Quasi-Experimentation: A Guide to Design and Analysis

Description

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. Dedication Series Editor’s Note Preface Contents 1 · Introduction 2 · Cause and Effect 3 · Threats to Validity 4 · Randomized Experiments 5 · One-Group Posttest‑Only Designs 6 · Pretest–Posttest Designs 7 · Nonequivalent Group Designs 8 · Regression Discontinuity Designs 9 · Interrupted Time‑Series Designs 10 · A Typology of Comparisons 11 · Methods of Design Elaboration 12 · Unfocused Design Elaboration and Pattern Matching 13 · Principles of Design and Analysis for Estimating Effects Appendix: The Problems of Overdetermination and Preemption Glossary References Author Index Subject Index About the Author