A Semiparametric Approach for Structural Equation Modeling with Ordinal Data. Issue 4 (4th July 2021)

• Record Type: Journal Article
• Authors: Vegelius, Johan; Jin, Shaobo
• Is Part Of: Structural equation modeling. Volume 28:Issue 4(2021)
• Page Start: 497
• Page End: 505
• Publication Date: 2021-07-04
• Subjects: SEMM -- adaptive Gauss-Hermite approximation -- EM -- direct maximization; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1848431
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: There is currently a lack of methods for non-linear structural equation modeling (NSEM) for non-parametric relationships between latent variables when data are ordinal. To this end, a semiparametric approach for flexible NSEMs without parametric forms is developed for ordinal data. An indirect application of a finite mixture of structural equation models (SEMM) is employed for modeling the conditional expected mean of endogenous latent variables. In this context, the latent classes are not to be interpreted as groups of observations belonging to those classes, rather they serve as means to model flexible non-linear functions as locally linear functions which together approximate a globally non-linear function. The proposed method is based on a hybrid of direct maximization and expectation-maximization algorithms. Two simulation studies are performed which show that parameter estimates are associated with low bias and a non-linear functional form is satisfactorily estimated using the proposed approach.