The Performance of Ten Modified Rescaled Statistics as the Number of Variables Increases

Among test statistics for assessing overall model fit in structural equation modeling (SEM), the Satorra-Bentler rescaled statistic is most widely used when the normality assumption is violated. However, many researchers have found that tends to overreject correct models when the number of variables...

Full description

Saved in:
Bibliographic Details
Published in:Structural equation modeling 2018-05, Vol.25 (3), p.414-438
Main Authors: Yang, Miao, Jiang, Ge, Yuan, Ke-Hai
Format: Article
Language:English
Subjects:
Errors
large number of variables
Mathematical models
nonnormality
rescaled statistic
small sample size
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Among test statistics for assessing overall model fit in structural equation modeling (SEM), the Satorra-Bentler rescaled statistic is most widely used when the normality assumption is violated. However, many researchers have found that tends to overreject correct models when the number of variables (p) is large and/or the sample size (N) is small. Modifications of have been proposed, but few studies have examined their performance against each other, especially when p is large. This article systematically evaluates 10 corrected versions of . Results show that the Bartlett correction and a recently proposed rank correction perform better than others in controlling Type I error rates, according to their deviations from the nominal rate. Nevertheless, the performance of both corrections depends heavily on p in addition to N. As p becomes relatively large, none of the corrected versions can properly control Type I errors even when N is rather large.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2017.1389612