Using Propensity Scores in Quasi-Experimental Designs

Name: Using Propensity Scores in Quasi-Experimental Designs
Availability: InStock
ISBN: 9781452205267

ISBN-10: 1452205264

ISBN-13: 9781452205267

Edition: 2014

Authors: William M. Holmes

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Description:

The author covers a wider range of software that is used with doing such analysis, and presents how propensity scores can be used to address issues in analyzing data from quasi-experimental designs, and which techniques should be used, and when. This book will clearly help students to understand the underlying concepts behind statistics, when they are used and how they are used.

Book details

Copyright year: 2014
Publisher: SAGE Publications, Incorporated
Publication date: 7/23/2013
Binding: Paperback
Pages: 360
Size: 7.50" wide x 9.00" long x 0.75" tall
Weight: 1.562
Language: English

William Holmes is a faculty member at the University of Massachusetts at Boston in the College of Public and Community Services. He has evaluated criminal justice and community programs serving families, children, the abused, and those with substance abuse problems. He coauthored Portrait of Divorce, with Kay Kitson, which won the William Goode Award from the Family Section of the American Sociological Association. He coauthored Family Abuse: Consequences, Theories, and Responses , with Calvin Larsen and Sylvia Mignon. Dr. Holmes has conducted research funded by the U.S. Bureau of Justice Statistics, the National Institute of Justice, the National Institute of Mental Health, the National…



Preface


Approach of the Book


Example Data


The General Social Survey Panel


The Health and Retirement Study


Improving Knowledge


Acknowledgments


About the Author



Quasi-Experiments and Nonequivalent Groups


Experiments and Inference


The Classic Experiment


Quasi-Experiments and Inference


Threats to Valid Inference


Propensity Scores


Quasi-Experiments and Observational Studies


Cross-Sectional Designs


Pre-Post Comparison Groups


Dose-Response Designs


Panel Studies


Longitudinal Studies


Broken Experiments


Adequacy and Sufficiency of Causal Inference



Causal Inference Using Control Variables


Controlling Confoundedness


Matching as Controlling


Stratifying as Controlling


Weighting as Controlling


Adjusting as Controlling


Multivariate Models for Controlling


Selecting Control Variables


Theory-Selected Controls


Research-Selected Controls


Ad Hoc Controls


Pretest Controls


Misspecification in Causal Models


Consistency in Using Controls


Getting Consistent Estimates


Instrumental Variable Controls


Estimating With Instrumental Variables


Two-Stage Least Squares


Detecting Selection Bias


Removing Selection Bias


Checking for Misspecification


Summary



Causal Inference Using Counterfactual Designs


Controlled Experiments


Random Assignment


Criteria Assignment


Dropping Out


Challenges to Counterfactual Designs


Natural Experiments


Matching Samples


Propensity Matching


Key Variable Matching


Identifying Key Variables


Using Key Variables and Propensity Scores


Distance Matching


Assessing Matching Results


Sample Weighting


Adequacy and Sufficiency of Matching


Causal Inference With Matching



Propensity Approaches for Quasi-Experiments


Estimating Propensity Scores


Regression Estimation of Propensities


Logistic Estimation of Propensities


Discriminant Analysis Estimation


Linking Estimation and Analysis


Estimation Complications


Checking Imbalance Reduction


Standard Deviation Criteria


Percent Reduction


Clinical/Substantive Criteria


Graphical Criteria


Improving Imbalance Reduction


Propensity Score Uses


Matching


Stratifying


Regressing


Adequacy and Sufficiency of Propensity Estimates



Propensity Matching


One-to-One Matching


Matching Similar Propensities


Using Calipers


Using Distance Criteria


Dealing With Dropped Cases


Computer Programs for One-to-One Matching


One-to-Many Matching


Greedy Matching


Nongreedy Matching


Using Calipers


Using Distance Criteria


Managing Unequal Cases in Groups


Assessing Adequacy and Sufficiency of Matching


Summary



Propensity Score Optimized Matching


Full Matching


Optimizing Criteria


Optimizing Procedures


Optimization and Network Flow


Genetic Optimized Matching


Adequacy and Sufficiency of Optimized Solutions



Propensities and Weighted Least Squares Regression


Propensities as Weights


Weighting Options


Inverse Proportional Weighting


Reseated Inverse Proportional Weighting


Reseated Inverse Propensity Weighting


Augmented Inverse Propensity Weights


Matching Weights


Choosing Weights


Weighted Regression


Assessing Regression Results


Assessing Adequacy and Sufficiency of Weighting



Propensities and Covariate Controls


Controlling Options


Adjustment Options


Propensities Versus Time 1 Controls


Propensities and Time 1 Controls


Assessing Covariate Results


Assessing Adequacy and Sufficiency of Covariates



Use with Generalized Linear Models


Generalized Linear Models


Logistic Regression


Matched Data With GZLM


Weighted Data With GZLM


Covariate Data With GZLM


Strata and GZLM


Adequacy and Sufficiency of GZLM



Propensity with Correlated Samples


Paired Samples


Paired Sample t Test


Paired Sample ANOVA and ANCOVA


Repeated Measures


Pre-Post Comparisons


Generalized Estimation Equations


Panel Studies


Longitudinal Panels


Mixed Repeated Designs


Geographically Correlated Samples


Adjacent Geographic Units


Geographic Units Sharing Commonalities


Repeated Variable ANOVA


Traditional Repeated ANOVA


Propensity-Adjusted Repeated ANOVA


Cox Regression


Proportional Hazards and Quasi-Experiments


Adequacy and Sufficiency With Correlated Samples



Handling Missing Data


Identifying Missing Data


Imputing Missing Data


Monotone Selection


FCS Method


Propensity Imputation


Imbalanced Missing Data


Imputation of Missing Data


Propensity Estimation With Missing Data


Generalized Propensity Scores


Matching With Missing Data


Stratifying With Missing Data


Covariance Control With Missing Data


Weights With Missing Data



Repairing Broken Experiments


When Things Go Wrong


Incomplete Randomization


Differential Compliance


Differential Mortality


Differential Events


Differential Missing Data


Reactive Effects


Subject Communication


Strong Placebo Effects


Propensities and Breakdowns


Assessing the Damage


Presence of Impact


Nature of Impact


Strength of Impact


Implications of Impact


Developing a Strategy


Ex Post Facto Matching


Propensity Score Weighting


Principal Stratification


Instrumental Variables


Multiple Imputation


Getting Missing Data


Appendixes



Stata Commands for Propensity Use



R Commands for Propensity Use



SPSS Commands for Propensity Use



SAS Commands for Propensity Use


References


Author Index


Subject Index