Open AccessCombined Approach to Multi-Informant Data Using Latent Factors and Latent Classes: Trifactor Mixture Model
Author(s) -
Eun Sook Kim,
Nathaniel P. von der Embse
Publication year - 2020
Publication title -
educational and psychological measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.819
H-Index - 95
eISSN - 1552-3888
pISSN - 0013-1644
DOI - 10.1177/0013164420973722
Subject(s) - mixture model , psychology , latent class model , perspective (graphical) , latent variable , factor analysis , latent variable model , structural equation modeling , econometrics , statistics , computer science , artificial intelligence , mathematics
Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common and unique perspectives of multiple informants on targets using latent factors and simultaneously take into account potential heterogeneity of targets using latent classes. We demonstrate this model using student self-rated and teacher-rated academic behaviors ( N = 24,094). Model specification and testing procedures are explicated in detail. Methodological and practical issues in conducting the trifactor mixture analysis are discussed.