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Using Propensity Scores in Quasi-Experimental Designs

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ISBN-10: 1452205264

ISBN-13: 9781452205267

Edition: 2014

Authors: William M. Holmes

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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.
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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…    

Approach of the Book
Example Data
The General Social Survey Panel
The Health and Retirement Study
Improving Knowledge
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
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
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
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
Stata Commands for Propensity Use
R Commands for Propensity Use
SPSS Commands for Propensity Use
SAS Commands for Propensity Use
Author Index
Subject Index