An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA

Autor/inn/en	Brosseau-Liard, Patricia E.; Savalei, Victoria; Li, Libo
Titel	An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA
Quelle	In: Multivariate Behavioral Research, 47 (2012) 6, S.904-930 (27 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1080/00273171.2012.715252
Schlagwörter	Structural Equation Models; Goodness of Fit; Maximum Likelihood Statistics; Robustness (Statistics); Comparative Analysis; Computation; Affective Measures + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit
Abstract	The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have been proposed, but the theoretical and empirical differences between the 2 have not been explored. In this article, we investigate the behavior of these 2 corrections. We show that the virtually unknown correction due to Li and Bentler (2006), which we label the sample-corrected robust RMSEA, is a consistent estimate of the population ML RMSEA yet drastically reduces bias due to nonnormality in small samples. On the other hand, the popular correction implemented in several SEM programs, which we label the population-corrected robust RMSEA, has poor properties because it estimates a quantity that decreases with increasing nonnormality. We recommend the use of the sample-corrected RMSEA with nonnormal data and its wide implementation. (Contains 1 table, 10 figures and 1 footnote.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10