Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences) Review

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(More customer reviews)I am a fourth year PhD student at University of Toronto and this textbook has been a very useful source on estimating treatment effects as part of my research.
I think it is particularly good at (1) explaining the differences between Heckman-type selection models and matching models; and at (2) summarizing different matching approaches including propensity score, matching estimators, and non-parametric techniques. Moreover, it provides details on how to perform sensitivity analysis and perform diagnostics for each technique. I liked that each chapter explains briefly the theoretical foundations of every approach and provides detailed guidance using examples on how to run STATA or R code.


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Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations of PSA and illustrates how it can be used for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems related to, strategies for employing, and the limitations of PSA. Unlike the existing textbooks on program evaluation, Propensity Score Analysis delves into statistical concepts, formulas, and models underlying the application.Key Features
Presents key information on model derivations
Summarizes complex statistical arguments but omits their proofs
Links each method found in this book to specific Stata programs and provides empirical examples
Guides readers using two conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman econometric model of causality
Contains examples representing real challenges commonly found in social behavioral research
Utilizes data simulation and Monte Carlo studies to illustrate key points
Presents descriptions of new statistical approaches necessary for understanding the four evaluation methods incorporated throughout the text

Intended AudienceThis text is appropriate for graduate and doctoral students taking Evaluation, Quantitative Methods, Survey Research, and Research Design courses across business, social work, public policy, psychology, sociology, and health/medicine disciplines.

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Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Very good introductory text for nonparametric regression. This book first gives motivation for the processes, outlines the methods of smoothing, and then covers various regression techniques. Not particularly mathematical, although some linear algebra knowledge is necessary. Very accessible as a first course. Does not go very in-depth into the theory of any of the methods, however.

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Incorporates mixed-effects modeling techniques for more powerful and efficient methodsThis book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques.The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis.Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices.With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

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Event History Modeling: A Guide for Social Scientists (Analytical Methods for Social Research) Review

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(More customer reviews)This is excerpted and slightly modified from a published review (Perspectives on Politics Volume 3, June 2005) I wrote of this book, which I like quite a bit and regularly recommend and assign to my political science graduate students.
Event History Modeling: A Guide for Social Scientists provides a broad and in-depth introduction to duration analysis for political scientists and for social scientists in general. This book will instantly become the go-to guide for most political scientists interested in event history analysis and should become a staple on syllabi for graduate courses for years to come. The authors cover a broad range of important topics, employing a combination of mathematical detail and verbal discussion; important concepts are illustrated with examples using political science data that readers can download. For a book on statistical methods, Event History Modeling is quite readable and the authors do a commendable job of presenting a great variety of issues and making clear recommendations.

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Here is an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences.The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus.Recent and critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit.The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.

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