Propensity Score Analysis - Fraser, Mark W

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Auteur(s) : Fraser, Mark W - Guo, Shenyang

Editeur : Sage Publications

Collection : Advanced Quantitative Techniques in the Social Sciences

Langue : Anglais

Parution : 30/06/2014

Format : Moyen, de 350g à 1kg

Nombre de pages : 448.0

Expédition : 980

Dimensions : 24 x 19.2 x 3.0

ISBN : 1452235007

Résumé :

With a strong focus on practical applications, the authors explore various strategies for employing PSA. In addition, they discuss the use of PSA with alternative types of data and limitations of PSA under a variety of constraints. Unlike the existing textbooks on program evaluation and causal inference, Propensity Score Analysis delves into statistical concepts, formulas, and models in the context of a robust and engaging focus on application.

Biographie:

Shenyang Guo, PhD, is the Kuralt Distinguished Professor at the School of Social Work, University of North Carolina. The author of numerous articles on statistical methods and research reports in child welfare, child mental health services, welfare, and health care, Guo has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, and program evaluation. He has given many invited workshops on statistical methods-including event history analysis and propensity score matching-at the NIH Summer Institute, Children's Bureau, and at conferences of the Society of Social Work and Research. He led the data analysis planning for the National Survey of Child and Adolescent Well-Being (NSCAW) longitudinal analysis.

Sommaire:
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 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 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 Chapter 6: Propensity Score Subclassification The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Chapter 7: Propensity Score Weighting Weighting Estimators Chapter 8: Matching Estimators Methods of Matching Estimators Overview of the Stata Program nnmatch Chapter 9: Propensity Score Analysis With Nonparametric Regression Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments 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 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 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