Propensity Score Analysis of Complex Survey Data with Structural Equation Modeling: A Tutorial with Mplus

被引：18

作者：

Leite, Walter L. ^{[1
]}

Stapleton, Laura M. ^{[2
]}

Bettini, Elizabeth F. ^{[3
]}

机构：

[1] Univ Florida, Gainesville, FL 32611 USA

[2] Univ Maryland, College Pk, MD 20742 USA

[3] Boston Univ, Boston, MA 02215 USA

来源：

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL | 2019年 / 26卷 / 03期

关键词：

causal inference; latent confounders; potential outcomes framework; propensity score analysis; propensity score weighting; Rubin's Causal model; sensitivity analysis; structural equation modeling; KINDERGARTEN RETENTION POLICY; DOUBLY ROBUST ESTIMATION; REDUCE SELECTION BIAS; CAUSAL INFERENCE; SENSITIVITY-ANALYSIS; MULTIPLE TREATMENTS; MAXIMUM-LIKELIHOOD; DESIGN; STRATIFICATION; ADJUSTMENT;

D O I：

10.1080/10705511.2018.1522591

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Propensity score (PS) analysis aims to reduce bias in treatment effect estimates obtained from observational studies, which may occur due to non-random differences between treated and untreated groups with respect to covariates related to the outcome. We demonstrate how to use structural equation modeling (SEM) for PS analysis to remove selection bias due to latent covariates and estimate treatment effects on latent outcomes. Following the discussion of the design and analysis stages of PS analysis with SEM, an example is presented which uses the Mplus software to analyze data from the 1999 School and Staffing Survey (SASS) and 2000 Teacher Follow-up Survey (TFS) to estimate the effects teacher's participation in a network of teachers on the teacher's perception of workload manageability.

引用

页码：448 / 469

页数：22

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