Multilevel Factor Mixture Modeling: A Tutorial for Multilevel Constructs

被引:0
|
作者
Cao, Chunhua [1 ,4 ]
Wang, Yan [2 ]
Kim, Eunsook [3 ]
机构
[1] Univ Alabama, Tuscaloosa, AL USA
[2] Univ Massachusetts Lowell, Lowell, MA USA
[3] Univ S Florida, Tampa, FL USA
[4] Univ Alabama, Dept Educ Studies Psychol Res Methodol & Counselin, 520 Colonial Dr, Tuscaloosa, AL 35401 USA
关键词
Heterogeneity; multilevel constructs; multilevel FMM; nonparametric approach; parametric approach; LATENT CLASS ANALYSIS; MEASUREMENT INVARIANCE; ANXIETY SENSITIVITY; HETEROGENEITY; NUMBER; INTERVENTIONS; PERFORMANCE; SELECTION;
D O I
10.1080/10705511.2024.2332257
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterogeneity at the within level, between level, or both levels. This tutorial focuses on explicating the model specification of multilevel FMM that considers the conceptualization of multilevel constructs. Empirical data sets are used to demonstrate the applications of multilevel FMM for within-level constructs, between-level constructs, and within- and between-level constructs. Detailed model specifications of integrating latent classes into multilevel constructs are provided. For modeling the heterogeneity at the between level, parametric and nonparametric approaches are compared both conceptually and substantively using demonstration data. The interpretations of results using multilevel FMM are also provided. The tutorial is concluded with a discussion of some important aspects of applying multilevel FMM.
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页码:155 / 171
页数:17
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