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ERIC Number: EJ791592
Record Type: Journal
Publication Date: 2008-Apr
Pages: 26
Abstractor: ERIC
Reference Count: 24
ISSN: ISSN-1070-5511
A Comparison of Four Estimators of a Population Measure of Model Fit in Covariance Structure Analysis
Zhang, Wei
Structural Equation Modeling: A Multidisciplinary Journal, v15 n2 p301-326 Apr 2008
A major issue in the utilization of covariance structure analysis is model fit evaluation. Recent years have witnessed increasing interest in various test statistics and so-called fit indexes, most of which are actually based on or closely related to F[subscript 0], a measure of model fit in the population. This study aims to provide a systematic investigation about the performance of four available estimators of F[subscript 0]: a conventional estimator based on noncentral chi-square approximation; a newly proposed estimator that does not assume noncentral chi-square approximation; and two variations of the newly proposed estimator. A Monte Carlo simulation study is conducted to examine how these four estimators of F[subscript 0] perform across varying model misspecifications, data distributions, model sizes, and sample sizes. The results show that, under normality, all four quantities estimate F[subscript 0] equally well; and, under nonnormality, the three newly proposed estimators outperform the conventional estimator. Issues related to these findings are discussed.(Contains 6 tables.)
Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A