Regularized Structural Equation Modeling to Detect Measurement Bias: Evaluation of Lasso, Adaptive Lasso, and Elastic Net. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Liang, Xinya; Jacobucci, Ross
• Is Part Of: Structural equation modeling. Volume 27:Issue 5(2020)
• Page Start: 722
• Page End: 734
• Publication Date: 2020-09-02
• Subjects: Adaptive lasso -- elastic net -- measurement bias detection -- regularized structural equation modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1693273
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Correct detection of measurement bias could help researchers revise models or refine psychological scales. Measurement bias detection can be viewed as a variable-selection problem, in which biased items are optimally selected from a set of items. This study investigated a number of regularization methods: ridge, lasso, elastic net (enet) and adaptive lasso (alasso), in comparison with maximum likelihood estimation (MLE) for detecting various forms of measurement bias in regard to a continuous violator using restricted factor analysis. Particularly, complex structural equation models with relatively small sample sizes were the study focus. Through a simulation study and an empirical example, results indicated that the enet outperformed other methods in small samples for identifying biased items. The alasso yielded low false positive rates for non-biased items outside of a high number of biased items. MLE performed well for the overall estimation of biased items.