what are the scientific research process

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Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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How the Scientific Method Works: An In-Depth Look

Though scientific research encompasses a broad spectrum of research, these experiments all follow the same scientific method..

What, exactly, is science? It's something people in lab coats do, right? Science has been a potent tool, providing us with technology we once never dreamt possible. It has also helped us answer questions that have sat dormant in the human psyche for millennia.

The history of science, however, is filled with revolutions or modifications of accepted theory. Newton described gravity as an immutable background entity, an ever-present force that permeated the cosmos.

That was until Einstein came along with general relativity and described how gravity emerged out of the interaction between mass and the fabric of spacetime. Scientists are constantly seeking a deeper explanation of reality, and so scientists have to be ready for a better theory or model to come along and replace it.

The journey with which scientific discovery and change occurs has been distilled into what is referred to as the scientific method.

What Is the Scientific Method?

The scientific method is a systematic approach used by scientists to investigate and understand natural phenomena. It consists of a series of steps that guide researchers in drawing conclusions from hypotheses.

"Science never achieves final truth in theories, but one theory can be objectively truer than another, even if we never know that for sure," says British physicist David Deutsch from the University of Oxford. Deutsch is the author of  The Beginning of Infinity , a book that argues science will never reach a point in which it can describe the entirety of phenomena in the physical world, as new theories will bring along with them deeper problems in need of explanation.

What Are the Steps of the Scientific Method?

The steps of the scientific method hold importance as they provide a structured and systematic approach to conducting scientific investigations. The following steps promote the credibility of scientific findings.

Step One — Identify the Question

Firstly, scientists identify phenomena they want to investigate. This could be based on an interesting observation that was collected from data, or it could be a mathematical problem that arises out of current theories. As such, the first step is to ask  why  something is the way it is — defining the research question in established terms, setting up a line of inquiry, and identifying possible methods for answering said question.

Step Two — Make Predictions 

After defining a research question, scientists are likely to develop a hypothesis or prediction based on what theoretical framework they adopt or the set of observations they have already made. This particular step in the scientific process is important because it relates to the 'testability' of certain theories or claims about the physical world. Generally, when distinguishing scientific predictions/claims from non-scientific predictions/claims, the difference is whether they are testable or not.

However, just because we cannot test something now doesn't mean it doesn't count as science. As science delves into the ever more extreme part of the physical world, whether they are very small or large in space or long or short in time, our ability to test theory is limited by the types of technology we have. That doesn't mean we shouldn't develop theories that attempt to explain the farthest reaches of the physical world.

For example, for a long time, astrophysicists developed mathematical models of the evolution of the early Universe. However, they did not possess an instrument to confirm their predictions. This did not mean their theories were unscientific. It just meant they had to rely on mathematics and general principles before the  James Webb Space Telescope  could observe that far back in the early Universe.

Step Three — Gather Evidence 

Once a testable prediction or hypothesis has been made, evidence is gathered to test the prediction. Evidence can be acquired in several different ways. Scientists can observe the natural world to see if their models match what is happening in reality; for example, astrophysicists use the James Webb Space Telescope to observe the early Universe to see if their models of galaxy formation match observations.

Scientists can also run experiments in a laboratory, like the particle physicists who smash subatomic particles together at CERN to see what happens next. Or they might input their parameters and run computer simulations. Sometimes scientists will combine each of these strategies, repeat them as many times as possible to replicate their findings and provide them to other scientists to critique their research and give valuable feedback. 

Step Four — Analyze the Data 

Once scientists have collected their data from their various methods, they then have to organize them into tables, graphs, or diagrams that might show interesting relationships, connections or anomalies that might be important when answering their research question.

Step Five — Form a Conclusion 

And lastly, scientists will evaluate their hypothesis or prediction in light of their observations to see if it was supported or not. Sometimes results won't provide a clear answer, and new ways of testing might have to be devised.

Or they might get clear results, send their findings to a scientific journal where it could then get published, peer-reviewed by other scientists and become part of the accepted corpus of knowledge on a particular subject. Sometimes new results might modify or overturn what exists on a given subject already.

Is Science Objective?

Science attempts to be as objective as possible by removing the bias people bring to the scientific process and interpretation of scientific results. Science has a number of ways to help correct these biases, such as using large data sets, peer review and controlling the parameters of experiments.

However, it is important to remember that science is carried out by humans, and things like bias, intuition, and historical contingencies can affect the results and direction of science. For example, scientific explanations are often accused of being ' reductionistic ' (e.g., consciousness is the firing of neurons in the brain). However, reductionist explanations of phenomena are largely an artifact of the historical contingencies of science. 

The sciences which developed the fastest (physics and chemistry) dealt with small scales of reality, and so scientists applied these approaches to try and explain macroscopic phenomena like consciousness.

All in all, science is the best system we have developed for discerning knowledge about the physical world. Like us, science is a work in progress, and the more we learn about the world and ourselves through science, the better we get at sharpening the tools and methods of science itself.

Read More: What Is the Scientific Method and How Did It Shape Science?

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Scientific Method

Illustration by J.R. Bee. ThoughtCo. 

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The scientific method is a series of steps followed by scientific investigators to answer specific questions about the natural world. It involves making observations, formulating a hypothesis , and conducting scientific experiments . Scientific inquiry starts with an observation followed by the formulation of a question about what has been observed. The steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. This is very important if you are doing a science project because you want your project to be focused on something that will hold your attention. Your observation can be on anything from plant movement to animal behavior, as long as it is something you really want to know more about.​ This is where you come up with the idea for your science project.

Once you've made your observation, you must formulate a question about what you have observed. Your question should tell what it is that you are trying to discover or accomplish in your experiment. When stating your question you should be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question may be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you've developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states very clearly how you plan to conduct your experiment. It is important that you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

The final step of the scientific method is developing a conclusion. This is where all of the results from the experiment are analyzed and a determination is reached about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

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Steps & Procedures for Conducting Scientific Research

Laboratory Observation Methods

A good scientist practices objectivity to avoid errors and personal biases that may lead to falsified research. The entire scientific research process--from defining the research question to drawing conclusions about data--requires the researcher to think critically and approach issues in an organized and systematic way. Scientific research can lead to the confirmation or re-evaluation of existing theories or to the development of entirely new theories.

Defining Problem and Research

The first step of the scientific research process involves defining the problem and conducting research. First, a broad topic is selected concerning some topic or a research question is asked. The scientist researches the question to determine if it has been answered or the types of conclusions other researchers have drawn and experiments that have been carried out in relation to the question. Research involves reading scholarly journal articles from other scientists, which can be found on the Internet via research databases and journals that publish academic articles online. During research, the scientist narrows down the broad topic into a specific research question about some issue.

The hypothesis is a concise, clear statement containing the main idea or purpose of your scientific research. A hypothesis must be testable and falsifiable, meaning there must be a way to test the hypothesis and it can either be supported or rejected based on examining data. Crafting a hypothesis requires you to define the variables you're researching (e.g., who or what you're studying), explain them with clarity and explain your position. When writing the hypothesis, scientists either make a specific cause-and-effect statement about the variables being studied or make a general statement about the relationship between such variables.

Design Experiment

Designing a scientific experiment involves planning how you're going to collect data. Often, the nature of the research question influences how the scientific research will be conducted. For example, researching people's opinions naturally requires conducting surveys. When designing the experiment, the scientists selects from where and how the sample being studied will be obtained, the dates and times for the experiment, the controls being used and the other measures needed to carry out the research.

Collect Data

Data collection involves carrying out the experiment the scientist designed. During this process, the scientists record the data and complete the tasks required to conduct the experiments. In other words, the scientist goes to the research site to perform the experiment, such as a laboratory or some other setting. Tasks involved with conducting the experiment vary depending on the type of research. For example, some experiments require bringing human participants in for a test, conducting observations in the natural environment or experimenting with animal subjects.

Analyze Data

Analyzing data for the scientific research process involves bringing the data together and calculating statistics. Statistical tests can help the scientist understand the data better and tell whether a significant result is found. Calculating the statistics for a scientific research experiment uses both descriptive statistics and inferential statistics measures. Descriptive statistics describe the data and samples collected, such as sample averages or means, as well as the standard deviation that tells the scientists how the data is distributed. Inferential statistics involves conducting tests of significance that have the power to either confirm or reject the original hypothesis.

Draw Conclusions

After the data from an experiment is analyzed, the scientist examines the information and makes conclusions based on the findings. The scientist compares the results both to the original hypothesis and the conclusions of previous experiments by other researchers. When drawing conclusions, the scientist explains what the results mean and how to view them in the context of the scientific field or real-world environment, as well as making suggestions for future research.

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How to eliminate bias in qualitative research, 5 components of a well-designed scientific experiment, types of observation in the scientific method, distinguishing between descriptive & causal studies, essential tenets of the scientific method, difference between proposition & hypothesis, how to write results for a science fair project, research methods in science, what are the 8 steps in scientific research, 10 characteristics of a science experiment, what is normative & descriptive science, the six parts of an experimental science project, why should you only test for one variable at a time..., what is the next step if an experiment fails to confirm..., writing objectives for lab reports, how to calculate success rate, the differences between concepts, theories & paradigms, how to use the scientific method in everyday life.

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Matthew Schieltz has been a freelance web writer since August 2006, and has experience writing a variety of informational articles, how-to guides, website and e-book content for organizations such as Demand Studios. Schieltz holds a Bachelor of Arts in psychology from Wright State University in Dayton, Ohio. He plans to pursue graduate school in clinical psychology.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions

The Scientific Method, as presented in many textbooks, is oversimplified.

Misconception:  There is a single Scientific Method that all scientists follow.

Correction:  The Scientific Method is an oversimplified representation of what is really a rich, complex, and unpredictable process.  Read more about it.

How science works

The Scientific Method is traditionally presented in the first chapter of science textbooks as a simple recipe for performing scientific investigations. Though many useful points are embodied in this method, it can easily be misinterpreted as linear and “cookbook”: pull a problem off the shelf, throw in an observation , mix in a few questions, sprinkle on a hypothesis , put the whole mixture into a 350° experiment — and voila , 50 minutes later you’ll be pulling a conclusion out of the oven! That might work if science were like Hamburger Helper®, but science is complex and cannot be reduced to a single, prepackaged recipe.

The linear, stepwise representation of the process of science is oversimplified, but it does get at least one thing right. It captures the core logic of science: testing ideas with evidence . However, this version of the scientific method is so simplified and rigid that it fails to accurately portray how real science works. It more accurately describes how science is summarized after the fact — in textbooks and journal articles — than how science is actually done.

The simplified, linear description of the scientific method implies that scientific studies follow an unvarying, linear recipe … but in reality, scientists engage in many different activities in many different sequences in their work.

The simplified, linear description of the scientific method implies that science is done by individual scientists working through these steps in isolation … but in reality, science depends on social interactions within the scientific community. Different parts of the process of science may be carried out by different people at different times.

The simplified, linear description of the scientific method implies that science has little room for creativity … but in reality, the process of science is exciting, dynamic, and unpredictable. Science relies on creative people thinking outside the box!

The simplified, linear description of the scientific method implies that science concludes … but in reality, scientific conclusions are always revisable if warranted by the evidence. Scientific investigations are often ongoing, raising new questions even as old ones are answered.

Here, we’ll examine a more accurate representation of the process of science. You can investigate:

The real process of science

• Testing scientific ideas
• Analysis within the scientific community
• Benefits of science
• Science at multiple levels

Or just click the Next button to dive right in!

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The Scientific Method emphasizes testing ideas with evidence — but just  how  does science do this? To learn more, visit our unit on  The core of science: Relating evidence and ideas .

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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.

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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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1.1: Chapter 1 Science and Scientific Research

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• William Pelz
• Herkimer College via Lumen Learning

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 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 the Internet to find the information they need to complete assigned projects or term papers. Graduate students working on research projects for a professor may see research as collecting or analyzing data related to their project. Businesses and consultants research different potential solutions to remedy organizational problems such as a supply chain bottleneck or to identify customer purchase patterns. However, none of the above can be considered “scientific research” unless: (1) it contributes to a body of science, and (2) 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 college-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 specialized equipment in their laboratories. Etymologically, the word “science” is derived from the Latin word scientia meaning knowledge. Science refers to a systematic and organized 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 behaviors. Social sciences can be classified into disciplines such as psychology (the science of human behaviors), 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 m aking 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 cognizant 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 for solving human ailments. Both basic and applied sciences are required for human development. However, applied sciences cannot stand on their own right, but instead relies on basic sciences for its progress. Of course, the 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 generalized body of laws and theories to explain a phenomenon or behavior of interest that are acquired using the scientific method. Laws are observed patterns of phenomena or behaviors, while theories are systematic explanations of the underlying phenomenon or behavior. 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 college level text book 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 is different from what they expected of that event, general deterrence theory explains why some people engage in improper or criminal behaviors, such as illegally download music or commit software piracy, and the theory of planned behavior 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 interrelated 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 to 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.

Graduate Research pp 55–78 Cite as

Principles of Scientific Research

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Scientific research has provided knowledge and understanding that has freed humankind from the ignorance that once promoted fear, mysticism, superstition, and illness. Developments in science and scientific methods, however, did not come easily. Many of our ancestors had to face persecution, even death, from religious and political groups because they dared to advance the notion that knowledge and understanding could be gained through systematic study and practice. Today, the benefits of scientific research are understood. We appreciate the advances in the biological and physical sciences that allow the control of environment, the probing of the universe, and communications around the globe. We also appreciate the advances in biochemistry and molecular biology that have led to curative drugs, to genetic counseling, and to an unparalleled understanding of structure—function relationships in living organisms. We look hopefully to the development of life itself and, in concert with social-behavioral scientists, the unraveling of the relationship between mind and brain. Despite the potential moral issues raised by the latter advances, the history of science provides us faith that knowledge and understanding can be advanced for the benefit of humanity.

... ever since the dawn of civilization, people have not been content to see events as unconnected and inexplicable. They have craved an understanding of the underlying order in the world.... Humanity’s deepest desire for knowledge is justification enough for our continuing quest. — Stephen Hawking

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A. Agresti and B. Finlay, Statistical Methods for Social. Scientists ( San Francisco: Macmillan, 1986 ).

Google Scholar  

N. C. Barford, Experimental Measurements: Precision , Error and Truth , 2nd ed. ( New York: Wiley, 1985 ).

W. I. B. Beveridge, The Art of Scientific Investigation ( New York: Vintage Books, 1960 ).

G. E. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters: An Introduction to Design , Data Analysis and Model Building ( New York: Wiley, 1978 ).

K. A. Brownlee, Statistical Theory and Methodology: In Science and Engineering (New York: Wiley, 1984).

W. G. Cochran and G. W. Snedecor, Statistical Methods , 7th ed. ( Ames: Iowa State University Press, 1980 ).

D. R. Cox, Applied Statistics: Principles and Examples ( New York: Chapman and Hall, 1981 ).

Book   Google Scholar  

R. A. Fisher, Statistical Methods for Research Workers , 14th ed. ( New York: Hafner, 1973 ).

M. Kendall and A. Stewart, The Advanced Theory of Statistics , 3 vols., 4th ed. ( New York: Hafner, 1977 ).

R. E. Kirk, Experimental Design: Procedures for the Behavorial Sciences , 2nd ed. ( Monterey, CA: Brooks/Cole, 1982 ).

R. Remington and M. A. Schork, Statistics with Applications to the Biological and Health Sciences , 2nd ed. ( Englewood Cliffs, NJ: Prentice-Hall, 1985 ).

R. R. Sokal and F. J. Rohlf, Biometry: The Principles and Practice of Statistics in Biological Research , 2nd ed. ( New York: Freeman, 1981 ).

J. W. Tukey, Exploratory Data Analysis ( Reading, MA: Addison-Wesley, 1977 ).

P. R. Bevington, Data Reduction and Error Analysis for the Physical Sciences ( New York: McGraw-Hill, 1969 ).

BMD: Biomedical Computer Programs ,rev. ed., W. J. Dixon and M. B. Brown, eds. (Berkeley: University of California Press, 1983).

S. Brandt, Statistical and Computational Methods in Data Analysis , 2nd rev. ed. ( Amsterdam: Elsevier, 1976 ).

N. H. Nie, C. H. Hull, J. G. Jenkins, K. Steinbrenner, and D. H. Bent, Statistical Package for the Social Sciences (SPSS) , 2nd ed. ( New York: McGraw-Hill, 1975 ).

T. A. Ryan, B. L. Joiner, and B. F. Ryan, Minitab Student Handbook ( North Scituate, MA: Duxbury Press, 1976 ).

SYSTAT, Mainframe Statistics Package for Microcomputers ( Evanston, IL: SYSTAT Inc., 1986 ).

P. B. Medawar, Advice to a Young Scientist ( New York: Harper and Row, 1979 ).

J. Kitfield, Laureates—Linus Pauling, Northwest Orient 17 (1) (1986), pp. 37–39.

H. C. Brown, Adventures in research, Chemical and Engineering News 59 (14) (1981), pp. 24–29.

Article   Google Scholar  

Rosalyn S. Yalow, Melange: Commencement 1988, The Chronicle of Higher Education34 (39) (1988), p. B-3.

E. B. Wilson, An Introduction to Scientific Research ( New York: McGraw-Hill, 1952 ).

W. Thomson (Lord Kelvin), Popular Lectures and Addresses by Sir William Thomson , 1891–1894 ( New York: Macmillan, 1894 ).

R. W. Hamming, The unreasonable effectiveness of mathematics, American Mathematics Monthly 87 (2) (1980), pp. 81–90.

Commission on Physical Sciences, Mathematics and Resources, National Academy of Sciences, Improving the Treatment of Scientific and Engineering Data Through Education (Washington, DC: National Academy Press, 1986 ).

Stephen W. Hawking, A Brief History of Time ( New York: Bantam Books, 1988 ).

A. H. Corwin, in Proceedings of the Robert A. Welch Conference on Chemical Research . XX. American Chemistry Bicentennial , W . O. Milligan, ed. ( Houston, TX: Robert A. Welch Foundation, 1977 ), pp. 45–69.

M. Gardner, Aha! Insight ( New York: Scientific American, 1978 ).

J. Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind ( Boston: Houghton Mifflin, 1976 ).

W. W. Rostow, The Barbaric Counterrevolution: Cause and Cure ( Austin: University of Texas Press, 1983 ).

E. A. Eschbach, Fostering creativity, PNL Profile Fall (1986), pp. 9–10; Battelle Pacific Northwest Laboratories, Document BN-FA 530 , Updated 3–88 ,Creativity, discovery, invention and the put-down.

J. C. Sheehan, The Enchanted Ring: The Untold Story of Penicillin ( Cambridge, MA: MIT Press, 1982 ).

C. W. Ceram, Gods , Graves , and Scholars , 2nd ed. ( New York: Knopf, 1982 ).

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The Scientific Process

Learning objectives.

• Explain the steps of the scientific method
• Differentiate between theories and hypotheses

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

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

Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

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

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 3). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

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

Link to Learning

Want to participate in a study? Visit this Psychological Research on the Net website and click on a link that sounds interesting to you in order to participate in online research.

Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Research Process: 8 Steps in Research Process

The research process starts with identifying a research problem and conducting a literature review to understand the context. The researcher sets research questions, objectives, and hypotheses based on the research problem.

A research study design is formed to select a sample size and collect data after processing and analyzing the collected data and the research findings presented in a research report.

What is the Research Process?

There are a variety of approaches to research in any field of investigation, irrespective of whether it is applied research or basic research. Each research study will be unique in some ways because of the particular time, setting, environment, and place it is being undertaken.

Nevertheless, all research endeavors share a common goal of furthering our understanding of the problem, and thus, all traverse through certain primary stages, forming a process called the research process.

Understanding the research process is necessary to effectively carry out research and sequence the stages inherent in the process.

How Research Process Work?

Eight steps research process is, in essence, part and parcel of a research proposal. It is an outline of the commitment that you intend to follow in executing a research study.

A close examination of the above stages reveals that each of these stages, by and large, is dependent upon the others.

One cannot analyze data (step 7) unless he has collected data (step 6). One cannot write a report (step 8) unless he has collected and analyzed data (step 7).

Research then is a system of interdependent related stages. Violation of this sequence can cause irreparable harm to the study.

It is also true that several alternatives are available to the researcher during each stage stated above. A research process can be compared with a route map.

The map analogy is useful for the researcher because several alternatives exist at each stage of the research process.

Choosing the best alternative in terms of time constraints, money, and human resources in our research decision is our primary goal.

Before explaining the stages of the research process, we explain the term ‘iterative’ appearing within the oval-shaped diagram at the center of the schematic diagram.

The key to a successful research project ultimately lies in iteration: the process of returning again and again to the identification of the research problems, methodology, data collection, etc., which leads to new ideas, revisions, and improvements.

By discussing the research project with advisers and peers, one will often find that new research questions need to be added, variables to be omitted, added or redefined, and other changes to be made. As a proposed study is examined and reexamined from different perspectives, it may begin to transform and take a different shape.

This is expected and is an essential component of a good research study.

Besides, examining study methods and data collected from different viewpoints is important to ensure a comprehensive approach to the research question.

In conclusion, there is seldom any single strategy or formula for developing a successful research study, but it is essential to realize that the research process is cyclical and iterative.

What is the primary purpose of the research process?

The research process aims to identify a research problem, understand its context through a literature review, set research questions and objectives, design a research study, select a sample, collect data, analyze the data, and present the findings in a research report.

Why is the research design important in the research process?

The research design is the blueprint for fulfilling objectives and answering research questions. It specifies the methods and procedures for collecting, processing, and analyzing data, ensuring the study is structured and systematic.

8 Steps of Research Process

Identifying the research problem.

The first and foremost task in the entire process of scientific research is to identify a research problem .

A well-identified problem will lead the researcher to accomplish all-important phases of the research process, from setting objectives to selecting the research methodology .

But the core question is: whether all problems require research.

We have countless problems around us, but all we encounter do not qualify as research problems; thus, these do not need to be researched.

Keeping this point in mind, we must draw a line between research and non-research problems.

Intuitively, researchable problems are those that have a possibility of thorough verification investigation, which can be effected through the analysis and collection of data. In contrast, the non-research problems do not need to go through these processes.

Researchers need to identify both;

Non-Research Problems

Statement of the problem, justifying the problem, analyzing the problem.

A non-research problem does not require any research to arrive at a solution. Intuitively, a non-researchable problem consists of vague details and cannot be resolved through research.

It is a managerial or built-in problem that may be solved at the administrative or management level. The answer to any question raised in a non-research setting is almost always obvious.

The cholera outbreak, for example, following a severe flood, is a common phenomenon in many communities. The reason for this is known. It is thus not a research problem.

Similarly, the reasons for the sudden rise in prices of many essential commodities following the announcement of the budget by the Finance Minister need no investigation. Hence it is not a problem that needs research.

How is a research problem different from a non-research problem?

A research problem is a perceived difficulty that requires thorough verification and investigation through data analysis and collection. In contrast, a non-research problem does not require research for a solution, as the answer is often obvious or already known.

Non-Research Problems Examples

A recent survey in town- A found that 1000 women were continuous users of contraceptive pills.

But last month’s service statistics indicate that none of these women were using contraceptive pills (Fisher et al. 1991:4).

The discrepancy is that ‘all 1000 women should have been using a pill, but none is doing so. The question is: why the discrepancy exists?

Well, the fact is, a monsoon flood has prevented all new supplies of pills from reaching town- A, and all old supplies have been exhausted. Thus, although the problem situation exists, the reason for the problem is already known.

Therefore, assuming all the facts are correct, there is no reason to research the factors associated with pill discontinuation among women. This is, thus, a non-research problem.

A pilot survey by University students revealed that in Rural Town-A, the goiter prevalence among school children is as high as 80%, while in the neighboring Rural Town-A, it is only 30%. Why is a discrepancy?

Upon inquiry, it was seen that some three years back, UNICEF launched a lipiodol injection program in the neighboring Rural Town-A.

This attempt acted as a preventive measure against the goiter. The reason for the discrepancy is known; hence, we do not consider the problem a research problem.

A hospital treated a large number of cholera cases with penicillin, but the treatment with penicillin was not found to be effective. Do we need research to know the reason?

Here again, there is one single reason that Vibrio cholera is not sensitive to penicillin; therefore, this is not the drug of choice for this disease.

In this case, too, as the reasons are known, it is unwise to undertake any study to find out why penicillin does not improve the condition of cholera patients. This is also a non-research problem.

In the tea marketing system, buying and selling tea starts with bidders. Blenders purchase open tea from the bidders. Over the years, marketing cost has been the highest for bidders and the lowest for blenders. What makes this difference?

The bidders pay exorbitantly higher transport costs, which constitute about 30% of their total cost.

Blenders have significantly fewer marketing functions involving transportation, so their marketing cost remains minimal.

Hence no research is needed to identify the factors that make this difference.

Here are some of the problems we frequently encounter, which may well be considered non-research problems:

• Rises in the price of warm clothes during winter;
• Preferring admission to public universities over private universities;
• Crisis of accommodations in sea resorts during summer
• Traffic jams in the city street after office hours;
• High sales in department stores after an offer of a discount.

Research Problem

In contrast to a non-research problem, a research problem is of primary concern to a researcher.

A research problem is a perceived difficulty, a feeling of discomfort, or a discrepancy between a common belief and reality.

As noted by Fisher et al. (1993), a problem will qualify as a potential research problem when the following three conditions exist:

• There should be a perceived discrepancy between “what it is” and “what it should have been.” This implies that there should be a difference between “what exists” and the “ideal or planned situation”;
• A question about “why” the discrepancy exists. This implies that the reason(s) for this discrepancy is unclear to the researcher (so that it makes sense to develop a research question); and
• There should be at least two possible answers or solutions to the questions or problems.

The third point is important. If there is only one possible and plausible answer to the question about the discrepancy, then a research situation does not exist.

It is a non-research problem that can be tackled at the managerial or administrative level.

Research Problem Examples

Research problem – example #1.

While visiting a rural area, the UNICEF team observed that some villages have female school attendance rates as high as 75%, while some have as low as 10%, although all villages should have a nearly equal attendance rate. What factors are associated with this discrepancy?

We may enumerate several reasons for this:

• Villages differ in their socio-economic background.
• In some villages, the Muslim population constitutes a large proportion of the total population. Religion might play a vital role.
• Schools are far away from some villages. The distance thus may make this difference.

Because there is more than one answer to the problem, it is considered a research problem, and a study can be undertaken to find a solution.

Research Problem – Example #2

The Government has been making all-out efforts to ensure a regular flow of credit in rural areas at a concession rate through liberal lending policy and establishing many bank branches in rural areas.

Knowledgeable sources indicate that expected development in rural areas has not yet been achieved, mainly because of improper credit utilization.

More than one reason is suspected for such misuse or misdirection.

These include, among others:

• Diversion of credit money to some unproductive sectors
• Transfer of credit money to other people like money lenders, who exploit the rural people with this money
• Lack of knowledge of proper utilization of the credit.

Here too, reasons for misuse of loans are more than one. We thus consider this problem as a researchable problem.

Research Problem – Example #3

Let’s look at a new headline: Stock Exchange observes the steepest ever fall in stock prices: several injured as retail investors clash with police, vehicles ransacked .

Investors’ demonstration, protest and clash with police pause a problem. Still, it is certainly not a research problem since there is only one known reason for the problem: Stock Exchange experiences the steepest fall in stock prices. But what causes this unprecedented fall in the share market?

Experts felt that no single reason could be attributed to the problem. It is a mix of several factors and is a research problem. The following were assumed to be some of the possible reasons:

• The merchant banking system;
• Liquidity shortage because of the hike in the rate of cash reserve requirement (CRR);
• IMF’s warnings and prescriptions on the commercial banks’ exposure to the stock market;
• Increase in supply of new shares;
• Manipulation of share prices;
• Lack of knowledge of the investors on the company’s fundamentals.

The choice of a research problem is not as easy as it appears. The researchers generally guide it;

• own intellectual orientation,
• level of training,
• experience,
• knowledge on the subject matter, and
• intellectual curiosity.

Theoretical and practical considerations also play a vital role in choosing a research problem. Societal needs also guide in choosing a research problem.

Once we have chosen a research problem, a few more related steps must be followed before a decision is taken to undertake a research study.

These include, among others, the following:

• Statement of the problem.
• Justifying the problem.
• Analyzing the problem.

A detailed exposition of these issues is undertaken in chapter ten while discussing the proposal development.

A clear and well-defined problem statement is considered the foundation for developing the research proposal.

It enables the researcher to systematically point out why the proposed research on the problem should be undertaken and what he hopes to achieve with the study’s findings.

A well-defined statement of the problem will lead the researcher to formulate the research objectives, understand the background of the study, and choose a proper research methodology.

Once the problem situation has been identified and clearly stated, it is important to justify the importance of the problem.

In justifying the problems, we ask such questions as why the problem of the study is important, how large and widespread the problem is, and whether others can be convinced about the importance of the problem and the like.

Answers to the above questions should be reviewed and presented in one or two paragraphs that justify the importance of the problem.

As a first step in analyzing the problem, critical attention should be given to accommodate the viewpoints of the managers, users, and researchers to the problem through threadbare discussions.

The next step is identifying the factors that may have contributed to the perceived problems.

Issues of Research Problem Identification

There are several ways to identify, define, and analyze a problem, obtain insights, and get a clearer idea about these issues. Exploratory research is one of the ways of accomplishing this.

The purpose of the exploratory research process is to progressively narrow the scope of the topic and transform the undefined problems into defined ones, incorporating specific research objectives.

The exploratory study entails a few basic strategies for gaining insights into the problem. It is accomplished through such efforts as:

Pilot Survey

A pilot survey collects proxy data from the ultimate subjects of the study to serve as a guide for the large study. A pilot study generates primary data, usually for qualitative analysis.

This characteristic distinguishes a pilot survey from secondary data analysis, which gathers background information.

Case Studies

Case studies are quite helpful in diagnosing a problem and paving the way to defining the problem. It investigates one or a few situations identical to the researcher’s problem.

Focus Group Interviews

Focus group interviews, an unstructured free-flowing interview with a small group of people, may also be conducted to understand and define a research problem .

Experience Survey

Experience survey is another strategy to deal with the problem of identifying and defining the research problem.

It is an exploratory research endeavor in which individuals knowledgeable and experienced in a particular research problem are intimately consulted to understand the problem.

These persons are sometimes known as key informants, and an interview with them is popularly known as the Key Informant Interview (KII).

Reviewing of Literature

A review of relevant literature is an integral part of the research process. It enables the researcher to formulate his problem in terms of the specific aspects of the general area of his interest that has not been researched so far.

Such a review provides exposure to a larger body of knowledge and equips him with enhanced knowledge to efficiently follow the research process.

Through a proper review of the literature, the researcher may develop the coherence between the results of his study and those of the others.

A review of previous documents on similar or related phenomena is essential even for beginning researchers.

Ignoring the existing literature may lead to wasted effort on the part of the researchers.

Why spend time merely repeating what other investigators have already done?

Suppose the researcher is aware of earlier studies of his topic or related topics . In that case, he will be in a much better position to assess his work’s significance and convince others that it is important.

A confident and expert researcher is more crucial in questioning the others’ methodology, the choice of the data, and the quality of the inferences drawn from the study results.

In sum, we enumerate the following arguments in favor of reviewing the literature:

• It avoids duplication of the work that has been done in the recent past.
• It helps the researcher discover what others have learned and reported on the problem.
• It enables the researcher to become familiar with the methodology followed by others.
• It allows the researcher to understand what concepts and theories are relevant to his area of investigation.
• It helps the researcher to understand if there are any significant controversies, contradictions, and inconsistencies in the findings.
• It allows the researcher to understand if there are any unanswered research questions.
• It might help the researcher to develop an analytical framework.
• It will help the researcher consider including variables in his research that he might not have thought about.

Why is reviewing literature crucial in the research process?

Reviewing literature helps avoid duplicating previous work, discovers what others have learned about the problem, familiarizes the researcher with relevant concepts and theories, and ensures a comprehensive approach to the research question.

What is the significance of reviewing literature in the research process?

Reviewing relevant literature helps formulate the problem, understand the background of the study, choose a proper research methodology, and develop coherence between the study’s results and previous findings.

Setting Research Questions, Objectives, and Hypotheses

After discovering and defining the research problem, researchers should make a formal statement of the problem leading to research objectives .

An objective will precisely say what should be researched, delineate the type of information that should be collected, and provide a framework for the scope of the study. A well-formulated, testable research hypothesis is the best expression of a research objective.

A hypothesis is an unproven statement or proposition that can be refuted or supported by empirical data. Hypothetical statements assert a possible answer to a research question.

Step #4: Choosing the Study Design

The research design is the blueprint or framework for fulfilling objectives and answering research questions .

It is a master plan specifying the methods and procedures for collecting, processing, and analyzing the collected data. There are four basic research designs that a researcher can use to conduct their study;

• experiment,
• secondary data study, and
• observational study.

The type of research design to be chosen from among the above four methods depends primarily on four factors:

• The type of problem
• The objectives of the study,
• The existing state of knowledge about the problem that is being studied, and
• The resources are available for the study.

Deciding on the Sample Design

Sampling is an important and separate step in the research process. The basic idea of sampling is that it involves any procedure that uses a relatively small number of items or portions (called a sample) of a universe (called population) to conclude the whole population.

It contrasts with the process of complete enumeration, in which every member of the population is included.

Such a complete enumeration is referred to as a census.

A population is the total collection of elements we wish to make some inference or generalization.

A sample is a part of the population, carefully selected to represent that population. If certain statistical procedures are followed in selecting the sample, it should have the same characteristics as the population. These procedures are embedded in the sample design.

Sample design refers to the methods followed in selecting a sample from the population and the estimating technique vis-a-vis the formula for computing the sample statistics.

The fundamental question is, then, how to select a sample.

To answer this question, we must have acquaintance with the sampling methods.

These methods are basically of two types;

• probability sampling , and
• non-probability sampling .

Probability sampling ensures every unit has a known nonzero probability of selection within the target population.

If there is no feasible alternative, a non-probability sampling method may be employed.

The basis of such selection is entirely dependent on the researcher’s discretion. This approach is called judgment sampling, convenience sampling, accidental sampling, and purposive sampling.

The most widely used probability sampling methods are simple random sampling , stratified random sampling , cluster sampling , and systematic sampling . They have been classified by their representation basis and unit selection techniques.

Two other variations of the sampling methods that are in great use are multistage sampling and probability proportional to size (PPS) sampling .

Multistage sampling is most commonly used in drawing samples from very large and diverse populations.

The PPS sampling is a variation of multistage sampling in which the probability of selecting a cluster is proportional to its size, and an equal number of elements are sampled within each cluster.

Collecting Data From The Research Sample

Data gathering may range from simple observation to a large-scale survey in any defined population. There are many ways to collect data. The approach selected depends on the objectives of the study, the research design, and the availability of time, money, and personnel.

With the variation in the type of data (qualitative or quantitative) to be collected, the method of data collection also varies .

The most common means for collecting quantitative data is the structured interview .

Studies that obtain data by interviewing respondents are called surveys. Data can also be collected by using self-administered questionnaires . Telephone interviewing is another way in which data may be collected .

Other means of data collection include secondary sources, such as the census, vital registration records, official documents, previous surveys, etc.

Qualitative data are collected mainly through in-depth interviews, focus group discussions , Key Informant Interview ( KII), and observational studies.

Process and Analyze the Collected Research Data

Data processing generally begins with the editing and coding of data . Data are edited to ensure consistency across respondents and to locate omissions if any.

In survey data, editing reduces errors in the recording, improves legibility, and clarifies unclear and inappropriate responses. In addition to editing, the data also need coding.

Because it is impractical to place raw data into a report, alphanumeric codes are used to reduce the responses to a more manageable form for storage and future processing.

This coding process facilitates the processing of the data. The personal computer offers an excellent opportunity for data editing and coding processes.

Data analysis usually involves reducing accumulated data to a manageable size, developing summaries, searching for patterns, and applying statistical techniques for understanding and interpreting the findings in light of the research questions.

Further, based on his analysis, the researcher determines if his findings are consistent with the formulated hypotheses and theories.

The techniques used in analyzing data may range from simple graphical techniques to very complex multivariate analyses depending on the study’s objectives, the research design employed, and the nature of the data collected.

As in the case of data collection methods, an analytical technique appropriate in one situation may not be suitable for another.

Writing Research Report – Developing Research Proposal, Writing Report, Disseminating and Utilizing Results

The entire task of a research study is accumulated in a document called a proposal or research proposal.

A research proposal is a work plan, prospectus, outline, offer, and a statement of intent or commitment from an individual researcher or an organization to produce a product or render a service to a potential client or sponsor .

The proposal will be prepared to keep the sequence presented in the research process. The proposal tells us what, how, where, and to whom it will be done.

It must also show the benefit of doing it. It always includes an explanation of the purpose of the study (the research objectives) or a definition of the problem.

It systematically outlines the particular research methodology and details the procedures utilized at each stage of the research process.

The end goal of a scientific study is to interpret the results and draw conclusions.

To this end, it is necessary to prepare a report and transmit the findings and recommendations to administrators, policymakers, and program managers to make a decision.

There are various research reports: term papers, dissertations, journal articles , papers for presentation at professional conferences and seminars, books, thesis, and so on. The results of a research investigation prepared in any form are of little utility if they are not communicated to others.

The primary purpose of a dissemination strategy is to identify the most effective media channels to reach different audience groups with study findings most relevant to their needs.

The dissemination may be made through a conference, a seminar, a report, or an oral or poster presentation.

The style and organization of the report will differ according to the target audience, the occasion, and the purpose of the research. Reports should be developed from the client’s perspective.

A report is an excellent means that helps to establish the researcher’s credibility. At a bare minimum, a research report should contain sections on:

• An executive summary;
• Background of the problem;
• Literature review;
• Methodology;
• Discussion;
• Conclusions and
• Recommendations.

The study results can also be disseminated through peer-reviewed journals published by academic institutions and reputed publishers both at home and abroad. The report should be properly evaluated .

These journals have their format and editorial policies. The contributors can submit their manuscripts adhering to the policies and format for possible publication of their papers.

There are now ample opportunities for researchers to publish their work online.

The researchers have conducted many interesting studies without affecting actual settings. Ideally, the concluding step of a scientific study is to plan for its utilization in the real world.

Although researchers are often not in a position to implement a plan for utilizing research findings, they can contribute by including in their research reports a few recommendations regarding how the study results could be utilized for policy formulation and program intervention.

Why is the dissemination of research findings important?

Dissemination of research findings is crucial because the results of a research investigation have little utility if not communicated to others. Dissemination ensures that the findings reach relevant stakeholders, policymakers, and program managers to inform decisions.

How should a research report be structured?

A research report should contain sections on an executive summary, background of the problem, literature review, methodology, findings, discussion, conclusions, and recommendations.

Why is it essential to consider the target audience when preparing a research report?

The style and organization of a research report should differ based on the target audience, occasion, and research purpose. Tailoring the report to the audience ensures that the findings are communicated effectively and are relevant to their needs.

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The Scientific Method

Introduction.

There are many scientific disciplines that address topics from medicine and astrophysics to agriculture and zoology. In each discipline, modern scientists use a process called the "Scientific Method" to advance their knowledge and understanding. This publication describes the method scientists use to conduct research and describe and explain nature, ultimately trying prove or disprove theories.

Scientists all over the world conduct research using the Scientific Method. The University of Nevada Cooperative Extension exists to provide unbiased, research-based information on topics important and relevant to society. The scientific research efforts, analyses, and subsequent information disseminated by Cooperative Extension is driven by careful review and synthesis of relevant scientific research. Cooperative Extension presents useful information based on the best science available, and today that science is based on knowledge obtained by application of the Scientific Method.

The Scientific Method – What it’s Not

The Scientific Method is a process for explaining the world we see. It is:

• Not a formula

The Scientific Method – What is it?

The Scientific Method is a process used to validate observations while minimizing observer bias. Its goal is for research to be conducted in a fair, unbiased and repeatable manner.

Long ago, people viewed the workings of nature and believed that the events and phenomena they observed were associated with the intrinsic nature of the beings or things being observed (Ackoff 1962, Wilson 1937). Today we view events and phenomena as having been caused , and science has evolved as a process to ask how and why things and events happen. Scientists seek to understand the relationships and intricacies between cause and effect in order to predict outcomes of future or similar events. To answer these questions and to help predict future happenings, scientists use the Scientific Method - a series of steps that lead to answers that accurately describe the things we observe, or at least improve our understanding of them.

The Scientific Method is not the only way, but is the best-known way to discover how and why the world works, without our knowledge being tainted by religious, political, or philosophical values. This method provides a means to formulate questions about general observations and devise theories of explanation. The approach lends itself to answering questions in fair and unbiased statements, as long as questions are posed correctly, in a hypothetical form that can be tested.

Definitions

It is important to understand three important terms before describing the Scientific Method.

This is a statement made by a researcher that is a working assumption to be tested and proven. It is something "considered true for the purpose of investigation" (Webster’s Dictionary 1995). An example might be “The earth is round.”

general principles drawn from facts that explain observations and can be used to predict new events. An example would be Newton’s theory of gravitation or Einstein’s theory of relativity. Each is based on falsifiable hypotheses of phenomenon we observe.

Falsifiable/ Null Hypothesis

to prove to be false (Webster’s Dictionary 1995). The hypothesis that is generated must be able to be tested, and either accepted or rejected. Scientists make hypotheses that they want to disprove in order that they may prove the working assumption describing the observed phenomena. This is done by declaring the statement or hypothesis as falsifiable . So, we would state the above hypothesis as “the earth is not round,” or “the earth is square” making it a working statement to be disproved.

The Scientific Method is not a formula, but rather a process with a number of sequential steps designed to create an explainable outcome that increases our knowledge base. This process is as follows:

STEP 1. Make an OBSERVATION

gather and assimilate information about an event, phenomenon, process, or an exception to a previous observation, etc.

STEP 2. Define the PROBLEM

ask questions about the observation that are relevant and testable. Define the null hypothesis to provide unbiased results.

STEP 3: Form the HYPOTHESIS

create an explanation, or educated guess, for the observation that is testable and falsifiable.

STEP 4: Conduct the EXPERIMENT

devise and perform an experiment to test the hypothesis.

STEP 5: Derive a THEORY

create a statement based in the outcome of the experiment that explains the observation(s) and predicts the likelihood of future observations.

Replication

Using the Scientific Method to answer questions about events or phenomena we observe can be repeated to fine-tune our theories. For example, if we conduct research using the Scientific Method and think we have answered a question, but different results occur the next time we make an observation, we may have to ask new questions and formulate new hypotheses that are tested by another experiment. Sometimes scientists must perform many experiments over many years or even decades using the Scientific Method to prove or disprove theories that are generated from one initial question. Numerous studies are often necessary to fully test the broad range of results that occur in order that scientists can formulate theories that truly account for the variation we see in our natural environment.

The Scientific Method – Is it worth all the effort?

Scientific knowledge can only advance when all scientists systematically use the same process to discover and disseminate new information. The advantage of all scientific research using the Scientific Method is that the experiments are repeatable by anyone, anywhere. When similar results occur in each experiment, these facts make the case for the theory stronger. If the same experiment is performed many times in many different locations, under a broad range of conditions, then the theory derived from these experiments is considered strong and widely applicable. If the questions are posed as testable hypotheses that rely on inductive reasoning and empiricism – that is, observations and data collection – then experiments can be devised to generate logical theories that explain the things we see. If we understand why the observed results occur, then we can accurately apply concepts derived from the experiment to other situations.

What do we need to consider when using the Scientific Method?

The Scientific Method requires that we ask questions and perform experiments to prove or disprove questions in ways that will lead to unbiased answers. Experiments must be well designed to provide accurate and repeatable (precise) results. If we test hypotheses correctly, then we can prove the cause of a phenomenon and determine the likelihood (probability) of the events to happen again. This provides predictive power. The Scientific Method enables us to test a hypothesis and distinguish between the correlation of two or more things happening in association with each other and the actual cause of the phenomenon we observe.

Correlation of two variables cannot explain the cause and effect of their relationship. Scientists design experiments using a number of methods to ensure the results reveal the likelihood of the observation happening (probability). Controlled experiments are used to analyze these relationships and develop cause and effect relationships. Statistical analysis is used to determine whether differences between treatments can be attributed to the treatment applied, if they are artifacts of the experimental design, or of natural variation.

In summary, the Scientific Method produces answers to questions posed in the form of a working hypothesis that enables us to derive theories about what we observe in the world around us. Its power lies in its ability to be repeated, providing unbiased answers to questions to derive theories. This information is powerful and offers opportunity to predict future events and phenomena.

Bibliography

• Ackoff, R. 1962. Scientific Method, Optimizing Applied Research Decisions. Wiley and Sons, New York, NY.
• Wilson, F. 1937. The Logic and Methodology of Science in Early Modern Thought. University of Toronto Press. Buffalo, NY.
• Committee on Science, Engineering, and Public Policy. Experimental Error. 1995. From: On Being a Scientist: Responsible Conduct in Research. Second Edition.
• The Gale Group. The Scientific Method. 2001. Gale Encyclopedia of Psychology. Second Edition.

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Psychological Research

The scientific process, learning objectives.

• Explain the steps of the scientific method
• Differentiate between theories and hypotheses

Figure 1 . Some of our ancestors, across the world and over the centuries, believed that trephination—the practice of making a hole in the skull, as shown here—allowed evil spirits to leave the body, thus curing mental illness and other disorders. (credit: “taiproject”/Flickr)

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

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

Process of Scientific Research

Figure 2 . The scientific method is a process for gathering data and processing information. It provides well-defined steps to standardize how scientific knowledge is gathered through a logical, rational problem-solving method.

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what they think the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test their hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Figure 3 . The scientific method involves deriving hypotheses from theories and then testing those hypotheses. If the results are consistent with the theory, then the theory is supported. If the results are not consistent, then the theory should be modified and new hypotheses will be generated.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

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

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

Figure 4 . Many of the specifics of (a) Freud’s theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.

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

Link to Learning

Want to participate in a study? Visit this Psychological Research on the Net website and click on a link that sounds interesting to you in order to participate in online research.

Why the Scientific Method Is Important for Psychology

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an introduction, which introduces the background information and outlines the hypotheses; a methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a discussion and conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Notable Researchers

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

Figure 5 . (a) Margaret Floy Washburn was the first woman to earn a doctorate degree in psychology. (b) Psychologist Inez Beverly Prosser, who was the first African American woman to earn a PhD in psychology.

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

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

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

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1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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1.2 Scientific Research in Psychology

Learning objectives.

• Describe a general model of scientific research in psychology and give specific examples that fit the model.
• Explain who conducts scientific research in psychology and why they do it.
• Distinguish between basic research and applied research.

A Model of Scientific Research in Psychology

Figure 1.2 “A Simple Model of Scientific Research in Psychology” presents a more specific model of scientific research in psychology. The researcher (who more often than not is really a small group of researchers) formulates a research question, conducts a study designed to answer the question, analyzes the resulting data, draws conclusions about the answer to the question, and publishes the results so that they become part of the research literature. Because the research literature is one of the primary sources of new research questions, this process can be thought of as a cycle. New research leads to new questions, which lead to new research, and so on. Figure 1.2 “A Simple Model of Scientific Research in Psychology” also indicates that research questions can originate outside of this cycle either with informal observations or with practical problems that need to be solved. But even in these cases, the researcher would start by checking the research literature to see if the question had already been answered and to refine it based on what previous research had already found.

Figure 1.2 A Simple Model of Scientific Research in Psychology

The research by Mehl and his colleagues is described nicely by this model. Their question—whether women are more talkative than men—was suggested to them both by people’s stereotypes and by published claims about the relative talkativeness of women and men. When they checked the research literature, however, they found that this question had not been adequately addressed in scientific studies. They conducted a careful empirical study, analyzed the results (finding very little difference between women and men), and published their work so that it became part of the research literature. The publication of their article is not the end of the story, however, because their work suggests many new questions (about the reliability of the result, about potential cultural differences, etc.) that will likely be taken up by them and by other researchers inspired by their work.

Scientific research has confirmed that cell phone use impairs a variety of driving behaviors.

Indiana Stan – CC BY-NC 2.0.

As another example, consider that as cell phones became more widespread during the 1990s, people began to wonder whether, and to what extent, cell phone use had a negative effect on driving. Many psychologists decided to tackle this question scientifically (Collet, Guillot, & Petit, 2010). It was clear from previously published research that engaging in a simple verbal task impairs performance on a perceptual or motor task carried out at the same time, but no one had studied the effect specifically of cell phone use on driving. Under carefully controlled conditions, these researchers compared people’s driving performance while using a cell phone with their performance while not using a cell phone, both in the lab and on the road. They found that people’s ability to detect road hazards, reaction time, and control of the vehicle were all impaired by cell phone use. Each new study was published and became part of the growing research literature on this topic.

Who Conducts Scientific Research in Psychology?

Scientific research in psychology is generally conducted by people with doctoral degrees (usually the doctor of philosophy [PhD] ) and master’s degrees in psychology and related fields, often supported by research assistants with bachelor’s degrees or other relevant training. Some of them work for government agencies (e.g., the National Institute of Mental Health), for nonprofit organizations (e.g., the American Cancer Society), or in the private sector (e.g., in product development). However, the majority of them are college and university faculty, who often collaborate with their graduate and undergraduate students. Although some researchers are trained and licensed as clinicians—especially those who conduct research in clinical psychology—the majority are not. Instead, they have expertise in one or more of the many other subfields of psychology: behavioral neuroscience, cognitive psychology, developmental psychology, personality psychology, social psychology, and so on. Doctoral-level researchers might be employed to conduct research full-time or, like many college and university faculty members, to conduct research in addition to teaching classes and serving their institution and community in other ways.

Of course, people also conduct research in psychology because they enjoy the intellectual and technical challenges involved and the satisfaction of contributing to scientific knowledge of human behavior. You might find that you enjoy the process too. If so, your college or university might offer opportunities to get involved in ongoing research as either a research assistant or a participant. Of course, you might find that you do not enjoy the process of conducting scientific research in psychology. But at least you will have a better understanding of where scientific knowledge in psychology comes from, an appreciation of its strengths and limitations, and an awareness of how it can be applied to solve practical problems in psychology and everyday life.

Scientific Psychology Blogs

A fun and easy way to follow current scientific research in psychology is to read any of the many excellent blogs devoted to summarizing and commenting on new findings. Among them are the following:

• Child-Psych, http://www.child-psych.org
• PsyBlog, http://www.spring.org.uk
• Research Digest, http://bps-research-digest.blogspot.com
• Social Psychology Eye, http://socialpsychologyeye.wordpress.com
• We’re Only Human, http://www.psychologicalscience.org/onlyhuman

You can also browse to http://www.researchblogging.org , select psychology as your topic, and read entries from a wide variety of blogs.

The Broader Purposes of Scientific Research in Psychology

People have always been curious about the natural world, including themselves and their behavior. (In fact, this is probably why you are studying psychology in the first place.) Science grew out of this natural curiosity and has become the best way to achieve detailed and accurate knowledge. Keep in mind that most of the phenomena and theories that fill psychology textbooks are the products of scientific research. In a typical introductory psychology textbook, for example, one can learn about specific cortical areas for language and perception, principles of classical and operant conditioning, biases in reasoning and judgment, and people’s surprising tendency to obey authority. And scientific research continues because what we know right now only scratches the surface of what we can know.

Scientific research is often classified as being either basic or applied. Basic research in psychology is conducted primarily for the sake of achieving a more detailed and accurate understanding of human behavior, without necessarily trying to address any particular practical problem. The research of Mehl and his colleagues falls into this category. Applied research is conducted primarily to address some practical problem. Research on the effects of cell phone use on driving, for example, was prompted by safety concerns and has led to the enactment of laws to limit this practice. Although the distinction between basic and applied research is convenient, it is not always clear-cut. For example, basic research on sex differences in talkativeness could eventually have an effect on how marriage therapy is practiced, and applied research on the effect of cell phone use on driving could produce new insights into basic processes of perception, attention, and action.

Key Takeaways

• Research in psychology can be described by a simple cyclical model. A research question based on the research literature leads to an empirical study, the results of which are published and become part of the research literature.
• Scientific research in psychology is conducted mainly by people with doctoral degrees in psychology and related fields, most of whom are college and university faculty members. They do so for professional and for personal reasons, as well as to contribute to scientific knowledge about human behavior.
• Basic research is conducted to learn about human behavior for its own sake, and applied research is conducted to solve some practical problem. Both are valuable, and the distinction between the two is not always clear-cut.
• Practice: Find a description of an empirical study in a professional journal or in one of the scientific psychology blogs. Then write a brief description of the research in terms of the cyclical model presented here. One or two sentences for each part of the cycle should suffice.
• Practice: Based on your own experience or on things you have already learned about psychology, list three basic research questions and three applied research questions of interest to you.

Collet, C., Guillot, A., & Petit, C. (2010). Phoning while driving I: A review of epidemiological, psychological, behavioural and physiological studies. Ergonomics, 53 , 589–601.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

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An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

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Facility for Rare Isotope Beams

At michigan state university, frib researchers lead team to merge nuclear physics experiments and astronomical observations to advance equation-of-state research, world-class particle-accelerator facilities and recent advances in neutron-star observation give physicists a new toolkit for describing nuclear interactions at a wide range of densities..

For most stars, neutron stars and black holes are their final resting places. When a supergiant star runs out of fuel, it expands and then rapidly collapses on itself. This act creates a neutron star—an object denser than our sun crammed into a space 13 to  18 miles wide. In such a heavily condensed stellar environment, most electrons combine with protons to make neutrons, resulting in a dense ball of matter consisting mainly of neutrons. Researchers try to understand the forces that control this process by creating dense matter in the laboratory through colliding neutron-rich nuclei and taking detailed measurements.

A research team—led by William Lynch and Betty Tsang at FRIB—is focused on learning about neutrons in dense environments. Lynch, Tsang, and their collaborators used 20 years of experimental data from accelerator facilities and neutron-star observations to understand how particles interact in nuclear matter under a wide range of densities and pressures. The team wanted to determine how the ratio of neutrons to protons influences nuclear forces in a system. The team recently published its findings in Nature Astronomy .

“In nuclear physics, we are often confined to studying small systems, but we know exactly what particles are in our nuclear systems. Stars provide us an unbelievable opportunity, because they are large systems where nuclear physics plays a vital role, but we do not know for sure what particles are in their interiors,” said Lynch, professor of nuclear physics at FRIB and in the Michigan State University (MSU) Department of Physics and Astronomy. “They are interesting because the density varies greatly within such large systems.  Nuclear forces play a dominant role within them, yet we know comparatively little about that role.” 

When a star with a mass that is 20-30 times that of the sun exhausts its fuel, it cools, collapses, and explodes in a supernova. After this explosion, only the matter in the deepest part of the star’s interior coalesces to form a neutron star. This neutron star has no fuel to burn and over time, it radiates its remaining heat into the surrounding space. Scientists expect that matter in the outer core of a cold neutron star is roughly similar to the matter in atomic nuclei but with three differences: neutron stars are much larger, they are denser in their interiors, and a larger fraction of their nucleons are neutrons. Deep within the inner core of a neutron star, the composition of neutron star matter remains a mystery. 

  “If experiments could provide more guidance about the forces that act in their interiors, we could make better predictions of their interior composition and of phase transitions within them. Neutron stars present a great research opportunity to combine these disciplines,” said Lynch.

Accelerator facilities like FRIB help physicists study how subatomic particles interact under exotic conditions that are more common in neutron stars. When researchers compare these experiments to neutron-star observations, they can calculate the equation of state (EOS) of particles interacting in low-temperature, dense environments. The EOS describes matter in specific conditions, and how its properties change with density. Solving EOS for a wide range of settings helps researchers understand the strong nuclear force’s effects within dense objects, like neutron stars, in the cosmos. It also helps us learn more about neutron stars as they cool.

“This is the first time that we pulled together such a wealth of experimental data to explain the equation of state under these conditions, and this is important,” said Tsang, professor of nuclear science at FRIB. “Previous efforts have used theory to explain the low-density and low-energy end of nuclear matter. We wanted to use all the data we had available to us from our previous experiences with accelerators to obtain a comprehensive equation of state.”   

Researchers seeking the EOS often calculate it at higher temperatures or lower densities. They then draw conclusions for the system across a wider range of conditions. However, physicists have come to understand in recent years that an EOS obtained from an experiment is only relevant for a specific range of densities. As a result, the team needed to pull together data from a variety of accelerator experiments that used different measurements of colliding nuclei to replace those assumptions with data. “In this work, we asked two questions,” said Lynch. “For a given measurement, what density does that measurement probe? After that, we asked what that measurement tells us about the equation of state at that density.”   

In its recent paper, the team combined its own experiments from accelerator facilities in the United States and Japan. It pulled together data from 12 different experimental constraints and three neutron-star observations. The researchers focused on determining the EOS for nuclear matter ranging from half to three times a nuclei’s saturation density—the density found at the core of all stable nuclei. By producing this comprehensive EOS, the team provided new benchmarks for the larger nuclear physics and astrophysics communities to more accurately model interactions of nuclear matter.

The team improved its measurements at intermediate densities that neutron star observations do not provide through experiments at the GSI Helmholtz Centre for Heavy Ion Research in Germany, the RIKEN Nishina Center for Accelerator-Based Science in Japan, and the National Superconducting Cyclotron Laboratory (FRIB’s predecessor). To enable key measurements discussed in this article, their experiments helped fund technical advances in data acquisition for active targets and time projection chambers that are being employed in many other experiments world-wide.   

In running these experiments at FRIB, Tsang and Lynch can continue to interact with MSU students who help advance the research with their own input and innovation. MSU operates FRIB as a scientific user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. FRIB is the only accelerator-based user facility on a university campus as one of 28 DOE-SC user facilities .  Chun Yen Tsang, the first author on the Nature Astronomy  paper, was a graduate student under Betty Tsang during this research and is now a researcher working jointly at Brookhaven National Laboratory and Kent State University. 

“Projects like this one are essential for attracting the brightest students, which ultimately makes these discoveries possible, and provides a steady pipeline to the U.S. workforce in nuclear science,” Tsang said.

The proposed FRIB energy upgrade ( FRIB400 ), supported by the scientific user community in the 2023 Nuclear Science Advisory Committee Long Range Plan , will allow the team to probe at even higher densities in the years to come. FRIB400 will double the reach of FRIB along the neutron dripline into a region relevant for neutron-star crusts and to allow study of extreme, neutron-rich nuclei such as calcium-68. 

Eric Gedenk is a freelance science writer.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.

Researchers develop a new way to safely boost immune cells to fight cancer

Researchers in Virginia Tech’s College of Engineering have developed a new cancer immunotherapy to localize cancer-killing cytokines in tumors to improve treatment effectiveness.

• Hailey Wade

19 Apr 2024

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Cancer is the monster of our society. Last year alone, more than 600,000 people in the United States died from cancer, according to the American Cancer Society . The relentless pursuit of understanding this complex disease has shaped medical progress on developing treatment procedures that are less invasive while still highly effective. 

Immunotherapy is on the rise as a possible solution. Immunotherapy involves harnessing the power of the body’s immune system to fight against cancer cells. Researchers in the College of Engineering have found a way to revamp a treatment procedure into a groundbreaking practice.

Rong Tong , associate professor in  chemical engineering , has teamed up with Wenjun "Rebecca" Cai , associate professor in  materials science and engineering , to explore a cancer immunotherapy treatment that has long been of interest to researchers. In their newly published article in the journal Science Advances , Tong and Cai detailed their approach, which involves activating the immune cells in the body and reprogramming them to attack and destroy the cancer cells. This therapeutic method is frequently implemented with the protein cytokine. Cytokines are small protein molecules that act as intercellular biochemical messengers and are released by the body's immune cells to coordinate their response.

“Cytokines are potent and highly effective at stimulating the immune cells to eliminate cancer cells,” Tong said. “The problem is they’re so potent that if they roam freely throughout the body, they’ll activate every immune cell they encounter, which can cause an overactive immune response and potentially fatal side effects.”

Tong and Cai, in collaboration with chemical engineering and materials science and engineering graduate students, have developed an innovative approach to employ cytokine proteins as a potential immunotherapy treatment. Unlike previous methods, their technique ensures that the immune cell stimulating cytokines effectively localize within the tumors for weeks while preserving the cytokine’s structure and reactivity levels. 

Combining forces to take down cancer cells

Current cancer treatments, such as chemotherapy, cannot distinguish between healthy cells and cancer cells. When someone with cancer is treated with chemotherapy, the treatment attacks all of the cells in their body, which can lead to side effects such as hair loss and fatigue. Stimulating the body’s immune system to attack tumors is a promising alternative to treat cancer. The delivery of cytokines can jump-start immune cells in the tumor, but overstimulating healthy cells can cause severe side effects.

“Scientists determined a while ago that cytokines can be used to activate and fight against tumors, but they didn’t know how to localize them inside the tumor while not exposing toxicity to the rest of the body,” said Tong. “Chemical engineers can look at this from an engineering approach and use their knowledge to help refine and elevate the effectiveness of the cytokines so they can work inside the body effectively.” 

The research team’s goal is to find a balance between killing cancer cells in the body while sparing healthy cells. 

To accomplish this goal, Tong and his students used their expertise to create specialized particles with distinctive sizes that help determine where the drug is going. These microparticles are designed to stay within the tumor environment after being injected into the body. Cai and her students worked on measuring these particles’ surface properties.

“In the field of materials science and engineering, we study the surface chemistry and mechanical behavior of materials, such as the specialized particle created for this project,” Cai said. “Surface engineering and characterization, along with particle size, play important roles in controlled drug delivery, ensuring prolonged drug presence and sustained therapeutic effectiveness.”

To ensure successful drug delivery, Tong and his chemical engineering students designed a novel strategy that: 

• Anchors cytokines to these new microparticles, limiting the harm of cytokines to healthy cells
• Allows the newly particle-anchored cytokines to jump-start immune systems and recruit immune cells to attack cancer cells

“Our strategy not only minimizes cytokine-induced harm to healthy cells, but also prolongs cytokine retention within the tumor,” Tong said. “This helps facilitate the recruitment of immune cells for targeted tumor attack.”

The next step in the process involves combining the new, localized cytokine therapy method with commercially available, Food and Drug Administration (FDA)-approved checkpoint blockade antibodies, which reactivate the tumor immune cells that have been silenced so they can fight back the cancer cells. 

“When there is a tumor inside the body, the body’s immune cells are being deactivated by the cancer cells,” Tong explained. “The FDA-approved checkpoint blocking antibody helps “take off the brakes” that tumors put on immune cells, while the cytokine molecules “step on the gas” to jump-start the immune system and get an immune cell army to fight cancer cells. These two approaches work together to activate immune cells.”

Combining the checkpoint antibodies with the particle-anchored cytokine proved to successfully eliminate many tumors in their study.

Engineering an impact on cancer treatment

Team members hope their impact on immunotherapy treatment is part of a greater movement toward cancer treatment approaches that are harmless to healthy cells. The new approach of attaching cytokines to particles also could be used in the future to deliver other types of immunostimulatory drugs, according to the team.

“Researchers are still looking for safer and more effective cancer treatments,” said Tong. “This motivation is what drives us to develop new technologies in the field. The whole class of drugs that are employed to jump-start the immune system to fight cancer cells has largely not yet succeeded. Our goal is to create novel solutions that allow researchers to test these drugs with existing FDA-approved therapeutics, ensuring both safety and enhanced efficacy.”

Cai said the nature of cancer treatment research requires expertise across engineering disciplines. 

“I view this project as a perfect marriage between chemical engineering and materials science,” Cai said. “The former focuses on the synthesis and drug delivery part, the latter on applying advanced materials characterization. This collaboration not only accelerates immunotherapy research, but also has the ability to transform cancer treatment.”

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Related Content

AI tool creates 'synthetic' images of cells for enhanced microscopy analysis

Observing individual cells through microscopes can reveal a range of important cell biological phenomena that frequently play a role in human diseases, but the process of distinguishing single cells from each other and their background is extremely time consuming -- and a task that is well-suited for AI assistance.

AI models learn how to carry out such tasks by using a set of data that are annotated by humans, but the process of distinguishing cells from their background, called "single-cell segmentation," is both time-consuming and laborious. As a result, there are limited amount of annotated data to use in AI training sets. UC Santa Cruz researchers have developed a method to solve this by building a microscopy image generation AI model to create realistic images of single cells, which are then used as "synthetic data" to train an AI model to better carry out single cell-segmentation.

The new software is described in a new paper published in the journal iScience . The project was led by Assistant Professor of Biomolecular Engineering Ali Shariati and his graduate student Abolfazl Zargari. The model, called cGAN-Seg, is freely available on GitHub.

"The images that come out of our model are ready to be used to train segmentation models," Shariati said. "In a sense we are doing microscopy without a microscope, in that we are able to generate images that are very close to real images of cells in terms of the morphological details of the single cell. The beauty of it is that when they come out of the model, they are already annotated and labeled. The images show a ton of similarities to real images, which then allows us to generate new scenarios that have not been seen by our model during the training."

Images of individual cells seen through a microscope can help scientists learn about cell behavior and dynamics over time, improve disease detection, and find new medicines. Subcellular details such as texture can help researchers answer important questions, like if a cell is cancerous or not.

Manually finding and labeling the boundaries of cells from their background is extremely difficult, however, especially in tissue samples where there are many cells in an image. It could take researchers several days to manually perform cell segmentation on just 100 microscopy images.

Deep learning can speed up this process, but an initial data set of annotated images is needed to train the models -- at least thousands of images are needed as a baseline to train an accurate deep learning model. Even if the researchers can find and annotate 1,000 images, those images may not contain the variation of features that appear across different experimental conditions.

"You want to show your deep learning model works across different samples with different cell types and different image qualities," Zargari said. "For example if you train your model with high quality images, it's not going to be able to segment the low quality cell images. We can rarely find such a good data set in the microscopy field."

To address this issue, the researchers created an image-to-image generative AI model that takes a limited set of annotated, labeled cell images and generates more, introducing more intricate and varied subcellular features and structures to create a diverse set of "synthetic" images. Notably, they can generate annotated images with a high density of cells, which are especially difficult to annotate by hand and are especially relevant for studying tissues. This technique works to process and generate images of different cell types as well as different imaging modalities, such as those taken using fluorescence or histological staining.

Zargari, who led the development of the generative model, employed a commonly used AI algorithm called a "cycle generative adversarial network" for creating realistic images. The generative model is enhanced with so-called "augmentation functions" and a "style injecting network," which helps the generator to create a wide variety of high quality synthetic images that show different possibilities for what the cells could look like. To the researchers' knowledge, this is the first time style injecting techniques have been used in this context.

Then, this diverse set of synthetic images created by the generator are used to train a model to accurately carry out cell segmentation on new, real images taken during experiments.

"Using a limited data set, we can train a good generative model. Using that generative model, we are able to generate a more diverse and larger set of annotated, synthetic images. Using the generated synthetic images we can train a good segmentation model -- that is the main idea," Zagari said.

The researchers compared the results of their model using synthetic training data to more traditional methods of training AI to carry out cell segmentation across different types of cells. They found that their model produces significantly improved segmentation compared to models trained with conventional, limited training data. This confirms to the researchers that providing a more diverse dataset during training of the segmentation model improves performance.

Through these enhanced segmentation capabilities, the researchers will be able to better detect cells and study variability between individual cells, especially among stem cells. In the future, the researchers hope to use the technology they have developed to move beyond still images to generate videos, which can help them pinpoint which factors influence the fate of a cell early in its life and predict their future.

"We are generating synthetic images that can also be turned into a time lapse movie, where we can generate the unseen future of cells," Shariati said. "With that, we want to see if we are able to predict the future states of a cell, like if the cell is going to grow, migrate, differentiate or divide."

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Story Source:

Materials provided by University of California - Santa Cruz . Original written by Emily Cerf. Note: Content may be edited for style and length.

Journal Reference :

• Abolfazl Zargari, Benjamin R. Topacio, Najmeh Mashhadi, S. Ali Shariati. Enhanced Cell Segmentation with Limited Training Datasets using Cycle Generative Adversarial Networks . iScience , 2024; 109740 DOI: 10.1016/j.isci.2024.109740

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Neolithic women in Europe were tied up and buried alive in ritual sacrifices, study suggests

The research found evidence of the "incaprettamento" method of murder at 14 Neolithic sites in Europe.

The murder of sacrificial victims by "incaprettamento" — tying their neck to their legs bent behind their back, so that they effectively strangled themselves — seems to have been a tradition across much of Neolithic Europe, with a new study identifying more than a dozen such murders over more than 2,000 years.

The study comes after a reassessment of an ancient tomb that was discovered more than 20 years ago at Saint-Paul-Trois-Châteaux near Avignon, in southern France. The tomb mimics a silo, or pit where grain was stored, and it held the remains of three women who were buried there about 5,500 years ago.

The new study, published Wednesday (April 10) in the journal Science Advances , reinterprets the positions of two of the skeletons and suggests the individuals were deliberately killed — first by tying them up in the manner called "incaprettamento" and then by burying them while they were still alive, perhaps for an agricultural ritual.

Study senior author Eric Crubézy , a biological anthropologist at Paul Sabatier University in Toulouse, France, told Live Science that there was a lot of agricultural symbolism to the tomb. He noted that a wooden structure built over it was aligned with the sun at the solstices and that several broken stones for grinding grain were found nearby. "You have the alignment, you have the silo, you have the broken stones — so it seems that it was a rite related to agriculture."

Related: Skull of Neolithic 'bog body' from Denmark was smashed by 8 heavy blows in violent murder

To investigate the idea of human sacrifice at Saint-Paul-Trois-Châteaux, Crubézy, who worked on the initial discovery of the tomb, and colleagues examined earlier archaeological studies of tomb sites throughout Europe. The team included forensic pathologist Bertrand Ludes , of Paris Cité University and the study's lead author.

They found evidence of 20 probable cases of sacrificial murders using incaprettamento at 14 Neolithic (New Stone Age) sites dating to between 5400 and 3500 B.C. They also found papers describing Mesolithic (Middle Stone Age) rock art in the Addaura Cave in Sicily, made between 14000 and 11000 B.C., that seems to depict two human figures bound in the incaprettamento manner.

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Crubézy said it appears incaprettamento originated as a sacrificial custom in the Mesolithic period, before agriculture, and later came to be used for human sacrifices associated with agriculture in the Neolithic period.

As a method of human sacrifice, incaprettamento seems to have been widespread across much of Neolithic Europe, with evidence of the practice at sites ranging from the Czech Republic to Spain. The earliest is a tomb near Brno-Bohunice in the Czech Republic that is dated to about 5400 B.C., and the latest is the tomb at Saint-Paul-Trois-Châteaux, suggesting that the practice persisted for more than 2,000 years, Crubézy said.

Gruesome murders

The bindings used to tie the two individuals at Saint-Paul-Trois-Châteaux have long since decayed, but a few features of their skeletons — such as the unusual positions of their legs — suggest how they died, Crubézy said.

The third woman in the tomb seems to have been older and likely died from natural causes, the researchers found. She was also interred normally for the time, on her side in the center of the tomb. This suggests that she had been ceremonially buried after her natural death and that the two younger women had been sacrificed to be buried with her, he said.

— 15 people were brutally murdered 5,000 years ago, but the bodies were buried with care

— 2 waves of mass murder struck prehistoric Denmark, genetic study reveals

— Why were dozens of people butchered 6,200 years ago and buried in a Neolithic death pit?

The two sacrificial victims seem to have been pinned down with heavy fragments of stones used for grinding grain, indicating that, despite their bindings, they were still alive when they were buried, he said.

Today, the gruesome incaprettamento murder method is associated with the Italian Mafia , who have sometimes used it as a form of warning or reprimand.

Crubézy said it wasn't known why incaprettamento was used for Stone Age human sacrifices, but it might have been because a person bound in this way could be seen as strangling themselves, rather than being killed by someone else.

Tom Metcalfe is a freelance journalist and regular Live Science contributor who is based in London in the United Kingdom. Tom writes mainly about science, space, archaeology, the Earth and the oceans. He has also written for the BBC, NBC News, National Geographic, Scientific American, Air & Space, and many others.

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• DAR "Crubézy said it wasn't known why incaprettamento was used for Stone Age human sacrifices, but it might have been because a person bound in this way could be seen as strangling themselves, rather than being killed by someone else." This would make no sense because a person obviously could not bind THEMSELVES in this manner! Reply
• Lemmy Caution The article rather confusingly presents us with two widely separate years in which the burial of the two sacrificial victims is thought to have taken place. First we are told these burials are estimated to have occurred around the year 5,400 BCE. Further along in the narrative, on the other hand, we are informed these sacrificial burials took place approximately 5,500 years ago. The obvious problem with this divergent chronology is that there is a substantial difference, which is to say all of 1900 years, between 5,500 years ago and the earlier noted figure of 5,400 BCE, which amounts to fully 7400 years ago. Reply
• View All 2 Comments

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Postdoctoral Research Associate (Senior Scientist) / Reference Code 81

Job Information

Offer description.

The Department of Material Sciences and Process Engineering, Institute of Physics and Materials Science is currently seeking a Postdoctoral Research Associate (Senior Scientist).

Extent of employment:         20 hours per week (with option for a temporary increase to 40 hours from 01.10.2024)

Duration of employment:       as of now - permanent

Workplace: 1190 Vienna, Peter-Jordan-Straße 82

Gross monthly salary and pay grade in terms of collective agreement for university staff (payable 14 times per year): B1 lit. b, € 2.376,20

Responsibilities:

• Independent research in the field of materials science with focus on defect tolerance of materials
• Planning, acquisition and management of research projects
• Establishing of a work group in the field of defect tolerance of materials under cyclic loading
• Publication of research results in peer-review journals
• Presentation of research results at national/international conferences
• Collaboration in research and teaching at the Institute of Physics and Materials Science
• Supervision of dissertations, Master’s and Bachelor’s theses

Required skills and qualifications:

• Doctorate degree/PhD in Material Science, Physics, Mechanical Engineering or related
• Very good written and spoken German and English or willingness to learn German in the first two years
• Sound scientific professional experience (postdoc)
• Experience in the planning, acquisition and management of research projects (public and private funding bodies)
• Experience with publication in peer-reviewed journals
• Experience with presentations at national and international conferences
• Sound theoretical and methodological knowledge in the field of defect tolerance of materials under cyclic loading in the range of high and very highload cycles

Desired skills and qualifications:

• Sound knowledge in the field of materials science and materials testing
• Experience in performing fatigue tests with the ultrasonic technique
• Experience in fractographic investigations (sample preparation, electron microscopy, etc.)
• Good international network in the scientific community
• Experience in knowledge transfer to society
• Ability to work in a team, communication skills, ability to work under pressure
• Experience in independent teaching in German and English
• Experience in the supervision of scientific theses

University of Natural Resources and Life Sciences Vienna seeks to increase the number of its female faculty and staff members. Therefore qualified women are strongly encouraged to apply. In case of equal qualification, female candidates will be given preference unless reasons specific to an individual male candidate tilt the balance in his favour.

People with disabilities and appropriate qualifications are specifically encouraged to apply.

Please send your job application incl.

• Motivation letter

to Personnel Management, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 70, 1190 Vienna; E-Mail: [email protected] . (Reference code: 81)

We regret that we cannot reimburse applicants travel and lodging expenses incurred as part of the selection and hiring process.

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Please note that the full job description is only available in German.

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