Applying and Interpreting Mixture Distribution Latent State-Trait Models. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Litson, Kaylee; Thornhill, Carly; Geiser, Christian; Burns, G. Leonard; Servera, Mateu
• Is Part Of: Structural equation modeling. Volume 26:Issue 6(2019)
• Page Start: 931
• Page End: 947
• Publication Date: 2019-11-02
• Subjects: Latent state-trait -- mixture distribution modeling -- consistency -- occasion-specificity -- longitudinal modeling -- latent classes; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1575741
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Latent state-trait (LST) models are commonly applied to determine the extent to which observed variables reflect trait-like versus state-like constructs. Mixture distribution LST (M-LST) models relax the assumption of population homogeneity made in traditional LST models, allowing researchers to identify subpopulations (latent classes) with differing trait- and state-like attributes. Applications of M-LST models are scarce, presumably because of the analysis complexity. We present a step-by-step tutorial for evaluating M-LST models based on an application to mother, father, and teacher reports of children's inattention ( n = 811). In the application, we found three latent classes for mother and father reports and four classes for teacher reports. All reporter solutions contained classes with very low, low, and moderate levels of inattention. The teacher solution also contained a class with high inattention. Comparable mother and father (but not teacher) classes exhibited similar levels of trait and state variance.