Small-Sample Robust Estimators of Noncentrality-Based and Incremental Model Fit
Journal
Structural Equation Modeling : A Multidisciplinary Journal
ISSN
1070-5511
ISSN-Digital
1532-8007
Type
journal article
Date Issued
2009-01-22
Author(s)
Herzog, Walter
;
Boomsma, Anne
Abstract
Traditional estimators of fit measures based on the noncentral chi-square distribution (root mean square error of approximation [RMSEA], Steiger's ?, etc.) tend to overreject acceptable models when the sample size is small. To handle this problem, it is proposed to employ Bartlett's (1950), Yuan's (2005), or Swain's (1975) correction of the maximum likelihood chi-square statistic for the estimation of noncentrality-based fit measures. In a Monte Carlo study, it is shown that Swain's correction especially produces reliable estimates and confidence intervals for different degrees of model misspecification (RMSEA range: 0.000-0.096) and sample sizes (50, 75, 100, 150, 200). In the second part of the article, the study is extended to incremental fit indexes (Tucker-Lewis Index, Comparative Fit Index, etc.). For their small-sample robust estimation, use of Swain's correction is recommended only for the target model, not for the independence model. The Swain-corrected estimators only require a ratio of sample size to estimated parameters of about 2:1 (sometimes even less) and are thus strongly recommended for applied research. R software is provided for convenient use.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Psychology Press, Taylor & Francis Group
Publisher place
Philadelphia, Pa.
Volume
16
Number
1
Start page
1
End page
27
Pages
27
Subject(s)
Division(s)
Eprints ID
45460