Asymptotic is Better than Bollen-Stine Bootstrapping to Assess Model Fit: The Effect of Model Size on the Chi-Square Statistic. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Corrêa Ferraz, Raul; Maydeu-Olivares, Alberto; Shi, Dexin
• Is Part Of: Structural equation modeling. Volume 29:Issue 5(2022)
• Page Start: 731
• Page End: 743
• Publication Date: 2022-09-03
• Subjects: Bootstrap -- goodness of fit -- model fit -- structural equation modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2022.2053128
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract: Previous research on bootstrapped p -values for the chi-square test of model fit has been limited to small models (around 10 variables), revealing that these p -values are accurate provided the sample size is not too small. For small sample sizes ( N < 100), usual p -values, obtained using asymptotic methods, are more accurate. However, as the number of variables increases asymptotic p -values incorrectly suggest that models fit poorly. We investigate whether Bollen-Stine (1992) bootstrapped p -values can overcome this problem using normal and non-normal data. We found that as model size increases, Bollen-Stine bootstrapped p -values become too conservative and less accurate than asymptotic p -values obtained using robust methods (i.e., mean and variance corrections). Bollen-Stine p -values cannot be recommended to assess model fit.