“This book represents an important contribution to the literature on research designs that may be implemented when randomized experiments are not feasible or are limited. Covering the full range of design alternatives, this is the first text that fuses important recent advances from statistics and econometrics into Campbell’s pioneering approach. A notable feature is Reichardt’s careful attention to issues that arise in each research design; he offers innovative design and analysis strategies that can minimize these issues and permit the strongest possible conclusions from research. Clearly written, this text is an outstanding choice for courses focusing on key issues of research design, and is suitable for graduate students with only a basic background in statistics. Established researchers will find it to be a valuable reference that offers new insights for strengthening research designs so that they yield the most credible possible evidence.”

—Stephen G. West, PhD, Department of Psychology, Arizona State University


“This book not only compiles a comprehensive list of methods on quasi-causal design, but also problematizes causes of biases even in perfectly executed experimental and quasi-experimental designs. The author’s take on the ways in which quasi-experiments could potentially render better results than randomized experiments is refreshing and important. Professors will want to discuss this book in their classes. I highly recommend it for students and even more experienced researchers—the author highlights the fundamentals of each approach along with its strengths and limitations.”

—Manuel González Canché, PhD, Higher Education, Quantitative Methods, and Education Policy Divisions, Graduate School of Education, University of Pennsylvania


“A 'must read.' After a thorough presentation of the strengths of randomized experiments, Reichardt provides a remarkably up-to-date review and synthesis of current thinking on the best, most useful alternatives. Notably, he uses simple and direct language to explain key concepts of the 'counterfactual outcomes' approach for estimating causal effects. While written for a graduate and professional audience, the book does not require advanced statistical knowledge. It is ideal as a supplemental text for a graduate course on experimental design and the analysis of variance, or as the primary source for a seminar on quasi-experimental design and analysis. Practicing scientists will want to own this book to understand how best to confront analytic issues in their empirical research and interpret their results.”

—Keith F. Widaman, PhD, Distinguished Professor, Graduate School of Education, University of California, Riverside


“Reichardt provides an expansive treatment of quasi-experimental designs, in the tradition of Shadish, Cook, and Campbell. This book includes up-to-date discussions of propensity scores, modern missing data procedures, and instrumental variables. Students will appreciate the numerous examples that help clarify the concepts. I would recommend this book for any graduate research methods class—I will certainly use it myself.”

—Felix J. Thoemmes, PhD, Department of Human Development and Department of Psychology, Cornell University


“Ever wonder how to best design a quasi-experimental study? This book will help you figure out which research questions best lend themselves to this type of experimental design. Have you collected data from a quasi-experiment and now want to make sure that you correctly analyze and interpret the results? This book addresses the assumptions that must be met, potential pitfalls, and statistical considerations. As an educational psychologist who teaches students across disciplines, I recommend this book as an up-to-date reference on quasi-experimental designs. Randomized controlled trials are not always feasible, for many reasons, so the way this text is framed is actually more useful for fields like education and the social sciences.”

—Meagan C. Arrastia-Chisholm, PhD, Department of Psychology, Counseling, and Family Therapy, Valdosta State University