Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

Autor/inn/en	Yuan, Ke-Hai; Zhang, Zhiyong
Titel	Robust Structural Equation Modeling with Missing Data and Auxiliary Variables
Quelle	In: Psychometrika, 77 (2012) 4, S.803-826 (24 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9282-4
Schlagwörter	Structural Equation Models; Tests; Federal Aid; Psychometrics; Statistical Analysis; Testing; Scoring; Maximum Likelihood Statistics; Error of Measurement; Robustness (Statistics); National Longitudinal Survey of Youth + Suchen Sie Ihr Suchwort? Examination; Prüfung; Examen; Psychometry; Psychometrie; Statistische Analyse; Testdurchführung; Testen; Bewertung; Messfehler; Widerstandsfähigkeit
Abstract	The paper develops a two-stage robust procedure for structural equation modeling (SEM) and an R package "rsem" to facilitate the use of the procedure by applied researchers. In the first stage, M-estimates of the saturated mean vector and covariance matrix of all variables are obtained. Those corresponding to the substantive variables are then fitted to the structural model in the second stage. A sandwich-type covariance matrix is used to obtain consistent standard errors (SE) of the structural parameter estimates. Rescaled, adjusted as well as corrected and F-statistics are proposed for overall model evaluation. Using R and EQS, the R package "rsem" combines the two stages and generates all the test statistics and consistent SEs. Following the robust analysis, multiple model fit indices and standardized solutions are provided in the corresponding output of EQS. An example with open/closed book examination data illustrates the proper use of the package. The method is further applied to the analysis of a data set from the National Longitudinal Survey of Youth 1997 cohort, and results show that the developed procedure not only gives a better endorsement of the substantive models but also yields estimates with uniformly smaller standard errors than the normal-distribution-based maximum likelihood. (Contains 5 tables, 1 figure, and 11 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10