書籍詳細

書籍詳細




洋書

傾向スコア分析(第2版)

Propensity Score Analysis : Statistical Methods and Applications

2ND

(Advanced Quantitative Techniques in the Social Sciences ; : 11)

Guo, Shenyang   Fraser, Mark W.

Sage 2014/07
421 p. illustrations (some color) ; 24 cm.   
装丁: Hrd    装丁について
テキストの言語: ENG    出版国: GB
ISBN: 9781452235004
KCN: 1020058378
紀伊國屋書店 選定タイトル
標準価格:¥20,169(本体 ¥18,336)   
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納期について
DDC: 519.53
KDC: C11 社会調査法・統計学
関連書リスト: RM1415
SB2713 セイジ社 リサーチ・メソッド 新刊・好評書
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Annotation

Provides readers with a systematic review of the origins, history, and statistical foundations of PSA and illustrates how it can be used be for solving evaluation and causal-inference problems.

Full Description

Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.
Detailed information

Table of Contents

List of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index