Resampling Based Bias Correction for Small Sample SEM. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Dhaene, Sara; Rosseel, Yves
• Is Part Of: Structural equation modeling. Volume 29:Issue 5(2022)
• Page Start: 755
• Page End: 771
• Publication Date: 2022-09-03
• Subjects: Bias correction -- Bootstrap -- Jackknife -- REML -- small sample bias; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2022.2057999
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract: Structural Equation Models (SEMs) are typically estimated via Maximum Likelihood. Grounded in large sample theory, estimates are prone to finite sample bias. Although Restricted Maximum Likelihood (REML) can alleviate this bias, its applicability is constrained to SEMs that are mathematically equivalent to mixed effect models. Via Monte Carlo simulations, we explored whether resampling based corrections could serve as viable, more broadly applicable alternatives. Results indicate that Bootstrap and Jackknife corrections effectively attenuate small sample bias, at the expected expense of an increase in variability. Similar conclusions are drawn with respect to a more recently proposed analytic approach by Ozenne et al., which was included for comparison. For all corrective methods, caution is advised when dealing with non-normal data and/or low reliability.