability support the web of relationships and responsibilities that define the research enterprise.

The accountability expected of individuals and organizations involved with research may be formally specified in policies or regulations. Accountability under institutional research misconduct policies, for example, could mean that researchers will face reprimand or other corrective actions if they fail to meet their responsibilities.

While responsibilities that are formally defined in policies or regulations are important to accountability in the research enterprise, responsibilities that may not be formally specified should also be included in the concept. For example, senior researchers who supervise others are accountable to their employers and the researchers whom they supervise to conduct themselves as professionals, as this is defined by formal organizational policies. On a less formal level, research supervisors are also accountable for being attentive to the educational and career development needs of students, postdoctoral fellows, and other junior researchers whom they oversee. The same principle holds for individuals working for research institutions, sponsoring organizations, and journals.

The scientific enterprise is filled with professional relationships. Many of them involve judging others’ work for purposes of funding, publication, or deciding who is hired or promoted. Being fair in these contexts means making professional judgments based on appropriate and announced criteria, including processes used to determine outcomes. Fairness in adhering to explicit criteria

and processes reinforces a system in which the core values can operate and trust among the parties can be maintained.

Fairness takes on another dimension in designing criteria and evaluation mechanisms. Research has demonstrated, for example, that grant proposals in which reviewers were blinded to applicant identity and institution receive systematically different funding decisions compared with the outcomes of unblinded reviews ( Ross et al., 2006 ). Truly blinded reviews may be difficult or impossible in a small field. Nevertheless, to the extent possible, the criteria and mechanisms involved in evaluation must be designed so as to ensure against unfair incentive structures or preexisting cultural biases. Fairness is also important in other review contexts, such as the process of peer reviewing articles and the production of book reviews for publication.

Fairness is a particularly important consideration in the list of authors for a publication and in the citations included in reports of research results. Investigators may be tempted to claim that senior or well-known authors played a larger role than they actually did so that their names may help carry the paper to publication and readership. But such a practice is unfair both to the people who actually did the work and to the honorary author, who may not want to be listed prominently or at all. Similarly, nonattribution of credit for contributions to the reported work or careless or negligent crediting of prior work violates the value of fairness. Best practices in authorship, which are based on the value of fairness, honesty, openness, and accountability, are discussed further in Chapter 9 .

Upholding fairness also requires researchers to acknowledge those whose work contributed to their advances. This is usually done through citing relevant work in reporting results. Also, since research is often a highly competitive activity, sometimes there is a race to make a discovery that results in clear winners and losers. Sometimes two groups of researchers make the same discovery nearly simultaneously. Being fair in these situations involves treating research competitors with generosity and magnanimity.

The importance of fairness is also evident in issues involving the duty of care toward human and animal research subjects. Researchers often depend on the use of human and animal subjects for their research, and they have an obligation to treat those subjects fairly—with respect in the case of human subjects and humanely in the case of laboratory animals. They also have obligations to other living things and to those aspects of the environment that affect humans and other living things. These responsibilities need to be balanced and informed by an appreciation for the potential benefits of research.

The research enterprise cannot continue to function unless the members of that system exhibit good stewardship both toward the other members of the system and toward the system itself. Good stewardship implies being aware of

and attending carefully to the dynamics of the relationships within the lab, at the institutional level, and at the broad level of the research enterprise itself. Although we have listed stewardship as the final value in the six we discuss in this report, it supports all the others. Here we take up stewardship within the research enterprise but pause to acknowledge the extension of this value to encompass the larger society.

One area where individual researchers exercise stewardship is by performing service for their institution, discipline, or the broader research enterprise that may not necessarily be recognized or rewarded. These service activities include reviewing, editing, serving on faculty committees, and performing various roles in scientific societies. Senior researchers may also serve as mentors to younger researchers whom they are not directly supervising or formally responsible for. At a broader level, researchers, institutions, sponsors, journals, and societies can contribute to the development and updating of policies and practices affecting research. As will be discussed in Chapter 9 , professional societies perform a valuable service by developing scientific integrity policies for their fields and keeping them updated. Individual journals, journal editors, and member organizations have contributed by developing standards and guidelines in areas such as authorship, data sharing, and the responsibilities of journals when they suspect that submitted work has been fabricated or plagiarized.

Stewardship also involves decisions about support and influences on science. Some aspects of the research system are influenced or determined by outside factors. Public demand, political considerations, concerns about national security, and even the prospects for our species’ survival can inform and influence decisions about the amount of public and private resources devoted to the research enterprise. Such forces also play important roles in determining the balance of resources invested in various fields of study (e.g., both among and within federal agencies), as well as the balance of effort devoted to fundamental versus applied work and the use of various funding mechanisms.

In some cases, good stewardship requires attending to situations in which the broader research enterprise may not be operating optimally. Chapter 6 discusses issues where problems have been identified and are being debated, such as workforce imbalances, the poor career prospects of academic researchers in some fields, and the incentive structures of modern research environments.

Stewardship is particularly evident in the commitment of the research enterprise to education, both of the next generation of researchers and of individuals who do not expect to become scientists. In particular, Chapter 10 discusses the need to educate all members of the research enterprise in the responsible conduct of research. Education is one way in which engaging in science provides benefits both to those within the research system and to the general public outside the system.

A DEFINITION OF RESEARCH INTEGRITY

Making judgments about definitions and terminology as they relate to research integrity and breaches of integrity is a significant component of this committee’s statement of task. Practicing integrity in research means planning, proposing, performing, reporting, and reviewing research in accordance with the values described above. These values should be upheld by research institutions, research sponsors, journals, and learned societies as well as by individual researchers and research groups. General norms and specific research practices that conform to these values have developed over time. Sometimes norms and practices need to be updated as technologies and the institutions that compose the research enterprise evolve. There are also disciplinary differences in some specific research practices, but norms and appropriate practices generally apply across science and engineering research fields. As described more fully in Chapter 9 , best practices in research are those actions undertaken by individuals and organizations that are based on the core values of science and enable good research. They should be embraced, practiced, and promoted.

The integrity of knowledge that emerges from research is based on individual and collective adherence to core values of objectivity, honesty, openness, fairness, accountability, and stewardship. Integrity in science means that the organizations in which research is conducted encourage those involved to exemplify these values in every step of the research process. Understanding the dynamics that support – or distort – practices that uphold the integrity of research by all participants ensures that the research enterprise advances knowledge.

The 1992 report Responsible Science: Ensuring the Integrity of the Research Process evaluated issues related to scientific responsibility and the conduct of research. It provided a valuable service in describing and analyzing a very complicated set of issues, and has served as a crucial basis for thinking about research integrity for more than two decades. However, as experience has accumulated with various forms of research misconduct, detrimental research practices, and other forms of misconduct, as subsequent empirical research has revealed more about the nature of scientific misconduct, and because technological and social changes have altered the environment in which science is conducted, it is clear that the framework established more than two decades ago needs to be updated.

Responsible Science served as a valuable benchmark to set the context for this most recent analysis and to help guide the committee's thought process. Fostering Integrity in Research identifies best practices in research and recommends practical options for discouraging and addressing research misconduct and detrimental research practices.

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The Value of Research

Many people ask why research is so important to UNLV and other universities. Below, we have answered a few common questions.

Research seeks to advance the existing body of knowledge in virtually all disciplines. Although stereotypical depictions suggest research is conducted in laboratories, it is actually performed in virtually all subjects – from English to physics, from health sciences to history, from chemistry to criminal justice. Different methods are employed, but, at its most fundamental level, research seeks to expand understanding.

Faculty members are tasked with discovering and creating new knowledge and sharing that knowledge – as well as their systematic methods of acquiring it – with students. Faculty members who perform research gain the respect of their colleagues, stay at the forefront of their fields, and are able to share their disciplines’ latest developments with students. They tend to collaborate with scholars from other universities, consider new interpretations and methods, and bring valuable grant funding to the university.

Teaching and research are far from mutually exclusive; they are, in fact, complementary activities. Students benefit tremendously from involvement in hands-on research experiences and develop valuable practical and analytical skills from their participation. Faculty who conduct research share up-to-date information with students and give them the opportunity to explore fields of interest in greater depth. The best and brightest students are often attracted to a university because of the opportunity to work closely with faculty; as a result, outstanding research programs tend to help recruit great students at both the undergraduate and graduate levels.

The public benefits when sophisticated faculty expertise is employed to improve quality of life. Research addresses a variety of pertinent local and state issues, solves practical problems, and encourages economic diversification. UNLV is particularly committed to conducting research that is beneficial to the community, state, and region.

University research creates knowledge that can lead to new technologies, commercial products, and development of industries that can have a significant impact on the economy. UNLV researchers are working to create such new technologies and intellectual property with commercialization potential. In addition to providing a revenue stream to the university, this could also bring business opportunities and jobs to our area.

Research is critical to the advancement of UNLV’s reputation among colleges and universities. Research success is a key indicator of the sophistication of a university; many believe it is the yardstick by which academic reputation is measured. UNLV continues to gain respect throughout the country because its research is highly regarded. If it seeks to gain even greater respect in the academic community, supporting research is the way to go about it.

UNLV has made tremendous strides in the last several years in building infrastructure that significantly advances the university’s research agenda. There are many performance measures that indicate research success, including research funding, student participation in doctoral programs, and scholarly publication productivity among faculty. One point of pride for UNLV is its inclusion in the Carnegie Foundation for the Advancement of Teaching category of “Research Universities (Higher Research Activity). This places UNLV in the company of many fine institutions and confirms its status as a nationally recognized research institution. However, UNLV aspires to become a “Top Tier University” and one measure of would be to earn the Carnegie Foundation’s highest distinction: “Research Universities (Highest Research Activity).” This would place UNLV among the very best universities in the U.S.

Rankings such as these demonstrate institutional research sophistication and most certainly aid the university as it endeavors to enhance its academic reputation. It is important to note, however, that these improvements are the result of strategic planning designed to build research infrastructure in recent years. The institution is just beginning to reap the benefits of this planning. Naturally, additional support of research infrastructure is necessary for the institution to continue its upward trajectory.

Research values

Modern science arose during what is called the scientific revolution. It is not entirely clear-cut how to characterize the underlying ideas and norms of this revolution, but there is much to suggest that the establishment of scientific academies, particularly in England, France and Italy, was a watershed of sorts. This starting point does not ignore the significance of individual contributions (such as those from Galileo and Newton), but highlights rather that scientific activity must necessarily be conducted within a socially organized and systematic framework.

The following text is mainly derived from the report Contract Research: Openness, Quality, Accountability (Kaiser et al. 2003).

Introduction

In 1986, Knut Erik Tranøy defined scientific activity as ‘the systematic and socially organized a) search for, b) appropriation and production of, and c) administration and communication of knowledge and insight’ (Tranøy 1986:59). Later works have followed this tradition of associating systematic approaches with social organization (Kaiser 2000:152).

According to Plato the “classic” definition of "science" (scientia, Latin forknowledge) is:

Knowledge is true belief/opinion combined with an account of the reason to believe it. Or, in brief, as is stated in the standard textbook version: Knowledge is justified true belief.

In essence the scientific revolution can boast of having altered perceptions of science/knowledge in two respects: (i) it added specific perceptions on scientific methodology, and (ii) it anchored knowledge in a socially organised scientific community. Thus a tentative definition may be given as:

Scientific knowledge is true belief/opinion combined with a scientifically justified account of its cause, and anchored in the norms of the scientific community.

Verifiability, public, criticism and universalism

The various phases of scientific institutionalisation can be categorised: amateur science (approx. 1640–1800), professional science (approx. 1800−1870), industrial science (1870–1940), and Big Science (1940–present day). It was the scientific academies that, in this initial and important stage of modern science (so-called amateur science), administered the normative basis for scientific activity. It is not easy to unambiguously characterize the prevailing norms. One important element was the experimental method and its underlying view of nature, something that was somewhat cryptically referred to as ‘Natural Philosophy’ or ‘Natural Knowledge’. Another important element concerned linguistic standards and the role that mathematics can play in attempting to describe the laws and regularities of nature. A universal canon of such methodological rules was not formulated, however. Theorists such as Francis Bacon and René Descartes made influential suggestions regarding methodology and scientific policy, but without achieving the same paradigmatic effect as practical examples of successful science.

How systematically or methodologically grounded this science in fact was has been debated for years, though we may assume, with certain caveats, that the underlying base was the concept of the intersubjective verifiability of results. Other basic norms were, on the other hand, successfully institutionalized. One such norm was the demand for public knowledge, which specifically entails that all scientific insights are to be included in a common arsenal of knowledge. Secrecy is disavowed and the personal possession of knowledge is rejected. The justification for such a norm lay in the assumption that the common benefit is greater when knowledge is publicly disclosed than when knowledge is considered to be private property. This was simultaneously the basis for scientific journals. The second norm that was institutionalized is a direct consequence of the first, and can be described as institutionalized and systematic criticism. When knowledge becomes public, either through lectures or publication, it is the task of the scientific community to critically examine the argumentation in order to detect potential mistakes. Bacon, in particular, had pointed out the danger of becoming the victim of wishful thinking and other ‘Idols of the Marketplace’, as he named them.

In the practice of the scientific academies lay also the seeds of yet another research norm, namely universalism. Bacon had vigorously argued that the affairs of science required the greatest minds of every country. The reality was that the tiny scientific community of the initial phase consisted of a few thousand scholars, who kept in constant touch with one another across national borders.

Merton on the ethos of science (CUDOS)

In 1942 the American sociologist Robert K. Merton formulated what he considered to be the basic ethos of science, i.e. its normative foundation (Merton 1973). His four primary norms are:

• Communalism (the demand for communal possession of scientific knowledge; public knowledge).
• Universalism (the rejection of any preferential rights to science; everyone has an equal opportunity irrespective of social background, nationality, etc).
• Disinterestedness (independence from special interests).
• Organized scepticism (the demand for systematic criticism of scientific claims).

Later on he added a fifth norm:

5. Originality (rewards in the form of special recognition are awarded to those who first bring to light new knowledge).

In light of the preceding comments regarding the scientific academies and the scientific revolution, it is easy to see that Merton’s 1st, 2nd and 4th norms are inspired by this history. It is, however, a slightly different case with the 3rd and 5th norms, and at the outset there is reason to believe that they stem from more recent times. The norm of disinterestedness seems to be clearly inspired by Max Weber’s (1864–1920) postulate of value neutrality. This can be related to the expectation that science should be objective in the sense that it is not steered by subjective values and prejudices, and provides balanced presentations.  

Merton’s norm of originality serves to remind us that an adequate understanding of the social dynamics of science must include the established system of scientific credit. We find the norm of originality explicitly expressed in the PhD regulations of most universities.

Justification of the norms:methodology

Merton justifies his norms on the basis of two parallel arguments: firstly the historic argument that the norms can be deduced from the institutional arrangements that have encompassed modern science since the scientific revolution; and secondly a functionalist argument that the norms, in tandem, enable the production and quality assurance of knowledge that society expects science to provide – an argument that many may consider to be problematic.      

However, the problem lies precisely in the fact that this demarcation between “scientific” and “non-scientific”is not clear cut, and in many cases this will lead to a debate also within the scientific community and among philosophers of science. By adhering to the pluralistic view, however, it would be possible to state a sort of minimal answer to the problem: “scientific” is that which competently uses the tools that at any given time are to be found in the scientific toolbox. Thus, the choice of (empirical) method does not have to be decisive as long as a) a method is used at all, b) this method is suitable for solving problems of the given type, and c) the method is used competently.

The entire point of using a method is that it serves as a quality assurance ofthe knowledge and insights that research produces. Methods should not only provide results, they should also, based on given preconditions, enable the systematic and intersubjective verifiability/quality control of the results. When knowledge is presented as being scientific or research-based, the conditions must be such that peers can review the fundamental data and based on a certain method, assess whether the conclusions are valid. This is the most important prerequisite for scientific quality assurance.

Traditions for justification

It is one thing to become fascinated by the underlying method of science and the inherent rules and regulations of the scientific community, but it is something quite else to explain to others why scientific activity is valuable. The latter aspect is what is normally called the justification of science. Justification pertains to the external aspects of science, and it has become customary to discern between two fundamental forms of justification. Aristotelian justification takes as its starting point the inherent worth of knowledge as a sort of project of self-realization for mankind; Baconian justification, on the other hand, takes as its starting point the utility of knowledge, where the utility is assessed in regard to people other than those who produce the knowledge (see Tranøy 1986, Kaiser 2000: chap. 2). Tranøy has introduced the general concept of welfare functions and welfare effects to take into account ‘the different ways in which insight, and in particular systematic scientific insight, affect the individual and social welfare of human beings, for good or for bad’ (Tranøy 1986: 78). Seen in relation to the problems of modern research policies, the two above-mentioned traditions of justification provide only a sketchy blueprint, but it is not unreasonable to imagine that also later ideas of justification will tend towards one of these two basic forms.

A more recent model of the justification of research is associated with John Desmond Bernal (1901 – 1971). From the 1930s on, in England in particular, there emerged a group of Marxist-inspired theorists who viewed Soviet science as an ideal. Øyvind Såtvedt summarizes Bernal’s programme of research policy in four points (Skoie and Såtvedt 1998: 33): 

• The principle that research must be systematically organized and that priority must be given to applied research that is beneficial to society.
• The principle that all researchers are ethically obligated to fight against any misuse of the knowledge that science has produced. There is no moral boundary between the production of knowledge on the one hand, and the application of this knowledge on the other.
• The principle that science is an instrument for social transformation (emancipation) and is rooted in practical life.
• A set of historiographical theses to be employed when describing the history of science.

There are many noteworthy aspects of this model (sometimes referred to as ‘Bernalism’): (i) all science is justified by its utility, (ii) freedom and self-determination are limited by governmental research policy, (iii) value neutrality and disinterestedness are replaced by a comprehensive ethical obligation towards the production and application of knowledge that is beneficial to society, and (iv) when the ethical basis of science is linked to social transformation and practical activities, then it is natural for science to forge close alliances with varying user groups in society.

Considering the reality of research policy today, it is striking how close it is to Bernalism, and how far removed it is from the ideals of Humboldt and Weber. Referring to Eirikur Baldursson, Såtvedt states that it is ‘a paradox that while Communism – which to a great degree inspired Bernal’s thinking on research policy – has largely been dethroned in recent decades, the ideas of Bernalism seem to be alive and well’ (Skoie and Såtvedt 1998: 35). He goes on to quote Aant Elzinga: ‘Bernal’s ideas have been “taken over by the captains of industry and ministers of government in the postwar period”’ (ibid. 1998: 35).

In the above, we outlined some of the historical developments and debates regarding the normative foundation of science, with regard to its inner social organization, its methods and its traditions of justification. These are the three areas that are particularly important for our present concern with for instance contract research.

Of course, the normative foundation of science involves more than those areas outlined above. An essential element that we have not dealt with pertains to problems of scientific integrity, and there are in that context several norms that stipulate scientifically correct behaviour.

The scientific community follows a set of internal scientific rules (norms) that allows for the emergence of scientific knowledge in a dynamic network of researchers in which each individual contributes insights, based on the previous insights of others, and submits them to the community to be critically tested and validated. Respect for the contributions of others and giving adequate credit are therefore important in the scientific community. This is expressed, for example, in the rules for crediting others’ contributions, i.e. usually in respect of references, as well as in the rules for authorship and publication. Plagiarism of the work of others damages the trust on which the scientific community is founded. The most damaging form of variance is dishonesty (fraudulent research), for example falsifying a test or test data. Scientific misconduct (dishonesty) is therefore often classified as so-called FFP (“fabrication, falsification, plagiarism”). Following the entry into force of the Norwegian Act on ethics and integrity in research, allegations of such misconduct are scrutinized by a separate commission. Less extreme violations of the internal norms of research are sometimes referred to as QRP (“questionable research practices”).

Another element of the normative foundation of science concerns research subjects, whether humans or animals, for example in medical research. No objective justifies the unethical use of research subjects. Clear standards have been drawn up for this, for example in the Declaration of Helsinki.

Thus if we consider research practice as a whole in the sense of a set of internal research rules as well as actions that are censurable from the point of view of research ethics, we must take into account different norms or sets of norms. An early publication on dishonesty in research published by the research ethics committees (Elgesem, Jåsund, Kaiser 1997) attempted to provide an overview of such internal research considerations in a table (see table 1).

Table 1: Overview of various sets of ethical norms of importance for research practice (from Elgesem, Jåsund, Kaiser 1997).

Freedom, openness and independence?

In terms of research policy, scientific research is often characterized by three key values/norms, i.e. freedom, openness and independence. The above-mentioned observations provide a platform for reflecting on these:

The norm of freedom

The norm of freedom in research has been asserted and defended in connection with the emergence of modern research universities in the 19th century. It refers at the outset to the universities’ freedom to determine themselves how the accumulation of scientific knowledge should be managed and renewed. Specifically, the norm has consequences for appointments, internal organization and teaching at the institutions. As a direct consequence of the norm, scientific personnel at the institution are given the freedom to choose their own field of work and their own research activity. The research activity is in other words not only exclusively controlled by researchers, but also initiated by researchers. The norm was ideologically associated with the perception of scientific knowledge as a cultural good, something that was indirectly beneficial for society in connection with formative education and schooling. The norm has been limited and modified in pace with the increase of institutes for applied research, which were exclusively utilitarian. In such institutes, research was still largely controlled by researchers, but not initiated by them – the objectives were defined externally. The development of Big Science after World War II, however, as well as later developments that took place on the outer limits of the universities, limited the researchers’ control over research. Larger and more complex projects, with predetermined objectives and a clear view towards technological application, demanded a form of supervision and organization that was not necessarily a matter of internal interest to science. Remnants of free research (i.e. research that is controlled and initiated by researchers) are still to be found in today’s universities, but when research funds are scarce and large resources are demanded in certain fields, researchers have in reality limited opportunity to initiate projects. The actuality of the norm of academic freedom, such as it is adapted to the modern world of research, is therefore open to debate (Menard et al. 1996).

The norm of independence

For individual researchers, it has always been a goal that their research should remain unaffected by external, non-scientific interests. At the same time, the social context of science has always required that alliances be struck with other parties and powers-that-be. The greater the need for funding, the closer these alliances became. Alliances always create a mutual dependence and limit the independence of a given party. This entails that the allied partner’s interests will colour the given researcher’s own activity, something that became ever more relevant as the technological and commercial dimensions of research came into focus. Today’s research is characterized by universities and autonomous institutes forging several such formal and informal alliances. Many have noticed a trend towards the increased commercialization of research, something that clearly created new forms of dependency. At the same time, such alliances do not necessarily have to lead to the deterioration of quality specifications in scientific research. In principle it is rather the opposite: it is precisely such quality specifications that make science an attractive alliance partner. It is also because of these quality specifications that the general public is able to have confidence in the results. The challenge lies therefore in the delicate balance between desired connections on the one hand, and a dependent relationship that is detrimental to research quality on the other.

The norm of openness

The norm of openness is not, on the surface, directly evident in traditional formulations of the normative foundation of science. What most resembles the norm of openness is the demand that knowledge be public, and perhaps also the methodological demand for the intersubjective verifiability of research results. Openness with respect to both data and method is a prerequisite for scientific quality assurance. Openness with respect to research results, in the form of publicly accessible publications, is a prerequisite for quality assurance in the form of peer review and for the practical application of research results to the benefit of society. It is well known that complete openness in this respect does not always exist, for example for reasons of national security or to protect industrial secrets. Modern patent practice usually entails a time-limited exclusivity. When discussing Bernalism and post-academic science, however, where special interests to a larger degree influence the contents of research, we asserted that the overriding interests of society linked to the potential welfare benefits of research imply that the norm of openness should include publicly accessible information about a) which research projects are in fact being conducted, b) who is funding this research and which user groups are collaborating on the project, c) how the research is being quality assured, and possibly d) how the results will fall into the public domain, even though this might take place after a certain time span.

Based on our preliminary discussion, we believe that research should also be controlled by two important norms that are not always explicitly mentioned, namely quality and accountability.

The norm of quality

Quality assurance of scientific knowledge has been a central part of scientific activity ever since the scientific revolution. The use of method, combined with intersubjective verifiability in the form of peer review, has always been promoted as key parts of such quality assurance. The system of peer review is relatively sophisticated and well established in today’s situation. As for method, we have emphasized the important distinction between having an awareness of method on the one hand, and the fact that researchers may, on the other hand, have differing estimations and viewpoints on how scientific problems should be tackled. Professional disagreement is therefore not an indicator of quality failure, nor does professional consensus on a given conclusion indicate that the research quality has been assured. Scientific quality assurance refers to processes rather than conclusions.

In research that is out-and-out utilitarian in nature, the required quality assurance will often include other parties than researchers, for example users. In research where common interests are at stake, and where there exists a significant degree of scientific uncertainty (post-normal science), it would be natural to expect that research should be open to input from various interested parties in regard to its preparation, transaction and conclusions. Although adequate quality assurance of scientific research is fundamental to the general public’s confidence in research, it can never guarantee valid, final and commonly accepted research results. Such a guarantee does not exist in cutting edge research. It turns out that the scientific community itself values professional quality highest among the goods that are demanded by ethical guidelines for research (cf. SCRES 2002).

The norm of accountability

Modern science, at least from the Enlightenment onwards, has always considered participation in the affairs of society, be they political, cultural or commercial, as a natural part of its activity. Nor

did Weber’s postulate of value neutrality lead to a lack of social commitment on the part of science. Weber’s postulate did not, however, demand a particular ethical responsibility on behalf of science. As long as the universities had sufficient institutional freedom to control and determine the research agenda themselves, it seemed certain that relevant knowledge would befall the general public. However, when utilitarian ideas exert ever greater influence on what is to be studied, how the research is to be conducted and who shall have access to the results, it seems imperative that research ethics are reinforced in order to compensate on behalf of the overriding interests of society. An empirical study of ethical guidelines for research, conducted by SCRES, shows that social responsibility is the most highly prioritized among the social values that science should seek to attain (SCRES 2002). It is hardly reasonable to claim, as Bernal does, that researchers are categorically responsible for how their results are applied. But it is not unreasonable to contend that researchers are jointly responsible for the use of science in the wider context of society (see Mitcham & Schomberg 2000).

It is precisely this form of ethical co-responsibility that we refer to here as accountability. Such a norm of research ethics essentially includes two aspects: 1) When certain decisions and/or technologies that were in essence made or created on the basis of scientific know-how turn out to have unfortunate, unforeseen side effects, and when there simultaneously is reason to believe that a better or broader preparation of the scientific decision-making basis could have forewarned against such possible side effects, then there is reason to believe that science is ethically co-responsible for the negative consequences. 2) When there exists either specific results or important scientific uncertainty which imply that a planned decision and/or technology could have serious consequences for e.g. society, health or the environment, then it is the ethical duty of science and the individual researcher to ensure that this information is effectively made known to the relevant decision makers and the general public.

We deem that such a norm of accountability overrides all other obligations, for example obligations towards a client or an institution. This norm entails furthermore that science engages itself in public debates where scientific and technological questions are on the agenda or form an essential part of the debate.

We have, in comparison with the norms that were discussed above, downplayed certain other norms, for example the norm of universality. This does not imply that we do not see the value of such a norm; it is rather due to our opinion that the norm is in reality quite complex, and that for example a certain amount of local knowledge also has a part to play in research. We refer here to the bibliography for further discussion (see for example Cole 1992). We have also downplayed Merton’s norm of disinterestedness, which we believe is better maintained by our norms of openness and accountability.

We believe there is reason to contend that openness, quality and accountability form the normative core of research ethics and are applicable to scientific activity in general. The norm of independence has a secondary meaning in the sense that the quality of science should not be unduly influenced by the given researcher’s inevitable relationships of dependency, and as such this norm is important for contract research. On the other hand, it is difficult to uphold freedom and independence as key norms when considered in the general form they were discussed above. Actual research is seldom of the kind these norms seem to imply, and it is difficult to see that they should comprise a minimum standard for research ethics in general – actual research is too complex for that. No researcher is completely independent: we are all dependent on something at all times. On the other hand, both of these norms have certain aspects, pertaining to quality assurance and accountability, that are important to uphold. Subsidiary aspects of these norms will not be included as consequences of our three overarching norms.

This article has been translated from Norwegian by Jennifer Follestad, Akasie språktjenester AS.

Bernal, J.D. 1967. The Social Function of Science. Oxford: Oxford University Press

Cole, S. 1992. Making Science – Between Nature and Society. Harvard: Harvard University Press

Elgesem, Dag; Jåsund Kjetil og Kaiser Matthias: ”Fusk i forskning”. De nasjonale forskningsetiske komiteer. Skriftserie nr. 8. 1997

Funtowicz, S. & Ravetz, J. 1993. “Science for the Post-Normal Age”, Futures 25/7: 735-755.

Kaiser, M. (2000). Hva er vitenskap? Oslo: Universitetsforlaget

Kaiser, M., K. Rønning, K. W. Ruyter, H.W. Nagell og M.E. Grung: Oppdragsforskning – åpenhet, kvalitet, etterrettelighet; De nasjonale forskningsetiske komiteer 2003

Menard, L. et al. 1996. The Future of Academic Freedom. London: The University of Chicago Press

Merton, R. K. 1973. The Sociology of Science – Theoretical and Empirical Investigations. Chicago & London: The University of Chicago Press.

Mitcham, C. & Schomberg, R.v. 2000. The Ethic of Scientist and Engineers: From Occupational Role Responsibility to Public Co-responsibility. Amsterdam: JAI Press.

SCRES 2002. Standards for ethics and responsibility in science – an empirical study. The Standing Committee for Responsibility and Ethics in Science, under The International Council for Science (ICSU), i dag International Science Council: https://council.science/, https://council.science/wp-content/uploads/2017/05/SCRES-Background.pdf                             

Skoie, H, & Såtvedt, Ø. 1998. Forskning, kultur og autonomi. Et bidrag til debatten om den frie universitetsforskning. Oslo: Norsk institutt for studier av norsk forskning og utdanning: 200.

Tranøy, K.E. Vitenskapen –samfunnsmakt og livsform Universitetsforlaget Oslo 1986, 1991.

What Is Research, and Why Do People Do It?

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

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

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Abstractspiepr Abs1

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

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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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2.1 Why Is Research Important?

Learning objectives.

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

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

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

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

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

Use of Research Information

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

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

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

Link to Learning

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

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

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

NOTABLE RESEARCHERS

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

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

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

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

The Process of Scientific Research

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

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

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

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

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

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

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

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

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

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

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Module 1: Introduction: What is Research?

Learning Objectives

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

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

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Recognizing the Value of Educational Research

• A recent survey shows that research on teaching and learning is not valued at many AACSB-accredited schools across the U.S. and Canada.
• One reason that business schools might not recognize research on teaching and learning is that the journal quality lists they commonly use to assess faculty intellectual contributions focus primarily on discipline-based scholarship.
• STEM fields already place equal value on research on teaching and learning within individual disciplines. By following their lead, two Canadian scholars argue, business schools will enrich their students’ learning experiences.    

If business educators were asked to define the purpose of business schools, they likely would emphasize the need to “prepare the next generation of leaders.” But if this is the case, why do so few business schools prioritize research that advances teaching and curricular design?

Researcher Sanobar Siddiqui first explored this question as the subject of her doctoral dissertation. “One of my thesis findings was that the tenure system’s lack of rewards impedes business academics from pursuing research in teaching and learning,” she explains.

Now an assistant professor of accounting at the University of Regina’s Faculty of Business Administration in Canada, Siddiqui wanted to learn why so many business schools do not value research on teaching and learning (RoTL). This response is puzzling, she says, given that Standard 7 of the  AACSB Business Accreditation Standards  accepts “scholarship of teaching and learning” as documentation to indicate a business school’s teaching effectiveness and impact.

She and Camillo Lento, a professor with the Faculty of Business Administration at Lakehead University in Thunder Bay, Ontario, published a  paper  on the status of RoTL in the April 2022 edition of the International Journal of Educational Management . The paper’s findings are based on a survey in which Siddiqui and Lento asked educators two questions:

• How do AACSB-accredited business schools in the U.S. and Canada define “teaching effectiveness,” according to AACSB’s Standard 7?
• Do these schools consider research on teaching and learning in their promotion and tenure decisions?

This topic is particularly important, says Siddiqui, because business schools serve such diverse student audiences. Moreover, learner success is integral to every business school’s mission. Many of the instructional strategies “that we use in class are not research-informed or evidence-based. Hence, we are shortchanging our students,” she says. “Our teaching needs to catch up with the changes we see in our classroom.”

‘A Last Priority’

Siddiqui and Lento received 78 responses to their survey; in the second phase of their study, they conducted semi-structured interviews with 11 educators in the U.S. and Canada.

Among survey respondents, 42 percent noted that they were “unaware of an explicit teaching effectiveness definition” at their schools, but 58 percent said the policies in place at their schools communicated “an implied definition.” Only one respondent could quote a definition of teaching effectiveness from the school’s website.

Respondents noted a lack of “perceived respect and value” for RoTL, describing this line of scholarship as “a last priority” at their schools. As one educator put it, “Our department does not really care about teaching as long as you are cranking out strong scholarship.”

Schools that consider educational research for tenure and faculty qualification tend to focus on journal quality alone, not on whether published articles are discipline-based.

The good news is that 55 percent of respondents noted that their schools did take RoTL into account when making tenure decisions. Siddiqui and Lento found that these schools have two things in common. First, they focus on journal quality alone for the purposes of tenure and faculty qualification, not on whether faculty’s published articles are discipline-based.

Second, these schools are more likely to consider RoTL when faculty include this work “as part of a larger research plan that includes discipline-based research.” Only faculty following teaching tracks are likely to receive tenure based solely on publications in education-focused journals. 

Additionally, teaching-oriented schools are more likely than research-oriented schools to recognize RoTL. While this makes outward sense, Siddiqui wonders why prolific faculty who produce innovative scholarship on pedagogical issues that are critical to business education cannot “be hired, promoted, and awarded just like discipline-based researchers” at research-oriented institutions.

What Perpetuates the Stigma?

Siddiqui and Lento point to several factors that could be driving the lack of recognition of RoTL among AACSB-accredited schools:

No consensus about teaching quality.  Although many individual educational institutions have defined teaching effectiveness based on existing research, business schools have not yet established a shared definition of what constitutes effective teaching. However, the co-authors emphasize, more dedicated research could produce findings that inspire a common language around teaching and learning.

The complex nature of determining teaching quality. Schools often evaluate the quality of faculty’s research by whether the work appears in academic journals that are rated highly by certain  journal quality lists . However, they find they cannot use a similar approach to evaluate the quality of faculty’s teaching, says Lento. “The evaluation of teaching effectiveness is much more complex and requires many more sources of information, possibly compiled into a teaching dossier that is unique to an educator.”

A lack of attention in business doctoral programs. Most doctoral programs train young researchers to study topics related to their disciplines of choice. As a result of this early training, RoTL “may come with a stigma as it is outside of traditional discipline-specific research,” Lento says.

Lento admits that the reasons listed above are speculative. He and Siddiqui would like to see other researchers conduct follow-up studies that take deeper dives into the broader stigma surrounding RoTL.

Changing Mindsets, Taking Action

In the meantime, Siddiqui and Lento call on business school administrators and faculty to work together to create a “shared and precise definition of teaching effectiveness.” Educators can start by defining teaching quality within their own institutions.

From there, Siddiqui and Lento say that schools can take any or all the following actions to change mindsets about RoTL:

• Set appropriate objectives, incentives, and evaluation mechanisms.
• Create and nurture communities of practice that help like-minded faculty pursue research focused on solving issues they face in their classrooms.
• Consider weighing education research in peer-reviewed articles more heavily, particularly for faculty in teaching-focused roles.
• Recognize RoTL for accreditation and tenure and normalize it as a legitimate form of scholarship.
• Make seed funds available to faculty who pursue RoTL.
• Give awards and incentives to faculty who use research-informed teaching in their classrooms.
• Consider hiring tenure-track academics who also are expert educators with an expressed interest in pursuing RoTL. These scholars can investigate and develop “research-informed teaching tools ready to be put into practice in almost any business classroom,” says Siddiqui. This outcome, she emphasizes, is an indication of how RoTL contributes to the advancement of business disciplines.
• Encourage and teach RoTL in doctoral programs, with the aim of improving and advancing the quality of teaching at business schools.

Siddiqui points out that information on the websites of AACSB-accredited schools “are replete with research centers, research chairs and scholars, core research focus areas, research awards, annual research celebration reports, intellectual contributions, and grant-funding awards.”

There is no reason, she says, that schools could not also highlight information about their teaching philosophies, teaching awards, student feedback, educational leadership and professional development, and faculty research on teaching and learning.

Two B-School Perspectives

So far, Siddiqui and Lento’s paper has captured the attention of other like-minded educators in the business school community. This includes Nicola Charwat, associate dean of teaching and learning and senior lecturer of business law and taxation at Monash University’s Monash Business School (MBS) in Caulfield East, Australia.

MBS prioritizes scholarship on teaching and learning (SoTL) where appropriate, she says, through efforts that include identifying quality education-oriented journals and valuing publication in those journals equally to publication in discipline-based journals. The school uses “a consultative process” to identify journals specializing in teaching and learning that are equivalent to discipline-based journals rated as A*, A, B, and C on the quality list compiled by the Australian Business Deans Council.

“We have also instituted a Business Education and Research Group, which has been awarding both practice- and research-output-focused grants to staff for three years,” Charwat says. “Alongside these efforts, of course, there are moves in the university in line with the broader trend of raising the profile of teaching and ensuring its status is on par with other work of the university.”

Educators in STEM disciplines have long recognized educational research in tenure decisions and regularly reward academics who pursue RoTL in their disciplines.

Despite these changes, Charwat notes that the perception remains that accomplishments related to educational research are “somehow lesser” than those related to discipline-related scholarship. Additionally, many faculty remain uncertain about how to approach educational research. In response, MBS has built communities of practice dedicated to teaching and is now working “to increase awareness of and opportunities to undertake SoTL and education research,” Charwat says.

Charwat says that the questions raised in Siddiqui and Lento’s paper are “essential” to business education, and that their article “has prompted us to start exploring the patterns of our own SoTL and education research.” MBS faculty, she adds, might also pursue a similar study focused on AACSB-accredited schools in Australia. 

Another educator who read the article with interest is Martin Lockett, former dean and professor of strategic management at Nottingham University Business School China (NUBS China) in Zhejiang. Lockett explains that NUBS China uses the Academic Journal Guide , which is produced by the Chartered Association of Business Schools (CABS), to support tenure decisions and to classify faculty under AACSB accreditation standards.

But in the CABS guide, only four journals focused on teaching and learning are rated as 3, 4, or 4*, which are the targets that NUBS China uses to qualify faculty as Scholarly Academics under AACSB accreditation or for internal recognition of quality research, Lockett says.

This has led to worry among the school’s teaching-oriented faculty that if they focus on RoTL, they risk being classified as “additional faculty,” unless they can consistently publish in the few education-focused journals listed by CABS. That concern, Lockett says, deters most faculty from pursuing RoTL in any substantial way.

While this scenario is all too common at institutions with research-focused missions, it is not mandated by AACSB accreditation standards, emphasizes Stephanie Bryant, AACSB’s chief accreditation officer. She clarifies that whether a business school considers educational scholarship for the purpose of accreditation or tenure is its choice, based on the parameters it has set for its individual mission. “The standards do not say anywhere, or imply, that educational research is not valued,” Bryant stresses. The devaluation of RoTL, she adds, “is a school perspective.”

Time to ‘Balance the Scales’

The stigma surrounding RoTL at AACSB-accredited business schools could be lifted, say Siddiqui and Lento, if administrators acknowledge the benefits that fostering cultures of teaching and learning bring to all business school stakeholders. These advantages include a wider scope of scholarship and more evidence-based pedagogical tools for faculty, richer learning experiences and better learning outcomes for students, and more well-rounded job candidates for employers.

Educators in science, technology, engineering, and mathematics (STEM) disciplines already know this, says Siddiqui. STEM departments have long recognized educational research in tenure decisions and regularly reward academics who pursue RoTL in their disciplines.

As one example, Siddiqui points to Carl Edwin Wieman, winner of the 2001 Nobel Peace Prize in Physics. Wieman established the  Carl Wieman Science Education Initiative  at the University of British Columbia in Canada to encourage evidence-based teaching methods focused on improving undergraduate science education. Since its inception, the initiative has hired fellows who are interested in conducting education research, particularly based in the disciplines in which they have earned their doctorates. It also has inspired the creation of teaching materials in science education, a dedicated website, and a sister initiative at the University of Colorado Boulder in the United States.

Business schools, says Siddiqui, could achieve comparable results by raising awareness of the importance of RoTL, disseminating RoTL findings beyond peer-reviewed journals, and driving research-informed teaching methods that advance business education.

This year, the co-authors published a second paper that finds that scholarly and practice academics who developed rigorous research skills in their doctoral programs and who publish discipline-based research are more likely to pursue RoTL research. Here, Siddiqui and Lento more directly call on business school deans to reward and incentivize this line of research by creating communities of practice and expanding their journal ranking frameworks to include relevant peer-reviewed publications.

It is imperative, Siddiqui and Lento argue, that business schools place studies based on classroom settings on equal footing with studies based on corporate settings. “Research on teaching and learning balances the scales,” Siddiqui says, “by utilizing evidence-based, efficient, and effective teaching to foster deep learning amongst diverse student audiences.”

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P-Value And Statistical Significance: What It Is & Why It Matters

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The p-value in statistics quantifies the evidence against a null hypothesis. A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01.

Hypothesis testing

When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis.

The null hypothesis (H0) states no relationship exists between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in supporting the idea being investigated. Thus, the null hypothesis assumes that whatever you try to prove did not happen.

The alternative hypothesis (Ha or H1) is the one you would believe if the null hypothesis is concluded to be untrue.

The alternative hypothesis states that the independent variable affected the dependent variable, and the results are significant in supporting the theory being investigated (i.e., the results are not due to random chance).

What a p-value tells you

A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true).

The level of statistical significance is often expressed as a p-value between 0 and 1.

The smaller the p -value, the less likely the results occurred by random chance, and the stronger the evidence that you should reject the null hypothesis.

Remember, a p-value doesn’t tell you if the null hypothesis is true or false. It just tells you how likely you’d see the data you observed (or more extreme data) if the null hypothesis was true. It’s a piece of evidence, not a definitive proof.

Example: Test Statistic and p-Value

Suppose you’re conducting a study to determine whether a new drug has an effect on pain relief compared to a placebo. If the new drug has no impact, your test statistic will be close to the one predicted by the null hypothesis (no difference between the drug and placebo groups), and the resulting p-value will be close to 1. It may not be precisely 1 because real-world variations may exist. Conversely, if the new drug indeed reduces pain significantly, your test statistic will diverge further from what’s expected under the null hypothesis, and the p-value will decrease. The p-value will never reach zero because there’s always a slim possibility, though highly improbable, that the observed results occurred by random chance.

P-value interpretation

The significance level (alpha) is a set probability threshold (often 0.05), while the p-value is the probability you calculate based on your study or analysis.

A p-value less than or equal to your significance level (typically ≤ 0.05) is statistically significant.

A p-value less than or equal to a predetermined significance level (often 0.05 or 0.01) indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis.

This suggests the effect under study likely represents a real relationship rather than just random chance.

For instance, if you set α = 0.05, you would reject the null hypothesis if your p -value ≤ 0.05. 

It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

Therefore, we reject the null hypothesis and accept the alternative hypothesis.

Example: Statistical Significance

Upon analyzing the pain relief effects of the new drug compared to the placebo, the computed p-value is less than 0.01, which falls well below the predetermined alpha value of 0.05. Consequently, you conclude that there is a statistically significant difference in pain relief between the new drug and the placebo.

What does a p-value of 0.001 mean?

A p-value of 0.001 is highly statistically significant beyond the commonly used 0.05 threshold. It indicates strong evidence of a real effect or difference, rather than just random variation.

Specifically, a p-value of 0.001 means there is only a 0.1% chance of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is correct.

Such a small p-value provides strong evidence against the null hypothesis, leading to rejecting the null in favor of the alternative hypothesis.

A p-value more than the significance level (typically p > 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis; we can only reject it or fail to reject it.

Note : when the p-value is above your threshold of significance,  it does not mean that there is a 95% probability that the alternative hypothesis is true.

One-Tailed Test

Two-Tailed Test

How do you calculate the p-value ?

Most statistical software packages like R, SPSS, and others automatically calculate your p-value. This is the easiest and most common way.

Online resources and tables are available to estimate the p-value based on your test statistic and degrees of freedom.

These tables help you understand how often you would expect to see your test statistic under the null hypothesis.

Understanding the Statistical Test:

Different statistical tests are designed to answer specific research questions or hypotheses. Each test has its own underlying assumptions and characteristics.

For example, you might use a t-test to compare means, a chi-squared test for categorical data, or a correlation test to measure the strength of a relationship between variables.

Be aware that the number of independent variables you include in your analysis can influence the magnitude of the test statistic needed to produce the same p-value.

This factor is particularly important to consider when comparing results across different analyses.

Example: Choosing a Statistical Test

If you’re comparing the effectiveness of just two different drugs in pain relief, a two-sample t-test is a suitable choice for comparing these two groups. However, when you’re examining the impact of three or more drugs, it’s more appropriate to employ an Analysis of Variance ( ANOVA) . Utilizing multiple pairwise comparisons in such cases can lead to artificially low p-values and an overestimation of the significance of differences between the drug groups.

How to report

A statistically significant result cannot prove that a research hypothesis is correct (which implies 100% certainty).

Instead, we may state our results “provide support for” or “give evidence for” our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct – e.g., less than 5%).

Example: Reporting the results

In our comparison of the pain relief effects of the new drug and the placebo, we observed that participants in the drug group experienced a significant reduction in pain ( M = 3.5; SD = 0.8) compared to those in the placebo group ( M = 5.2; SD  = 0.7), resulting in an average difference of 1.7 points on the pain scale (t(98) = -9.36; p < 0.001).

The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values:

“When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001.

The tradition of reporting p values in the form p < .10, p < .05, p < .01, and so forth, was appropriate in a time when only limited tables of critical values were available.” (p. 114)

• Do not use 0 before the decimal point for the statistical value p as it cannot equal 1. In other words, write p = .001 instead of p = 0.001.
• Please pay attention to issues of italics ( p is always italicized) and spacing (either side of the = sign).
• p = .000 (as outputted by some statistical packages such as SPSS) is impossible and should be written as p < .001.
• The opposite of significant is “nonsignificant,” not “insignificant.”

Why is the p -value not enough?

A lower p-value  is sometimes interpreted as meaning there is a stronger relationship between two variables.

However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).

To understand the strength of the difference between the two groups (control vs. experimental) a researcher needs to calculate the effect size .

When do you reject the null hypothesis?

In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used significance levels are 0.01, 0.05, and 0.10.

Remember, rejecting the null hypothesis doesn’t prove the alternative hypothesis; it just suggests that the alternative hypothesis may be plausible given the observed data.

The p -value is conditional upon the null hypothesis being true but is unrelated to the truth or falsity of the alternative hypothesis.

What does p-value of 0.05 mean?

If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.

Are all p-values below 0.05 considered statistically significant?

No, not all p-values below 0.05 are considered statistically significant. The threshold of 0.05 is commonly used, but it’s just a convention. Statistical significance depends on factors like the study design, sample size, and the magnitude of the observed effect.

A p-value below 0.05 means there is evidence against the null hypothesis, suggesting a real effect. However, it’s essential to consider the context and other factors when interpreting results.

Researchers also look at effect size and confidence intervals to determine the practical significance and reliability of findings.

How does sample size affect the interpretation of p-values?

Sample size can impact the interpretation of p-values. A larger sample size provides more reliable and precise estimates of the population, leading to narrower confidence intervals.

With a larger sample, even small differences between groups or effects can become statistically significant, yielding lower p-values. In contrast, smaller sample sizes may not have enough statistical power to detect smaller effects, resulting in higher p-values.

Therefore, a larger sample size increases the chances of finding statistically significant results when there is a genuine effect, making the findings more trustworthy and robust.

Can a non-significant p-value indicate that there is no effect or difference in the data?

No, a non-significant p-value does not necessarily indicate that there is no effect or difference in the data. It means that the observed data do not provide strong enough evidence to reject the null hypothesis.

There could still be a real effect or difference, but it might be smaller or more variable than the study was able to detect.

Other factors like sample size, study design, and measurement precision can influence the p-value. It’s important to consider the entire body of evidence and not rely solely on p-values when interpreting research findings.

Can P values be exactly zero?

While a p-value can be extremely small, it cannot technically be absolute zero. When a p-value is reported as p = 0.000, the actual p-value is too small for the software to display. This is often interpreted as strong evidence against the null hypothesis. For p values less than 0.001, report as p < .001

Further Information

• P-values and significance tests (Kahn Academy)
• Hypothesis testing and p-values (Kahn Academy)
• Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “ p “< 0.05”.
• Criticism of using the “ p “< 0.05”.
• Publication manual of the American Psychological Association
• Statistics for Psychology Book Download

Bland, J. M., & Altman, D. G. (1994). One and two sided tests of significance: Authors’ reply.  BMJ: British Medical Journal ,  309 (6958), 874.

Goodman, S. N., & Royall, R. (1988). Evidence and scientific research.  American Journal of Public Health ,  78 (12), 1568-1574.

Goodman, S. (2008, July). A dirty dozen: twelve p-value misconceptions . In  Seminars in hematology  (Vol. 45, No. 3, pp. 135-140). WB Saunders.

Lang, J. M., Rothman, K. J., & Cann, C. I. (1998). That confounded P-value.  Epidemiology (Cambridge, Mass.) ,  9 (1), 7-8.

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Article Contents

Consumer research and value hierarchy, value hierarchy as inequality, a future research agenda, concluding remarks, data collection statement, author notes.

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After 50 Years, It Is Time to Talk about Value Hierarchy and Inequality

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Lez E Trujillo-Torres, Benét DeBerry-Spence, Sonya A Grier, Søren Askegaard, After 50 Years, It Is Time to Talk about Value Hierarchy and Inequality, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 79–90, https://doi.org/10.1093/jcr/ucad040

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This research enriches the field’s perspective on value and argues that to seriously address inequality during the next 50 years, consumer research must explore processual aspects of value hierarchy and consider its relationship to inequality. Doing so recognizes the duality of structures as embodying outcomes and agency, as well as the need to view value not only as what it is but also as what it does. To begin to address limitations in the literature, we use empirical evidence from an investigation of the cancer care market from 1970 to 2021 to understand how value hierarchy shapes and manifests as inequality. This is conceptualized as: distribution of multilevel resources, consolidation of consumer power, stratification of consumer agency, and (de)credentialization of worthiness. Building on each of these, we discuss a research agenda for future JCR inquiries and introduce “value hierarchy as inequality” as a big-tent issue for consumer research.

During the past 50 years, the concept of value has had implications, directly or indirectly, for each and every Journal of Consumer Research ( JCR ) article published. This is because value, defined as a sense of benefit, meaning, or worth of marketplace entities, is critical to consumer decision-making ( Figueiredo and Scaraboto 2016 ; Zeithaml 1988 ). Marketplace entities can encompass concrete actors such as products, people, and institutions and also abstract ideas and causes. Certainly, then, value is a big-tent issue that is central to the development of new consumer behavior topics, given that it is at the core of “what” is being exchanged in a marketing context. But value also entails hierarchization. For example, as the cancer awareness marketing campaign in figure 1 illustrates, not all health consumption experiences are valued the same; rather, inequality permeates markets and many consumer experiences and is “a global issue that defines our time” ( United Nations 2023 ). We argue that to seriously address inequality over the next 50 years, consumer researchers must foreground the often ignored role, functioning, and impact of value hierarchy.

EXAMPLES OF PERCEIVED INEQUALITIES BETWEEN CANCER TYPES BY PANCREATIC CANCER PATIENTS

Source.— Pancreatic Cancer Action.

We define “value hierarchy” as a structure in which entities are layered according to a value rank or order ( Lenski 1984 ). Our understanding of value hierarchy is informed by structural perspectives of hierarchy and inequality found in organizational studies, in which hierarchy in everyday life plays a crucial role in the formation and reproduction of inequalities ( Bunderson et al 2016 ; Harrison and Klein 2007 ). In marketplaces, for example, consumers value brands, products, or experiences and establish value hierarchy among them ( Moreau, Lehmann, and Markman 2001 ). Inequalities are pronounced differences between marketplace entities ( Crockett and Grier 2021 ; DeBerry-Spence et al. 2023 ), which can limit privileges, opportunities, or resources; hinder market participation; support discriminatory practices; and lead to unequal outcomes ( Poole et al. 2021 ; Trujillo-Torres and DeBerry-Spence 2019 ). As such, hierarchy and inequalities mark imbalances in social arrangements ( Sewell 1992 ) as differences in the social valuation of groups ( Lamont 2012 ) and as moral justifications aligned with higher-order principles ( Boltanski and Thévenot 2006 ).

A structural perspective of value hierarchy is necessary to expose its foundational connections with inequality. Specifically, value hierarchy can result in and unfold as both centralized inequality ( Argote, Turner, and Fichman 1989 ; Harrison and Klein 2007 ) and steep inequality ( Bunderson et al. 2016 ). When centralized, inequality is embedded in the number and type of resources entities receive within a value hierarchy. For example, some consumers have historically received more social and material resources than others ( Poole et al. 2021 ). When steep, inequality is associated with the sheer depth or magnitude differences between entities within a value hierarchy ( Halevy et al. 2012 ; Leonard 1990 ). This is present in studies that note dramatic differences between consumers in power, status, and self-perception ( Bone, Christensen, and Williams 2014 ). These examples in the marketplace are consistent with studies’ findings that centralization and steepness are linked to negative implications. Both can manifest in “competition, differentiation, and (resentful) deviance among some [organizational] unit members” ( Harrison and Klein 2007 , 1206); in conflict ( Bendersky and Hays 2012 ; Bunderson et al. 2016 ); and in enduring discrimination of racial and ethnic groups ( Ray 2019 ). Taken as a whole, a structuralist perspective of hierarchy, then, provides a path for our work, which investigates value hierarchy as inequality. See web appendix A for key constructs summary.

We argue that marketers need to view value hierarchy as a process of inequality (i.e., a means to establish, sustain, and alter inequalities) and as “hierarchy resulting in inequality across valued characteristics or outcomes” ( Bunderson et al. 2016 , 1267). While a robust legacy that examines how consumers shape value in the marketplace exists in consumer research ( Gollnhofer, Weijo, and Schouten 2019 ; Nøjgaard 2023 ; Schau, Muñiz, and Arnould 2009 ; Zeithaml 1988 ), the emphasis is on inequality as solely an outcome of value hierarchy. Thus, theorizing value hierarchy as a process brings to the fore the processual aspects of its relationship to inequalities and moves the field forward to explore the structural relationships of value among entities . These can include the structures that arise and/or are sustained, the resources that are distributed, and the consequences and depth of inequality. Value hierarchy as inequality also recognizes the duality of structures as embodying outcomes and agency, and importantly, it addresses the need to view value not only as what it is but also as what it does. The relevance of this is perhaps obvious, given that value hierarchy implicates and links a multitude of market and social processes, such as legitimation, in which entities change value positions when these become (de)legitimized ( Humphreys 2010 ; Mimoun, Trujillo-Torres, and Sobande 2022 ), and categorization, in which value is assigned and sorted ( Moreau et al. 2001 ).

Our research, therefore, underscores the nascent theoretical conversation about consumers, value hierarchy, and inequality. We argue that value hierarchy shapes and manifests as inequality as distribution of multilevel resources, consolidation of consumer power, stratification of consumer agency, and (de)credentialization of worthiness. We draw empirical evidence demonstrating each of these elements from the context of the cancer care market and the experiences of people with breast, leukemia, or lung cancer from 1970 to 2021. This market, which represents approximately 7% (nearly $209 billion) of all U.S. health expenditures related to diseases ( National Cancer Institute 2020 ), is characterized by value hierarchy with marked inequalities in resources (public, governmental, and medical), product innovation, service provider distribution, and funding ( Wailoo 2010 ). The implications for health services consumption and related consumer experiences are indisputable. As such, it is an ideal context to illustrate value hierarchy as inequality. Details about our context, data collection, and analysis approaches appear in web appendices B and C . We use our conceptualization and the literature as building blocks to identify future research priorities for JCR inquiries.

While the literature on value is extensive, surprisingly, it has paid scant attention to the value hierarchies implicit in marketing and consumer practices, despite strong evidence of consumers’ power and capacity to establish, sustain, and alter value structures. Thus, a structural perspective of value hierarchy can complement and extend the historically prominent position that value and value creation practices have occupied in consumer research. In table 1 , we provide a brief synopsis of the consumer research literature vis-à-vis value hierarchies. For example, consumers, in groups and/or through consumer advocacy organizations, can place or rank entities at different value positions that not only change markets ( Nøjgaard 2023 ) but also influence the distributive process that dictates who gets what within these markets, such as attention, prestige, and power. Similarly, consumer movements can assign greater value to products by devising new value regimes and value flows that alter privilege positions among entities (e.g., wasted food vs. traditional supermarket food; Gollnhofer et al. 2019 ).

SELECT CONSUMER RESEARCH AND VALUE HIERARCHIES

Note.— Illustrative exemplars are not exhaustive and may be relevant to multiple consumer research areas.

Of note, our review reveals that value hierarchy is implicit in much of consumer research and marketing strategy, from specific theoretical perspectives, such as social marketing and segmenting, targeting, and positioning, to academic movements, such as transformative consumer research and Race in the Marketplace. Each of the theoretical areas in table 1 presents broad opportunities to expand the accumulated knowledge on value hierarchy. For example, the value perception and creation literature assigns and sorts value across entities ( Moreau et al. 2001 ), whereas the (de)stigmatization literature adds or removes (de)valuing characteristics ( Eichert and Luedicke 2022 ). Furthermore, value hierarchy is interconnected with core elements of marketing practices, such as segmenting, targeting, and positioning. Diaz Ruiz and Kjellberg (2020 , 431) show that through feral segmentation, consumers “coin customer categories” with differential worth. These consumer research studies are consistent with those that emphasize that value hierarchy is intertwined with “inequality in member power, status, or privilege” ( Bunderson et al. 2016 , 1266) and the perspective that markets create structures of inequality that involve schemas, rules, and social and material resources. This underscores the need to “better understand stability, change, and the institutionalization” ( Ray 2019 , 26) of inequality.

An implication for consumer research is that inequalities are not simply an epiphenomenon of value relationships (i.e., a by-product or tangentially related), but rather structurally co-constitutive of the positions that entities occupy within a value hierarchy. For example, inequalities can create significant drawbacks for consumers ( Trujillo-Torres and DeBerry-Spence 2023 ) and are associated with sociocultural factors such as “age, gender, race, ethnicity, migrant status and disability” ( United Nations 2023 ). These factors can lead to systemic injustices, such as racist and discriminatory practices ( Crockett and Grier 2021 ), or stigmatizing practices ( Wallendorf 2001 ). This is consistent with Ray’s (2019) observation that “racial inequality is not merely ‘in’ organizations but ‘of’ them (48)” and that, correspondingly, “cultural schemas [are] connected to social resources” (30). However, inequalities are not always detrimental. Pronounced differences can reflect consumers’ rankings of preferred products ( Moreau et al. 2001 ), brands ( Schau et al. 2009 ), or people ( Grier and Deshpandé 2001 ) or support competition and market creation ( Gollnhofer et al. 2019 ). Thus, a structural perspective of value hierarchy as inequality suggests that inequalities are not just stand-alone by-products of or distinct from structures and processes.

Taken as a whole, our research acknowledges the need to examine value hierarchy as outcomes and processes and highlights the structural relationships of value among entities. Such an examination can provide greater insight into “who gets what and why” ( Lenski 1984 , 1) and extend understanding of not only what value is but also what value does. Importantly, we make explicit the relationship between value hierarchy and inequalities and address the broader “moral imperative” ( United Nations 2023 ) related to inequality. Collectively, these considerations create a path for marketing scholarship to contribute to this global policy debate.

To begin to address the limitations in the literature, we conceptualize how value hierarchy shapes and manifests as inequality. Building on existing literature and drawing on 50 years’ worth of data from the cancer care market and consumer health care experiences (see table W5 for more examples), we conceptualize value hierarchy as the distribution of multilevel resources, consolidation of consumer power, stratification of consumer agency, and (de)credentialization of worthiness. These elements underscore the outcomes and processual aspects embedded in value hierarchy, consistent with structures as embodying outcomes and agency. Our work highlights the role of structure as a medium and an outcome ( Giddens 1984 ; Sewell 1992 ) and therefore acknowledges the duality of value hierarchy. While we detail each of these independently for analytical purposes, we note that they can co-occur and overlap.

Distribution of Multilevel Resources

The distribution of multilevel resources is when consumers influence the allocation of resources that are situated at multiple levels within a value hierarchy. This can involve consumers linking with a variety of actors to establish, sustain, or change their position within a hierarchy. Resources can vary from the intangible (e.g., social support, knowledge, skills, rules), to the material (e.g., capital, commodities), to any vehicle of power that consumers “[use] to gain, enhance, contest, or maintain” a certain position ( Ray 2019 , 31). As an outcome and a process, the distribution of multilevel resources implicates attributes and actions; that is, it both characterizes observed market inequalities and consumer experiences of resource differences and acts to sustain and (re)create value hierarchies. The distribution of these (i.e., what, to whom, and when) also shapes value hierarchy as inequality by implicating multiple levels of actors and resources, from the micro (e.g., consumer), to the meso (e.g., marketers, market), to the macro (e.g., government, legislation). For example, Huff, Humphreys, and Wilner (2021) show that the legitimacy of cannabis consumption, which implicates an improved position within a value hierarchy, depends on different types of consumers, producers, and the meta market, with the last including regulatory bodies, news media, and public opinion. These micro and macro hierarchical processes are also present in the Black Lives Matter movement, in which consumers marshal resources (e.g., hashtags, its name) to influence positions within a value hierarchy. As might be expected, then, if select entities receive resources (i.e., centralization) that are dramatically different from what others receive within a value hierarchy (i.e., steepness), structural dominance and inequalities are likely to arise over time.

Breast cancer activism illustrates value hierarchy as inequality vis-à-vis the distribution of multilevel resources in the cancer care market. In the 1990s, breast cancer activists, with help from political actors, instituted a new value hierarchy that directed resources (funding, attention) to breast cancer (the disease) and brought attention to and elevated in public discourse these patient experiences. In other words, activists created micro–macro linkages (e.g., Congress “lobby” days and rallies; web appendix C ) that resulted in outcomes such as the dramatic increase in funding in 1993 by the Clinton Administration for breast cancer research and a role in research funding oversight. These efforts also created inequalities in the cancer care market. For example, web appendix D shows that from 1993 to 2018, breast cancer was the highest U.S. government-funded cancer, far ahead of other cancers also with high rates of disease incidences and mortality. Breast cancer also led (followed by leukemia) across many measures of success (e.g., private donations, media coverage) and benefited from micro–meso linkages of consumers with marketers and other market institutions interested in supporting breast cancer and/or the experiences of patients through cause marketing ( Jain 2013 ). Indeed, in 2003, cancer awareness, driven by breast cancer, became the number one health problem in the United States in the national Gallup survey, reflecting another value hierarchy among various health concerns. By contrast, lung cancer had fewer resources, its patients were stigmatized, and patient advocacy was limited, reflecting prejudices against smoking ( Chapple, Ziebland, and McPherson 2004 ).

Consolidation of Consumer Power

The consolidation of consumer power is when consumers concurrently wield power and create outcomes from the mobilization of power. Acknowledging this duality recognizes the ability of consumers to successfully consolidate resources ( Sewell 1992 ) and to achieve certain outcomes. It also opens the door for greater examination of how consumers wield power to create “novel mechanisms” ( Ray 2019 , 35) that reproduce hierarchical relationships as inequality. After all, consumers create or support value hierarchies that differentially grant resources; those who are resource dominant (resource deprived) have significantly more (less) power. In other words, resource distribution involves centralization of power and steepness in power ( Sewell 1992 ), with obvious implications for markets and consumer experiences of inequality. For example, gentrification, in which lower-income residents are replaced with higher-income residents, reflects hierarchical differences with significant power implications for local commercial development and residents, including the power to create a sense of community and racial harmony ( Grier and Perry 2018 ).

Empirical evidence from the cancer care market shows consolidation and use of consumer power in consumer-driven product innovation by leukemia patients and activists. Such is the case of the Team in Training fundraising initiative, a program created in 1988 by a leukemia patient’s father, who raised $322,000 participating in the New York City marathon ( LLS 2023 ). This fundraising initiative showcases athletic humanitarians who self-sacrifice pursuing an athletic goal and a cancer cure, marshaling social and material resources to leukemia institutions, such as the Leukemia and Lymphoma Society (LLS), and leukemia patients. Initiatives such as these catapulted the power of the “patient hero” in public discourse. Not surprising, LLS became one of the most successful fundraising charities in the United States for private donations from 1970 to 2021, far above other cancer charities that represent diseases with higher mortality and incidence rates ( web appendix E ). Team in Training activities were also associated with extensive commercial solidarity and were key enablers of the power leveraged by leukemia organizations ( web appendix F ). As a result, the power of leukemia activism translated into the ability to foster commercially available treatment innovations for various leukemia types with private funding. Leukemia is only second to breast cancer in the number of Food and Drug Administration-approved drugs and treatments (75 and 108, respectively) ( web appendix G ). By contrast, lung cancer in third place has benefited from leukemia and breast cancer drug discoveries and research, showing that dominant entities can also uplift others. This suggests that value hierarchy as inequality may not be as steep among some types of cancer.

Stratification of Consumer Agency

Stratification of consumer agency entails consumers creating categories of entities with distinct levels of agency. This concerns central tenets of value hierarchy; that is, stratification implicates structure and agency and links value hierarchy as inequality that stems from the allocation of consumer agency. Agency involves deliberate action and exercise of subjectivity and free will in making decisions and communicating ( White and Wyn 1998 ), while structure is the organized rules and resources that actors create and reproduce in social interactions and that constitute a social system ( Giddens 1984 ). Agency differentials contribute to inequality processes through the maintenance, reproduction, or disruption of hierarchy. In this way, value hierarchy as inequality can typify the push and pull of structure and agency associated with Giddens’s (1984) theory of structuration. For example, Figueiredo et al. (2015) identify consumer agency as both constrained and fostered. They note that consumers in informal markets exert agency by transgressing the boundaries of formal economies but also acknowledge that respecting consumer agency is not always achievable. This suggests that consumers do not all have the same agency and that consumers’ agencies are not all valued the same.

We turn to the cancer market to empirically demonstrate the stratification of consumer agency. In the 1990s, disease-specific advocacy organizations such as the LLS actively shaped and reproduced notions of agency in leukemia patients, who often faced exclusion, stigma, and discrimination in school, employment, medical access, and other areas ( Wailoo 2010 ). For example, an LLS employee in a 1990 Chicago Tribune article argued for deliberate action and the exercise of free will: “Jeff is a survivor. Years ago, leukemia was a death sentence. But today many more people survive … we're winning the battle in a lot of cases” ( Ogintz 1990 ). Agency here refers to the ability of these patients to seek, access, and employ treatments successfully (e.g., bone marrow transplant, drugs) and communicate success. The active inclusion of leukemia patients in Team in Training activities such as marathons and triathlons complemented these rhetorical efforts. As a patient in remission stated: “You can do anything, if you want it badly enough” ( Willismaon 1994 ). These efforts have led to the reduction of inequalities for leukemia patients by enabling the use of products and technical innovations, reduced mortality, and increased public support (e.g., media coverage, private donations). However, tensions related to who benefits from agency allocations and which patients can enact agency remain. For example, lung cancer patients’ ability to seek treatments has been limited—truncated by changing social conditions (e.g., stigma against smoking, secondhand smoke danger), their perceived culpability, and nonsmoker victimization. Consequently, less public, scientific, and government attention has been given to the disease, patients, and advocacy organizations.

(De)Credentialization of Worthiness

The (de)credentialization of worthiness entails consumers having (or not having) credentials and the process of establishing (removing) credentials within a value hierarchy. In other words, (de)credentialization determines what is worthy within a value hierarchy. This focus is important because credentials and the process of credentialization implicate resources, power, agency, and, correspondingly, inequalities. Thus, credentials and credentialization can bear fewer inequalities (e.g., through inclusion) or more inequalities (e.g., through exclusion). For example, Ray (2019) notes that within organizations, whiteness has been an enduring credential for the allocation of resources, power, and agency to organizational actors, with negative implications (e.g., racism, discrimination) for non-white actors. In consumer research, various credentials of worthiness, from race to gender to class to sexual orientation, pertain to value hierarchy and relatedly inequality ( Mimoun et al. 2022 ).

(De)credentialization of worthiness occurs in the cancer care market. Until the late 2000s, the common credentials to obtain cancer treatments were survivability prospects (usually an early detected cancer) and considerations that often related to patients’ race and socioeconomic status ( Jain 2013 ). Patients with curable cancers and from middle-class and educated backgrounds ( Wailoo 2010 ) had more consumption opportunities than other cancer patients. Then, biomarker matching emerged as a new credential, aligning patients’ biological markers (e.g., proteins, cells, genes, nucleotides) with available therapeutic and preventive options. This decredentialization provided opportunities for patients who had previously experienced significant inequality to seek innovative treatments. This is the case for women with metastatic breast cancers, or “metavivors” whose cancer migrated from the breast to different organs or systems, who often feel excluded, as they are counternormative examples of “cured” early-stage survivors ( Jain 2013 ) and advocate for more attention to their disease and experiences. Though increasing these patients’ visibility, biomarker matching offers no guarantee of longevity (see web appendix H for metavivors’ photos), as only one-third of metastatic breast cancer patients live for at least 5 years, given the lack of treatments ( National Cancer Institute 2020 ). Biomarkers have also rendered visible the “previvors,” who are predisposed to breast and ovarian cancer due to BRCA1 and BRCA2 genes but who are not diagnosed with cancer. Their experiences with preventive cancer surgeries are often legitimized by public figures (e.g., Angelina Jolie) but can be minimized by actual cancer patients. Thus, biomarker matching has created a dynamic decredentialization of worthiness requirements in the cancer care market, removing some inequalities and revealing multiple hierarchies within the breast cancer patient community.

Building on our conceptualization of how value hierarchy both shapes and manifests as inequality as well as the literature, we discuss research areas for future JCR inquiries. Table 2 summarizes these efforts and highlights not only a variety of conceptual domains (consumer research areas) in which our conceptualization can find application but also multiple empirical contexts and questions that would benefit from further exploration.

FUTURE RESEARCH PRIORITIES AND QUESTIONS ON VALUE HIERARCHY AS INEQUALITY

Regarding the distribution of multilevel resources, we encourage the continued examination of how resource distribution can further enable or constrain inequalities in consumer actions, organizations, and outcomes within value hierarchy. Historically, consumer research on value has focused on the micro–meso linkages in markets ( Schau et al. 2009 ; Zeithaml 1988 ), despite evidence that macro-level institutions (e.g., governments, legislation; Humphreys 2010 ) and ideologies ( Giesler and Veresiu 2014 ) are key sources of legitimacy. Moreover, inequality, whether in the marketplace or society, is systemic and institutionalized ( Poole et al. 2021 ), which is evident in studies on race in the marketplace ( Trujillo-Torres and DeBerry-Spence 2023 ). We can therefore also leverage Ray’s (2019) tenets of racialized organizations for the study of consumer value hierarchies. For example, future examinations could investigate the factors that determine the roles of conflict, political considerations, allies, coalitions, and ideologies on the levels and boundaries of resource distribution within value hierarchies in consumer markets. Such research could enrich understanding of how value hierarchy affects the “stability and change” ( Ray 2019 , 30) of inequalities by “accumulating, managing, monopolizing, and apportioning … resources.”

In terms of the consolidation of consumer power, prior research has demonstrated the status games that consumers play to compensate for an underprivileged position in a static value hierarchy ( Rucker and Galinsky 2008 ), but also the variety of ways value orientations can shift through consumer agency. For example, Gollnhofer et al. (2019) showcase the collaboration of consumers with marketers to assign greater value to wasted food by devising alternative “flows of food to waste” that position wasted food as worthy of consumption ( Gollnhofer et al. 2019 , 461), thereby subverting one value hierarchy and (being in the process of) establishing another. Looking ahead, future consumer research on value hierarchy as inequality could investigate how consolidated and consolidating consumer power opens new avenues for consumer-driven innovation and/or collaboration ( Kjeldgaard et al. 2017 ). Scholars might also examine how consumers consolidate and deploy resources from governmental and commercial resources to, for example, resist changes that occur in neighborhoods following urban revitalization that prioritizes some consumers’ interests over others. Moreover, studies on consumer power across gentrifying markets in diverse contexts would be fruitful, as particular market configurations differ given competing ideologies, historical influences, and regulations.

Regarding the stratification of consumer agency, future research could benefit from a greater understanding of the tensions that underlie consumer agency and value hierarchy within markets, societies, and institutions and specifically the tensions that arise when consumers desire to enact agency but are either limited or restricted in their ability to do so. This is the case for “fatshionistas,” who are subject to inequality in terms of product options and consumption opportunities ( Scaraboto and Fischer 2013 ). These consumers exhibit agency by blogging and participating in the Fat Acceptance Movement, but their agency is bound within the logics of the fashion system. Conversely, Adkins and Ozanne (2005) demonstrate how low-literate consumers can mobilize “consumer literacy” through compensating coping skills. Here, value hierarchy is dominant but not hegemonic. Consumer research could also examine how these tensions manifest and, importantly, how they relate to the processes associated with the maintenance and/or reproduction of value hierarchy as inequality.

Future studies could also shed greater light on the stratification of consumer agency by examining periods of destabilization in consumption and markets. For example, the COVID-19 pandemic revealed the significance of long-standing health disparities between population groups ( DeBerry-Spence and Trujillo-Torres 2022 ), and thus, research could benefit from a societal-level understanding of value hierarchy, agency allocation, and resource distribution. This underscores the need to realize inequality in agency to identify novel intervention points for transformative consumer research efforts. Such a focus would enhance understanding of how “agency, motive, and action” relate “to resources and cultural schemas” ( Ray 2019 , 35).

Future research on (de)credentialization of worthiness would give the field a better understanding of the dynamism of value hierarchy as inequality, including its stability, change, and institutionalization. For example, Mimoun et al. (2022) identify hierarchical differences based on worthiness credentials that impeded (facilitated) fertility technology consumption. In their study, single women, women over the age of 40, and LGBTQ+ individuals defied the worthiness requirements of those who were considered “good parents”: young, married, heterosexual couples. Importantly, credentialization can be dynamic to reflect changes that alter conceptions of worthiness, such as a changing historical context, power relations, or new systems of thought ( Ray 2019 ). In Mimoun et al. (2022) , for example, developments such as higher rates of female participation in the workforce, higher earnings of women, and changing societal acceptance of sexual identity have decredentialized the old value hierarchy, legitimized consumer segments, and decreased inequalities in this market. Conversely, Nøjgaard (2023) examines the credentialization processes through which value hierarchies are established from “objectifying” value translation processes in a formalized consumer activism setting. Future studies could extend the dynamism of (de)credentialization of worthiness by investigating, in more depth, its intersection with other social processes, such as legitimation and stigmatization, and the transferability or inheritance of credentials over time. Other areas that have received scant research attention include how the valence or neutrality of credentials is (re)constructed and the role of (de)credentialization in market evolution and disruption. These inquiries could further delineate the processes and effects of value hierarchy.

Our research highlights the significance of inequality in markets and the relationship to value hierarchy. Of note, our understanding of value hierarchy infers a “valorization process,” as social hierarchies are not given but are necessarily the outcome of social processes. We introduce the concept “value hierarchy as inequality” and draw from the literature and empirical evidence to conceptualize and demonstrate how value hierarchy shapes and manifests as inequality. Doing so helps move the field forward to comprehend value hierarchy as both an outcome and a process and brings to the fore the structural relationships of value among entities. It also makes clear the need to view value not only as what it is but also as what it does. Our conceptualization is supported by more than 50 years of empirical evidence drawn from the cancer care market. Thus, our work provides a springboard for consumer behavior researchers to pursue more influential scholarship that addresses value hierarchies and their importance for inequality. It also aligns with the global recognition that inequalities are not inconsequential. Thus, we call for future JCR inquiries over the next 50 years to address value hierarchy as inequality.

The first author led the data collection efforts from 2015 to 2021. The archival data were collected from library databases, news media, blogs and social media, and other secondary sources. The authors also conducted observations. The data were analyzed jointly by all authors. The data are currently stored in a project directory on Research Box.

Lez E. Trujillo-Torres ( [email protected] ) is an assistant professor of marketing at the University of Illinois Chicago, Chicago, IL 60607, USA.

Benét DeBerry-Spence ( [email protected] ) is a professor of marketing at the University of Illinois Chicago, Chicago, IL 60607, USA.

Sonya A. Grier ( [email protected] ) is a professor at the Department of Marketing, American University, Washington, DC 20016, USA.

Søren Askegaard ( [email protected] ) is a professor at the Department of Business & Management and chair at Danish Institute for Advanced Study (DIAS), University of Southern Denmark, 5230 Odense M, Denmark.

The first two authors contributed equally to this work. This article is based on the first author’s dissertation, which was completed at the University of Illinois Chicago. The authors thank the editor, associate editor, and three reviewers for their support and constructive feedback. The authors report no conflicts of interest. Supplementary materials are included in the web appendix accompanying the online version of this article.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Fisher Restoration, Management, and Research Supported Through Wildlife Restoration Funds

Found only in North America, fishers live in forested and semi-forested areas in Canada and the northern United States. Sometimes called fisher cats, these mammals are one of the largest members of the weasel family. They have the typical weasel shape with a long, slender body, short legs, and thick fur. Pressure from logging and habitat changes for agriculture and development in the late 18th and early 19th century led to the decline of fisher across most of the eastern U.S. The value of fisher fur and their unregulated historic harvest also contributed to population decline. 

Today, thanks to conservation efforts, research, and regulated harvest, fisher populations are sustainably managed by state fish and wildlife agencies. In New England and the Mid-Atlantic region Wildlife Restoration grants, funded through a partnership that uses manufacture excise tax on sporting arms, ammunition, and archery equipment as well as revenue from state hunting license and permit fees, have funded population assessments to expand and increase fisher populations and funded research into the needs of the species. 

“New Hampshire is lucky, the state did not experience the same level of development as our neighbors and we have always maintained a fisher population in the state,” said Patrick Tate, Wildlife Biologist at New Hampshire Fish and Game Department. Other states experienced larger fisher declines and some experienced total loss of fisher within their borders. “Fisher from our state have been moved to help build populations in states like Connecticut, Pennsylvania, and West Virginia,” added Tate. “For decades our state has used Wildlife Restoration funds for a variety of research projects to ensure we maintain the fisher for future generations.” In recent years New Hampshire Fish and Game Department has used Wildlife Restoration funds to work alongside the University of New Hampshire to conduct a trail camera study with over 175 cameras capturing images of furbearer species including fishers, coyotes, bobcats, and foxes. Along with the network of cameras, researchers are using GPS collars to track and monitor survival and mortalities of fisher. Upon the death of a fisher the collars transmit a mortality signal for field recovery. Once recovered, the Veterinary Diagnostics Laboratory at UNH will run an analysis for any toxins, viruses, bacteria and to identify the likely cause of death. “Through this partnership we are learning more about the current and future threats to fisher, and we can make informed management decisions,” added Tate.

“Fishers are a native species in Connecticut and regularly prey on small mammals like squirrels and mice, which is essential for a balanced, healthy ecosystem,” said Jason Hawley, Furbearer Program biologists at Connecticut Department of Energy & Environmental Protection. Key to maintaining healthy fisher populations after their reintroduction in 1988 has been research to understand habitat needs such as den sites and areas for highway crossings and the historic and current monitoring of population size and dynamics of fisher. Connecticut has seen a fisher decline in recent years and Hawley and his colleagues are using Wildlife Restoration funds to conduct a three-year long research project to study fisher mortality and changes to population size. “The project totals around $350,000 and is entirely supported through Wildlife Restoration funds,” added Hawley. “The funds support staff time, purchase of the collars for adult fisher, and transmitters for young fisher known as kits.” Connecticut is currently in the first year of the study capturing and collaring 28 total fishers to test the best collar types for the study. Like in New Hampshire, these collars will help researchers monitor fisher movements and habitat preferences and send a signal to Hawley and his team if the collared fisher dies. Hawley’s team is including transmitters for kits in this study to gain data on the survival of young fisher as well as adults. “It is important for species management to understand the age of a population and survival at different life stages.” During the next two years 100 fishers will be captured, collared, and monitored with the support of Wildlife Restoration funds. 

In neighboring Rhode Island, Wildlife Restoration funds are used to support staff time that work on furbearer species including fisher, support trail camera and collar monitoring research, and aid in course development for trapping education. “The Rhode Island Department of Environmental Management’s Division of Fish and Wildlife and former University of Rhode Island PhD student Laken Ganoe collaborated to trap and collar 56 fisher to collect data on resource selection and population demographics across the state,” said Morgan Lucot, biologist with the Division of Fish and Wildlife. The collaring and trail camera studies have been completed, and Dr. Ganoe is currently in the process of publishing the results from these projects. The fishers in this study are the first fishers that have been collared for monitoring in Rhode Island history. Prior to this study, data on fisher in Rhode Island was primarily limited to information collected during harvest by trappers with trappers often acting as citizen scientists sharing their observation and harvest data with state agency officials. “Our licensed trappers are valuable partners in fisher management, they provide harvest data, and this community was one of the first to report a decline in fisher numbers with some self-selecting to not harvest fisher in recent years.” Lucot goes on to explain that furbearer management efforts in Rhode Island go beyond field research. “Along with supporting research, Wildlife Restoration funds have also supported course development for Rhode Islanders to better understand the regulated trapping fundamentals and the role trappers can play.” The state agency offers a day-long course covering various aspects of trapping including the history of trapping, conservation and trapping regulations, and the biology of different target species in Rhode Island. 

In New York State, fisher can be found throughout approximately 26,000 square miles of forested habitat. Biologists at the New York Department of Environmental Conservation have placed radio and GPS collars on fisher in different parts of the state including the Adirondack Mountains. “Through this Wildlife Restoration funded research, we were able to see areas where the fisher is doing well and where populations may be declining, with this information we can expand our research into why a certain area may be experiencing a decline,” said Mandy Watson, biologist with the agency. Along with the collar monitoring, the agency has used a network of cameras to capture images of fisher, and also collected fisher fur samples. The samples were collected using gun brushes mounted on a tree below bait to collect hair from fisher. These samples allow researchers to collect data on individual fisher. “With the data from this research we reduced the fisher trapping season in select Adirondack Wildlife Management Units where populations had declined,” added Watson. “Like in other states we saw trappers self-selecting to not harvest fisher.” Watson goes on to add that trappers were vital in the data collection effort as they helped researchers capture fishers to be collared as part of the monitoring study. “Thanks to the help of the trapping community sharing their knowledge and the researchers collecting data we have more information about the current state of fisher in New York, and we can share these findings with other agencies.” 

For decades, Wildlife Restoration grant funds have supported fisher restoration, reintroduction, research, and monitoring across much of the fisher’s range. Most of our fisher knowledge including their habitat requirements, movements and travel corridors, the factors affecting survival, food habits, and the impacts of pesticides has been gained by studies funded with Wildlife Restoration grants. Research efforts conducted by state fish and wildlife agencies and their partners have provided important information for management decisions to ensure fisher are scientifically managed and remain part of the New England and Mid-Atlantic ecosystems now and into the future.

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Experimental R&D Value Added Statistics for the U.S. and States Now Available

Research and development activity accounted for 2.3 percent of the U.S. economy in 2021, according to new experimental statistics released today by the Bureau of Economic Analysis. R&D as a share of each state’s gross domestic product, or GDP, ranged from 0.3 percent in Louisiana and Wyoming to 6.3 percent in New Mexico, home to federally funded Los Alamos National Laboratory and Sandia National Laboratories.

These statistics are part of a new Research and Development Satellite Account  BEA is developing in partnership with the National Center for Science and Engineering Statistics of the National Science Foundation . The statistics complement BEA’s national data on R&D investment  and provide BEA’s first state-by-state numbers on R&D.

The new statistics, covering 2017 to 2021, provide information on the contribution of R&D to GDP (known as R&D value added), compensation, and employment for the nation, all 50 states, and the District of Columbia. In the state statistics, R&D is attributed to the state where the R&D is performed.

Some highlights from the newly released statistics:

R&D activity is highly concentrated in the United States. The top ten R&D-producing states account for 70 percent of U.S. R&D value added. California alone accounts for almost a third of U.S. R&D. Other top R&D-producing states include Washington, Massachusetts, Texas, and New York.

Treating R&D as a sector allows for comparisons with other industries and sectors of the U.S. economy. For instance, R&D’s share of U.S. value added in 2021 is similar to hospitals (2.4 percent) and food services and drinking places (2.2 percent).

Eighty-five percent of R&D value added is generated by the business sector, followed by government, and nonprofit institutions serving households.

Within the business sector, the professional, scientific, and technical services industry accounts for 40 percent of business R&D value added.    Information (15 percent), chemical manufacturing (12 percent), and computer and electronic product manufacturing (11 percent) also account for sizable shares.

Visit the R&D Satellite Account on BEA’s website for the full set of experimental statistics and accompanying information. To help refine the methodology and presentation of these statistics, BEA is seeking your feedback. Please submit comments to  [email protected] .

The 'healthiest' way to spend 24 hours depends on what you value most

Health The 'healthiest' way to spend 24 hours depends on what you value most

It's known as the "Goldilocks day": the "just right" way to allocate your time to various activities for optimal health.

Sounds like a handy guide to life, right? But is it even possible?

We already have guidelines around how much physical activity adults should get each week . So how many hours each day should we spend standing, sitting or sleeping?

New Australian research published in Diabetologica provides an hour-by-hour breakdown of daily activities to reduce the risk of cardiometabolic diseases, which include disorders of the heart, diabetes and chronic kidney disease.

The study, from Swinburne University and the Baker Heart and Diabetes Institute, analysed more than 2,000 people in the Netherlands, 684 of whom had type 2 diabetes.

Over seven days, they had their waist circumference, blood glucose and insulin levels, cholesterol, blood pressure and triglycerides (a type of fat found in blood) measured.

By examining how participants with the healthiest results divvied up their time, the researchers came up with what they say is an optimum day for cardiometabolic health.

Christian Brakenridge from Swinburne's Centre for Urban Transitions led the research, and says the activity plan is "like a North Star" — something to aim towards.

"I think people might kind of baulk at the idea of these strong quantitative guidelines, but the take home message here is we really want people to sit less, move more and sleep for appropriate durations," Dr Brakenridge says.

The average Australian sits for about eight hours a day but desk-based office workers can spend around 10 hours seated.

And most of us only get two hours of physical activity each day (that's light and moderate activity combined), which is about half of what the study recommends.

Light physical activity includes slow walking or doing chores, and moderate to vigorous activity can be brisk walking, jogging or difficult tasks like shovelling.

Dot Dumuid is a time-use epidemiologist at the University of South Australia. For years she's studied the healthiest ways to spend our time.

She provided statistics for the new study, and noted its narrow focus on cardiometabolic   risk factors.

"I like when studies put other outcomes in there as well, like cognition, for example."

Dr Dumuid says very few study participants managed four hours of activity day in, day out.

" There'd be a few super-achievers … but that's not feasible for heaps of people.

"You could do it, but you'd have to give up something else."

And that activity trade-off is where things get interesting.

Adjusting the levers of your life

The perfect day for your heart might be quite different to the perfect day for your brain.

Dr Dumuid has studied the "optimum" 24 hours for a range of health outcomes, and is particularly interested in what happens when you take time from one category   and put it in another.

For example, physical activity is great for heart health. But if it comes at the cost of sleep, Dr Dumuid says that can be detrimental for those with anxiety and depression.

And people need to spend more hours sitting than moving if they want to optimise academic performance and cognitive function, as that's when we usually do things like study, read or play music.

While Dr Dumuid is yet to come up with a "Goldilocks day" for adults, she has one that she says is most beneficial for the mental, physical and cognitive function of children aged 11 and 12.

But even with children, priorities can shift, and if exams are approaching, a student might need to temporarily adjust the dial to manage their time differently.

To help with this, Dr Dumuid developed an online tool which lets students rank what's most important to them to give a more personalised 24-hour breakdown.

"One size rarely fits all in population health," she says.

More than one optimum day

No matter how much time we want to invest in being happy and healthy, not everyone has complete agency over how they spend their day.

There can be many limitations depending on where you live, what you earn and whether your capacity is restricted, for example, by chronic health conditions.

And the daily activity combinations researchers looked at in the new study didn't incorporate things like social interactions, which can improve mental and physical health.

So how many hours a day should we spend socialising? Recent research in Nature found there's no universal balance between solitude and socialising.

In fact, solitude (when the person chooses it) can reduce stress levels.

This is another reason why Dr Dumuid thinks we'll never have one single optimum day for overall health.

Instead, perhaps we'll one day have multiple "best days" with different purposes.

"In the future you might wake up and decide 'OK, today I want to preference my mental health, let me see what my options are.'

"Then you focus on something else the next day, and then over a week you can balance it out to be a good, healthy week."

Dr Brakenridge hopes his findings will be used by the federal government to update current health guidelines so they can better reflect the full spectrum of human behaviour.

He says Australia should look to Canada, which has the world's first 24-hour movement guidelines that lay out how much time adults should spend doing aerobic activities, muscle strengthening, sleeping, sitting and using a screen.

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