This book offers interesting examples from various sciences and explains how to use modern approaches in quasi-experimentation to derive reliable estimates of treatment effects under demanding field conditions. Leading expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest-posttest, non-equivalent groups, regression discontinuity, and time series interruption designs. He analyzes their respective strengths and weaknesses, offering practical advice for their use. Comparing quasi-experiments with randomized experimental methods, Reichardt discusses when and why the former may be the best choice. Modern methods for removing biases from treatment effect estimates, as well as tactics for handling missing data and non-compliance with treatment assignments, are described. Throughout the book, mathematical equations are translated into words to facilitate accessibility. In addition to discussing standard quasi-experiments, the book also provides a comprehensive typology of quasi-experiment design options to assist the reader in formulating the best research design for the conditions of any particular study.