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. Reichardt compares quasi-experiments to randomized experiments and discusses when and why the former might be a better choice. Modern moethods 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.

Cause and effect --Threats to validity -- Randomized experiments -- One-group posttest-only designs -- Pretest-posttest designs -- Nonequivalent group designs -- Regression discontinuity designs -- Interrupted time-series designs -- A typology of comparisons -- Methods of design elaboration -- Unfocused design elaboration and pattern matching -- Principles of design and analysis for estimating effects.