Resampling Based Bias Correction for Small Sample SEM

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 equiva...

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Bibliographic Details
Published inStructural equation modeling Vol. 29; no. 5; pp. 755 - 771
Main Authors Dhaene, Sara, Rosseel, Yves
Format Journal Article
LanguageEnglish
Published Hove Routledge 03.09.2022
Psychology Press
Subjects
Bias correction
Bootstrap
Jackknife
REML
small sample bias
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Summary: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.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2022.2057999