Answers to these questions lie in realms other than science. Scientific theories are, in essence, conceptual models that explain some phenomenon. Indeed, much of science is fundamentally concerned with developing and testing theories, hypotheses, models, conjectures, or conceptual frameworks that can explain aspects of the physical and social world.
Examples of well-known scientific theories include evolution, quantum theory, and the theory of relativity. To be sure, generalized theoretical understanding is still a goal. However, some research in the social sciences seeks to achieve deep understanding of particular events or circumstances rather than theoretical understanding that will generalize across situations or events. Between these extremes lies the bulk of social science theory or models, what Merton called.
Philosophers of science have long debated the meaning of the term empirical. As we state here, in one sense the empirical nature of science means that assertions about the world must be warranted by, or at least constrained by, explicit observation of it. However, we recognize that in addition to direct observation, strategies like logical reasoning and mathematical analysis can also provide empirical support for scientific assertions. Subsequently, however, analysis of light energy absorbed by Earth, measured from the content of organic material in geological sediment cores, raised doubts about this correlation as a causal mechanism e.
The modest change in eccentricity did not make nearly enough difference in incident sunlight to produce the required change in thermal absorption. Examples of such mid-range theories or explanatory models can be found in the physical and the social sciences. These theories are representations or abstractions of some aspect of reality that one can only approximate by such models.
Molecules, fields, or black holes are classic explanatory models in physics; the genetic code and the contractile filament model of muscle are two in biology. He based the hypothesis on astronomical observations showing that the regions above and below the ecliptic are laden with cosmic dust, which would cool the planet.
Farley had begun his research project in an effort to refute the Muller inclination model, but discovered—to his surprise— that cosmic dust levels did indeed wax and wane in sync with the ice ages. As an immediate cause of the temperature change, Muller proposed that dust from space would influence the cloud cover on Earth and the amount of greenhouse gases—mainly carbon dioxide—in the atmosphere. Indeed, measurements of oxygen isotopes in trapped air bubbles and other properties from a ,year-long Antarctic ice core by paleoceanographer Nicholas Shackleton provided more confirming evidence.
Still, no one knows how orbital variations would send the carbon dioxide into and out of the atmosphere. And there are likely to be other significant geologic factors besides carbon dioxide that control climate. There is much work still to be done to sort out the complex variables that are probably responsible for the ice ages.
Theory enters the research process in two important ways. First, scientific research may be guided by a conceptual framework, model, or theory. Researchers seek to test whether a theory holds up under certain circumstances. Here the link between question and theory is straightforward. For example, Putnam based his work on a theoretical conception of institutional performance that related civic engagement and modernization.
A research question can also devolve from a practical problem Stokes, ; see discussion above. In this case, addressing a complex problem like the relationship between class size and student achievement may require several theories. Indeed, the findings from the Tennessee class size reduction study see Box have led to several efforts to devise theoretical understandings of how class size reduction may lead to better student achievement.
Scientists are developing models to understand differences in classroom behavior between large and small classes that may ultimately explain and predict changes in achievement Grissmer and Flannagan, That is, the choice of what to observe and how to observe it is driven by an organizing conception—explicit or tacit— of the problem or topic.
Thus, theory drives the research question, the use of methods, and the interpretation of results. Research methods—the design for collecting data and the measurement and analysis of variables in the design—should be selected in light of a research question, and should address it directly. Methods linked directly to problems permit the development of a logical chain of reasoning based. The process of posing significant questions or hypotheses may occur, as well, at the end of a study e.
For clarity of discussion, we separate out the link between question and method see Principle 3 and the rigorous reasoning from evidence to theory see Principle 4.
In the actual practice of research, such a separation cannot be achieved. Debates about method—in many disciplines and fields—have raged for centuries as researchers have battled over the relative merit of the various techniques of their trade.
The simple truth is that the method used to conduct scientific research must fit the question posed, and the investigator must competently implement the method. Particular methods are better suited to address some questions rather than others.
The rare choice in the mid s in Tennessee to conduct a randomized field trial, for example, enabled stronger inferences about the effects of class size reduction on student achievement see Box than would have been possible with other methods. This link between question and method must be clearly explicated and justified; a researcher should indicate how a particular method will enable competent investigation of the question of interest.
Moreover, a detailed description of method—measurements, data collection procedures, and data analyses—must be available to permit others to critique or replicate the study see Principle 5. Finally, investigators should identify potential methodological limitations such as insensitivity to potentially important variables, missing data, and potential researcher bias.
The choice of method is not always straightforward because, across all disciplines and fields, a wide range of legitimate methods—both quantitative and qualitative—are available to the researcher.
For example when considering questions about the natural universe—from atoms to cells to black holes—profoundly different methods and approaches characterize each sub-field. While investigations in the natural sciences are often dependent on the use of highly sophisticated instrumentation e.
For example, in two Danish zoologists identified an entirely new phylum of animals from a species of tiny rotifer-like creatures found living on the mouthparts of lobsters, using only a hand lens and light microscope Wilson, , p. However, the Glass and Smith study was criticized e. Some subsequent reviews reached conclusions similar to Glass and Smith e. In the midst of controversy, the Tennessee state legislature asked just this question and funded a randomized experiment to find out, an experiment that Harvard statistician Frederick Mosteller , p.
If a research conjecture or hypothesis can withstand scrutiny by multiple methods its credibility is enhanced greatly. As Webb, Campbell, Schwartz, and Sechrest , pp. The experiment began with a cohort of students who entered kindergarten in , and lasted 4 years. After third grade, all students returned to regular size classes. Although students were supposed to stay in their original treatment conditions for four years, not all did.
Three findings from this experiment stand out. First, students in small classes outperformed students in regular size classes with or without aides. Second, the benefits of class-size reduction were much greater for minorities primarily African American and inner-city children than others see, e. And third, even though students returned to regular classes in fourth grade, the reduced class-size effect persisted in affecting whether they took college entrance examinations and on their examination performance Krueger and Whitmore, Interestingly, in balancing the size of the effects of class size reduction with the costs, the Tennessee legislature decided not to reduce class size in the state Ritter and Boruch, New theories about the periodicity of the ice ages, similarly, were informed by multiple methods e.
The integration and interaction of multiple disciplinary perspectives—with their varying methods—often accounts for scientific progress Wilson, ; this is evident, for example, in the advances in understanding early reading skills described in Chapter 2.
This line of work features methods that range from neuroimaging to qualitative classroom observation. We close our discussion of this principle by noting that in many sciences, measurement is a key aspect of research method.
This is true for many research endeavors in the social sciences and education research, although not for all of them. If the concepts or variables are poorly specified or inadequately measured, even the best methods will not be able to support strong scientific inferences. The history of the natural sciences is one of remarkable development of concepts and variables, as well as the tools instrumentation to measure them.
Measurement reliability and validity is particularly challenging in the social sciences and education Messick, Sometimes theory is not strong enough to permit clear specification and justification of the concept or variable. Sometimes the tool e. Sometimes the use of the measurement has an unintended social consequence e. And sometimes error is an inevitable part of the measurement process.
In the physical sciences, many phenomena can be directly observed or have highly predictable properties; measurement error is often minimal. However, see National Research Council [] for a discussion of when and how measurement in the physical sciences can be imprecise. In sciences that involve the study of humans, it is essential to identify those aspects of measurement error that attenuate the estimation of the relationships of interest e.
By investigating those aspects of a social measurement that give rise to measurement error, the measurement process itself will often be improved. Regardless of field of study, scientific measurements should be accompanied by estimates of uncertainty whenever possible see Principle 4 below. The extent to which the inferences that are made in the course of scientific work are warranted depends on rigorous reasoning that systematically and logically links empirical observations with the underlying theory and the degree to which both the theory and the observations are linked to the question or problem that lies at the root of the investigation.
This chain of reasoning must be coherent, explicit one that another researcher could replicate , and persuasive to a skeptical reader so that, for example, counterhypotheses are addressed. All rigorous research—quantitative and qualitative—embodies the same underlying logic of inference King, Keohane, and Verba, This inferential reasoning is supported by clear statements about how the research conclusions were reached: What assumptions were made?
How was evidence judged to be relevant? How were alternative explanations considered or discarded? How were the links between data and the conceptual or theoretical framework made? The nature of this chain of reasoning will vary depending on the design of the study, which in turn will vary depending on the question that is being investigated.
Will the research develop, extend, modify, or test a hypothesis? Does it aim to determine: What works? How does it work? Under what circumstances does it work? If the goal is to produce a description of a complex system, such as a subcellular organelle or a hierarchical social organization, successful inference may rather depend on issues of fidelity and internal consistency of the observational techniques applied to diverse components and the credibility of the evidence gathered.
The research design and the inferential reasoning it enables must demonstrate a thorough understanding of the subtleties of the questions to be asked and the procedures used to answer them. Putnam used multiple methods to subject to rigorous testing his hypotheses about what affects the success or failure of democratic institutions as they develop in diverse social environments to rigorous testing, and found the weight of the evidence favored.
This principle has several features worthy of elaboration. Assumptions underlying the inferences made should be clearly stated and justified.
Moreover, choice of design should both acknowledge potential biases and plan for implementation challenges. Estimates of error must also be made. Claims to knowledge vary substantially according to the strength of the research design, theory, and control of extraneous variables and by systematically ruling out possible alternative explanations.
Although scientists always reason in the presence of uncertainty, it is critical to gauge the magnitude of this uncertainty. In the physical and life sciences, quantitative estimates of the error associated with conclusions are often computed and reported. In the social sciences and education, such quantitative measures are sometimes difficult to generate; in any case, a statement about the nature and estimated magnitude of error must be made in order to signal the level of certainty with which conclusions have been drawn.
To make valid inferences, plausible counterexplanations must be dealt with in a rational, systematic, and compelling way. Well-known research designs e. In reporting, too, it is important to clarify that rival hypotheses are possible and that conclusions are not presented as if they were gospel.
A cell biologist, for example, might unintentionally place select heart cells with a slight glimmer into an experimental group and others into a control group, thus potentially biasing the comparison between the groups of cells.
The potential for a biased—or unfair—comparison arises because the shiny cells could differ systematically from the others in ways that affect what is being studied.
Selection bias is a pervasive problem in the social sciences and education research. To illustrate, in studying the effects of class-size reduction, credentialed teachers are more likely to be found in wealthy school districts that have the resources to reduce class size than in poor districts.
This fact raises the possibility that higher achievement will be observed in the smaller classes due to factors other than class size e. For example, U. One popular explanation of this finding was that the effect was due to their schooling and the emphasis on ecology in U. A third prevalent class of alternative interpretations contends that an outcome was biased by the measurement used.
For example, education effects are often judged by narrowly defined achievement tests that focus on factual knowledge and therefore favor direct-instruction teaching tech-.
Multiple achievement measures with high reliability consistency and validity accuracy help to counter potential measurement bias. The Tennessee class-size study was designed primarily to eliminate all possible known explanations, except for reduced class size, in comparing the achievement of children in regular classrooms against achievement in reduced size classrooms. It did this. Complications remained, however. Freedom, rights and technology Why Free Software is Important.
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Even the researchers whose results were exaggerated to popularize this idea do not embrace it any more, and argue that the true challenge is learning how best to target corrective information B. Nyhan Proc. Natl Acad. USA , e; Meanwhile, evidence is growing that rebuttals can be effective. Science deniers — whether on vaccines, evolution or climate — all draw on the same flawed reasoning techniques: cherry-picking evidence, relying on conspiracy theories and fake experts, engaging in illogical reasoning and insisting that science must be perfect.
A landmark study P. Betsch Nature Hum. Admittedly, this study did not examine whether this works in person or with hard-core deniers. When I attended the Flat Earth International Conference in , I chose to say nothing on the first day, although it was hard to keep my mouth shut when I heard that Antarctica is a wall of ice that keeps the sea from flowing off Earth.
I let them speak, then followed up with questions once the dialogue was rolling. If they shared a conspiracy theory, I asked why they trusted the evidence for it. And trust has to be built, with patience, respect, empathy and interpersonal connections. Because I spent the first day listening, even committed deniers were interested in what I had to say.
Lemaitre et al. Coronavirus misinformation, and how scientists can help to fight it.