The Statistical Analysis of Quasi-Experiments by Christopher H. Achen is a foundational work for social scientists grappling with the realities of imperfect data. While randomized experiments remain the gold standard for causal inference, most research in political science, sociology, economics, and public policy relies on nonrandomized designs. Achen demonstrates that quasi-experiments—studies with nonrandom treatment groups or censored samples—pose unique statistical challenges that traditional regression or matching techniques cannot fully solve. Through careful critique and innovative solutions, he shows how common assumptions of control variables and model specification often fail, leaving policy evaluations and empirical findings biased or misleading.The book develops practical alternatives. Achen introduces methods ranging from two-stage least squares to advanced single-equation instrumental variable estimators, extending them to contexts with heteroskedasticity, nonlinearities, and censored samples. Drawing on case studies such as school desegregation, college admissions, and pretrial release systems, he illustrates both the pitfalls of conventional analysis and the power of more appropriate estimators. Written with applied researchers in mind, **The Statistical Analysis of Quasi-Experiments** balances theoretical rigor with accessibility, offering tools that can be computed with widely available software. More than a technical manual, it is a call for greater care and intellectual honesty in social science, highlighting how better statistical practice can produce more reliable insights into pressing issues of law, policy, and society.This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1986.

The Statistical Analysis of Quasi-Experiments by Christopher H. Achen is a foundational work for social scientists grappling with the realities of imperfect data. While randomized experiments remain the gold standard for causal inference, most research in political science, sociology, economics, and public policy relies on nonrandomized designs. Achen demonstrates that quasi-experiments—studies with nonrandom treatment groups or censored samples—pose unique statistical challenges that traditional regression or matching techniques cannot fully solve. Through careful critique and innovative solutions, he shows how common assumptions of control variables and model specification often fail, leaving policy evaluations and empirical findings biased or misleading.The book develops practical alternatives. Achen introduces methods ranging from two-stage least squares to advanced single-equation instrumental variable estimators, extending them to contexts with heteroskedasticity, nonlinearities, and censored samples. Drawing on case studies such as school desegregation, college admissions, and pretrial release systems, he illustrates both the pitfalls of conventional analysis and the power of more appropriate estimators. Written with applied researchers in mind, **The Statistical Analysis of Quasi-Experiments** balances theoretical rigor with accessibility, offering tools that can be computed with widely available software. More than a technical manual, it is a call for greater care and intellectual honesty in social science, highlighting how better statistical practice can produce more reliable insights into pressing issues of law, policy, and society.This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1986.