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Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.

The development and introduction of a new technology to society can be viewed as an experimental process, full of uncertainties, which are only gradually reduced as the technology is employed. Unexpected developments may trigger an experimental process in which society must find new ways to deal with the uncertainties posed. This book explores how the experimental perspective determines what ethical issues new technologies raise and how it helps morally evaluate their introduction. Expert contributors highlight the uncertainties that accompany the process, identify the social and ethical challenges they give rise to, and propose strategies to manage them. Focusing on the introduction of new technologies and experimentation as ways to perceive new developments and changing contexts, a key theme of the book is how to approach the moral issues raised by new technology and understand the role of experimentation in exploring these matters.

While there are many books available on statistical analysis of data from experiments, there is significantly less available on the design, development, and actual conduct of the experiments. Laboratory Experiments in the Social Sciences summarizes how to design and conduct scientifically sound experiments, be they from surveys, interviews, observations, or experimental methods. The book encompasses how to collect reliable data, the appropriate uses of different methods, and how to avoid or resolve common problems in experimental research. Case study examples illustrate how multiple methods can be used to answer the same research questions and what kinds of outcome would result from each methodology. Sound data begins with effective data collection. This book will assist students and professionals alike in sociology, marketing, political science, anthropology, economics, and psychology. Provides a comprehensive summary of issues in social science experimentation, from ethics to design, management, and financing Offers'how-to'explanations of the problems and challenges faced by everyone involved in social science experiments Pays attention to both practical problems and to theoretical and philosophical arguments Defines commonalities and distinctions within and among experimental situations across the social sciences

How do we know which social and economic policies work, which should be continued, and which should be changed? Jim Manzi argues that throughout history, various methods have been attempted -- except for controlled experimentation. Experiments provide the feedback loop that allows us, in certain limited ways, to identify error in our beliefs as a first step to correcting them. Over the course of the first half of the twentieth century, scientists invented a methodology for executing controlled experiments to evaluate certain kinds of proposed social interventions. This technique goes by many names in different contexts (randomized control trials, randomized field experiments, clinical trials, etc.). Over the past ten to twenty years this has been increasingly deployed in a wide variety of contexts, but it remains the red-haired step child of modern social science. This is starting to change, and this change should be encouraged and accelerated, even though the staggering complexity of human society creates severe limits to what social science could be realistically expected to achieve. Randomized trials have shown, for example, that work requirements for welfare recipients have succeeded like nothing else in encouraging employment, that charter school vouchers have been successful in increasing educational attainment for underprivileged children, and that community policing has worked to reduce crime, but also that programs like Head Start and Job Corps, which might be politically attractive, fail to attain their intended objectives. Business leaders can also use experiments to test decisions in a controlled, low-risk environment before investing precious resources in large-scale changes -- the philosophy behind Manzi's own successful software company. In a powerful and masterfully-argued book, Manzi shows us how the methods of science can be applied to social and economic policy in order to ensure progress and prosperity.

This unique book is the first comprehensive guide to the discovery, analysis, and evaluation of natural experiments - an increasingly popular methodology in the social sciences. Thad Dunning provides an introduction to key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis. Surveying many examples of standard natural experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights both the strengths and potential weaknesses of these methods, aiding researchers in better harnessing the promise of natural experiments while avoiding the pitfalls. Dunning also demonstrates the contribution of qualitative methods to natural experiments and proposes new ways to integrate qualitative and quantitative techniques. Chapters complete with exercises and appendices covering specialized topics such as cluster-randomized natural experiments, make this an ideal teaching tool as well as a valuable book for professional researchers.

In Methods of Randomization in Experimental Design, author Valentim R. Alferes presents the main procedures of random assignment and local control in between-subjects experimental designs and the counterbalancing schemes in within-subjects or cross-over experimental designs. Alferes uses a pedagogical strategy that allows the reader to implement all randomization methods by relying on the materials given in the appendices and using common features included in most word processor software.A companion website provides downloadable IBM SPSS and R versions of SCRAED, a package that performs simple and complex random assignment in experimental design, including the 18 randomization methods presented in Chapters 2 and 3.

Laboratory Experiments in the Social Sciences is the only book providing core information for researchers about the ways and means to conduct experiments. Its comprehensive regard for laboratory experiments encompasses “how-to” explanations, investigations of philosophies and ethics, explorations of experiments in specific social science disciplines, and summaries of both the history and future of social science laboratories. No other book offers such a direct avenue to enlarging our knowledge in the social sciences.This collection of original chapters combines instructions and advice about the design of laboratory experiments in the social sciences with the array of other issues. While there are books on experimental design and chapters in more general methods books on design, theory, and ethical issues, no other book attempts to discuss the fundamental ideas of the philosophy of science or lays out the methods comprehensively or in such detail. Experimentation has recently prospered because of increasing interest in cross-disciplinary syntheses, and this book of advice, guidelines, and observations underline its potential and increasing importance.· Provides a comprehensive summary of issues in social science experimentation, from ethics to design, management, and financing· Offers'how-to'explanations of the problems and challenges faced by everyone involved in social science experiments· Pays attention to both practical problems and to theoretical and philosophical arguments· Defines commonalities and distinctions within and among experimental situations across the social sciences

Policy analysis has grown increasingly reliant on the random assignment experiment—a research method whereby participants are sorted by chance into either a program group that is subject to a government policy or program, or a control group that is not. Because the groups are randomly selected, they do not differ from one another systematically. Therefore any differences between the groups at the end of the study can be attributed solely to the influence of the program or policy. But there are many questions that randomized experiments have not been able to address. What component of a social policy made it successful? Did a given program fail because it was designed poorly or because it suffered from low participation rates? In Learning More from Social Experiments, editor Howard Bloom and a team of innovative social researchers profile advancements in the scientific underpinnings of social policy research that can improve randomized experimental studies. Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random assignment studies can be overcome by combining data from these studies with statistical methods from other research designs. Carolyn Hill, James Riccio, and Bloom profile a new statistical model that allows researchers to pool data from multiple randomized-experiments in order to determine what characteristics of a program made it successful. Lisa Gennetian, Pamela Morris, Johannes Bos, and Bloom discuss how a statistical estimation procedure can be used with experimental data to single out the effects of a program's intermediate outcomes (e.g., how closely patients in a drug study adhere to the prescribed dosage) on its ultimate outcomes (the health effects of the drug). Sometimes, a social policy has its true effect on communities and not individuals, such as in neighborhood watch programs or public health initiatives. In these cases, researchers must randomly assign treatment to groups or clusters of individuals, but this technique raises different issues than do experiments that randomly assign individuals. Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that arise from its use. He pays particular attention to the way in which the movement of individuals into and out of clusters over time complicates the design, execution, and interpretation of a study. Learning More from Social Experiments represents a substantial leap forward in the analysis of social policies. By supplementing theory with applied research examples, this important new book makes the case for enhancing the scope and relevance of social research by combining randomized experiments with non-experimental statistical methods, and it serves as a useful guide for researchers who wish to do so.