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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.
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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.
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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.
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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-.
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Multiple achievement measures with high reliability consistency and validity accuracy help to counter potential measurement bias. The Tennessee class-size study was designed primarily to eliminate all possible known explanations, except for reduced class size, in comparing the achievement of children in regular classrooms against achievement in reduced size classrooms.
It did this. Complications remained, however. About ten percent of students moved out of their originally assigned condition class size , weakening the design because the comparative groups did not remain intact to enable strict comparisons. However, most scholars who subsequently analyzed the data e.
Students in classes of students outperformed their peers in larger classes, on average, by a small margin. Replication and generalization strengthen and clarify the limits of scientific conjectures and theories.
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By replication we mean, at an elementary level, that if one investigator makes a set of observations, another investigator can make a similar set of observations under the same conditions. Replication in this sense comes close to what psychometricians call reliability—consistency of measurements from one observer to another, from one task to another parallel task, from one occasion to another occasion.
At a somewhat more complex level, replication means the ability to repeat an investigation in more than one setting from one laboratory to another or from one field site to a similar field site and reach similar conclusions. To be sure, replication in the physical sciences, especially with inanimate objects, is more easily achieved than in social science or education; put another way, the margin of error in social science replication is usually.
The role of contextual factors and the lack of control that characterizes work in the social realm require a more nuanced notion of replication. Nevertheless, the typically large margins of error in social science replications do not preclude their identification. Having evidence of replication, an important goal of science is to understand the extent to which findings generalize from one object or person to another, from one setting to another, and so on.
To this end, a substantial amount of statistical machinery has been built both to help ensure that what is observed in a particular study is representative of what is of larger interest i. Nonstatistical means of generalization e. Subsequent applications, implementations, or trials are often necessary to assure generalizability or to clarify its limits.
For example, since the Tennessee experiment, additional studies of the effects of class size reduction on student learning have been launched in settings other than Tennessee to assess the extent to which the findings generalize e. In the social sciences and education, many generalizations are limited to particular times and particular places Cronbach, Consider, again, the Tennessee class-size research; it was undertaken in a set of schools that had the desire to participate, the physical facilities to accommodate an increased number of classrooms, and adequate teaching staff.
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Not surprisingly, most researchers studying California have. We argue in Chapter 2 that a characteristic of scientific knowledge accumulation is its contested nature. Here we suggest that science is not only characterized by professional scrutiny and criticism, but also that such criticism is essential to scientific progress.
Scientific studies usually are elements of a larger corpus of work; furthermore, the scientists carrying out a particular study always are part of a larger community of scholars. Reporting and reviewing research results are essential to enable wide and meaningful peer review. Results are traditionally published in a specialty journal, in books published by academic presses, or in other peer-reviewed publications.