scientific research basic knowledge

What is Basic Research?

Introduction

What is the meaning of basic research, examples of basic research, how do i perform basic research.

Basic science research is an essential pillar of scientific knowledge, because it extends understanding, provides new insights, and contributes to the advancement of science and fundamental knowledge across disciplines. In contrast, applied research aims for the discovery of practical solutions, which can involve using a technology or innovation that stems from existing knowledge. Basic science research potentially allows for generating ideas on which applied science can build novel inquiry and useful applications.

The process for conducting basic research is essentially the same as in an applied research orientation, but a better understanding of the distinction may prove increasingly important when crafting your research inquiry. In this article, we'll detail the characteristics and importance of basic research.

One of the key distinctions in science is the divide between basic and applied research . Applied research is directly associated with practical applications such as:

career development
program evaluation
policy reform
community action

In inquiries regarding each of these applications, researchers identify a specific problem to be solved and design a study intentionally aimed at developing solutions to that problem. Basic research is less concerned about specific problems and more focused on the nature of understanding.

Characteristics of basic research

Research that advances understanding of knowledge has distinguishing characteristics and important considerations.

Focus on theoretical development . Rather than focus on practical applications, scholars in basic science research are more interested in ordering data and understanding in a scientific manner. This means expanding the consensus understanding of theory and the proposal of new theoretical frameworks that ultimately further research.
Exploratory research questions . Basic research tends to look at areas where there is insufficient theoretical coherence to empirically understand phenomena. In other words, basic research often employs research questions that seek greater definition of knowledge.
Funding for basic science . The nature of the support available for research depends on whether the science is basic or applied . Government agencies, national institutes, and private organizations all have different objectives, making some more appropriate for basic research than others.
Writing for research dissemination . Academic journals exist on a continuum between theoretical and practical orientations. Journals that are more interested in theoretical and methodological discussions are more appropriate for basic research than are journals that look for more practical implications arising from research.

The brief survey of these characteristics should guide researchers about how they should approach research design in terms of feasibility, methods, and execution. This discussion shouldn't preclude you from pursuing basic research if it is more appropriate to your research inquiry. Instead, it should inform you of the opportunities, advantages, and challenges of basic research.

Importance of basic research

Basic research may seem aimless and unfocused if it doesn't yield any direct practical implications. However, its contribution to scholarly discussion cannot be overstated as it guides the development of theories and facilitates critical discussion about what applied studies to pursue next.

Basic science has guided fields such as microbiology, engineering, and chemistry. Scientists ultimately use its findings to develop new methods in treating disease and innovating on new technology.

Its contribution to the social sciences through observation and longitudinal study is also immeasurable. While basic research is often a precursor to more applied science, the theories it generates spur further study that ultimately leads to professional development programs and policy reform in social institutions.

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Different fields rely on both applied and basic science for generating new knowledge. While applied research looks to yield direct benefits through real-world applications, basic research provides the necessary theoretical foundation for practical research in various fields.

Basic research example in education

Basic research in schooling contexts focuses on understanding the nature of teaching and learning or the processes within educational environments before any focused investigation can be designed, let alone conducted. Basic research is necessary in this case because of the various situated differences across learners who come from different cultures and backgrounds.

Basic research in education looks at various inquiries such as how teachers and students interact with each other and how alternative assessments can create positive learning outcomes. Ultimately, this may lead to applied research that can facilitate the creation of teacher education and professional development programs.

Basic research example in psychology

Psychology is a field that is under constant development. Basic research is essential to developing theories related to human behavior and mental processes. The subfield of cognition is a significant benefactor of basic research as it relies on novel theoretical frameworks relating to memory and learning.

Basic research example in health

A great deal of health research that reaches public consciousness is undoubtedly applied research. The development of vaccines and other medicine to combat the COVID-19 pandemic was one such line of inquiry that addressed a practical need.

That said, scientists will undoubtedly credit basic research as a precursor to medical breakthroughs in applied science research. The knowledge gained through basic research laid the foundation for genomic sequencing of the COVID-19 virus, while experiments on living systems created knowledge about how to safely vaccinate the human body.

The National Institute of Health sponsors such basic research and research in other areas such as human DNA, while the National Science Foundation funds basic research on topics such as gender stereotypes and stress levels.

At its core, all scientific inquiry seeks to identify causal factors, relationships, and distinguishing characteristics among concepts and phenomena. As a result, the process is essentially the same for basic or applied science. Nonetheless, it is worth reviewing the process.

Research design . Identify gaps in existing research that novel inquiry can address. A rigorous literature review can help identify theoretical or methodological gaps that a new study with an exploratory research question can address.
Data collection . Exploratory research questions tend to prioritize data collection methods such as interviews , focus groups , and observations . Basic research, as a result, casts a wide net for any and all potential data that can facilitate generation of theoretical developments.
Data analysis . At this stage, the goal is to organize and view your data in such a way that facilitates the identification of key insights. Analysis in basic research serves the dual purpose of filtering data through existing theoretical frameworks and generating new theory.
Research dissemination . Once you determine your findings, you will want to present your insights in an empirical and rigorous manner. Visualizing data in your papers and presentations is useful for pointing out the most relevant data and analysis in your study.

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Published: 16 January 2024

Basic science is not just a foundation

Ruth Lehmann ORCID: orcid.org/0000-0002-8454-5651 1

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Intellectual freedom for scientists, unconstrained by commercial interests and direct application, fuels unexpected discoveries. Curiosity-driven, basic science has yielded a deeper understanding of how life forms develop and function in their environment and has had wide implications for health and our planet. Investing in this is vital for scientific progress and is worth protecting in a democracy.

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Acknowledgements

I am grateful for many discussions with friends and colleagues in conceptualizing the opinions expressed here, but most importantly to my collaborator on this essay, L. Girard, Director of Strategic Communication, Whitehead Institute. Much of the opinions expressed here were shaped by my experience as a trainee and investigator at the Max Planck Institute for Developmental Biology in Tübingen Germany, the Medical Research Council in Cambridge UK, the Skirball Institute at New York University Langone Medical Center, the Whitehead Institute, the Massachusetts Institute of Technology and the Howard Hughes Medical Institute.

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Discoveries in Basic Science

A Perfectly Imperfect Process

Illustration of two scientists talking in front of a puzzle of a complex molecule

Have you ever wondered why science takes so long? Maybe you haven’t thought about it much. But waiting around to hear more about COVID-19 may have you frustrated with the process.

Science can be slow and unpredictable. Each research advance builds on past discoveries, often in unexpected ways. It can take many years to build up enough basic knowledge to apply what scientists learn to improve human health.

“You really can’t understand how a disease occurs if you don’t understand how the basic biological processes work in the first place,” says Dr. Jon Lorsch, director of NIH’s National Institute of General Medical Sciences. “And of course, if you don’t understand how the underlying processes work, you don’t have any hope of actually fixing them and curing those diseases.”

Basic research asks fundamental questions about how life works. Scientists study cells, genes, proteins, and other building blocks of life. What they find can lead to better ways to predict, prevent, diagnose, and treat disease.

How Basic Research Works

When scientists are interested in a topic, they first read previous studies to find out what’s known. This lets them figure out what questions still need to be asked.

Using what they learn, scientists design new experiments to answer important unresolved questions. They collect and analyze data, and evaluate what the findings might mean.

The type of experiment depends on the question and the field of science. A lot of what we know about basic biology so far has come from studying organisms other than people.

“If one wants to delve into the intricate details of how cells work or how the molecules inside the cells work together to make processes happen, it can be very difficult to study them in humans,” Lorsch explains. “But you can study them in a less complicated life form.”

These are called research organisms. The basic biology of these organisms can be similar to ours, and much is already known about their genetic makeup. They can include yeast, fruit flies, worms, zebrafish, and mice.

Computers can also help answer basic science questions. “You can use computers to look for patterns and to try to understand how the different data you’ve collected can fit together,” Lorsch says.

But computer models have limits. They often rely on what’s already known about a process or disease. So it’s important that the models include the most up-to-date information. Scientists usually have more confidence in predictions when different computer models come up with similar answers.

This is true for other types of studies, too. One study usually only uncovers a piece of a much larger puzzle. It takes a lot of data from many different scientists to start piecing the puzzle together.

Building Together

Science is a collective effort. Researchers often work together and communicate with each other regularly. They chat with other scientists about their work, both in their lab and beyond. They present their findings at national and international conferences. Networking with their peers lets them get feedback from other experts while doing their research.

Once they’ve collected enough evidence to support their idea, researchers go through a more formal peer-review process. They write a paper summarizing their study and try to get it published in a scientific journal. After they submit their study to a journal, editors review it and decide whether to send it to other scientists for peer review.

“Peer review keeps us all informed of each other’s work, makes sure we’re staying on the cutting-edge with our techniques, and maintains a level of integrity and honesty in science,” says Dr. Windy Boyd, a senior science editor who oversees the peer-review process at NIH’s scientific journal of environmental health research and news.

Different experts evaluate the quality of the research. They look at the methods and how the results were gathered.

“Peer reviewers can all be looking at slightly different parts of the work,” Boyd explains. “One reviewer might be an expert in one specific method, where another reviewer might be more of an expert in the type of study design, and someone else might be more focused on the disease itself.”

Peer reviewers may see problems with the experiments or think different experiments are needed. They might offer new ways to interpret the data. They can also reject the paper because of poor quality, a lack of new information, or other reasons. But if the research passes this peer review process, the study is published.

Just because a study is published doesn’t mean its interpretation of the data is “right.” Other studies may support a different hypothesis.

Scientists work to develop different explanations, or models, for the various findings. They usually favor the model that can explain the most data that’s available.

“At some point, the weight of the evidence from different research groups points strongly to an answer being the most likely,” Lorsch explains. “You should be able to use that model to make predictions that are testable, which further strengthens the likelihood that that answer is the correct one.”

An Ever-Changing Process

Science is always a work in progress. It takes many studies to figure out the “most accurate” model—which doesn’t mean the “right” model.

It’s a self-correcting process. Sometimes experiments can give different results when they’re repeated. Other times, when the results are combined with later studies, the current model no longer can explain all the data and needs to be updated.

“Science is constantly evolving; new tools are being discovered,” Boyd says. “So our understanding can also change over time as we use these different tools.”

Science looks at a question from many different angles with many different techniques. Stories you may see or read about a new study may not explain how it fits into the bigger picture.

“It can seem like, at times, studies contradict each other,” Boyd explains. “But the studies could have different designs and often ask different questions.”

The details of how studies are different aren’t always explained in stories in the media. Only over time does enough evidence accumulate to point toward an explanation of all the different findings on a topic.

“The storybook version of science is that the scientist is doing something, and there’s this eureka moment where everything is revealed,” Lorsch says. “But that’s really not how it happens. Everything is done one increment at a time.”

Federal Support for Medical Research and AAMC’s Role

The AAMC advocates for basic research, as part of the continuum from laboratory-based science to clinical and translational investigation to studies in and with communities and whole populations. The Association strongly supports the work of the U.S. National Institutes of Health (NIH), the American people’s leading organization in support of basic as well as general health research, reflected in the NIH mission statement:

To seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability.

We also support the Agency for Healthcare Research and Quality, the Centers for Disease Control and Prevention, the Department of Veterans Affairs, and other agencies and organizations that sponsor or conduct medical research.

In addition to advocacy , the AAMC also provides analysis and advice on development of policies and regulations that guide basic and other research. The peer review (or merit review) is one example of a critically important system necessary for supporting the research enterprise. AAMC also examines federal and institutional policies promoting team science (increasingly important to research across the continuum) and the advancement and promotion of individual scientists working collaboratively within teams. We also support professional development programs for senior leaders of research programs at medical schools and teaching hospitals, and for those who guide training and career development of new scientists.

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National Academy of Sciences (US), National Academy of Engineering (US) and Institute of Medicine (US) Panel on Scientific Responsibility and the Conduct of Research. Responsible Science: Ensuring the Integrity of the Research Process: Volume I. Washington (DC): National Academies Press (US); 1992.

Responsible Science: Ensuring the Integrity of the Research Process: Volume I.

Hardcopy Version at National Academies Press

2 Scientific Principles and Research Practices

Until the past decade, scientists, research institutions, and government agencies relied solely on a system of self-regulation based on shared ethical principles and generally accepted research practices to ensure integrity in the research process. Among the very basic principles that guide scientists, as well as many other scholars, are those expressed as respect for the integrity of knowledge, collegiality, honesty, objectivity, and openness. These principles are at work in the fundamental elements of the scientific method, such as formulating a hypothesis, designing an experiment to test the hypothesis, and collecting and interpreting data. In addition, more particular principles characteristic of specific scientific disciplines influence the methods of observation; the acquisition, storage, management, and sharing of data; the communication of scientific knowledge and information; and the training of younger scientists. 1 How these principles are applied varies considerably among the several scientific disciplines, different research organizations, and individual investigators.

The basic and particular principles that guide scientific research practices exist primarily in an unwritten code of ethics. Although some have proposed that these principles should be written down and formalized, 2 the principles and traditions of science are, for the most part, conveyed to successive generations of scientists through example, discussion, and informal education. As was pointed out in an early Academy report on responsible conduct of research in the health sciences, “a variety of informal and formal practices and procedures currently exist in the academic research environment to assure and maintain the high quality of research conduct” (IOM, 1989a, p. 18).

Physicist Richard Feynman invoked the informal approach to communicating the basic principles of science in his 1974 commencement address at the California Institute of Technology (Feynman, 1985):

[There is an] idea that we all hope you have learned in studying science in school—we never explicitly say what this is, but just hope that you catch on by all the examples of scientific investigation. . . . It's a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty—a kind of leaning over backwards. For example, if you're doing an experiment, you should report everything that you think might make it invalid—not only what you think is right about it; other causes that could possibly explain your results; and things you thought of that you've eliminated by some other experiment, and how they worked—to make sure the other fellow can tell they have been eliminated.

Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can—if you know anything at all wrong, or possibly wrong—to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it. In summary, the idea is to try to give all the information to help others to judge the value of your contribution, not just the information that leads to judgment in one particular direction or another. (pp. 311-312)

Many scholars have noted the implicit nature and informal character of the processes that often guide scientific research practices and inference. 3 Research in well-established fields of scientific knowledge, guided by commonly accepted theoretical paradigms and experimental methods, involves few disagreements about what is recognized as sound scientific evidence. Even in a revolutionary scientific field like molecular biology, students and trainees have learned the basic principles governing judgments made in such standardized procedures as cloning a new gene and determining its sequence.

In evaluating practices that guide research endeavors, it is important to consider the individual character of scientific fields. Research fields that yield highly replicable results, such as ordinary organic chemical structures, are quite different from fields such as cellular immunology, which are in a much earlier stage of development and accumulate much erroneous or uninterpretable material before the pieces fit together coherently. When a research field is too new or too fragmented to support consensual paradigms or established methods, different scientific practices can emerge.

THE NATURE OF SCIENCE

In broadest terms, scientists seek a systematic organization of knowledge about the universe and its parts. This knowledge is based on explanatory principles whose verifiable consequences can be tested by independent observers. Science encompasses a large body of evidence collected by repeated observations and experiments. Although its goal is to approach true explanations as closely as possible, its investigators claim no final or permanent explanatory truths. Science changes. It evolves. Verifiable facts always take precedence. . . .

Scientists operate within a system designed for continuous testing, where corrections and new findings are announced in refereed scientific publications. The task of systematizing and extending the understanding of the universe is advanced by eliminating disproved ideas and by formulating new tests of others until one emerges as the most probable explanation for any given observed phenomenon. This is called the scientific method.

An idea that has not yet been sufficiently tested is called a hypothesis. Different hypotheses are sometimes advanced to explain the same factual evidence. Rigor in the testing of hypotheses is the heart of science, if no verifiable tests can be formulated, the idea is called an ad hoc hypothesis—one that is not fruitful; such hypotheses fail to stimulate research and are unlikely to advance scientific knowledge.

A fruitful hypothesis may develop into a theory after substantial observational or experimental support has accumulated. When a hypothesis has survived repeated opportunities for disproof and when competing hypotheses have been eliminated as a result of failure to produce the predicted consequences, that hypothesis may become the accepted theory explaining the original facts.

Scientific theories are also predictive. They allow us to anticipate yet unknown phenomena and thus to focus research on more narrowly defined areas. If the results of testing agree with predictions from a theory, the theory is provisionally corroborated. If not, it is proved false and must be either abandoned or modified to account for the inconsistency.

Scientific theories, therefore, are accepted only provisionally. It is always possible that a theory that has withstood previous testing may eventually be disproved. But as theories survive more tests, they are regarded with higher levels of confidence. . . .

In science, then, facts are determined by observation or measurement of natural or experimental phenomena. A hypothesis is a proposed explanation of those facts. A theory is a hypothesis that has gained wide acceptance because it has survived rigorous investigation of its predictions. . . .

. . . science accommodates, indeed welcomes, new discoveries: its theories change and its activities broaden as new facts come to light or new potentials are recognized. Examples of events changing scientific thought are legion. . . . Truly scientific understanding cannot be attained or even pursued effectively when explanations not derived from or tested by the scientific method are accepted.

SOURCE: National Academy of Sciences and National Research Council (1984), pp. 8-11.

A well-established discipline can also experience profound changes during periods of new conceptual insights. In these moments, when scientists must cope with shifting concepts, the matter of what counts as scientific evidence can be subject to dispute. Historian Jan Sapp has described the complex interplay between theory and observation that characterizes the operation of scientific judgment in the selection of research data during revolutionary periods of paradigmatic shift (Sapp, 1990, p. 113):

What “liberties” scientists are allowed in selecting positive data and omitting conflicting or “messy” data from their reports is not defined by any timeless method. It is a matter of negotiation. It is learned, acquired socially; scientists make judgments about what fellow scientists might expect in order to be convincing. What counts as good evidence may be more or less well-defined after a new discipline or specialty is formed; however, at revolutionary stages in science, when new theories and techniques are being put forward, when standards have yet to be negotiated, scientists are less certain as to what others may require of them to be deemed competent and convincing.

Explicit statements of the values and traditions that guide research practice have evolved through the disciplines and have been given in textbooks on scientific methodologies. 4 In the past few decades, many scientific and engineering societies representing individual disciplines have also adopted codes of ethics (see Volume II of this report for examples), 5 and more recently, a few research institutions have developed guidelines for the conduct of research (see Chapter 6 ).

But the responsibilities of the research community and research institutions in assuring individual compliance with scientific principles, traditions, and codes of ethics are not well defined. In recent years, the absence of formal statements by research institutions of the principles that should guide research conducted by their members has prompted criticism that scientists and their institutions lack a clearly identifiable means to ensure the integrity of the research process.

FACTORS AFFECTING THE DEVELOPMENT OF RESEARCH PRACTICES

In all of science, but with unequal emphasis in the several disciplines, inquiry proceeds based on observation and experimentation, the exercising of informed judgment, and the development of theory. Research practices are influenced by a variety of factors, including:

The general norms of science;

The nature of particular scientific disciplines and the traditions of organizing a specific body of scientific knowledge;

The example of individual scientists, particularly those who hold positions of authority or respect based on scientific achievements;

The policies and procedures of research institutions and funding agencies; and

Socially determined expectations.

The first three factors have been important in the evolution of modern science. The latter two have acquired more importance in recent times.

Norms of Science

As members of a professional group, scientists share a set of common values, aspirations, training, and work experiences. 6 Scientists are distinguished from other groups by their beliefs about the kinds of relationships that should exist among them, about the obligations incurred by members of their profession, and about their role in society. A set of general norms are imbedded in the methods and the disciplines of science that guide individual, scientists in the organization and performance of their research efforts and that also provide a basis for nonscientists to understand and evaluate the performance of scientists.

But there is uncertainty about the extent to which individual scientists adhere to such norms. Most social scientists conclude that all behavior is influenced to some degree by norms that reflect socially or morally supported patterns of preference when alternative courses of action are possible. However, perfect conformity with any relevant set of norms is always lacking for a variety of reasons: the existence of competing norms, constraints, and obstacles in organizational or group settings, and personality factors. The strength of these influences, and the circumstances that may affect them, are not well understood.

In a classic statement of the importance of scientific norms, Robert Merton specified four norms as essential for the effective functioning of science: communism (by which Merton meant the communal sharing of ideas and findings), universalism, disinterestedness, and organized skepticism (Merton, 1973). Neither Merton nor other sociologists of science have provided solid empirical evidence for the degree of influence of these norms in a representative sample of scientists. In opposition to Merton, a British sociologist of science, Michael Mulkay, has argued that these norms are “ideological” covers for self-interested behavior that reflects status and politics (Mulkay, 1975). And the British physicist and sociologist of science John Ziman, in an article synthesizing critiques of Merton's formulation, has specified a set of structural factors in the bureaucratic and corporate research environment that impede the realization of that particular set of norms: the proprietary nature of research, the local importance and funding of research, the authoritarian role of the research manager, commissioned research, and the required expertise in understanding how to use modern instruments (Ziman, 1990).

It is clear that the specific influence of norms on the development of scientific research practices is simply not known and that further study of key determinants is required, both theoretically and empirically. Commonsense views, ideologies, and anecdotes will not support a conclusive appraisal.

Individual Scientific Disciplines

Science comprises individual disciplines that reflect historical developments and the organization of natural and social phenomena for study. Social scientists may have methods for recording research data that differ from the methods of biologists, and scientists who depend on complex instrumentation may have authorship practices different from those of scientists who work in small groups or carry out field studies. Even within a discipline, experimentalists engage in research practices that differ from the procedures followed by theorists.

Disciplines are the “building blocks of science,” and they “designate the theories, problems, procedures, and solutions that are prescribed, proscribed, permitted, and preferred” (Zuckerman, 1988a, p. 520). The disciplines have traditionally provided the vital connections between scientific knowledge and its social organization. Scientific societies and scientific journals, some of which have tens of thousands of members and readers, and the peer review processes used by journals and research sponsors are visible forms of the social organization of the disciplines.

The power of the disciplines to shape research practices and standards is derived from their ability to provide a common frame of reference in evaluating the significance of new discoveries and theories in science. It is the members of a discipline, for example, who determine what is “good biology” or “good physics” by examining the implications of new research results. The disciplines' abilities to influence research standards are affected by the subjective quality of peer review and the extent to which factors other than disciplinary quality may affect judgments about scientific achievements. Disciplinary departments rely primarily on informal social and professional controls to promote responsible behavior and to penalize deviant behavior. These controls, such as social ostracism, the denial of letters of support for future employment, and the withholding of research resources, can deter and penalize unprofessional behavior within research institutions. 7

Many scientific societies representing individual disciplines have adopted explicit standards in the form of codes of ethics or guidelines governing, for example, the editorial practices of their journals and other publications. 8 Many societies have also established procedures for enforcing their standards. In the past decade, the societies' codes of ethics—which historically have been exhortations to uphold high standards of professional behavior—have incorporated specific guidelines relevant to authorship practices, data management, training and mentoring, conflict of interest, reporting research findings, treatment of confidential or proprietary information, and addressing error or misconduct.

The Role of Individual Scientists and Research Teams

The methods by which individual scientists and students are socialized in the principles and traditions of science are poorly understood. The principles of science and the practices of the disciplines are transmitted by scientists in classroom settings and, perhaps more importantly, in research groups and teams. The social setting of the research group is a strong and valuable characteristic of American science and education. The dynamics of research groups can foster—or inhibit—innovation, creativity, education, and collaboration.

One author of a historical study of research groups in the chemical and biochemical sciences has observed that the laboratory director or group leader is the primary determinant of a group's practices (Fruton, 1990). Individuals in positions of authority are visible and are also influential in determining funding and other support for the career paths of their associates and students. Research directors and department chairs, by virtue of personal example, thus can reinforce, or weaken, the power of disciplinary standards and scientific norms to affect research practices.

To the extent that the behavior of senior scientists conforms with general expectations for appropriate scientific and disciplinary practice, the research system is coherent and mutually reinforcing. When the behavior of research directors or department chairs diverges from expectations for good practice, however, the expected norms of science become ambiguous, and their effects are thus weakened. Thus personal example and the perceived behavior of role models and leaders in the research community can be powerful stimuli in shaping the research practices of colleagues, associates, and students.

The role of individuals in influencing research practices can vary by research field, institution, or time. The standards and expectations for behavior exemplified by scientists who are highly regarded for their technical competence or creative insight may have greater influence than the standards of others. Individual and group behaviors may also be more influential in times of uncertainty and change in science, especially when new scientific theories, paradigms, or institutional relationships are being established.

Institutional Policies

Universities, independent institutes, and government and industrial research organizations create the environment in which research is done. As the recipients of federal funds and the institutional sponsors of research activities, administrative officers must comply with regulatory and legal requirements that accompany public support. They are required, for example, “to foster a research environment that discourages misconduct in all research and that deals forthrightly with possible misconduct” (DHHS, 1989a, p. 32451).

Academic institutions traditionally have relied on their faculty to ensure that appropriate scientific and disciplinary standards are maintained. A few universities and other research institutions have also adopted policies or guidelines to clarify the principles that their members are expected to observe in the conduct of scientific research. 9 In addition, as a result of several highly publicized incidents of misconduct in science and the subsequent enactment of governmental regulations, most major research institutions have now adopted policies and procedures for handling allegations of misconduct in science.

Institutional policies governing research practices can have a powerful effect on research practices if they are commensurate with the norms that apply to a wide spectrum of research investigators. In particular, the process of adopting and implementing strong institutional policies can sensitize the members of those institutions to the potential for ethical problems in their work. Institutional policies can establish explicit standards that institutional officers then have the power to enforce with sanctions and penalties.

Institutional policies are limited, however, in their ability to specify the details of every problematic situation, and they can weaken or displace individual professional judgment in such situations. Currently, academic institutions have very few formal policies and programs in specific areas such as authorship, communication and publication, and training and supervision.

Government Regulations and Policies

Government agencies have developed specific rules and procedures that directly affect research practices in areas such as laboratory safety, the treatment of human and animal research subjects, and the use of toxic or potentially hazardous substances in research.

But policies and procedures adopted by some government research agencies to address misconduct in science (see Chapter 5 ) represent a significant new regulatory development in the relationships between research institutions and government sponsors. The standards and criteria used to monitor institutional compliance with an increasing number of government regulations and policies affecting research practices have been a source of significant disagreement and tension within the research community.

In recent years, some government research agencies have also adopted policies and procedures for the treatment of research data and materials in their extramural research programs. For example, the National Science Foundation (NSF) has implemented a data-sharing policy through program management actions, including proposal review and award negotiations and conditions. The NSF policy acknowledges that grantee institutions will “keep principal rights to intellectual property conceived under NSF sponsorship” to encourage appropriate commercialization of the results of research (NSF, 1989b, p. 1). However, the NSF policy emphasizes “that retention of such rights does not reduce the responsibility of researchers and institutions to make results and supporting materials openly accessible” (p. 1).

In seeking to foster data sharing under federal grant awards, the government relies extensively on the scientific traditions of openness and sharing. Research agency officials have observed candidly that if the vast majority of scientists were not so committed to openness and dissemination, government policy might require more aggressive action. But the principles that have traditionally characterized scientific inquiry can be difficult to maintain. For example, NSF staff have commented, “Unless we can arrange real returns or incentives for the original investigator, either in financial support or in professional recognition, another researcher's request for sharing is likely to present itself as ‘hassle'—an unwelcome nuisance and diversion. Therefore, we should hardly be surprised if researchers display some reluctance to share in practice, however much they may declare and genuinely feel devotion to the ideal of open scientific communication” (NSF, 1989a, p. 4).

Social Attitudes and Expectations

Research scientists are part of a larger human society that has recently experienced profound changes in attitudes about ethics, morality, and accountability in business, the professions, and government. These attitudes have included greater skepticism of the authority of experts and broader expectations about the need for visible mechanisms to assure proper research practices, especially in areas that affect the public welfare. Social attitudes are also having a more direct influence on research practices as science achieves a more prominent and public role in society. In particular, concern about waste, fraud, and abuse involving government funds has emerged as a factor that now directly influences the practices of the research community.

Varying historical and conceptual perspectives also can affect expectations about standards of research practice. For example, some journalists have criticized several prominent scientists, such as Mendel, Newton, and Millikan, because they “cut corners in order to make their theories prevail” (Broad and Wade, 1982, p. 35). The criticism suggests that all scientists at all times, in all phases of their work, should be bound by identical standards.

Yet historical studies of the social context in which scientific knowledge has been attained suggest that modern criticism of early scientific work often imposes contemporary standards of objectivity and empiricism that have in fact been developed in an evolutionary manner. 10 Holton has argued, for example, that in selecting data for publication, Millikan exercised creative insight in excluding unreliable data resulting from experimental error. But such practices, by today's standards, would not be acceptable without reporting the justification for omission of recorded data.

In the early stages of pioneering studies, particularly when fundamental hypotheses are subject to change, scientists must be free to use creative judgment in deciding which data are truly significant. In such moments, the standards of proof may be quite different from those that apply at stages when confirmation and consensus are sought from peers. Scientists must consistently guard against self-deception, however, particularly when theoretical prejudices tend to overwhelm the skepticism and objectivity basic to experimental practices.

In discussing “the theory-ladenness of observations,” Sapp (1990) observed the fundamental paradox that can exist in determining the “appropriateness” of data selection in certain experiments done in the past: scientists often craft their experiments so that the scientific problems and research subjects conform closely with the theory that they expect to verify or refute. Thus, in some cases, their observations may come closer to theoretical expectations than what might be statistically proper.

This source of bias may be acceptable when it is influenced by scientific insight and judgment. But political, financial, or other sources of bias can corrupt the process of data selection. In situations where both kinds of influence exist, it is particularly important for scientists to be forthcoming about possible sources of bias in the interpretation of research results. The coupling of science to other social purposes in fostering economic growth and commercial technology requires renewed vigilance to maintain acceptable standards for disclosure and control of financial or competitive conflicts of interest and bias in the research environment. The failure to distinguish between appropriate and inappropriate sources of bias in research practices can lead to erosion of public trust in the autonomy of the research enterprise.

RESEARCH PRACTICES

In reviewing modern research practices for a range of disciplines, and analyzing factors that could affect the integrity of the research process, the panel focused on the following four areas:

Data handling—acquisition, management, and storage;

Communication and publication;

Correction of errors; and

Research training and mentorship.

Commonly understood practices operate in each area to promote responsible research conduct; nevertheless, some questionable research practices also occur. Some research institutions, scientific societies, and journals have established policies to discourage questionable practices, but there is not yet a consensus on how to treat violations of these policies. 11 Furthermore, there is concern that some questionable practices may be encouraged or stimulated by other institutional factors. For example, promotion or appointment policies that stress quantity rather than the quality of publications as a measure of productivity could contribute to questionable practices.

Data Handling

Acquisition and management.

Scientific experiments and measurements are transformed into research data. The term “research data” applies to many different forms of scientific information, including raw numbers and field notes, machine tapes and notebooks, edited and categorized observations, interpretations and analyses, derived reagents and vectors, and tables, charts, slides, and photographs.

Research data are the basis for reporting discoveries and experimental results. Scientists traditionally describe the methods used for an experiment, along with appropriate calibrations, instrument types, the number of repeated measurements, and particular conditions that may have led to the omission of some datain the reported version. Standard procedures, innovations for particular purposes, and judgments concerning the data are also reported. The general standard of practice is to provide information that is sufficiently complete so that another scientist can repeat or extend the experiment.

When a scientist communicates a set of results and a related piece of theory or interpretation in any form (at a meeting, in a journal article, or in a book), it is assumed that the research has been conducted as reported. It is a violation of the most fundamental aspect of the scientific research process to set forth measurements that have not, in fact, been performed (fabrication) or to ignore or change relevant data that contradict the reported findings (falsification).

On occasion what is actually proper research practice may be confused with misconduct in science. Thus, for example, applying scientific judgment to refine data and to remove spurious results places special responsibility on the researcher to avoid misrepresentation of findings. Responsible practice requires that scientists disclose the basis for omitting or modifying data in their analyses of research results, especially when such omissions or modifications could alter the interpretation or significance of their work.

In the last decade, the methods by which research scientists handle, store, and provide access to research data have received increased scrutiny, owing to conflicts, over ownership, such as those described by Nelkin (1984); advances in the methods and technologies that are used to collect, retain, and share data; and the costs of data storage. More specific concerns have involved the profitability associated with the patenting of science-based results in some fields and the need to verify independently the accuracy of research results used in public or private decision making. In resolving competing claims, the interests of individual scientists and research institutions may not always coincide: researchers may be willing to exchange scientific data of possible economic significance without regard for financial or institutional implications, whereas their institutions may wish to establish intellectual property rights and obligations prior to any disclosure.

The general norms of science emphasize the principle of openness. Scientists are generally expected to exchange research data as well as unique research materials that are essential to the replication or extension of reported findings. The 1985 report Sharing Research Data concluded that the general principle of data sharing is widely accepted, especially in the behavioral and social sciences (NRC, 1985). The report catalogued the benefits of data sharing, including maintaining the integrity of the research process by providing independent opportunities for verification, refutation, or refinement of original results and data; promoting new research and the development and testing of new theories; and encouraging appropriate use of empirical data in policy formulation and evaluation. The same report examined obstacles to data sharing, which include the criticism or competition that might be stimulated by data sharing; technical barriers that may impede the exchange of computer-readable data; lack of documentation of data sets; and the considerable costs of documentation, duplication, and transfer of data.

The exchange of research data and reagents is ideally governed by principles of collegiality and reciprocity: scientists often distribute reagents with the hope that the recipient will reciprocate in the future, and some give materials out freely with no stipulations attached. 12 Scientists who repeatedly or flagrantly deviate from the tradition of sharing become known to their peers and may suffer subtle forms of professional isolation. Such cases may be well known to senior research investigators, but they are not well documented.

Some scientists may share materials as part of a collaborative agreement in exchange for co-authorship on resulting publications. Some donors stipulate that the shared materials are not to be used for applications already being pursued by the donor's laboratory. Other stipulations include that the material not be passed on to third parties without prior authorization, that the material not be used for proprietary research, or that the donor receive prepublication copies of research publications derived from the material. In some instances, so-called materials transfer agreements are executed to specify the responsibilities of donor and recipient. As more academic research is being supported under proprietary agreements, researchers and institutions are experiencing the effects of these arrangements on research practices.

Governmental support for research studies may raise fundamental questions of ownership and rights of control, particularly when data are subsequently used in proprietary efforts, public policy decisions, or litigation. Some federal research agencies have adopted policies for data sharing to mitigate conflicts over issues of ownership and access (NIH, 1987; NSF, 1989b).

Many research investigators store primary data in the laboratories in which the data were initially derived, generally as electronic records or data sheets in laboratory notebooks. For most academic laboratories, local customary practice governs the storage (or discarding) of research data. Formal rules or guidelines concerning their disposition are rare.

Many laboratories customarily store primary data for a set period (often 3 to 5 years) after they are initially collected. Data that support publications are usually retained for a longer period than are those tangential to reported results. Some research laboratories serve as the proprietor of data and data books that are under the stewardship of the principal investigator. Others maintain that it is the responsibility of the individuals who collected the data to retain proprietorship, even if they leave the laboratory.

Concerns about misconduct in science have raised questions about the roles of research investigators and of institutions in maintaining and providing access to primary data. In some cases of alleged misconduct, the inability or unwillingness of an investigator to provide primary data or witnesses to support published reports sometimes has constituted a presumption that the experiments were not conducted as reported. 13 Furthermore, there is disagreement about the responsibilities of investigators to provide access to raw data, particularly when the reported results have been challenged by others. Many scientists believe that access should be restricted to peers and colleagues, usually following publication of research results, to reduce external demands on the time of the investigator. Others have suggested that raw data supporting research reports should be accessible to any critic or competitor, at any time, especially if the research is conducted with public funds. This topic, in particular, could benefit from further research and systematic discussion to clarify the rights and responsibilities of research investigators, institutions, and sponsors.

Institutional policies have been developed to guide data storage practices in some fields, often stimulated by desires to support the patenting of scientific results and to provide documentation for resolving disputes over patent claims. Laboratories concerned with patents usually have very strict rules concerning data storage and note keeping, often requiring that notes be recorded in an indelible form and be countersigned by an authorized person each day. A few universities have also considered the creation of central storage repositories for all primary data collected by their research investigators. Some government research institutions and industrial research centers maintain such repositories to safeguard the record of research developments for scientific, historical, proprietary, and national security interests.

In the academic environment, however, centralized research records raise complex problems of ownership, control, and access. Centralized data storage is costly in terms of money and space, and it presents logistical problems of cataloguing and retrieving data. There have been suggestions that some types of scientific data should be incorporated into centralized computerized data banks, a portion of which could be subject to periodic auditing or certification. 14 But much investigator-initiated research is not suitable for random data audits because of the exploratory nature of basic or discovery research. 15

Some scientific journals now require that full data for research papers be deposited in a centralized data bank before final publication. Policies and practices differ, but in some fields support is growing for compulsory deposit to enhance researchers' access to supporting data.

Issues Related to Advances in Information Technology

Advances in electronic and other information technologies have raised new questions about the customs and practices that influence the storage, ownership, and exchange of electronic data and software. A number of special issues, not addressed by the panel, are associated with computer modeling, simulation, and other approaches that are becoming more prevalent in the research environment. Computer technology can enhance research collaboration; it can also create new impediments to data sharing resulting from increased costs, the need for specialized equipment, or liabilities or uncertainties about responsibilities for faulty data, software, or computer-generated models.

Advances in computer technology may assist in maintaining and preserving accurate records of research data. Such records could help resolve questions about the timing or accuracy of specific research findings, especially when a principal investigator is not available or is uncooperative in responding to such questions. In principle, properly managed information technologies, utilizing advances in nonerasable optical disk systems, might reinforce openness in scientific research and make primary data more transparent to collaborators and research managers. For example, the so-called WORM (write once, read many) systems provide a high-density digital storage medium that supplies an ineradicable audit trail and historical record for all entered information (Haas, 1991).

Advances in information technologies could thus provide an important benefit to research institutions that wish to emphasize greater access to and storage of primary research data. But the development of centralized information systems in the academic research environment raises difficult issues of ownership, control, and principle that reflect the decentralized character of university governance. Such systems are also a source of additional research expense, often borne by individual investigators. Moreover, if centralized systems are perceived by scientists as an inappropriate or ineffective form of management or oversight of individual research groups, they simply may not work in an academic environment.

Communication and Publication

Scientists communicate research results by a variety of formal and informal means. In earlier times, new findings and interpretations were communicated by letter, personal meeting, and publication. Today, computer networks and facsimile machines have supplemented letters and telephones in facilitating rapid exchange of results. Scientific meetings routinely include poster sessions and press conferences as well as formal presentations. Although research publications continue to document research findings, the appearance of electronic publications and other information technologies heralds change. In addition, incidents of plagiarism, the increasing number of authors per article in selected fields, and the methods by which publications are assessed in determining appointments and promotions have all increased concerns about the traditions and practices that have guided communication and publication.

Journal publication, traditionally an important means of sharing information and perspectives among scientists, is also a principal means of establishing a record of achievement in science. Evaluation of the accomplishments of individual scientists often involves not only the numbers of articles that have resulted from a selected research effort, but also the particular journals in which the articles have appeared. Journal submission dates are often important in establishing priority and intellectual property claims.

Authorship of original research reports is an important indicator of accomplishment, priority, and prestige within the scientific community. Questions of authorship in science are intimately connected with issues of credit and responsibility. Authorship practices are guided by disciplinary traditions, customary practices within research groups, and professional and journal standards and policies. 16 There is general acceptance of the principle that each named author has made a significant intellectual contribution to the paper, even though there remains substantial disagreement over the types of contributions that are judged to be significant.

A general rule is that an author must have participated sufficiently in the work to take responsibility for its content and vouch for its validity. Some journals have adopted more specific guidelines, suggesting that credit for authorship be contingent on substantial participation in one or more of the following categories: (1) conception and design of the experiment, (2) execution of the experiment and collection and storage of the supporting data, (3) analysis and interpretation of the primary data, and (4) preparation and revision of the manuscript. The extent of participation in these four activities required for authorship varies across journals, disciplines, and research groups. 17

“Honorary,” “gift,” or other forms of noncontributing authorship are problems with several dimensions. 18 Honorary authors reap an inflated list of publications incommensurate with their scientific contributions (Zen, 1988). Some scientists have requested or been given authorship as a form of recognition of their status or influence rather than their intellectual contribution. Some research leaders have a custom of including their own names in any paper issuing from their laboratory, although this practice is increasingly discouraged. Some students or junior staff encourage such “gift authorship” because they feel that the inclusion of prestigious names on their papers increases the chance of publication in well-known journals. In some cases, noncontributing authors have been listed without their consent, or even without their being told. In response to these practices, some journals now require all named authors to sign the letter that accompanies submission of the original article, to ensure that no author is named without consent.

“Specialized” authorship is another issue that has received increasing attention. In these cases, a co-author may claim responsibility for a specialized portion of the paper and may not even see or be able to defend the paper as a whole. 19 “Specialized” authorship may also result from demands that co-authorship be given as a condition of sharing a unique research reagent or selected data that do not constitute a major contribution—demands that many scientists believe are inappropriate. “Specialized” authorship may be appropriate in cross-disciplinary collaborations, in which each participant has made an important contribution that deserves recognition. However, the risks associated with the inabilities of co-authors to vouch for the integrity of an entire paper are great; scientists may unwittingly become associated with a discredited publication.

Another problem of lesser importance, except to the scientists involved, is the order of authors listed on a paper. The meaning of author order varies among and within disciplines. For example, in physics the ordering of authors is frequently alphabetical, whereas in the social sciences and other fields, the ordering reflects a descending order of contribution to the described research. Another practice, common in biology, is to list the senior author last.

Appropriate recognition for the contributions of junior investigators, postdoctoral fellows, and graduate students is sometimes a source of discontent and unease in the contemporary research environment. Junior researchers have raised concerns about treatment of their contributions when research papers are prepared and submitted, particularly if they are attempting to secure promotions or independent research funding or if they have left the original project. In some cases, well-meaning senior scientists may grant junior colleagues undeserved authorship or placement as a means of enhancing the junior colleague's reputation. In others, significant contributions may not receive appropriate recognition.

Authorship practices are further complicated by large-scale projects, especially those that involve specialized contributions. Mission teams for space probes, oceanographic expeditions, and projects in high-energy physics, for example, all involve large numbers of senior scientists who depend on the long-term functioning of complex equipment. Some questions about communication and publication that arise from large science projects such as the Superconducting Super Collider include: Who decides when an experiment is ready to be published? How is the spokesperson for the experiment determined? Who determines who can give talks on the experiment? How should credit for technical or hardware contributions be acknowledged?

Apart from plagiarism, problems of authorship and credit allocation usually do not involve misconduct in science. Although some forms of “gift authorship,” in which a designated author made no identifiable contribution to a paper, may be viewed as instances of falsification, authorship disputes more commonly involve unresolved differences of judgment and style. Many research groups have found that the best method of resolving authorship questions is to agree on a designation of authors at the outset of the project. The negotiation and decision process provides initial recognition of each member's effort, and it may prevent misunderstandings that can arise during the course of the project when individuals may be in transition to new efforts or may become preoccupied with other matters.

Plagiarism. Plagiarism is using the ideas or words of another person without giving appropriate credit. Plagiarism includes the unacknowledged use of text and ideas from published work, as well as the misuse of privileged information obtained through confidential review of research proposals and manuscripts.

As described in Honor in Science, plagiarism can take many forms: at one extreme is the exact replication of another's writing without appropriate attribution (Sigma Xi, 1986). At the other is the more subtle “borrowing” of ideas, terms, or paraphrases, as described by Martin et al., “so that the result is a mosaic of other people's ideas and words, the writer's sole contribution being the cement to hold the pieces together.” 20 The importance of recognition for one's intellectual abilities in science demands high standards of accuracy and diligence in ensuring appropriate recognition for the work of others.

The misuse of privileged information may be less clear-cut because it does not involve published work. But the general principles of the importance of giving credit to the accomplishments of others are the same. The use of ideas or information obtained from peer review is not acceptable because the reviewer is in a privileged position. Some organizations, such as the American Chemical Society, have adopted policies to address these concerns (ACS, 1986).

Additional Concerns. Other problems related to authorship include overspecialization, overemphasis on short-term projects, and the organization of research communication around the “least publishable unit.” In a research system that rewards quantity at the expense of quality and favors speed over attention to detail (the effects of “publish or perish”), scientists who wait until their research data are complete before releasing them for publication may be at a disadvantage. Some institutions, such as Harvard Medical School, have responded to these problems by limiting the number of publications reviewed for promotion. Others have placed greater emphasis on major contributions as the basis for evaluating research productivity.

As gatekeepers of scientific journals, editors are expected to use good judgment and fairness in selecting papers for publication. Although editors cannot be held responsible for the errors or inaccuracies of papers that may appear in their journals, editors have obligations to consider criticism and evidence that might contradict the claims of an author and to facilitate publication of critical letters, errata, or retractions. 21 Some institutions, including the National Library of Medicine and professional societies that represent editors of scientific journals, are exploring the development of standards relevant to these obligations (Bailar et al., 1990).

Should questions be raised about the integrity of a published work, the editor may request an author's institution to address the matter. Editors often request written assurances that research reported conforms to all appropriate guidelines involving human or animal subjects, materials of human origin, or recombinant DNA.

In theory, editors set standards of authorship for their journals. In practice, scientists in the specialty do. Editors may specify the. terms of acknowledgment of contributors who fall short of authorship status, and make decisions regarding appropriate forms of disclosure of sources of bias or other potential conflicts of interest related to published articles. For example, the New England Journal of Medicine has established a category of prohibited contributions from authors engaged in for-profit ventures: the journal will not allow such persons to prepare review articles or editorial commentaries for publication. Editors can clarify and insist on the confidentiality of review and take appropriate actions against reviewers who violate it. Journals also may require or encourage their authors to deposit reagents and sequence and crystallographic data into appropriate databases or storage facilities. 22

Peer Review

Peer review is the process by which editors and journals seek to be advised by knowledgeable colleagues about the quality and suitability of a manuscript for publication in a journal. Peer review is also used by funding agencies to seek advice concerning the quality and promise of proposals for research support. The proliferation of research journals and the rewards associated with publication and with obtaining research grants have put substantial stress on the peer review system. Reviewers for journals or research agencies receive privileged information and must exert great care to avoid sharing such information with colleagues or allowing it to enter their own work prematurely.

Although the system of peer review is generally effective, it has been suggested that the quality of refereeing has declined, that self-interest has crept into the review process, and that some journal editors and reviewers exert inappropriate influence on the type of work they deem publishable. 23

Correction of Errors

At some level, all scientific reports, even those that mark profound advances, contain errors of fact or interpretation. In part, such errors reflect uncertainties intrinsic to the research process itself—a hypothesis is formulated, an experimental test is devised, and based on the interpretation of the results, the hypothesis is refined, revised, or discarded. Each step in this cycle is subject to error. For any given report, “correctness” is limited by the following:

The precision and accuracy of the measurements. These in turn depend on available technology, the use of proper statistical and analytical methods, and the skills of the investigator.

Generality of the experimental system and approach. Studies must often be carried out using “model systems.” In biology, for example, a given phenomenon is examined in only one or a few among millions of organismal species.

Experimental design—a product of the background and expertise of the investigator.

Interpretation and speculation regarding the significance of the findings—judgments that depend on expert knowledge, experience, and the insightfulness and boldness of the investigator.

Viewed in this context, errors are an integral aspect of progress in attaining scientific knowledge. They are consequences of the fact that scientists seek fundamental truths about natural processes of vast complexity. In the best experimental systems, it is common that relatively few variables have been identified and that even fewer can be controlled experimentally. Even when important variables are accounted for, the interpretation of the experimental results may be incorrect and may lead to an erroneous conclusion. Such conclusions are sometimes overturned by the original investigator or by others when new insights from another study prompt a reexamination of older reported data. In addition, however, erroneous information can also reach the scientific literature as a consequence of misconduct in science.

What becomes of these errors or incorrect interpretations? Much has been made of the concept that science is “self-correcting”—that errors, whether honest or products of misconduct, will be exposed in future experiments because scientific truth is founded on the principle that results must be verifiable and reproducible. This implies that errors will generally not long confound the direction of thinking or experimentation in actively pursued areas of research. Clearly, published experiments are not routinely replicated precisely by independent investigators. However, each experiment is based on conclusions from prior studies; repeated failure of the experiment eventually calls into question those conclusions and leads to reevaluation of the measurements, generality, design, and interpretation of the earlier work.

Thus publication of a scientific report provides an opportunity for the community at large to critique and build on the substance of the report, and serves as one stage at which errors and misinterpretations can be detected and corrected. Each new finding is considered by the community in light of what is already known about the system investigated, and disagreements with established measurements and interpretations must be justified. For example, a particular interpretation of an electrical measurement of a material may implicitly predict the results of an optical experiment. If the reported optical results are in disagreement with the electrical interpretation, then the latter is unlikely to be correct—even though the measurements themselves were carefully and correctly performed. It is also possible, however, that the contradictory results are themselves incorrect, and this possibility will also be evaluated by the scientists working in the field. It is by this process of examination and reexamination that science advances.

The research endeavor can therefore be viewed as a two-tiered process: first, hypotheses are formulated, tested, and modified; second, results and conclusions are reevaluated in the course of additional study. In fact, the two tiers are interrelated, and the goals and traditions of science mandate major responsibilities in both areas for individual investigators. Importantly, the principle of self-correction does not diminish the responsibilities of the investigator in either area. The investigator has a fundamental responsibility to ensure that the reported results can be replicated in his or her laboratory. The scientific community in general adheres strongly to this principle, but practical constraints exist as a result of the availability of specialized instrumentation, research materials, and expert personnel. Other forces, such as competition, commercial interest, funding trends and availability, or pressure to publish may also erode the role of replication as a mechanism for fostering integrity in the research process. The panel is unaware of any quantitative studies of this issue.

The process of reevaluating prior findings is closely related to the formulation and testing of hypotheses. 24 Indeed, within an individual laboratory, the formulation/testing phase and the reevaluation phase are ideally ongoing interactive processes. In that setting, the precise replication of a prior result commonly serves as a crucial control in attempts to extend the original findings. It is not unusual that experimental flaws or errors of interpretation are revealed as the scope of an investigation deepens and broadens.

If new findings or significant questions emerge in the course of a reevaluation that affect the claims of a published report, the investigator is obliged to make public a correction of the erroneous result or to indicate the nature of the questions. Occasionally, this takes the form of a formal published retraction, especially in situations in which a central claim is found to be fundamentally incorrect or irreproducible. More commonly, a somewhat different version of the original experiment, or a revised interpretation of the original result, is published as part of a subsequent report that extends in other ways the initial work. Some concerns have been raised that such “revisions” can sometimes be so subtle and obscure as to be unrecognizable. Such behavior is, at best, a questionable research practice. Clearly, each scientist has a responsibility to foster an environment that encourages and demands rigorous evaluation and reevaluation of every key finding.

Much greater complexity is encountered when an investigator in one research group is unable to confirm the published findings of another. In such situations, precise replication of the original result is commonly not attempted because of the lack of identical reagents, differences in experimental protocols, diverse experimental goals, or differences in personnel. Under these circumstances, attempts to obtain the published result may simply be dropped if the central claim of the original study is not the major focus of the new study. Alternatively, the inability to obtain the original finding may be documented in a paper by the second investigator as part of a challenge to the original claim. In any case, such questions about a published finding usually provoke the initial investigator to attempt to reconfirm the original result, or to pursue additional studies that support and extend the original findings.

In accordance with established principles of science, scientists have the responsibility to replicate and reconfirm their results as a normal part of the research process. The cycles of theoretical and methodological formulation, testing, and reevaluation, both within and between laboratories, produce an ongoing process of revision and refinement that corrects errors and strengthens the fabric of research.

Research Training and Mentorship

The panel defined a mentor as that person directly responsible for the professional development of a research trainee. 25 Professional development includes both technical training, such as instruction in the methods of scientific research (e.g., research design, instrument use, and selection of research questions and data), and socialization in basic research practices (e.g., authorship practices and sharing of research data).

Positive Aspects of Mentorship

The relationship of the mentor and research trainee is usually characterized by extraordinary mutual commitment and personal involvement. A mentor, as a research advisor, is generally expected to supervise the work of the trainee and ensure that the trainee's research is completed in a sound, honest, and timely manner. The ideal mentor challenges the trainee, spurs the trainee to higher scientific achievement, and helps socialize the trainee into the community of scientists by demonstrating and discussing methods and practices that are not well understood.

Research mentors thus have complex and diverse roles. Many individuals excel in providing guidance and instruction as well as personal support, and some mentors are resourceful in providing funds and securing professional opportunities for their trainees. The mentoring relationship may also combine elements of other relationships, such as parenting, coaching, and guildmastering. One mentor has written that his “research group is like an extended family or small tribe, dependent on one another, but led by the mentor, who acts as their consultant, critic, judge, advisor, and scientific father” (Cram, 1989, p. 1). Another mentor described as “orphaned graduate students” trainees who had lost their mentors to death, job changes, or in other ways (Sindermann, 1987). Many students come to respect and admire their mentors, who act as role models for their younger colleagues.

Difficulties Associated with Mentorship

However, the mentoring relationship does not always function properly or even satisfactorily. Almost no literature exists that evaluates which problems are idiosyncratic and which are systemic. However, it is clear that traditional practices in the area of mentorship and training are under stress. In some research fields, for example, concerns are being raised about how the increasing size and diverse composition of research groups affect the quality of the relationship between trainee and mentor. As the size of research laboratories expands, the quality of the training environment is at risk (CGS, 1990a).

Large laboratories may provide valuable instrumentation and access to unique research skills and resources as well as an opportunity to work in pioneering fields of science. But as only one contribution to the efforts of a large research team, a graduate student's work may become highly specialized, leading to a narrowing of experience and greater dependency on senior personnel; in a period when the availability of funding may limit research opportunities, laboratory heads may find it necessary to balance research decisions for the good of the team against the individual educational interests of each trainee. Moreover, the demands of obtaining sufficient resources to maintain a laboratory in the contemporary research environment often separate faculty from their trainees. When laboratory heads fail to participate in the everyday workings of the laboratory—even for the most beneficent of reasons, such as finding funds to support young investigators—their inattention may harm their trainees' education.

Although the size of a research group can influence the quality of mentorship, the more important issues are the level of supervision received by trainees, the degree of independence that is appropriate for the trainees' experience and interests, and the allocation of credit for achievements that are accomplished by groups composed of individuals with different status. Certain studies involving large groups of 40 to 100 or more are commonly carried out by collaborative or hierarchical arrangements under a single investigator. These factors may affect the ability of research mentors to transmit the methods and ethical principles according to which research should be conducted.

Problems also arise when faculty members are not directly rewarded for their graduate teaching or training skills. Although faculty may receive indirect rewards from the contributions of well-trained graduate students to their own research as well as the satisfaction of seeing their students excelling elsewhere, these rewards may not be sufficiently significant in tenure or promotion decisions. When institutional policies fail to recognize and reward the value of good teaching and mentorship, the pressures to maintain stable funding for research teams in a competitive environment can overwhelm the time allocated to teaching and mentorship by a single investigator.

The increasing duration of the training period in many research fields is another source of concern, particularly when it prolongs the dependent status of the junior investigator. The formal period of graduate and postdoctoral training varies considerably among fields of study. In 1988, the median time to the doctorate from the baccalaureate degree was 6.5 years (NRC, 1989). The disciplinary median varied: 5.5 years in chemistry; 5.9 years in engineering; 7.1 years in health sciences and in earth, atmospheric, and marine sciences; and 9.0 years in anthropology and sociology. 26

Students, research associates, and faculty are currently raising various questions about the rights and obligations of trainees. Sexist behavior by some research directors and other senior scientists is a particular source of concern. Another significant concern is that research trainees may be subject to exploitation because of their subordinate status in the research laboratory, particularly when their income, access to research resources, and future recommendations are dependent on the goodwill of the mentor. Foreign students and postdoctoral fellows may be especially vulnerable, since their immigration status often depends on continuation of a research relationship with the selected mentor.

Inequalities between mentor and trainee can exacerbate ordinary conflicts such as the distribution of credit or blame for research error (NAS, 1989). When conflicts arise, the expectations and assumptions that govern authorship practices, ownership of intellectual property, and the giving of references and recommendations are exposed for professional—and even legal—scrutiny (Nelkin, 1984; Weil and Snapper, 1989).

Making Mentorship Better

Ideally, mentors and trainees should select each other with an eye toward scientific merit, intellectual and personal compatibility, and other relevant factors. But this situation operates only under conditions of freely available information and unconstrained choice—conditions that usually do not exist in academic research groups. The trainee may choose to work with a faculty member based solely on criteria of patronage, perceived influence, or ability to provide financial support.

Good mentors may be well known and highly regarded within their research communities and institutions. Unfortunately, individuals who exploit the mentorship relationship may be less visible. Poor mentorship practices may be self-correcting over time, if students can detect and avoid research groups characterized by disturbing practices. However, individual trainees who experience abusive relationships with a mentor may discover only too late that the practices that constitute the abuse were well known but were not disclosed to new initiates.

It is common practice for a graduate student to be supervised not only by an individual mentor but also by a committee that represents the graduate department or research field of the student. However, departmental oversight is rare for the postdoctoral research fellow. In order to foster good mentorship practices for all research trainees, many groups and institutions have taken steps to clarify the nature of individual and institutional responsibilities in the mentor–trainee relationship. 27

FINDINGS AND CONCLUSIONS

The self-regulatory system that characterizes the research process has evolved from a diverse set of principles, traditions, standards, and customs transmitted from senior scientists, research directors, and department chairs to younger scientists by example, discussion, and informal education. The principles of honesty, collegiality, respect for others, and commitment to dissemination, critical evaluation, and rigorous training are characteristic of all the sciences. Methods and techniques of experimentation, styles of communicating findings, the relationship between theory and experimentation, and laboratory groupings for research and for training vary with the particular scientific disciplines. Within those disciplines, practices combine the general with the specific. Ideally, research practices reflect the values of the wider research community and also embody the practical skills needed to conduct scientific research.

Practicing scientists are guided by the principles of science and the standard practices of their particular scientific discipline as well as their personal moral principles. But conflicts are inherent among these principles. For example, loyalty to one's group of colleagues can be in conflict with the need to correct or report an abuse of scientific practice on the part of a member of that group.

Because scientists and the achievements of science have earned the respect of society at large, the behavior of scientists must accord not only with the expectations of scientific colleagues, but also with those of a larger community. As science becomes more closely linked to economic and political objectives, the processes by which scientists formulate and adhere to responsible research practices will be subject to increasing public scrutiny. This is one reason for scientists and research institutions to clarify and strengthen the methods by which they foster responsible research practices.

Accordingly, the panel emphasizes the following conclusions:

The panel believes that the existing self-regulatory system in science is sound. But modifications are necessary to foster integrity in a changing research environment, to handle cases of misconduct in science, and to discourage questionable research practices.
Individual scientists have a fundamental responsibility to ensure that their results are reproducible, that their research is reported thoroughly enough so that results are reproducible, and that significant errors are corrected when they are recognized. Editors of scientific journals share these last two responsibilities.
Research mentors, laboratory directors, department heads, and senior faculty are responsible for defining, explaining, exemplifying, and requiring adherence to the value systems of their institutions. The neglect of sound training in a mentor's laboratory will over time compromise the integrity of the research process.
Administrative officials within the research institution also bear responsibility for ensuring that good scientific practices are observed in units of appropriate jurisdiction and that balanced reward systems appropriately recognize research quality, integrity, teaching, and mentorship. Adherence to scientific principles and disciplinary standards is at the root of a vital and productive research environment.
At present, scientific principles are passed on to trainees primarily by example and discussion, including training in customary practices. Most research institutions do not have explicit programs of instruction and discussion to foster responsible research practices, but the communication of values and traditions is critical to fostering responsible research practices and detering misconduct in science.
Efforts to foster responsible research practices in areas such as data handling, communication and publication, and research training and mentorship deserve encouragement by the entire research community. Problems have also developed in these areas that require explicit attention and correction by scientists and their institutions. If not properly resolved, these problems may weaken the integrity of the research process.

1. See, for example, Kuyper (1991).

2. See, for example, the proposal by Pigman and Carmichael (1950).

3. See, for example, Holton (1988) and Ravetz (1971).

4. Several excellent books on experimental design and statistical methods are available. See, for example, Wilson (1952) and Beveridge (1957).

5. For a somewhat dated review of codes of ethics adopted by the scientific and engineering societies, see Chalk et al. (1981).

6. The discussion in this section is derived from Mark Frankel's background paper, “Professional Societies and Responsible Research Conduct,” included in Volume II of this report.

7. For a broader discussion on this point, see Zuckerman (1977).

8. For a full discussion of the roles of scientific societies in fostering responsible research practices, see the background paper prepared by Mark Frankel, “Professional Societies and Responsible Research Conduct,” in Volume II of this report.

9. Selected examples of academic research conduct policies and guidelines are included in Volume II of this report.

10. See, for example, Holton's response to the criticisms of Millikan in Chapter 12 of Thematic Origins of Scientific Thought (Holton, 1988). See also Holton (1978).

11. See, for example, responses to the Proceedings of the National Academy of Sciences action against Friedman: Hamilton (1990) and Abelson et al. (1990). See also the discussion in Bailar et al. (1990).

12. Much of the discussion in this section is derived from a background paper, “Reflections on the Current State of Data and Reagent Exchange Among Biomedical Researchers,” prepared by Robert Weinberg and included in Volume II of this report.

13. See, for example, Culliton (1990) and Bradshaw et al. (1990). For the impact of the inability to provide corroborating data or witnesses, also see Ross et al. (1989).

14. See, for example, Rennie (1989) and Cassidy and Shamoo (1989).

15. See, for example, the discussion on random data audits in Institute of Medicine (1989a), pp. 26-27.

16. For a full discussion of the practices and policies that govern authorship in the biological sciences, see Bailar et al. (1990).

17. Note that these general guidelines exclude the provision of reagents or facilities or the supervision of research as a criteria of authorship.

18. A full discussion of problematic practices in authorship is included in Bailar et al. (1990). A controversial review of the responsibilities of co-authors is presented by Stewart and Feder (1987).

19. In the past, scientific papers often included a special note by a named researcher, not a co-author of the paper, who described, for example, a particular substance or procedure in a footnote or appendix. This practice seems to.have been abandoned for reasons that are not well understood.

20. Martin et al. (1969), as cited in Sigma Xi (1986), p. 41.

21. Huth (1988) suggests a “notice of fraud or notice of suspected fraud” issued by the journal editor to call attention to the controversy (p. 38). Angell (1983) advocates closer coordination between institutions and editors when institutions have ascertained misconduct.

22. Such facilities include Cambridge Crystallographic Data Base, GenBank at Los Alamos National Laboratory, the American Type Culture Collection, and the Protein Data Bank at Brookhaven National Laboratory. Deposition is important for data that cannot be directly printed because of large volume.

23. For more complete discussions of peer review in the wider context, see, for example, Cole et al. (1977) and Chubin and Hackett (1990).

24. The strength of theories as sources of the formulation of scientific laws and predictive power varies among different fields of science. For example, theories derived from observations in the field of evolutionary biology lack a great deal of predictive power. The role of chance in mutation and natural selection is great, and the future directions that evolution may take are essentially impossible to predict. Theory has enormous power for clarifying understanding of how evolution has occurred and for making sense of detailed data, but its predictive power in this field is very limited. See, for example, Mayr (1982, 1988).

25. Much of the discussion on mentorship is derived from a background paper prepared for the panel by David Guston. A copy of the full paper, “Mentorship and the Research Training Experience,” is included in Volume II of this report.

26. Although the time to the doctorate is increasing, there is some evidence that the magnitude of the increase may be affected by the organization of the cohort chosen for study. In the humanities, the increased time to the doctorate is not as large if one chooses as an organizational base the year in which the baccalaureate was received by Ph.D. recipients, rather than the year in which the Ph.D. was completed; see Bowen et al. (1991).

27. Some universities have written guidelines for the supervision or mentorship of trainees as part of their institutional research policy guidelines (see, for example, the guidelines adopted by Harvard University and the University of Michigan that are included in Volume II of this report). Other groups or institutions have written “guidelines” (IOM, 1989a; NIH, 1990), “checklists” (CGS, 1990a), and statements of “areas of concern” and suggested “devices” (CGS, 1990c).

The guidelines often affirm the need for regular, personal interaction between the mentor and the trainee. They indicate that mentors may need to limit the size of their laboratories so that they are able to interact directly and frequently with all of their trainees. Although there are many ways to ensure responsible mentorship, methods that provide continuous feedback, whether through formal or informal mechanisms, are apt to be the most successful (CGS, 1990a). Departmental mentorship awards (comparable to teaching or research prizes) can recognize, encourage, and enhance the mentoring relationship. For other discussions on mentorship, see the paper by David Guston in Volume II of this report.

One group convened by the Institute of Medicine has suggested “that the university has a responsibility to ensure that the size of a research unit does not outstrip the mentor's ability to maintain adequate supervision” (IOM, 1989a, p. 85). Others have noted that although it may be desirable to limit the number of trainees assigned to a senior investigator, there is insufficient information at this time to suggest that numbers alone significantly affect the quality of research supervision (IOM, 1989a, p. 33).

Cite this Page National Academy of Sciences (US), National Academy of Engineering (US) and Institute of Medicine (US) Panel on Scientific Responsibility and the Conduct of Research. Responsible Science: Ensuring the Integrity of the Research Process: Volume I. Washington (DC): National Academies Press (US); 1992. 2, Scientific Principles and Research Practices.
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Home » Scientific Research – Types, Purpose and Guide

Scientific Research – Types, Purpose and Guide

Table of Contents

Scientific Research

Definition:

Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge.

It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses. Scientific research can be conducted in various fields, such as natural sciences, social sciences, and engineering, and may involve experiments, observations, surveys, or other forms of data collection. The goal of scientific research is to advance knowledge, improve understanding, and contribute to the development of solutions to practical problems.

Types of Scientific Research

There are different types of scientific research, which can be classified based on their purpose, method, and application. In this response, we will discuss the four main types of scientific research.

Descriptive Research

Descriptive research aims to describe or document a particular phenomenon or situation, without altering it in any way. This type of research is usually done through observation, surveys, or case studies. Descriptive research is useful in generating ideas, understanding complex phenomena, and providing a foundation for future research. However, it does not provide explanations or causal relationships between variables.

Exploratory Research

Exploratory research aims to explore a new area of inquiry or develop initial ideas for future research. This type of research is usually conducted through observation, interviews, or focus groups. Exploratory research is useful in generating hypotheses, identifying research questions, and determining the feasibility of a larger study. However, it does not provide conclusive evidence or establish cause-and-effect relationships.

Experimental Research

Experimental research aims to test cause-and-effect relationships between variables by manipulating one variable and observing the effects on another variable. This type of research involves the use of an experimental group, which receives a treatment, and a control group, which does not receive the treatment. Experimental research is useful in establishing causal relationships, replicating results, and controlling extraneous variables. However, it may not be feasible or ethical to manipulate certain variables in some contexts.

Correlational Research

Correlational research aims to examine the relationship between two or more variables without manipulating them. This type of research involves the use of statistical techniques to determine the strength and direction of the relationship between variables. Correlational research is useful in identifying patterns, predicting outcomes, and testing theories. However, it does not establish causation or control for confounding variables.

Scientific Research Methods

Scientific research methods are used in scientific research to investigate phenomena, acquire knowledge, and answer questions using empirical evidence. Here are some commonly used scientific research methods:

Observational Studies

This method involves observing and recording phenomena as they occur in their natural setting. It can be done through direct observation or by using tools such as cameras, microscopes, or sensors.

Experimental Studies

This method involves manipulating one or more variables to determine the effect on the outcome. This type of study is often used to establish cause-and-effect relationships.

Survey Research

This method involves collecting data from a large number of people by asking them a set of standardized questions. Surveys can be conducted in person, over the phone, or online.

Case Studies

This method involves in-depth analysis of a single individual, group, or organization. Case studies are often used to gain insights into complex or unusual phenomena.

Meta-analysis

This method involves combining data from multiple studies to arrive at a more reliable conclusion. This technique can be used to identify patterns and trends across a large number of studies.

Qualitative Research

This method involves collecting and analyzing non-numerical data, such as interviews, focus groups, or observations. This type of research is often used to explore complex phenomena and to gain an understanding of people’s experiences and perspectives.

Quantitative Research

This method involves collecting and analyzing numerical data using statistical techniques. This type of research is often used to test hypotheses and to establish cause-and-effect relationships.

Longitudinal Studies

This method involves following a group of individuals over a period of time to observe changes and to identify patterns and trends. This type of study can be used to investigate the long-term effects of a particular intervention or exposure.

Data Analysis Methods

There are many different data analysis methods used in scientific research, and the choice of method depends on the type of data being collected and the research question. Here are some commonly used data analysis methods:

Descriptive statistics: This involves using summary statistics such as mean, median, mode, standard deviation, and range to describe the basic features of the data.
Inferential statistics: This involves using statistical tests to make inferences about a population based on a sample of data. Examples of inferential statistics include t-tests, ANOVA, and regression analysis.
Qualitative analysis: This involves analyzing non-numerical data such as interviews, focus groups, and observations. Qualitative analysis may involve identifying themes, patterns, or categories in the data.
Content analysis: This involves analyzing the content of written or visual materials such as articles, speeches, or images. Content analysis may involve identifying themes, patterns, or categories in the content.
Data mining: This involves using automated methods to analyze large datasets to identify patterns, trends, or relationships in the data.
Machine learning: This involves using algorithms to analyze data and make predictions or classifications based on the patterns identified in the data.

Application of Scientific Research

Scientific research has numerous applications in many fields, including:

Medicine and healthcare: Scientific research is used to develop new drugs, medical treatments, and vaccines. It is also used to understand the causes and risk factors of diseases, as well as to develop new diagnostic tools and medical devices.
Agriculture : Scientific research is used to develop new crop varieties, to improve crop yields, and to develop more sustainable farming practices.
Technology and engineering : Scientific research is used to develop new technologies and engineering solutions, such as renewable energy systems, new materials, and advanced manufacturing techniques.
Environmental science : Scientific research is used to understand the impacts of human activity on the environment and to develop solutions for mitigating those impacts. It is also used to monitor and manage natural resources, such as water and air quality.
Education : Scientific research is used to develop new teaching methods and educational materials, as well as to understand how people learn and develop.
Business and economics: Scientific research is used to understand consumer behavior, to develop new products and services, and to analyze economic trends and policies.
Social sciences : Scientific research is used to understand human behavior, attitudes, and social dynamics. It is also used to develop interventions to improve social welfare and to inform public policy.

How to Conduct Scientific Research

Conducting scientific research involves several steps, including:

Identify a research question: Start by identifying a question or problem that you want to investigate. This question should be clear, specific, and relevant to your field of study.
Conduct a literature review: Before starting your research, conduct a thorough review of existing research in your field. This will help you identify gaps in knowledge and develop hypotheses or research questions.
Develop a research plan: Once you have a research question, develop a plan for how you will collect and analyze data to answer that question. This plan should include a detailed methodology, a timeline, and a budget.
Collect data: Depending on your research question and methodology, you may collect data through surveys, experiments, observations, or other methods.
Analyze data: Once you have collected your data, analyze it using appropriate statistical or qualitative methods. This will help you draw conclusions about your research question.
Interpret results: Based on your analysis, interpret your results and draw conclusions about your research question. Discuss any limitations or implications of your findings.
Communicate results: Finally, communicate your findings to others in your field through presentations, publications, or other means.

Purpose of Scientific Research

The purpose of scientific research is to systematically investigate phenomena, acquire new knowledge, and advance our understanding of the world around us. Scientific research has several key goals, including:

Exploring the unknown: Scientific research is often driven by curiosity and the desire to explore uncharted territory. Scientists investigate phenomena that are not well understood, in order to discover new insights and develop new theories.
Testing hypotheses: Scientific research involves developing hypotheses or research questions, and then testing them through observation and experimentation. This allows scientists to evaluate the validity of their ideas and refine their understanding of the phenomena they are studying.
Solving problems: Scientific research is often motivated by the desire to solve practical problems or address real-world challenges. For example, researchers may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
Advancing knowledge: Scientific research is a collective effort to advance our understanding of the world around us. By building on existing knowledge and developing new insights, scientists contribute to a growing body of knowledge that can be used to inform decision-making, solve problems, and improve our lives.

Examples of Scientific Research

Here are some examples of scientific research that are currently ongoing or have recently been completed:

Clinical trials for new treatments: Scientific research in the medical field often involves clinical trials to test new treatments for diseases and conditions. For example, clinical trials may be conducted to evaluate the safety and efficacy of new drugs or medical devices.
Genomics research: Scientists are conducting research to better understand the human genome and its role in health and disease. This includes research on genetic mutations that can cause diseases such as cancer, as well as the development of personalized medicine based on an individual’s genetic makeup.
Climate change: Scientific research is being conducted to understand the causes and impacts of climate change, as well as to develop solutions for mitigating its effects. This includes research on renewable energy technologies, carbon capture and storage, and sustainable land use practices.
Neuroscience : Scientists are conducting research to understand the workings of the brain and the nervous system, with the goal of developing new treatments for neurological disorders such as Alzheimer’s disease and Parkinson’s disease.
Artificial intelligence: Researchers are working to develop new algorithms and technologies to improve the capabilities of artificial intelligence systems. This includes research on machine learning, computer vision, and natural language processing.
Space exploration: Scientific research is being conducted to explore the cosmos and learn more about the origins of the universe. This includes research on exoplanets, black holes, and the search for extraterrestrial life.

When to use Scientific Research

Some specific situations where scientific research may be particularly useful include:

Solving problems: Scientific research can be used to investigate practical problems or address real-world challenges. For example, scientists may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
Decision-making: Scientific research can provide evidence-based information to inform decision-making. For example, policymakers may use scientific research to evaluate the effectiveness of different policy options or to make decisions about public health and safety.
Innovation : Scientific research can be used to develop new technologies, products, and processes. For example, research on materials science can lead to the development of new materials with unique properties that can be used in a range of applications.
Knowledge creation : Scientific research is an important way of generating new knowledge and advancing our understanding of the world around us. This can lead to new theories, insights, and discoveries that can benefit society.

Advantages of Scientific Research

There are many advantages of scientific research, including:

Improved understanding : Scientific research allows us to gain a deeper understanding of the world around us, from the smallest subatomic particles to the largest celestial bodies.
Evidence-based decision making: Scientific research provides evidence-based information that can inform decision-making in many fields, from public policy to medicine.
Technological advancements: Scientific research drives technological advancements in fields such as medicine, engineering, and materials science. These advancements can improve quality of life, increase efficiency, and reduce costs.
New discoveries: Scientific research can lead to new discoveries and breakthroughs that can advance our knowledge in many fields. These discoveries can lead to new theories, technologies, and products.
Economic benefits : Scientific research can stimulate economic growth by creating new industries and jobs, and by generating new technologies and products.
Improved health outcomes: Scientific research can lead to the development of new medical treatments and technologies that can improve health outcomes and quality of life for people around the world.
Increased innovation: Scientific research encourages innovation by promoting collaboration, creativity, and curiosity. This can lead to new and unexpected discoveries that can benefit society.

Limitations of Scientific Research

Scientific research has some limitations that researchers should be aware of. These limitations can include:

Research design limitations : The design of a research study can impact the reliability and validity of the results. Poorly designed studies can lead to inaccurate or inconclusive results. Researchers must carefully consider the study design to ensure that it is appropriate for the research question and the population being studied.
Sample size limitations: The size of the sample being studied can impact the generalizability of the results. Small sample sizes may not be representative of the larger population, and may lead to incorrect conclusions.
Time and resource limitations: Scientific research can be costly and time-consuming. Researchers may not have the resources necessary to conduct a large-scale study, or may not have sufficient time to complete a study with appropriate controls and analysis.
Ethical limitations : Certain types of research may raise ethical concerns, such as studies involving human or animal subjects. Ethical concerns may limit the scope of the research that can be conducted, or require additional protocols and procedures to ensure the safety and well-being of participants.
Limitations of technology: Technology may limit the types of research that can be conducted, or the accuracy of the data collected. For example, certain types of research may require advanced technology that is not yet available, or may be limited by the accuracy of current measurement tools.
Limitations of existing knowledge: Existing knowledge may limit the types of research that can be conducted. For example, if there is limited knowledge in a particular field, it may be difficult to design a study that can provide meaningful results.

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Researcher, Academic Writer, Web developer

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Scientific Research in Education (2002)

Chapter: 3 guiding principles for scientific inquiry, 3 guiding principles for scientific inquiry.

In Chapter 2 we present evidence that scientific research in education accumulates just as it does in the physical, life, and social sciences. Consequently, we believe that such research would be worthwhile to pursue to build further knowledge about education, and about education policy and practice. Up to this point, however, we have not addressed the questions “What constitutes scientific research?” and “Is scientific research on education different from scientific research in the social, life, and physical sciences?” We do so in this chapter.

These are daunting questions that philosophers, historians, and scientists have debated for several centuries (see Newton-Smith [2000] for a current assessment). Merton (1973), for example, saw commonality among the sciences. He described science as having four aims: universalism, the quest for general laws; organization, the quest to organize and conceptualize a set of related facts or observations; skepticism, the norm of questioning and looking for counter explanations; and communalism, the quest to develop a community that shares a set of norms or principles for doing science. In contrast, some early modern philosophers (the logical positivists) attempted to achieve unity across the sciences by reducing them all to physics, a program that ran into insuperable technical difficulties (Trant, 1991).

In short, we hold that there are both commonalities and differences across the sciences. At a general level, the sciences share a great deal in common, a set of what might be called epistemological or fundamental

principles that guide the scientific enterprise. They include seeking conceptual (theoretical) understanding, posing empirically testable and refutable hypotheses, designing studies that test and can rule out competing counterhypotheses, using observational methods linked to theory that enable other scientists to verify their accuracy, and recognizing the importance of both independent replication and generalization. It is very unlikely that any one study would possess all of these qualities. Nevertheless, what unites scientific inquiry is the primacy of empirical test of conjectures and formal hypotheses using well-codified observation methods and rigorous designs, and subjecting findings to peer review. It is, in John Dewey’s expression, “competent inquiry” that produces what philosophers call “knowledge claims” that are justified or “warranted” by pertinent, empirical evidence (or in mathematics, deductive proof). Scientific reasoning takes place amid (often quantifiable) uncertainty (Schum, 1994); its assertions are subject to challenge, replication, and revision as knowledge is refined over time. The long-term goal of much of science is to produce theory that can offer a stable encapsulation of “facts” that generalizes beyond the particular. In this chapter, then, we spell out what we see as the commonalities among all scientific endeavors.

As our work began, we attempted to distinguish scientific investigations in education from those in the social, physical, and life sciences by exploring the philosophy of science and social science; the conduct of physical, life, and social science investigations; and the conduct of scientific research on education. We also asked a panel of senior government officials who fund and manage research in education and the social and behavioral sciences, and a panel of distinguished scholars from psychometrics, linguistic anthropology, labor economics and law, to distinguish principles of evidence across fields (see National Research Council, 2001d). Ultimately, we failed to convince ourselves that at a fundamental level beyond the differences in specialized techniques and objects of inquiry across the individual sciences, a meaningful distinction could be made among social, physical, and life science research and scientific research in education. At times we thought we had an example that would demonstrate the distinction, only to find our hypothesis refuted by evidence that the distinction was not real.

Thus, the committee concluded that the set of guiding principles that apply to scientific inquiry in education are the same set of principles that

can be found across the full range of scientific inquiry. Throughout this chapter we provide examples from a variety of domains—in political science, geophysics, and education—to demonstrate this shared nature. Although there is no universally accepted description of the elements of scientific inquiry, we have found it convenient to describe the scientific process in terms of six interrelated, but not necessarily ordered, 1 principles of inquiry:

Pose significant questions that can be investigated empirically.

Link research to relevant theory.

Use methods that permit direct investigation of the question.

Provide a coherent and explicit chain of reasoning.

Replicate and generalize across studies.

Disclose research to encourage professional scrutiny and critique.

We choose the phrase “guiding principles” deliberately to emphasize the vital point that they guide, but do not provide an algorithm for, scientific inquiry. Rather, the guiding principles for scientific investigations provide a framework indicating how inferences are, in general, to be supported (or refuted) by a core of interdependent processes, tools, and practices. Although any single scientific study may not fulfill all the principles—for example, an initial study in a line of inquiry will not have been replicated independently—a strong line of research is likely to do so (e.g., see Chapter 2 ).

We also view the guiding principles as constituting a code of conduct that includes notions of ethical behavior. In a sense, guiding principles operate like norms in a community, in this case a community of scientists; they are expectations for how scientific research will be conducted. Ideally, individual scientists internalize these norms, and the community monitors them. According to our analysis these principles of science are common to systematic study in such disciplines as astrophysics, political science, and economics, as well as to more applied fields such as medicine, agriculture, and education. The principles emphasize objectivity, rigorous thinking, open-mindedness, and honest and thorough reporting. Numerous scholars

For example, inductive, deductive, and abductive modes of scientific inquiry meet these principles in different sequences.

have commented on the common scientific “conceptual culture” that pervades most fields (see, e.g., Ziman, 2000, p. 145; Chubin and Hackett, 1990).

These principles cut across two dimensions of the scientific enterprise: the creativity, expertise, communal values, and good judgment of the people who “do” science; and generalized guiding principles for scientific inquiry. The remainder of this chapter lays out the communal values of the scientific community and the guiding principles of the process that enable well-grounded scientific investigations to flourish.

THE SCIENTIFIC COMMUNITY

Science is a communal “form of life” (to use the expression of the philosopher Ludwig Wittgenstein [1968]), and the norms of the community take time to learn. Skilled investigators usually learn to conduct rigorous scientific investigations only after acquiring the values of the scientific community, gaining expertise in several related subfields, and mastering diverse investigative techniques through years of practice.

The culture of science fosters objectivity through enforcement of the rules of its “form of life”—such as the need for replicability, the unfettered flow of constructive critique, the desirability of blind refereeing—as well as through concerted efforts to train new scientists in certain habits of mind. By habits of mind, we mean things such as a dedication to the primacy of evidence, to minimizing and accounting for biases that might affect the research process, and to disciplined, creative, and open-minded thinking. These habits, together with the watchfulness of the community as a whole, result in a cadre of investigators who can engage differing perspectives and explanations in their work and consider alternative paradigms. Perhaps above all, the communally enforced norms ensure as much as is humanly possible that individual scientists—while not necessarily happy about being proven wrong—are willing to open their work to criticism, assessment, and potential revision.

Another crucial norm of the scientific “form of life,” which also depends for its efficacy on communal enforcement, is that scientists should be ethical and honest. This assertion may seem trite, even naïve. But scientific knowledge is constructed by the work of individuals, and like any other enterprise, if the people conducting the work are not open and candid, it

can easily falter. Sir Cyril Burt, a distinguished psychologist studying the heritability of intelligence, provides a case in point. He believed so strongly in his hypothesis that intelligence was highly heritable that he “doctored” data from twin studies to support his hypothesis (Tucker, 1994; Mackintosh, 1995); the scientific community reacted with horror when this transgression came to light. Examples of such unethical conduct in such fields as medical research are also well documented (see, e.g., Lock and Wells, 1996).

A different set of ethical issues also arises in the sciences that involve research with animals and humans. The involvement of living beings in the research process inevitably raises difficult ethical questions about a host of potential risks, ranging from confidentiality and privacy concerns to injury and death. Scientists must weigh the relative benefits of what might be learned against the potential risks to human research participants as they strive toward rigorous inquiry. (We consider this issue more fully in Chapters 4 and 6 .)

GUIDING PRINCIPLES

Throughout this report we argue that science is competent inquiry that produces warranted assertions (Dewey, 1938), and ultimately develops theory that is supported by pertinent evidence. The guiding principles that follow provide a framework for how valid inferences are supported, characterize the grounds on which scientists criticize one another’s work, and with hindsight, describe what scientists do. Science is a creative enterprise, but it is disciplined by communal norms and accepted practices for appraising conclusions and how they were reached. These principles have evolved over time from lessons learned by generations of scientists and scholars of science who have continually refined their theories and methods.

SCIENTIFIC PRINCIPLE 1 Pose Significant Questions That Can Be Investigated Empirically

This principle has two parts. The first part concerns the nature of the questions posed: science proceeds by posing significant questions about the world with potentially multiple answers that lead to hypotheses or conjectures that can be tested and refuted. The second part concerns how these questions are posed: they must be posed in such a way that it is

possible to test the adequacy of alternative answers through carefully designed and implemented observations.

Question Significance

A crucial but typically undervalued aspect of successful scientific investigation is the quality of the question posed. Moving from hunch to conceptualization and specification of a worthwhile question is essential to scientific research. Indeed, many scientists owe their renown less to their ability to solve problems than to their capacity to select insightful questions for investigation, a capacity that is both creative and disciplined:

The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill. To raise new questions, new possibilities, to regard old questions from a new angle, requires creative imagination and marks real advance in science (Einstein and Infeld, 1938, p. 92, quoted in Krathwohl, 1998).

Questions are posed in an effort to fill a gap in existing knowledge or to seek new knowledge, to pursue the identification of the cause or causes of some phenomena, to describe phenomena, to solve a practical problem, or to formally test a hypothesis. A good question may reframe an older problem in light of newly available tools or techniques, methodological or theoretical. For example, political scientist Robert Putnam challenged the accepted wisdom that increased modernity led to decreased civic involvement (see Box 3-1 ) and his work has been challenged in turn. A question may also be a retesting of a hypothesis under new conditions or circumstances; indeed, studies that replicate earlier work are key to robust research findings that hold across settings and objects of inquiry (see Principle 5 ). A good question can lead to a strong test of a theory, however explicit or implicit the theory may be.

The significance of a question can be established with reference to prior research and relevant theory, as well as to its relationship with important claims pertaining to policy or practice. In this way, scientific knowledge grows as new work is added to—and integrated with—the body of material that has come before it. This body of knowledge includes theo-

In 1970 political scientist Robert Putnam was in Rome studying Italian politics when the government decided to implement a new system of regional governments throughout the country. This situation gave Putnam and his colleagues an opportunity to begin a long-term study of how government institutions develop in diverse social environments and what affects their success or failure as democratic institutions (Putnam, Leonardi, and Nanetti, 1993). Based on a conceptual framework about “institutional performance,” Putnam and his colleagues carried out three or four waves of personal interviews with government officials and local leaders, six nationwide surveys, statistical measures of institutional performance, analysis of relevant legislation from 1970 to 1984, a one-time experiment in government responsiveness, and indepth case studies in six regions from 1976 to 1989.

The researchers found converging evidence of striking differences by region that had deep historical roots. The results also cast doubt on the then-prevalent view that increased modernity leads to decreased civic involvement. “The least civic areas of Italy are precisely the traditional southern villages. The civic ethos of traditional communities must not be idealized. Life in much of traditional Italy today is marked by hierarchy and exploitation, not by share-and-share alike” (p. 114). In contrast, “The most civic regions of Italy—the communities where citizens feel empowered to engage in collective deliberation about public choices and where those choices are translated most fully into effective public policies—include some of the most modern towns and cities of the peninsula. Modernization does not signal the demise of the civic community” (p. 115).

The findings of Putnam and his colleagues about the relative influence of economic development and civic traditions on democratic success are less conclusive, but the weight of the evidence favors the assertion that civic tradition matters more than economic affluence. This and subsequent work on social capital (Putnam, 1995) has led to a flurry of investigations and controversy that continues today.

ries, models, research methods (e.g., designs, measurements), and research tools (e.g., microscopes, questionnaires). Indeed, science is not only an effort to produce representations (models) of real-world phenomena by going from nature to abstract signs. Embedded in their practice, scientists also engage in the development of objects (e.g., instruments or practices); thus, scientific knowledge is a by-product of both technological activities and analytical activities (Roth, 2001). A review of theories and prior research relevant to a particular question can simply establish that it has not been answered before. Once this is established, the review can help shape alternative answers, the design and execution of a study by illuminating if and how the question and related conjectures have already been examined, as well as by identifying what is known about sampling, setting, and other important context. 2

Donald Stokes’ work (Stokes, 1997) provides a useful framework for thinking about important questions that can advance scientific knowledge and method (see Figure 3-1 ). In Pasteur’s Quadrant , he provided evidence that the conception of research-based knowledge as moving in a linear progression from fundamental science to applied science does not reflect how science has historically advanced. He provided several examples demonstrating that, instead, many advancements in science occurred as a result of “use-inspired research,” which simultaneously draws on both basic and applied research. Stokes (1997, p. 63) cites Brooks (1967) on basic and applied work:

Work directed toward applied goals can be highly fundamental in character in that it has an important impact on the conceptual structure or outlook of a field. Moreover, the fact that research is of such a nature that it can be applied does not mean that it is not also basic.

We recognize that important scientific discoveries are sometimes made when a competent observer notes a strange or interesting phenomenon for the first time. In these cases, of course, no prior literature exists to shape the investigation. And new fields and disciplines need to start somewhere. Our emphasis on linking to prior literature in this principle, then, applies generally to relatively established domains and fields.

FIGURE 3-1. Quadrant model of scientific research.

SOURCE: Stokes (1997, p. 73). Reprinted with permission.

Stokes’ model clearly applies to research in education, where problems of practice and policy provide a rich source for important—and often highly fundamental in character—research questions.

Empirically Based

Put simply, the term “empirical” means based on experience through the senses, which in turn is covered by the generic term observation. Since science is concerned with making sense of the world, its work is necessarily grounded in observations that can be made about it. Thus, research questions

must be posed in ways that potentially allow for empirical investigation. 3 For example, both Milankovitch and Muller could collect data on the Earth’s orbit to attempt to explain the periodicity in ice ages (see Box 3-2 ). Likewise, Putnam could collect data from natural variations in regional government to address the question of whether modernization leads to the demise of civic community ( Box 3-1 ), and the Tennessee state legislature could empirically assess whether reducing class size improves students’ achievement in early grades ( Box 3-3 ) because achievement data could be collected on students in classes of varying sizes. In contrast, questions such as: “ Should all students be required to say the pledge of allegiance?” cannot be submitted to empirical investigation and thus cannot be examined scientifically. Answers to these questions lie in realms other than science.

SCIENTIFIC PRINCIPLE 2 Link Research to Relevant Theory

Scientific theories are, in essence, conceptual models that explain some phenomenon. They are “nets cast to catch what we call ‘the world’…we endeavor to make the mesh ever finer and finer” (Popper, 1959, p. 59). Indeed, much of science is fundamentally concerned with developing and testing theories, hypotheses, models, conjectures, or conceptual frameworks that can explain aspects of the physical and social world. Examples of well-known scientific theories include evolution, quantum theory, and the theory of relativity.

In the social sciences and in education, such “grand” theories are rare. To be sure, generalized theoretical understanding is still a goal. However, some research in the social sciences seeks to achieve deep understanding of particular events or circumstances rather than theoretical understanding that will generalize across situations or events. Between these extremes lies the bulk of social science theory or models, what Merton (1973) called

Philosophers of science have long debated the meaning of the term empirical. As we state here, in one sense the empirical nature of science means that assertions about the world must be warranted by, or at least constrained by, explicit observation of it. However, we recognize that in addition to direct observation, strategies like logical reasoning and mathematical analysis can also provide empirical support for scientific assertions.

During the past 1 billion years, the earth’s climate has fluctuated between cold periods, when glaciers scoured the continents, and ice-free warm periods. Serbian mathematician Milutin Milankovitch in the 1930s posited the textbook explanation for these cycles, which was accepted as canon until recently (Milankovitch, 1941/1969; Berger, Imbrie, Hays, Kukla, and Saltzman, 1984). He based his theory on painstaking measurements of the eccentricity—or out-of-roundness—of the Earth’s orbit, which changed from almost perfectly circular to slightly oval and back every 100,000 years, matching the interval between glaciation periods. Subsequently, however, analysis of light energy absorbed by Earth, measured from the content of organic material in geological sediment cores, raised doubts about this correlation as a causal mechanism (e.g., MacDonald and Sertorio, 1990). The modest change in eccentricity did not make nearly enough difference in incident sunlight to produce the required change in thermal absorption. Another problem with Milankovitch’s explanation was that the geologic record showed some glaciation periods beginning before the orbital changes that supposedly caused them (Broecker, 1992; Winograd, Coplen, and Landwehr, 1992).

Astrophysicist Richard Muller then suggested an alternative mechanism, based on a different aspect of the Earth’s orbit (Muller, 1994; Karner and Muller, 2000; also see Grossman, 2001). Muller hypothesized that it is the Earth’s orbit in and out of the ecliptic that has been responsible for Earth’s cycli

mid-range theories that attempt to account for some aspect of the social world. Examples of such mid-range theories or explanatory models can be found in the physical and the social sciences.

These theories are representations or abstractions of some aspect of reality that one can only approximate by such models. Molecules, fields, or black holes are classic explanatory models in physics; the genetic code and the contractile filament model of muscle are two in biology. Similarly,

cal glaciation periods. He based the hypothesis on astronomical observations showing that the regions above and below the ecliptic are laden with cosmic dust, which would cool the planet. Muller’s “inclination theory” received major support when Kenneth Farley (1995) published a paper on cosmic dust in sea sediments.

Farley had begun his research project in an effort to refute the Muller inclination model, but discovered—to his surprise— that cosmic dust levels did indeed wax and wane in sync with the ice ages. As an immediate cause of the temperature change, Muller proposed that dust from space would influence the cloud cover on Earth and the amount of greenhouse gases—mainly carbon dioxide—in the atmosphere. Indeed, measurements of oxygen isotopes in trapped air bubbles and other properties from a 400,000-year-long Antarctic ice core by paleoceanographer Nicholas Shackleton (2001) provided more confirming evidence.

To gain greater understanding of these processes, geochronologists are seeking new “clocks” to determine more accurately the timing of events in the Earth’s history (e.g., Feng and Vasconcelos, 2001), while geochemists look for new ways of inferring temperature from composition of gasses trapped deep in ice or rock (see Pope and Giles, 2001). Still, no one knows how orbital variations would send the carbon dioxide into and out of the atmosphere. And there are likely to be other significant geologic factors besides carbon dioxide that control climate. There is much work still to be done to sort out the complex variables that are probably responsible for the ice ages.

culture, socioeconomic status, and poverty are classical models in anthropology, sociology, and political science. In program evaluation, program developers have ideas about the mechanism by which program inputs affect targeted outcomes; evaluations translate and test these ideas through a “program theory” that guides the work (Weiss, 1998a).

Theory enters the research process in two important ways. First, scientific research may be guided by a conceptual framework, model, or theory

that suggests possible questions to ask or answers to the question posed. 4 The process of posing significant questions typically occurs before a study is conducted. Researchers seek to test whether a theory holds up under certain circumstances. Here the link between question and theory is straightforward. For example, Putnam based his work on a theoretical conception of institutional performance that related civic engagement and modernization.

A research question can also devolve from a practical problem (Stokes, 1997; see discussion above). In this case, addressing a complex problem like the relationship between class size and student achievement may require several theories. Different theories may give conflicting predictions about the problem’s solution, or various theories might have to be reconciled to address the problem. Indeed, the findings from the Tennessee class size reduction study (see Box 3-3 ) have led to several efforts to devise theoretical understandings of how class size reduction may lead to better student achievement. Scientists are developing models to understand differences in classroom behavior between large and small classes that may ultimately explain and predict changes in achievement (Grissmer and Flannagan, 2000).

A second more subtle way that theoretical understanding factors into the research process derives from the fact that all scientific observations are “theory laden” (Kuhn, 1962). That is, the choice of what to observe and how to observe it is driven by an organizing conception—explicit or tacit— of the problem or topic. Thus, theory drives the research question, the use of methods, and the interpretation of results.

SCIENTIFIC PRINCIPLE 3

Use methods that permit direct investigation of the question.

Research methods—the design for collecting data and the measurement and analysis of variables in the design—should be selected in light of a research question, and should address it directly. Methods linked directly to problems permit the development of a logical chain of reasoning based

The process of posing significant questions or hypotheses may occur, as well, at the end of a study (e.g., Agar, 1996), or over the course of an investigation as understanding of the facets of the problem evolves (e.g., Brown, 1992).

on the interplay among investigative techniques, data, and hypotheses to reach justifiable conclusions. For clarity of discussion, we separate out the link between question and method (see Principle 3 ) and the rigorous reasoning from evidence to theory (see Principle 4 ). In the actual practice of research, such a separation cannot be achieved.

Debates about method—in many disciplines and fields—have raged for centuries as researchers have battled over the relative merit of the various techniques of their trade. The simple truth is that the method used to conduct scientific research must fit the question posed, and the investigator must competently implement the method. Particular methods are better suited to address some questions rather than others. The rare choice in the mid 1980s in Tennessee to conduct a randomized field trial, for example, enabled stronger inferences about the effects of class size reduction on student achievement (see Box 3-3 ) than would have been possible with other methods.

This link between question and method must be clearly explicated and justified; a researcher should indicate how a particular method will enable competent investigation of the question of interest. Moreover, a detailed description of method—measurements, data collection procedures, and data analyses—must be available to permit others to critique or replicate the study (see Principle 5 ). Finally, investigators should identify potential methodological limitations (such as insensitivity to potentially important variables, missing data, and potential researcher bias).

The choice of method is not always straightforward because, across all disciplines and fields, a wide range of legitimate methods—both quantitative and qualitative—are available to the researcher. For example when considering questions about the natural universe—from atoms to cells to black holes—profoundly different methods and approaches characterize each sub-field. While investigations in the natural sciences are often dependent on the use of highly sophisticated instrumentation (e.g., particle accelerators, gene sequencers, scanning tunneling microscopes), more rudimentary methods often enable significant scientific breakthroughs. For example, in 1995 two Danish zoologists identified an entirely new phylum of animals from a species of tiny rotifer-like creatures found living on the mouthparts of lobsters, using only a hand lens and light microscope (Wilson, 1998, p. 63).

Although research on the effects of class size reduction on students’ achievement dates back 100 years, Glass and Smith (1978) reported the first comprehensive statistical synthesis (meta-analysis) of the literature and concluded that, indeed, there were small improvements in achievement when class size was reduced (see also Glass, Cahen, Smith, and Filby, 1982; Bohrnstedt and Stecher, 1999). However, the Glass and Smith study was criticized (e.g., Robinson and Wittebols, 1986; Slavin, 1989) on a number of grounds, including the selection of some of the studies for the meta-analysis (e.g., tutoring, college classes, atypically small classes). Some subsequent reviews reached conclusions similar to Glass and Smith (e.g., Bohrnstedt and Stetcher, 1999; Hedges, Laine, and Greenwald, 1994; Robinson and Wittebols, 1986) while others did not find consistent evidence of a positive effect (e.g., Hanushek, 1986, 1999a; Odden, 1990; Slavin, 1989).

Does reducing class size improve students’ achievement? In the midst of controversy, the Tennessee state legislature asked just this question and funded a randomized experiment to find out, an experiment that Harvard statistician Frederick Mosteller (1995, p. 113) called “. . . one of the most important educational investigations ever carried out.” A total of 11,600 elementary school students and their teachers in 79 schools across the state were randomly assigned to one of three class-size conditions: small class (13-17 students), regular class

If a research conjecture or hypothesis can withstand scrutiny by multiple methods its credibility is enhanced greatly. As Webb, Campbell, Schwartz, and Sechrest (1966, pp. 173-174) phrased it: “When a hypothesis can survive the confrontation of a series of complementary methods of testing, it contains a degree of validity unattainable by one tested within the more constricted framework of a single method.” Putnam’s study (see Box 3-1 ) provides an example in which both quantitative and qualitative methods were applied in a longitudinal design (e.g., interview, survey, statistical estimate of institutional performance, analysis of legislative docu-

(22-26 students), or regular class (22-26 students) with a full-time teacher’s aide (for descriptions of the experiment, see Achilles, 1999; Finn and Achilles, 1990; Folger and Breda, 1989; Krueger, 1999; Word et al., 1990). The experiment began with a cohort of students who entered kindergarten in 1985, and lasted 4 years. After third grade, all students returned to regular size classes. Although students were supposed to stay in their original treatment conditions for four years, not all did. Some were randomly reassigned between regular and regular/aide conditions in the first grade while about 10 percent switched between conditions for other reasons (Krueger and Whitmore, 2000).

Three findings from this experiment stand out. First, students in small classes outperformed students in regular size classes (with or without aides). Second, the benefits of class-size reduction were much greater for minorities (primarily African American) and inner-city children than others (see, e.g., Finn and Achilles, 1990, 1999; but see also Hanushek, 1999b). And third, even though students returned to regular classes in fourth grade, the reduced class-size effect persisted in affecting whether they took college entrance examinations and on their examination performance (Krueger and Whitmore, 2001).

Interestingly, in balancing the size of the effects of class size reduction with the costs, the Tennessee legislature decided to reduce class size in the state (Ritter and Boruch, 1999).

ments) to generate converging evidence about the effects of modernization on civic community. New theories about the periodicity of the ice ages, similarly, were informed by multiple methods (e.g., astronomical observations of cosmic dust, measurements of oxygen isotopes). The integration and interaction of multiple disciplinary perspectives—with their varying methods—often accounts for scientific progress (Wilson, 1998); this is evident, for example, in the advances in understanding early reading skills described in Chapter 2 . This line of work features methods that range from neuroimaging to qualitative classroom observation.

We close our discussion of this principle by noting that in many sciences, measurement is a key aspect of research method. This is true for many research endeavors in the social sciences and education research, although not for all of them. If the concepts or variables are poorly specified or inadequately measured, even the best methods will not be able to support strong scientific inferences. The history of the natural sciences is one of remarkable development of concepts and variables, as well as the tools (instrumentation) to measure them. Measurement reliability and validity is particularly challenging in the social sciences and education (Messick, 1989). Sometimes theory is not strong enough to permit clear specification and justification of the concept or variable. Sometimes the tool (e.g., multiple-choice test) used to take the measurement seriously under-represents the construct (e.g., science achievement) to be measured. Sometimes the use of the measurement has an unintended social consequence (e.g., the effect of teaching to the test on the scope of the curriculum in schools).

And sometimes error is an inevitable part of the measurement process. In the physical sciences, many phenomena can be directly observed or have highly predictable properties; measurement error is often minimal. (However, see National Research Council [1991] for a discussion of when and how measurement in the physical sciences can be imprecise.) In sciences that involve the study of humans, it is essential to identify those aspects of measurement error that attenuate the estimation of the relationships of interest (e.g., Shavelson, Baxter, and Gao, 1993). By investigating those aspects of a social measurement that give rise to measurement error, the measurement process itself will often be improved. Regardless of field of study, scientific measurements should be accompanied by estimates of uncertainty whenever possible (see Principle 4 below).

SCIENTIFIC PRINCIPLE 4 Provide Coherent, Explicit Chain of Reasoning

The extent to which the inferences that are made in the course of scientific work are warranted depends on rigorous reasoning that systematically and logically links empirical observations with the underlying theory and the degree to which both the theory and the observations are linked to the question or problem that lies at the root of the investigation. There

is no recipe for determining how these ingredients should be combined; instead, what is required is the development of a logical “chain of reasoning” (Lesh, Lovitts, and Kelly, 2000) that moves from evidence to theory and back again. This chain of reasoning must be coherent, explicit (one that another researcher could replicate), and persuasive to a skeptical reader (so that, for example, counterhypotheses are addressed).

All rigorous research—quantitative and qualitative—embodies the same underlying logic of inference (King, Keohane, and Verba, 1994). This inferential reasoning is supported by clear statements about how the research conclusions were reached: What assumptions were made? How was evidence judged to be relevant? How were alternative explanations considered or discarded? How were the links between data and the conceptual or theoretical framework made?

The nature of this chain of reasoning will vary depending on the design of the study, which in turn will vary depending on the question that is being investigated. Will the research develop, extend, modify, or test a hypothesis? Does it aim to determine: What works? How does it work? Under what circumstances does it work? If the goal of the research is to test a hypothesis, stated in the form of an “if-then” rule, successful inference may depend on measuring the extent to which the rule predicts results under a variety of conditions. If the goal is to produce a description of a complex system, such as a subcellular organelle or a hierarchical social organization, successful inference may rather depend on issues of fidelity and internal consistency of the observational techniques applied to diverse components and the credibility of the evidence gathered. The research design and the inferential reasoning it enables must demonstrate a thorough understanding of the subtleties of the questions to be asked and the procedures used to answer them.

Muller (1994), for example, collected data on the inclination of the Earth’s orbit over a 100,000 year cycle, correlated it with the occurrence of ice ages, ruled out the plausibility of orbital eccentricity as a cause for the occurrence of ice ages, and inferred that the bounce in the Earth’s orbit likely caused the ice ages (see Box 3-2 ). Putnam used multiple methods to subject to rigorous testing his hypotheses about what affects the success or failure of democratic institutions as they develop in diverse social environments to rigorous testing, and found the weight of the evidence favored

the assertion that civic tradition matters more than economic affluence (see Box 3-1 ). And Baumeister, Bratslavsky, Muraven, and Tice (1998) compared three competing theories and used randomized experiments to conclude that a “psychic energy” hypothesis best explained the important psychological characteristic of “will power” (see “ Application of the Principles ”).

This principle has several features worthy of elaboration. Assumptions underlying the inferences made should be clearly stated and justified. Moreover, choice of design should both acknowledge potential biases and plan for implementation challenges.

Estimates of error must also be made. Claims to knowledge vary substantially according to the strength of the research design, theory, and control of extraneous variables and by systematically ruling out possible alternative explanations. Although scientists always reason in the presence of uncertainty, it is critical to gauge the magnitude of this uncertainty. In the physical and life sciences, quantitative estimates of the error associated with conclusions are often computed and reported. In the social sciences and education, such quantitative measures are sometimes difficult to generate; in any case, a statement about the nature and estimated magnitude of error must be made in order to signal the level of certainty with which conclusions have been drawn.

Perhaps most importantly, the reasoning about evidence should identify, consider, and incorporate, when appropriate, the alternative, competing explanations or rival “answers” to the research question. To make valid inferences, plausible counterexplanations must be dealt with in a rational, systematic, and compelling way. 5 The validity—or credibility—of a hypothesis is substantially strengthened if alternative counterhypotheses can be ruled out and the favored one thereby supported. Well-known research designs (e.g., Campbell and Stanley [1963] in educational psychology; Heckman [1979, 1980a, 1980b, 2001] and Goldberger [1972, 1983] in

In reporting, too, it is important to clarify that rival hypotheses are possible and that conclusions are not presented as if they were gospel. Murphy and colleagues call this “‘fair-dealing’—wariness of presenting the perspective of one group as if it defined a single truth about the phenomenon, while paying scant attention to other perspectives” (Murphy, Dingwall, Greatbatch, Parker, and Watson, 1998, p. 192).

economics; and Rosenbaum and Rubin [1983, 1984] in statistics) have been crafted to guard researchers against specific counterhypotheses (or “threats to validity”). One example, often called “selectivity bias,” is the counterhypothesis that differential selection (not the treatment) caused the outcome—that participants in the experimental treatment systematically differed from participants in the traditional (control) condition in ways that mattered importantly to the outcome. A cell biologist, for example, might unintentionally place (select) heart cells with a slight glimmer into an experimental group and others into a control group, thus potentially biasing the comparison between the groups of cells. The potential for a biased—or unfair—comparison arises because the shiny cells could differ systematically from the others in ways that affect what is being studied.

Selection bias is a pervasive problem in the social sciences and education research. To illustrate, in studying the effects of class-size reduction, credentialed teachers are more likely to be found in wealthy school districts that have the resources to reduce class size than in poor districts. This fact raises the possibility that higher achievement will be observed in the smaller classes due to factors other than class size (e.g.. teacher effects). Random assignment to “treatment” is the strongest known antidote to the problem of selection bias (see Chapter 5 ).

A second counterhypothesis contends that something in the research participants’ history that co-occurred with the treatment caused the outcome, not the treatment itself. For example, U.S. fourth-grade students outperformed students in others countries on the ecology subtest of the Third International Mathematics and Science Study. One (popular) explanation of this finding was that the effect was due to their schooling and the emphasis on ecology in U.S. elementary science curricula. A counter-hypothesis, one of history, posits that their high achievement was due to the prevalence of ecology in children’s television programming. A control group that has the same experiences as the experimental group except for the “treatment” under study is the best antidote for this problem.

A third prevalent class of alternative interpretations contends that an outcome was biased by the measurement used. For example, education effects are often judged by narrowly defined achievement tests that focus on factual knowledge and therefore favor direct-instruction teaching tech-

niques. Multiple achievement measures with high reliability (consistency) and validity (accuracy) help to counter potential measurement bias.

The Tennessee class-size study was designed primarily to eliminate all possible known explanations, except for reduced class size, in comparing the achievement of children in regular classrooms against achievement in reduced size classrooms. It did this. Complications remained, however. About ten percent of students moved out of their originally assigned condition (class size), weakening the design because the comparative groups did not remain intact to enable strict comparisons. However, most scholars who subsequently analyzed the data (e.g., Krueger and Whitmore, 2001), while limited by the original study design, suggested that these infidelities did not affect the main conclusions of the study that smaller class size caused slight improvements in achievement. Students in classes of 13-17 students outperformed their peers in larger classes, on average, by a small margin.

SCIENTIFIC PRINCIPLE 5 Replicate and Generalize Across Studies

Replication and generalization strengthen and clarify the limits of scientific conjectures and theories. By replication we mean, at an elementary level, that if one investigator makes a set of observations, another investigator can make a similar set of observations under the same conditions. Replication in this sense comes close to what psychometricians call reliability—consistency of measurements from one observer to another, from one task to another parallel task, from one occasion to another occasion. Estimates of these different types of reliability can vary when measuring a given construct: for example, in measuring performance of military personnel (National Research Council, 1991), multiple observers largely agreed on what they observed within tasks; however, enlistees’ performance across parallel tasks was quite inconsistent.

At a somewhat more complex level, replication means the ability to repeat an investigation in more than one setting (from one laboratory to another or from one field site to a similar field site) and reach similar conclusions. To be sure, replication in the physical sciences, especially with inanimate objects, is more easily achieved than in social science or education; put another way, the margin of error in social science replication is usually

much greater than in physical science replication. The role of contextual factors and the lack of control that characterizes work in the social realm require a more nuanced notion of replication. Nevertheless, the typically large margins of error in social science replications do not preclude their identification.

Having evidence of replication, an important goal of science is to understand the extent to which findings generalize from one object or person to another, from one setting to another, and so on. To this end, a substantial amount of statistical machinery has been built both to help ensure that what is observed in a particular study is representative of what is of larger interest (i.e., will generalize) and to provide a quantitative measure of the possible error in generalizing. Nonstatistical means of generalization (e.g., triangulation, analytic induction, comparative analysis) have also been developed and applied in genres of research, such as ethnography, to understand the extent to which findings generalize across time, space, and populations. Subsequent applications, implementations, or trials are often necessary to assure generalizability or to clarify its limits. For example, since the Tennessee experiment, additional studies of the effects of class size reduction on student learning have been launched in settings other than Tennessee to assess the extent to which the findings generalize (e.g., Hruz, 2000).

In the social sciences and education, many generalizations are limited to particular times and particular places (Cronbach, 1975). This is because the social world undergoes rapid and often significant change; social generalizations, as Cronbach put it, have a shorter “half-life” than those in the physical world. Campbell and Stanley (1963) dubbed the extent to which the treatment conditions and participant population of a study mirror the world to which generalization is desired the “external validity” of the study. Consider, again, the Tennessee class-size research; it was undertaken in a set of schools that had the desire to participate, the physical facilities to accommodate an increased number of classrooms, and adequate teaching staff. Governor Wilson of California “overgeneralized” the Tennessee study, ignoring the specific experimental conditions of will and capacity and implemented class-size reduction in more than 95 percent of grades K-3 in the state. Not surprisingly, most researchers studying California have

concluded that the Tennessee findings did not entirely generalize to a different time, place, and context (see, e.g., Stecher and Bohrnstedt, 2000). 6

SCIENTIFIC PRINCIPLE 6 Disclose Research to Encourage Professional Scrutiny and Critique

We argue in Chapter 2 that a characteristic of scientific knowledge accumulation is its contested nature. Here we suggest that science is not only characterized by professional scrutiny and criticism, but also that such criticism is essential to scientific progress. Scientific studies usually are elements of a larger corpus of work; furthermore, the scientists carrying out a particular study always are part of a larger community of scholars. Reporting and reviewing research results are essential to enable wide and meaningful peer review. Results are traditionally published in a specialty journal, in books published by academic presses, or in other peer-reviewed publications. In recent years, an electronic version may accompany or even substitute for a print publication. 7 Results may be debated at professional conferences. Regardless of the medium, the goals of research reporting are to communicate the findings from the investigation; to open the study to examination, criticism, review, and replication (see Principle 5 ) by peer investigators; and ultimately to incorporate the new knowledge into the prevailing canon of the field. 8

	A question arises as to whether this is a failure to generalize or a problem of poor implementation. The conditions under which Tennessee implemented the experiment were not reproduced in California with the now known consequence of failure to replicate and generalize.
	The committee is concerned that the quality of peer review in electronic modes of dissemination varies greatly and sometimes cannot be easily assessed from its source. While the Internet is providing new and exciting ways to connect scientists and promote scientific debate, the extent to which the principles of science are met in some electronically posted work is often unclear.
	Social scientists and education researchers also commonly publish information about new knowledge for practitioners and the public. In those cases, the research must be reported in accessible ways so that readers can understand the researcher’s procedures and evaluate the evidence, interpretations, and arguments.

The goal of communicating new knowledge is self-evident: research results must be brought into the professional and public domain if they are to be understood, debated, and eventually become known to those who could fruitfully use them. The extent to which new work can be reviewed and challenged by professional peers depends critically on accurate, comprehensive, and accessible records of data, method, and inferential reasoning. This careful accounting not only makes transparent the reasoning that led to conclusions—promoting its credibility—but it also allows the community of scientists and analysts to comprehend, to replicate, and otherwise to inform theory, research, and practice in that area.

Many nonscientists who seek guidance from the research community bemoan what can easily be perceived as bickering or as an indication of “bad” science. Quite the contrary: intellectual debate at professional meetings, through research collaborations, and in other settings provide the means by which scientific knowledge is refined and accepted; scientists strive for an “open society” where criticism and unfettered debate point the way to advancement. Through scholarly critique (see, e.g., Skocpol, 1996) and debate, for example, Putnam’s work has stimulated a series of articles, commentary, and controversy in research and policy circles about the role of “social capital” in political and other social phenomena (Winter, 2000). And the Tennessee class size study has been the subject of much scholarly debate, leading to a number of follow-on analyses and launching new work that attempts to understand the process by which classroom behavior may shift in small classes to facilitate learning. However, as Lagemann (2000) has observed, for many reasons the education research community has not been nearly as critical of itself as is the case in other fields of scientific study.

APPLICATION OF THE PRINCIPLES

The committee considered a wide range of literature and scholarship to test its ideas about the guiding principles. We realized, for example, that empiricism, while a hallmark of science, does not uniquely define it. A poet can write from first-hand experience of the world, and in this sense is an empiricist. And making observations of the world, and reasoning about their experience, helps both literary critics and historians create the

interpretive frameworks that they bring to bear in their scholarship. But empirical method in scientific inquiry has different features, like codified procedures for making observations and recognizing sources of bias associated with particular methods, 9 and the data derived from these observations are used specifically as tools to support or refute knowledge claims. Finally, empiricism in science involves collective judgments based on logic, experience, and consensus.

Another hallmark of science is replication and generalization. Humanists do not seek replication, although they often attempt to create work that generalizes (say) to the “human condition.” However, they have no formal logic of generalization, unlike scientists working in some domains (e.g., statistical sampling theory). In sum, it is clear that there is no bright line that distinguishes science from nonscience or high-quality science from low-quality science. Rather, our principles can be used as general guidelines for understanding what can be considered scientific and what can be considered high-quality science (see, however, Chapters 4 and 5 for an elaboration).

To show how our principles help differentiate science from other forms of scholarship, we briefly consider two genres of education inquiry published in refereed journals and books. We do not make a judgment about the worth of either form of inquiry; although we believe strongly in the merits of scientific inquiry in education research and more generally, that “science” does not mean “good.” Rather, we use them as examples to illustrate the distinguishing character of our principles of science. The first— connoisseurship —grew out of the arts and humanities (e.g., Eisner, 1991) and does not claim to be scientific. The second— portraiture —claims to straddle the fence between humanistic and scientific inquiry (e.g., Lawrence-Lightfoot and Davis, 1997).

Eisner (1991, p. 7) built a method for education inquiry firmly rooted in the arts and humanities, arguing that “there are multiple ways in which the world can be known: Artists, writers, and dancers, as well as scientists, have important things to tell about the world.” His method of inquiry combines connoisseurship (the art of appreciation), which “aims to

We do not claim that any one investigator or observational method is “objective.” Rather, the guiding principles are established to guard against bias through rigorous methods and a critical community.

appreciate the qualities . . . that constitute an act, work, or object and, typically . . . to relate these to the contextual and antecedent conditions” (p. 85) with educational criticism (the art of disclosure), which provides “connoisseurship with a public face” (p. 85). The goal of this genre of research is to enable readers to enter an event and to participate in it. To this end, the educational critic—through educational connoisseurship— must capture the key qualities of the material, situation, and experience and express them in text (“criticism”) to make what the critic sees clear to others. “To know what schools are like, their strengths and their weaknesses, we need to be able to see what occurs in them, and we need to be able to tell others what we have seen in ways that are vivid and insightful” (Eisner, 1991, p. 23, italics in original).

The grounds for his knowledge claims are not those in our guiding principles. Rather, credibility is established by: (1) structural corroboration—“multiple types of data are related to each other” (p. 110) and “ disconfirming evidence and contradictory interpretations ” (p. 111; italics in original) are considered; (2) consensual validation—“agreement among competent others that the description, interpretation, evaluation, and thematics of an educational situation are right” (p. 112); and (3) referential adequacy— “the extent to which a reader is able to locate in its subject matter the qualities the critic addresses and the meanings he or she ascribes to these” (p. 114). While sharing some features of our guiding principles (e.g., ruling out counterinterpretations to the favored interpretation), this humanistic approach to knowledge claims builds on a very different epistemology; the key scientific concepts of reliability, replication, and generalization, for example, are quite different. We agree with Eisner that such approaches fall outside the purview of science and conclude that our guiding principles readily distinguish them.

Portraiture (Lawrence-Lightfoot, 1994; Lawrence-Lightfoot and Davis, 1997) is a qualitative research method that aims to “record and interpret the perspectives and experience of the people they [the researchers] are studying, documenting their [the research participants’] voices and their visions—their authority, knowledge, and wisdom” (Lawrence-Lightfoot and Davis, 1997, p. xv). In contrast to connoisseurship’s humanist orientation, portraiture “seeks to join science and art” (Lawrence-Lightfoot and Davis, 1997, p. xv) by “embracing the intersection of aesthetics and empiricism” (p. 6). The standard for judging the quality of portraiture is authenticity,

“. . . capturing the essence and resonance of the actors’ experience and perspective through the details of action and thought revealed in context” (p. 12). When empirical and literary themes come together (called “resonance”) for the researcher, the actors, and the audience, “we speak of the portrait as achieving authenticity” (p. 260).

In I’ve Known Rivers , Lawrence-Lightfoot (1994) explored the life stories of six men and women:

. . . using the intensive, probing method of ‘human archeology’—a name I [Lawrence-Lightfoot] coined for this genre of portraiture as a way of trying to convey the depth and penetration of the inquiry, the richness of the layers of human experience, the search for ancestral and generational artifacts, and the painstaking, careful labor that the metaphorical dig requires. As I listen to the life stories of these individuals and participate in the ‘co-construction’ of narrative, I employ the themes, goals, and techniques of portraiture. It is an eclectic, interdisciplinary approach, shaped by the lenses of history, anthropology, psychology and sociology. I blend the curiosity and detective work of a biographer, the literary aesthetic of a novelist, and the systematic scrutiny of a researcher (p. 15).

Some scholars, then, deem portraiture as “scientific” because it relies on the use of social science theory and a form of empiricism (e.g., interview). While both empiricism and theory are important elements of our guiding principles, as we discuss above, they are not, in themselves, defining. The devil is in the details. For example, independent replication is an important principle in our framework but is absent in portraiture in which researcher and subject jointly construct a narrative. Moreover, even when our principles are manifest, the specific form and mode of application can make a big difference. For example, generalization in our principles is different from generalization in portraiture. As Lawrence-Lightfoot and Davis (1997) point out, generalization as used in the social sciences does not fit portraiture. Generalization in portraiture “. . . is not the classical conception . . . where the investigator uses codified methods for generalizing from specific findings to a universe, and where there is little interest in findings that reflect only the characteristics of the sample. . . .” By contrast, the portraitist seeks to “document and illuminate the complexity

and detail of a unique experience or place, hoping the audience will see itself reflected in it, trusting that the readers will feel identified. The portraitist is very interested in the single case because she believes that embedded in it the reader will discover resonant universal themes” (p. 15). We conclude that our guiding principles would distinguish portraiture from what we mean by scientific inquiry, although it, like connoisseurship, has some traits in common.

To this point, we have shown how our principles help to distinguish science and nonscience. A large amount of education research attempts to base knowledge claims on science; clearly, however, there is great variation with respect to scientific rigor and competence. Here we use two studies to illustrate how our principles demonstrate this gradation in scientific quality.

The first study (Carr, Levin, McConnachie, Carlson, Kemp, Smith, and McLaughlin, 1999) reported on an educational intervention carried out on three nonrandomly selected individuals who were suffering severe behavioral disorders and who were residing in group-home settings. Since earlier work had established remedial procedures involving “simulations and analogs of the natural environment” (p. 6), the focus of the study was on the generalizability (or external validity) to the “real world” of the intervention (places, caregivers).

Over a two to three week period, “baseline” frequencies of their problem behaviors were established, these behaviors were remeasured after an intervention lasting for some years was carried out. The researchers took a third measurement during the maintenance phase of the study. While care was taken in describing behavioral observations, variable construction and reliability, the paper reporting on the study did not provide clear, detailed depictions of the interventions or who carried them out (research staff or staff of the group homes). Furthermore, no details were given of the changes in staffing or in the regimens of the residential settings—changes that were inevitable over a period of many years (the timeline itself was not clearly described). Finally, in the course of daily life over a number of years, many things would have happened to each of the subjects, some of which might be expected to be of significance to the study, but none of them were documented. Over the years, too, one might expect some developmental changes to occur in the aggressive behavior displayed by the research subjects, especially in the two teenagers. In short, the study focused on

generalizability at too great an expense relative to internal validity. In the end, there were many threats to internal validity in this study, and so it is impossible to conclude (as the authors did) from the published report that the “treatment” had actually caused the improvement in behavior that was noted.

Turning to a line of work that we regard as scientifically more successful, in a series of four randomized experiments, Baumeister, Bratslavsky, Muraven, and Tice (1998) tested three competing theories of “will power” (or, more technically, “self-regulation”)—the psychological characteristic that is posited to be related to persistence with difficult tasks such as studying or working on homework assignments. One hypothesis was that will power is a developed skill that would remain roughly constant across repeated trials. The second theory posited a self-control schema “that makes use of information about how to alter one’s own response” (p. 1254) so that once activated on one trial, it would be expected to increase will power on a second trial. The third theory, anticipated by Freud’s notion of the ego exerting energy to control the id and superego, posits that will power is a depletable resource—it requires the use of “psychic energy” so that performance from trial 1 to trial 2 would decrease if a great deal of will power was called for on trial 1. In one experiment, 67 introductory psychology students were randomly assigned to a condition in which either no food was present or both radishes and freshly baked chocolate chip cookies were present, and the participants were instructed either to eat two or three radishes (resisting the cookies) or two or three cookies (resisting the radishes). Immediately following this situation, all participants were asked to work on two puzzles that unbeknownst to them, were unsolvable, and their persistence (time) in working on the puzzles was measured. The experimental manipulation was checked for every individual participating by researchers observing their behavior through a one-way window. The researchers found that puzzle persistence was the same in the control and cookie conditions and about 2.5 times as long, on average, as in the radish condition, lending support to the psychic energy theory—arguably, resisting the temptation to eat the cookies evidently had depleted the reserve of self-control, leading to poor performance on the second task. Later experiments extended the findings supporting the energy theory to situations involving choice, maladaptive performance, and decision making.

However, as we have said, no single study or series of studies satisfy all of our guiding principles, and these will power experiments are no exception. They all employed small samples of participants, all drawn from a college population. The experiments were contrived—the conditions of the study would be unlikely outside a psychology laboratory. And the question of whether these findings would generalize to more realistic (e.g., school) settings was not addressed.

Nevertheless, the contrast in quality between the two studies, when observed through the lens of our guiding principles, is stark. Unlike the first study, the second study was grounded in theory and identified three competing answers to the question of self-regulation, each leading to a different empirically refutable claim. In doing so, the chain of reasoning was made transparent. The second study, unlike the first, used randomized experiments to address counterclaims to the inference of psychic energy, such as selectivity bias or different history during experimental sessions. Finally, in the second study, the series of experiments replicated and extended the effects hypothesized by the energy theory.

CONCLUDING COMMENT

Nearly a century ago, John Dewey (1916) captured the essence of the account of science we have developed in this chapter and expressed a hopefulness for the promise of science we similarly embrace:

Our predilection for premature acceptance and assertion, our aversion to suspended judgment, are signs that we tend naturally to cut short the process of testing. We are satisfied with superficial and immediate short-visioned applications. If these work out with moderate satisfactoriness, we are content to suppose that our assumptions have been confirmed. Even in the case of failure, we are inclined to put the blame not on the inadequacy and incorrectness of our data and thoughts, but upon our hard luck and the hostility of circumstances. . . . Science represents the safeguard of the [human] race against these natural propensities and the evils which flow from them. It consists of the special appliances and methods... slowly worked out in order to conduct reflection under conditions whereby its procedures and results are tested.

Researchers, historians, and philosophers of science have debated the nature of scientific research in education for more than 100 years. Recent enthusiasm for "evidence-based" policy and practice in education—now codified in the federal law that authorizes the bulk of elementary and secondary education programs—have brought a new sense of urgency to understanding the ways in which the basic tenets of science manifest in the study of teaching, learning, and schooling.

Scientific Research in Education describes the similarities and differences between scientific inquiry in education and scientific inquiry in other fields and disciplines and provides a number of examples to illustrate these ideas. Its main argument is that all scientific endeavors share a common set of principles, and that each field—including education research—develops a specialization that accounts for the particulars of what is being studied. The book also provides suggestions for how the federal government can best support high-quality scientific research in education.

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Getting started with research

What is Research?

Research is the pursuit of new knowledge through the process of discovery. Scientific research involves diligent inquiry and systematic observation of phenomena. Most scientific research projects involve experimentation, often requiring testing the effect of changing conditions on the results. The conditions under which specific observations are made must be carefully controlled, and records must be meticulously maintained. This ensures that observations and results can be are reproduced. Scientific research can be basic (fundamental) or applied. What is the difference? The National Science Foundation uses the following definitions in its resource surveys:

Basic research:

The objective of basic research is to gain more comprehensive knowledge or understanding of the subject under study, without specific applications in mind. In industry, basic research is defined as research that advances scientific knowledge but does not have specific immediate commercial objectives, although it may be in fields of present or potential commercial interest.

Applied research:

Applied research is aimed at gaining knowledge or understanding to determine the means by which a specific, recognized need may be met. In industry, applied research includes investigations oriented to discovering new scientific knowledge that has specific commercial objectives with respect to products, processes, or services.

What is research at the undergraduate level?

At the undergraduate level, research is self-directed work under the guidance and supervision of a mentor/advisor ― usually a university professor. A gradual transition towards independence is encouraged as a student gains confidence and is able to work with minor supervision. Students normally participate in an ongoing research project and investigate phenomena of interest to them and their advisor.

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1 Science and scientific research

What is research? Depending on who you ask, you will likely get very different answers to this seemingly innocuous question. Some people will say that they routinely research different online websites to find the best place to buy the goods or services they want. Television news channels supposedly conduct research in the form of viewer polls on topics of public interest such as forthcoming elections or government-funded projects. Undergraduate students research on the Internet to find the information they need to complete assigned projects or term papers. Postgraduate students working on research projects for a professor may see research as collecting or analysing data related to their project. Businesses and consultants research different potential solutions to remedy organisational problems such as a supply chain bottleneck or to identify customer purchase patterns. However, none of the above can be considered ‘scientific research’ unless: it contributes to a body of science, and it follows the scientific method. This chapter will examine what these terms mean.

What is science? To some, science refers to difficult high school or university-level courses such as physics, chemistry, and biology meant only for the brightest students. To others, science is a craft practiced by scientists in white coats using specialised equipment in their laboratories. Etymologically, the word ‘science’ is derived from the Latin word scientia meaning knowledge. Science refers to a systematic and organised body of knowledge in any area of inquiry that is acquired using ‘the scientific method’ (the scientific method is described further below). Science can be grouped into two broad categories: natural science and social science. Natural science is the science of naturally occurring objects or phenomena, such as light, objects, matter, earth, celestial bodies, or the human body. Natural sciences can be further classified into physical sciences, earth sciences, life sciences, and others. Physical sciences consist of disciplines such as physics (the science of physical objects), chemistry (the science of matter), and astronomy (the science of celestial objects). Earth sciences consist of disciplines such as geology (the science of the earth). Life sciences include disciplines such as biology (the science of human bodies) and botany (the science of plants). In contrast, social science is the science of people or collections of people, such as groups, firms, societies, or economies, and their individual or collective behaviours. Social sciences can be classified into disciplines such as psychology (the science of human behaviours), sociology (the science of social groups), and economics (the science of firms, markets, and economies).

The natural sciences are different from the social sciences in several respects. The natural sciences are very precise, accurate, deterministic, and independent of the person making the scientific observations. For instance, a scientific experiment in physics, such as measuring the speed of sound through a certain media or the refractive index of water, should always yield the exact same results, irrespective of the time or place of the experiment, or the person conducting the experiment. If two students conducting the same physics experiment obtain two different values of these physical properties, then it generally means that one or both of those students must be in error. However, the same cannot be said for the social sciences, which tend to be less accurate, deterministic, or unambiguous. For instance, if you measure a person’s happiness using a hypothetical instrument, you may find that the same person is more happy or less happy (or sad) on different days and sometimes, at different times on the same day. One’s happiness may vary depending on the news that person received that day or on the events that transpired earlier during that day. Furthermore, there is not a single instrument or metric that can accurately measure a person’s happiness. Hence, one instrument may calibrate a person as being ‘more happy’ while a second instrument may find that the same person is ‘less happy’ at the same instant in time. In other words, there is a high degree of measurement error in the social sciences and there is considerable uncertainty and little agreement on social science policy decisions. For instance, you will not find many disagreements among natural scientists on the speed of light or the speed of the earth around the sun, but you will find numerous disagreements among social scientists on how to solve a social problem such as reduce global terrorism or rescue an economy from a recession. Any student studying the social sciences must be cognisant of and comfortable with handling higher levels of ambiguity, uncertainty, and error that come with such sciences, which merely reflects the high variability of social objects.

Sciences can also be classified based on their purpose. Basic sciences , also called pure sciences, are those that explain the most basic objects and forces, relationships between them, and laws governing them. Examples include physics, mathematics, and biology. Applied sciences , also called practical sciences, are sciences that apply scientific knowledge from basic sciences in a physical environment. For instance, engineering is an applied science that applies the laws of physics and chemistry for practical applications such as building stronger bridges or fuel efficient combustion engines, while medicine is an applied science that applies the laws of biology to solving human ailments. Both basic and applied sciences are required for human development. However, applied science cannot stand on its own right, but instead relies on basic sciences for its progress. Of course, industry and private enterprises tend to focus more on applied sciences given their practical value, while universities study both basic and applied sciences.

Scientific knowledge

The purpose of science is to create scientific knowledge. Scientific knowledge refers to a generalised body of laws and theories for explaining a phenomenon or behaviour of interest that is acquired using the scientific method. Laws are observed patterns of phenomena or behaviours, while theories are systematic explanations of the underlying phenomenon or behaviour. For instance, in physics, the Newtonian Laws of Motion describe what happens when an object is in a state of rest or motion (Newton’s First Law), what force is needed to move a stationary object or stop a moving object (Newton’s Second Law), and what happens when two objects collide (Newton’s Third Law). Collectively, the three laws constitute the basis of classical mechanics—a theory of moving objects. Likewise, the theory of optics explains the properties of light and how it behaves in different media, electromagnetic theory explains the properties of electricity and how to generate it, quantum mechanics explains the properties of subatomic particles, and thermodynamics explains the properties of energy and mechanical work. An introductory university level textbook in physics will likely contain separate chapters devoted to each of these theories. Similar theories are also available in social sciences. For instance, cognitive dissonance theory in psychology explains how people react when their observations of an event are different from what they expected of that event, general deterrence theory explains why some people engage in improper or criminal behaviours, such as to illegally download music or commit software piracy, and the theory of planned behaviour explains how people make conscious reasoned choices in their everyday lives.

The goal of scientific research is to discover laws and postulate theories that can explain natural or social phenomena, or in other words, build scientific knowledge. It is important to understand that this knowledge may be imperfect or even quite far from the truth. Sometimes, there may not be a single universal truth, but rather an equilibrium of ‘multiple truths.’ We must understand that the theories upon which scientific knowledge is based are only explanations of a particular phenomenon as suggested by a scientist. As such, there may be good or poor explanations depending on the extent to which those explanations fit well with reality, and consequently, there may be good or poor theories. The progress of science is marked by our progression over time from poorer theories to better theories, through better observations using more accurate instruments and more informed logical reasoning.

We arrive at scientific laws or theories through a process of logic and evidence. Logic (theory) and evidence (observations) are the two, and only two, pillars upon which scientific knowledge is based. In science, theories and observations are inter-related and cannot exist without each other. Theories provide meaning and significance to what we observe, and observations help validate or refine existing theory or construct new theory. Any other means of knowledge acquisition, such as faith or authority cannot be considered science.

Scientific research

Given that theories and observations are the two pillars of science, scientific research operates at two levels: a theoretical level and an empirical level. The theoretical level is concerned with developing abstract concepts about a natural or social phenomenon and relationships between those concepts (i.e., build ‘theories’), while the empirical level is concerned with testing the theoretical concepts and relationships to see how well they reflect our observations of reality, with the goal of ultimately building better theories. Over time, a theory becomes more and more refined (i.e., fits the observed reality better), and the science gains maturity. Scientific research involves continually moving back and forth between theory and observations. Both theory and observations are essential components of scientific research. For instance, relying solely on observations for making inferences and ignoring theory is not considered valid scientific research.

Depending on a researcher’s training and interest, scientific inquiry may take one of two possible forms: inductive or deductive. In inductive research , the goal of a researcher is to infer theoretical concepts and patterns from observed data. In deductive research , the goal of the researcher is to test concepts and patterns known from theory using new empirical data. Hence, inductive research is also called theory-building research, and deductive research is theory-testing research. Note here that the goal of theory testing is not just to test a theory, but possibly to refine, improve, and extend it. Figure 1.1 depicts the complementary nature of inductive and deductive research. Note that inductive and deductive research are two halves of the research cycle that constantly iterates between theory and observations. You cannot do inductive or deductive research if you are not familiar with both the theory and data components of research. Naturally, a complete researcher is one who can traverse the entire research cycle and can handle both inductive and deductive research.

It is important to understand that theory-building (inductive research) and theory-testing (deductive research) are both critical for the advancement of science. Elegant theories are not valuable if they do not match with reality. Likewise, mountains of data are also useless until they can contribute to the construction of meaningful theories. Rather than viewing these two processes in a circular relationship, as shown in Figure 1.1, perhaps they can be better viewed as a helix, with each iteration between theory and data contributing to better explanations of the phenomenon of interest and better theories. Though both inductive and deductive research are important for the advancement of science, it appears that inductive (theory-building) research is more valuable when there are few prior theories or explanations, while deductive (theory-testing) research is more productive when there are many competing theories of the same phenomenon and researchers are interested in knowing which theory works best and under what circumstances.

Theory building and theory testing are particularly difficult in the social sciences, given the imprecise nature of the theoretical concepts, inadequate tools to measure them, and the presence of many unaccounted for factors that can also influence the phenomenon of interest. It is also very difficult to refute theories that do not work. For instance, Karl Marx’s theory of communism as an effective means of economic production withstood for decades, before it was finally discredited as being inferior to capitalism in promoting economic growth and social welfare. Erstwhile communist economies like the Soviet Union and China eventually moved toward more capitalistic economies characterised by profit-maximising private enterprises. However, the recent collapse of the mortgage and financial industries in the United States demonstrates that capitalism also has its flaws and is not as effective in fostering economic growth and social welfare as previously presumed. Unlike theories in the natural sciences, social science theories are rarely perfect, which provides numerous opportunities for researchers to improve those theories or build their own alternative theories.

Conducting scientific research, therefore, requires two sets of skills—theoretical and methodological—needed to operate in the theoretical and empirical levels respectively. Methodological skills (‘know-how’) are relatively standard, invariant across disciplines, and easily acquired through doctoral programs. However, theoretical skills (‘know-what’) are considerably harder to master, require years of observation and reflection, and are tacit skills that cannot be ‘taught’ but rather learned though experience. All of the greatest scientists in the history of mankind, such as Galileo, Newton, Einstein, Niels Bohr, Adam Smith, Charles Darwin, and Herbert Simon, were master theoreticians, and they are remembered for the theories they postulated that transformed the course of science. Methodological skills are needed to be an ordinary researcher, but theoretical skills are needed to be an extraordinary researcher!

Scientific method

In the preceding sections, we described science as knowledge acquired through a scientific method. So what exactly is the ‘scientific method’? Scientific method refers to a standardised set of techniques for building scientific knowledge, such as how to make valid observations, how to interpret results, and how to generalise those results. The scientific method allows researchers to independently and impartially test pre-existing theories and prior findings, and subject them to open debate, modifications, or enhancements. The scientific method must satisfy four key characteristics:

Replicability : Others should be able to independently replicate or repeat a scientific study and obtain similar, if not identical, results. Precision : Theoretical concepts, which are often hard to measure, must be defined with such precision that others can use those definitions to measure those concepts and test that theory. Falsifiability : A theory must be stated in such a way that it can be disproven. Theories that cannot be tested or falsified are not scientific theories and any such knowledge is not scientific knowledge. A theory that is specified in imprecise terms or whose concepts are not accurately measureable cannot be tested, and is therefore not scientific. Sigmund Freud’s ideas on psychoanalysis fall into this category and are therefore not considered a ‘theory’, even though psychoanalysis may have practical utility in treating certain types of ailments. Parsimony: When there are multiple different explanations of a phenomenon, scientists must always accept the simplest or logically most economical explanation. This concept is called parsimony or ‘Occam’s razor’. Parsimony prevents scientists from pursuing overly complex or outlandish theories with an endless number of concepts and relationships that may explain a little bit of everything but nothing in particular. Any branch of inquiry that does not allow the scientific method to test its basic laws or theories cannot be called ‘science’. For instance, theology (the study of religion) is not science because theological ideas—such as the presence of God—cannot be tested by independent observers using a logical, confirmable, repeatable, and scrutinisable. Similarly, arts, music, literature, humanities, and law are also not considered science, even though they are creative and worthwhile endeavours in their own right.

The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques for collecting and analysing qualitative or quantitative data. These methods include laboratory experiments, field surveys, case research, ethnographic research, action research, and so forth. Much of this book is devoted to learning about these different methods. However, recognise that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyse these observations. Very little of this method is directly pertinent to the theoretical level, which is really the more challenging part of scientific research.

Types of scientific research

Depending on the purpose of research, scientific research projects can be grouped into three types: exploratory, descriptive, and explanatory. Exploratory research is often conducted in new areas of inquiry, where the goals of the research are: to scope out the magnitude or extent of a particular phenomenon, problem, or behaviour, to generate some initial ideas (or ‘hunches’) about that phenomenon, or to test the feasibility of undertaking a more extensive study regarding that phenomenon. For instance, if the citizens of a country are generally dissatisfied with governmental policies during an economic recession, exploratory research may be directed at measuring the extent of citizens’ dissatisfaction, understanding how such dissatisfaction is manifested, such as the frequency of public protests, and the presumed causes of such dissatisfaction, such as ineffective government policies in dealing with inflation, interest rates, unemployment, or higher taxes. Such research may include examination of publicly reported figures, such as estimates of economic indicators, such as gross domestic product (GDP), unemployment, and consumer price index (CPI), as archived by third-party sources, obtained through interviews of experts, eminent economists, or key government officials, and/or derived from studying historical examples of dealing with similar problems. This research may not lead to a very accurate understanding of the target problem, but may be worthwhile in scoping out the nature and extent of the problem and serve as a useful precursor to more in-depth research.

Descriptive research is directed at making careful observations and detailed documentation of a phenomenon of interest. These observations must be based on the scientific method (i.e., must be replicable, precise, etc.), and therefore, are more reliable than casual observations by untrained people. Examples of descriptive research are tabulation of demographic statistics by the United States Census Bureau or employment statistics by the Bureau of Labor, who use the same or similar instruments for estimating employment by sector or population growth by ethnicity over multiple employment surveys or censuses. If any changes are made to the measuring instruments, estimates are provided with and without the changed instrumentation to allow the readers to make a fair before-and-after comparison regarding population or employment trends. Other descriptive research may include chronicling ethnographic reports of gang activities among adolescent youth in urban populations, the persistence or evolution of religious, cultural, or ethnic practices in select communities, and the role of technologies such as Twitter and instant messaging in the spread of democracy movements in Middle Eastern countries.

Explanatory research seeks explanations of observed phenomena, problems, or behaviours. While descriptive research examines the what, where, and when of a phenomenon, explanatory research seeks answers to questions of why and how. It attempts to ‘connect the dots’ in research, by identifying causal factors and outcomes of the target phenomenon. Examples include understanding the reasons behind adolescent crime or gang violence, with the goal of prescribing strategies to overcome such societal ailments. Most academic or doctoral research belongs to the explanation category, though some amount of exploratory and/or descriptive research may also be needed during initial phases of academic research. Seeking explanations for observed events requires strong theoretical and interpretation skills, along with intuition, insights, and personal experience. Those who can do it well are also the most prized scientists in their disciplines.

History of scientific thought

Before closing this chapter, it may be interesting to go back in history and see how science has evolved over time and identify the key scientific minds in this evolution. Although instances of scientific progress have been documented over many centuries, the terms ‘science’, ’scientists’, and the ‘scientific method’ were coined only in the nineteenth century. Prior to this time, science was viewed as a part of philosophy, and coexisted with other branches of philosophy such as logic, metaphysics, ethics, and aesthetics, although the boundaries between some of these branches were blurred.

In the earliest days of human inquiry, knowledge was usually recognised in terms of theological precepts based on faith. This was challenged by Greek philosophers such as Plato, Aristotle, and Socrates during the third century BC, who suggested that the fundamental nature of being and the world can be understood more accurately through a process of systematic logical reasoning called rationalism . In particular, Aristotle’s classic work Metaphysics (literally meaning ‘beyond physical [existence]’) separated theology (the study of Gods) from ontology (the study of being and existence) and universal science (the study of first principles, upon which logic is based). Rationalism (not to be confused with ‘rationality’) views reason as the source of knowledge or justification, and suggests that the criterion of truth is not sensory but rather intellectual and deductive, often derived from a set of first principles or axioms (such as Aristotle’s ‘law of non-contradiction’).

The next major shift in scientific thought occurred during the sixteenth century, when British philosopher Francis Bacon (1561–1626) suggested that knowledge can only be derived from observations in the real world. Based on this premise, Bacon emphasised knowledge acquisition as an empirical activity (rather than as a reasoning activity), and developed empiricism as an influential branch of philosophy. Bacon’s works led to the popularisation of inductive methods of scientific inquiry, the development of the ‘scientific method’ (originally called the ‘Baconian method’), consisting of systematic observation, measurement, and experimentation, and may have even sowed the seeds of atheism or the rejection of theological precepts as ‘unobservable’.

Empiricism continued to clash with rationalism throughout the Middle Ages, as philosophers sought the most effective way of gaining valid knowledge. French philosopher Rene Descartes sided with the rationalists, while British philosophers John Locke and David Hume sided with the empiricists. Other scientists, such as Galileo Galilei and Sir Isaac Newton, attempted to fuse the two ideas into natural philosophy (the philosophy of nature), to focus specifically on understanding nature and the physical universe, which is considered to be the precursor of the natural sciences. Galileo (1564–1642) was perhaps the first to state that the laws of nature are mathematical, and contributed to the field of astronomy through an innovative combination of experimentation and mathematics.

In the eighteenth century, German philosopher Immanuel Kant sought to resolve the dispute between empiricism and rationalism in his book Critique of pure r eason by arguing that experiences are purely subjective and processing them using pure reason without first delving into the subjective nature of experiences will lead to theoretical illusions. Kant’s ideas led to the development of German idealism , which inspired later development of interpretive techniques such as phenomenology, hermeneutics, and critical social theory.

At about the same time, French philosopher Auguste Comte (1798–1857), founder of the discipline of sociology, attempted to blend rationalism and empiricism in a new doctrine called positivism . He suggested that theory and observations have circular dependence on each other. While theories may be created via reasoning, they are only authentic if they can be verified through observations. The emphasis on verification started the separation of modern science from philosophy and metaphysics and further development of the ‘scientific method’ as the primary means of validating scientific claims. Comte’s ideas were expanded by Emile Durkheim in his development of sociological positivism (positivism as a foundation for social research) and Ludwig Wittgenstein in logical positivism.

In the early twentieth century, strong accounts of positivism were rejected by interpretive sociologists (antipositivists) belonging to the German idealism school of thought. Positivism was typically equated with quantitative research methods such as experiments and surveys and without any explicit philosophical commitments, while antipositivism employed qualitative methods such as unstructured interviews and participant observation. Even practitioners of positivism, such as American sociologist Paul Lazarsfield who pioneered large-scale survey research and statistical techniques for analysing survey data, acknowledged potential problems of observer bias and structural limitations in positivist inquiry. In response, antipositivists emphasised that social actions must be studied though interpretive means based upon understanding the meaning and purpose that individuals attach to their personal actions, which inspired Georg Simmel’s work on symbolic interactionism, Max Weber’s work on ideal types, and Edmund Husserl’s work on phenomenology.

In the mid-to-late twentieth century, both positivist and antipositivist schools of thought were subjected to criticisms and modifications. British philosopher Sir Karl Popper suggested that human knowledge is based not on unchallengeable, rock solid foundations, but rather on a set of tentative conjectures that can never be proven conclusively, but only disproven. Empirical evidence is the basis for disproving these conjectures or ‘theories’. This metatheoretical stance, called postpositivism (or postempiricism), amends positivism by suggesting that it is impossible to verify the truth although it is possible to reject false beliefs, though it retains the positivist notion of an objective truth and its emphasis on the scientific method.

Likewise, antipositivists have also been criticised for trying only to understand society but not critiquing and changing society for the better. The roots of this thought lie in Das k apital , written by German philosophers Karl Marx and Friedrich Engels, which critiqued capitalistic societies as being socially inequitable and inefficient, and recommended resolving this inequity through class conflict and proletarian revolutions. Marxism inspired social revolutions in countries such as Germany, Italy, Russia, and China, but generally failed to accomplish the social equality that it aspired. Critical research (also called critical theory) propounded by Max Horkheimer and Jürgen Habermas in the twentieth century, retains similar ideas of critiquing and resolving social inequality, and adds that people can and should consciously act to change their social and economic circumstances, although their ability to do so is constrained by various forms of social, cultural and political domination. Critical research attempts to uncover and critique the restrictive and alienating conditions of the status quo by analysing the oppositions, conflicts and contradictions in contemporary society, and seeks to eliminate the causes of alienation and domination (i.e., emancipate the oppressed class). More on these different research philosophies and approaches will be covered in future chapters of this book.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Basic Research

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Sohvi Leih 3 &
David J. Teece 4

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Basic research can be defined as systematic inquiry that involves a quest for some fundamental scientific aspects of phenomena without any specific practical applications in mind. The pay-off of basic research is often uncertain and, once published, difficult to appropriate. Accordingly, the social returns to basic research exceed the private returns, rendering it a ‘public good’. Basic research results in contributions to the world stock of scientific knowledge. It ultimately supports long-term economic growth, increased productivity and subsequent practical applications on a global basis.

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Leih, S., Teece, D.J. (2016). Basic Research. In: Augier, M., Teece, D. (eds) The Palgrave Encyclopedia of Strategic Management. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-94848-2_332-1

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From Tread to Watershed: How Tire Wear Particle Chemicals 6PPD and 6PPD-quinone Are Impacting Waterways

Publications

Tire and road wear particles (TRWPs) are generated during normal driving conditions and contain both rubber and roadway materials. These particles travel from the roadways and urban environments into surrounding ecosystems where there are deleterious impacts from both the TRWPs and leached rubber chemicals. Recent studies have demonstrated the widespread occurrence and toxicity of TRWPs and their associated chemicals (e.g. 6PPD, 6PPD-quinone, benzothiazoles, phthalate esters, polycyclic aromatic hydrocarbons, etc.) in both aquatic and terrestrial environments. The Kansas Water Science Center's (KS WSC) Organic Geochemistry Research Laboratory (OGRL) is conducting research to further understand the ecological impacts of these ubiquitous anthropogenic materials which necessitates an interdisciplinary approach to address the many knowledge gaps relating to the sources, transport, fate, toxicity, and reduction strategies of TRWPs and their associated chemicals.

6PPD-quinone is a transformation product of 6PPD, an antiozonant used in tires.

Currently, 6PPD is used in all tires and can contaminate stormwater anywhere tires are used.

Conceptual diagram showing how compounds from tire wear 6PPD and 6PPDQ are released and move through the environment.

The rubber-derived ozone transformation product 6PPD-quinone (6PPDQ) was discovered in 2020 as a causative agent for urban runoff mortality syndrome in coho salmon. Tire and road wear particles are mobilized during urban storm events and transport 6PPD-quinone into surrounding aquatic ecosystems with concentrations that exceed acute toxicity thresholds for several salmonid species and the Environmental Protection Agency’s (EPA) aquatic life screening values of 11ng/L. These urban runoff events create sampling challenges and storm chasing to capture and characterize the transient exposure.

The widespread use of rubber in products such as tires and the documented harmful environmental effects necessitates an improved understanding of the occurrence, fate, and bioavailability of 6PPD-quinone.

Highlighted Science

After the discovery of the tire antiozonant transformation product 6PPD-quinone in 2020, research into rubber antiozonants chemicals began in earnest. The KS WSC OGRL developed a method for the analysis of 6PPD and 6PPD-quinone in water in 2021 to support toxicological studies and understand the environmental prevalence, fate, and bioavailability. The analytical method was published in 2024 to inform sampling procedures and hold times and advance understanding of stream exposures. Results from a nationwide reconnaissance of 6PPD and 6PPD-quinone in surface waters from 94 sites with varying land-use (urban, agricultural, and forested) and streamflow found that storm events mobilize 6PPD-quinone primarily from urban watersheds; downstream concentrations exceeded EPAs recently released 6PPD-quinone 11 ng/L freshwater acute aquatic life screening value for the protection of aquatic life. https://doi.org/10.1016/j.chemosphere.2024.142830

With a short half-life, 6PPD has a low aqueous detection frequency
Urban-impacted stormwater has the highest observed aqueous concentrations of 6PPD-quinone
6PPD-quinone stream exposures are transient pulse(s) at concentrations toxic to coho salmon.
Sampling protocols and monitoring should account for the episodic nature of exposure.

In FY25 research is continuing at the KS WSC OGRL to advance knowledge about the ecosystem occurrence, partitioning, and fate of 6PPD and 6PPD-quinone:

Ecological Implications of 6PPD/6PPD-quinone Partitioning: Occurrence of 6PPD and 6PPD-quinone in suspended and bed sediments
Passive Sampling: Use of diffusive gradients in thin film (DGT) passive samplers for 6PPD-quinone
Bioavailability (Occurrence Sampling): collaborative studies across the USGS to determine 6PPD and 6PPD-quinone levels in ecologically relevant habitats
Toxicology Studies: collaborations with USGS Western Fisheries Research Center and Columbia Environmental Research Center exploring facets of 6PPD and 6PPD-quinone toxicity
Method Validation: Participation in an inter-laboratory validation study and seeking accreditation with Washington State Department of Ecology

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine
Tire antiozonant that prevents rubber breakdown
Ozone in the atmosphere interacts with tires

6PPD-quinone

6PPD and ozone reaction protects the tire, but also produces 6PPD-quinone (6PPDQ)

Current Analytical Capabilities

Methods for 6ppd-quinone and 6ppd.

Image of person kneeling in river next to a discarded tire taking a sample to be analyzed for 6PPD and 6PPD-Quinone

Analysis Code: LCTR

Direct Inject Water Method

Instrumentation: Liquid chromatography/tandem mass spectrometry

Matrix: Water

Reporting Limit: 0.002 µg/L

Compound/CAS#:

6PPD-quinone / 2754428-18-5

6PPD/ 793-24-8

Analysis Code: LCTR-EPA

Image of the Kansas River with a person kneeling in the water taking a sample from inside of a tire to be analyzed for the presence of 6PPD and 6PPD-Quinone.

EPA Draft Method 1634

Instrumentation: liquid chromatography/tandem mass spectrometry

Matrix: Water (predominantly stormwater and surface water)

Reporting Limit: 2.00 ng/L

In Development

Analysis Code: LCTW

Image of sampling bottles next to a parking lot drainage pipe outlet after a rain event.

Whole Water (Aqueous and Suspended Material) Method

Sample Prep: Energized Dispersive Guided Extraction and Solid-phase extraction

Instrumentation: Liquid chromatography/tandem mass spectrometry

Matrix: Stormwater or Turbid Waters

Reporting Limit: in development

Compound/CAS#:

Analysis Code: LCTS

Solid Method

Image of person Kneeling to collect a sample from a limestone shelf in the Kansas River

Matrix: Sediments and Soils

Reporting Limit: in development

Contact Rachael Lane (785-760-4540; [email protected] ) for details about bottles or additional tire wear compounds of interest

Highlighted Outreach

Lane, R.F. subgroup co-lead for Environmental Assessments Strategies chapters
Lane R.F. subgroup co-lead Training and Outreach material for 6PPD and 6PPD-quinone
Lane, R.F. (Presenter, Invited) 2023. Analytical Capabilities for 6PPD. ITRC Tire Anti-Degradants (6PPD) Monthly Meeting
Lane, R.F. 2025. ITRC 6PPD CLU-IN Trainer

Lane, R.F (Presenter, Invited), Kolpin, D.W.; Bradley, P.M., Smalling, K.L. 2023. Understanding the environmental occurrence and fate of the tire leachate transformation product 6PPD-Quinone. USGS CMWSC Seminar Series

† Hypertext links to non-USGS products and services; and the use of trade names, trademarks, company names, or other references to non-USGS products and services are provided for information only and do not constitute endorsement or warranty by the U.S. Geological Survey (USGS), U.S. Department of the Interior, or U.S. Government.

Graphic depicting urban stormwater pollution impacting coho salmon.

6PPD-Quinone

Acute and chronic toxicity of Ni and Zn to a mayfly

6PPD Research at Columbia Environmental Research Center

Integrated science for the study of microplastics in the environment—a strategic science vision for the u.s. geological survey, tire-derived transformation product 6ppd-quinone induces mortality and transcriptionally disrupts vascular permeability pathways in developing coho salmon, establishing an in vitro model to assess the toxicity of 6ppd-quinone and other tire wear transformation products, u.s. environmental protection agency (epa), washington state department of ecology.

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This year, the APN awarded 17 scholars with Individual Research Fellowships (IRF), 6 researchers with 1 Collaborative Working Group Fellowship (CWG), and 1 scholar with the Research Policy Fellowship (RPF); while Next Gen awarded 34 scholars with Doctoral Dissertation Fellowships, including: 8 Dissertation Proposal fellowships, 14 Dissertation Research fellowships, and 12 Dissertation Completion fellowships . For more information on the fellowships, please visit our main pages for APN and Next Gen.

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APN IRF Fellows (PDF)
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APN Collaborative Working Group Fellowship (PDF)
Next Gen Proposal Fellows (PDF)
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2024 African Peacebuilding Network (APN) Fellows

Apn irf fellows.

Issahaku Adam | University of Cape Coast, Ghana
Sani Yakubu Adam | Bayero University Kano, Nigeria
Benyin Akande | University of Calabar, Nigeria
Gloria Longba’am-Alli | University of Jos, Nigeria
Justine Ayuk | University of Buea, Cameroon
Madinatu Bello | University of Cape Coast, Ghana
Fadzai Chipato | Great Zimbabwe University, Zimbabwe
Thembani Dube | University of Stellenbosch, South Africa
Angela Kavishe | The Mwalimu Nyerere Memorial Academy, Tanzania
Aleu Garang Aleu Kuek | University of Juba, South Sudan
Malephoto Lephoto | National University of Lesotho, Lesotho
Sean Maliehe | National University of Lesotho, Lesotho
Thatshisiwe Ndlovu | University of the Witwatersrand, South Africa
Sebastian Paalo | Kwame Nkrumah University of Science and Technology, Ghana
Zaid Sekito | Makerere University, Uganda
Rosette Sifa Vuninga | University of Cape Town, South Africa
Yilkal Ayalew Workneh | Debre Tabor University, Ethiopia

APN RPF FELLOW

Ernest Bagson | SD Dombo University, Ghana

MEMBERS OF THE APN COLLABORATIVE WORKING GROUP

Nicolette Roman | University of Western Cape, South Africa (Team Leader)
Joshua Mugumbwa | Makerere University, Uganda
Moses Mutua Mutiso | Moi University, Kenya
Anja Human-Hendricks | University of the Western Cape, South Africa
Edna Masita | Moi University, Kenya
Proscovia Nalwadda | Makerere University, Uganda

2024 Next Generation Social Sciences (Next Gen) Fellows

Next gen proposal fellows.

Kehinde Adebayo Afolabi | United States International University – Africa, Kenya
Robert Birungi | Makerere University, Uganda
Lameck Kachena | University of Cape Town, South Africa
Patrick Mutinda | Kenyatta University, Kenya
Clemente Ntauazi | University of the Western Cape, South Africa
Alloice Salex Okumu | University of Nairobi, Kenya
Sasha Claude Rai | University of the Witwatersrand, South Africa
Patience Shawarira | University of Cape Town, South Africa

NEXT GEN RESEARCH FELLOWS

Tamia Botes | University of the Witwatersrand, South Africa
Nyasha Blessed Bushu | University of The Free State, South Africa
Freweini Tekle Kidane | Makerere University, Uganda
Stanley Elias Kiswaga | Makerere University, Uganda
Kgomotso Komane | University of Pretoria, South Africa
Olivia Komuhangi | Makerere University, Uganda
Jacinta Mwongeli Matheka | Makerere University, Uganda
Kagiso Nko | University of the Western Cape, South Africa
Lerato Nkosi | University of KwaZulu Natal, South Africa
Kenechukwu Nwachukwu | Makerere University, Uganda
Nicholas Odoyo | Makerere University, Uganda
Zuko Sikhafungana | University of Western Cape, South Africa
Ham Ssenoga | Makerere University, Uganda
Freedman Delali Woledzi | University of Ghana, Ghana

NEXT GEN COMPLETION FELLOWS

Milcah Abasabyona | Makerere University, Uganda
Asasira Simon Rwabyoma | The Open University of Tanzania, Tanzania
Maryline Chepngetich | Moi University, Kenya
Stephanie Effevottu | University of Ibadan, Nigeria
Marie Grace Kagoyire Gasinzigwa | Stellenbosch University, South Africa
Olive Lomokol | Makerere University, Uganda
Silindile Mlilo | University of Witwatersrand, South Africa
Medina Moosa | University of the Western Cape, South Africa
Matseliso Motsoane | University of the Witwatersrand, South Africa
Japhet Ringo | Ardhi University, Tanzania
Tyne Williams | University of Pretoria, South Africa

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What Are The 4 Parts Of The Scientific Method
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Basic research
Basic research, also called pure research, fundamental research, basic science, ... Basic research advances fundamental knowledge about the world. It focuses on creating and refuting or supporting theories that explain observed phenomena. Pure research is the source of most new scientific ideas and ways of thinking about the world.
BASIC RESEARCH, ITS APPLICATION AND BENEFITS
Basic Research G-SCIENCE ACADEMIES STATEMENT 2020 BACKGROUND "Basic research leads to new knowledge. It provides scientific capital. It creates the fund from which the ... Since much of the knowledge developed by basic research is publicly accessible and benefits global society as a whole, it is a public good that cannot easily be ...
The Concept of Basic Research
Animal research is also important in another type of research, called basic research. Basic research experiments are performed to further scientific knowledge without an obvious or immediate benefit. The goal of basic research is to understand the function of newly discovered molecules and cells, strange phenomena, or little-understood processes.
Basic Research
Basic research is defined by the National Science Foundation (NSF 2010: 9) in the US as 'systematic study directed toward fuller knowledge or understanding of the fundamental aspects of phenomena and of observable facts without specific applications towards processes or products in mind'.Similarly, 'basic research is experimental or theoretical work undertaken primarily to acquire new ...
PDF Curiosity Creates Cures: The Value and Impact of Basic Science
Basic science, sometimes called "pure" or "fundamental" science, helps researchers understand living systems and life processes. This knowledge leads to better ways to predict, prevent, diagnose, and treat disease. Through basic science, researchers try to answer fundamental questions about how life works. Examples include: How do cells ...
What is Basic Research?
Basic science research is an essential pillar of scientific knowledge, because it extends understanding, provides new insights, and contributes to the advancement of science and fundamental knowledge across disciplines. ... scientists will undoubtedly credit basic research as a precursor to medical breakthroughs in applied science research. The ...
How to Conduct Scientific Research?
Scientific method should be neutral, objective, rational, and as a result, should be able to approve or disapprove the hypothesis. The research plan should include the procedure to obtain data and evaluate the variables. It should ensure that analyzable data are obtained. It should also include plans on the statistical analysis to be performed.
Basic Research: Understanding The Way Things Work ...
Discovery in science does not follow a straightforward path. Scientific research is conducted using models that are still being developed, in the context of dozens of unanswered questions, and using techniques and approaches no one else in the world has used before. According to the Association of American Medical Colleges (AAMC), basic science "provides the foundation of knowledge for the ...
Basic science is not just a foundation
Basic science is not just a foundation | Nature Cell Biology
Discoveries in Basic Science
These are called research organisms. The basic biology of these organisms can be similar to ours, and much is already known about their genetic makeup. They can include yeast, fruit flies, worms, zebrafish, and mice. Computers can also help answer basic science questions. "You can use computers to look for patterns and to try to understand ...
Basic Research
Basic Research. Definition: Basic Research, also known as Fundamental or Pure Research, is scientific research that aims to increase knowledge and understanding about the natural world without necessarily having any practical or immediate applications.It is driven by curiosity and the desire to explore new frontiers of knowledge rather than by the need to solve a specific problem or to develop ...
Basic Science
All scientific research conducted at medical schools and teaching hospitals ultimately aims to improve health and ability. Basic science research—often called fundamental or bench research—provides the foundation of knowledge for the applied science that follows.This type of research encompasses familiar scientific disciplines such as biochemistry, microbiology, physiology, and ...
What is Basic Research and Why is it Useful?
Scientific research can be categorized as basic, translational, or clinical. Basic research is curiosity-driven and asks fundamental questions (How? What? Why?) about the core building blocks of life. The purpose of basic research is to understand how nature works. Translational research is more focused and applies information from basic research to ask how scientists can use this knowledge to ...
Definitions of Basic, Applied, and Fundamental Research
DOD Financial Management Regulation, DOD 7000.14-R, Vol. 2B, Ch. 5: Basic research is systematic study directed toward greater knowledge or understanding of the fundamental aspects of phenomena and of observable facts without specific applications towards processes or products in mind. It includes all scientific study and experimentation directed toward increasing fundamental knowledge and ...
2 Scientific Principles and Research Practices
2. Scientific Principles and Research Practices. Until the past decade, scientists, research institutions, and government agencies relied solely on a system of self-regulation based on shared ethical principles and generally accepted research practices to ensure integrity in the research process. Among the very basic principles that guide ...
Scientific Research
Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge. It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses.
Guiding Principles for Scientific Inquiry
But scientific knowledge is constructed by the work of individuals, and like any other enterprise, if the people conducting the work are not open and candid, it. Page 54 ... many advancements in science occurred as a result of "use-inspired research," which simultaneously draws on both basic and applied research. Stokes (1997, p. 63) cites ...
What is Research?
Basic research: The objective of basic research is to gain more comprehensive knowledge or understanding of the subject under study, without specific applications in mind. In industry, basic research is defined as research that advances scientific knowledge but does not have specific immediate commercial objectives, although it may be in fields ...
Scientific Knowledge Definition, Overview & Examples
Some examples of these scientific knowledge and breakthroughs include: The remarkable research and discovery of the Covid-19 vaccine to fight coronavirus is a product of scientific knowledge on ...
Science and scientific research
Applied sciences, also called practical sciences, are sciences that apply scientific knowledge from basic sciences in a physical environment. For instance, engineering is an applied science that applies the laws of physics and chemistry for practical applications such as building stronger bridges or fuel efficient combustion engines, while ...
What is Scientific Research and How is it Conducted?
It is important for anyone intending to report science news to have some basic knowledge about what scientific research is, how it is done and how it fits with the news media. This chapter begins by discussing the definition of scientific research, describing the process of conducting such research, and highlighting the application of ...
Basic Research
Basic research is defined by the National Science Foundation (NSF 2010: 9) in the US as 'systematic study directed toward fuller knowledge or understanding of the fundamental aspects of phenomena and of observable facts without specific applications towards processes or products in mind'.Similarly, 'basic research is experimental or theoretical work undertaken primarily to acquire new ...
PDF WHAT IS BASIC RESEARCHP
Basic research leads to new knowledge. It provides scientific capital. It creates the fund from which the practical applications of knowledge must be drawn. . . . Today it is truer than ever that basic research is the pacemaker of technological progress. . . .
Principal Scientist II, Emerging Technologies (Neuroscience)
Major Accountabilities:Contribute to the Biomedical Research drug discovery process by advancing the development and discovery of new in-vivo digital biomarkers.Explore and validate novel in-vivo digital biomarker hypotheses by leveraging expertise in physiology and behavioral neuroscience, combined with an understanding of human disease mechanisms. Demonstrated ability to set up in-vivo study ...
From Tread to Watershed: How Tire Wear Particle Chemicals 6PPD and 6PPD
Highlighted Science . After the discovery of the tire antiozonant transformation product 6PPD-quinone in 2020, research into rubber antiozonants chemicals began in earnest. A water method for 6PPD and 6PPD-quinone was brought online in 2021 to support toxicological studies and understand the environmental prevalence, fate, and bioavailability.
2024 African Peacebuilding Network (APN) Fellows
The Social Science Research Council fosters innovative research, nurtures new generations of social scientists, deepens how inquiry is practiced within and across disciplines, and mobilizes necessary knowledge on important public issues. ... MediaWell - An online knowledge platform sharing research about societally beneficial information ...

What is Basic Research?

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Characteristics of basic research

Importance of basic research

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Basic science is not just a foundation

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Discoveries in Basic Science

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Federal Support for Medical Research and AAMC’s Role

Responsible Science: Ensuring the Integrity of the Research Process: Volume I.

2 Scientific Principles and Research Practices

THE NATURE OF SCIENCE

Norms of Science

Individual Scientific Disciplines

The Role of Individual Scientists and Research Teams

Institutional Policies

Government Regulations and Policies

Social Attitudes and Expectations

Data Handling

Issues Related to Advances in Information Technology

Communication and Publication

Peer Review

Correction of Errors

Research Training and Mentorship

Positive Aspects of Mentorship

Difficulties Associated with Mentorship

Making Mentorship Better

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Scientific Research – Types, Purpose and Guide

Scientific Research

Types of Scientific Research

Descriptive Research

Exploratory Research

Experimental Research

Correlational Research

Scientific Research Methods

Observational Studies

Experimental Studies

Survey Research

Case Studies

Meta-analysis

Qualitative Research

Quantitative Research

Longitudinal Studies

Data Analysis Methods

Application of Scientific Research

How to Conduct Scientific Research

Purpose of Scientific Research

Examples of Scientific Research

When to use Scientific Research

Advantages of Scientific Research

Limitations of Scientific Research

About the author

Muhammad Hassan

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