Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling

Autor/in	Hoshino, Takahiro
Titel	Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling
Quelle	In: Psychometrika, 72 (2007) 4, S.535-549 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-007-9007-2
Schlagwörter	Structural Equation Models; Simulation; Observation; Behavioral Science Research; Social Science Research; Regression (Statistics); Evaluation Methods; Computation + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Beobachtung; Social scientific research; Sozialwissenschaftliche Forschung; Regression; Regressionsanalyse
Abstract	Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a "doubly robust"-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10