Multilevel Factor Mixture Modeling: A Tutorial for Multilevel Constructs
被引:0
|
作者:
Cao, Chunhua
论文数: 0引用数: 0
h-index: 0
机构:
Univ Alabama, Tuscaloosa, AL USA
Univ Alabama, Dept Educ Studies Psychol Res Methodol & Counselin, 520 Colonial Dr, Tuscaloosa, AL 35401 USAUniv Alabama, Tuscaloosa, AL USA
Cao, Chunhua
[1
,4
]
Wang, Yan
论文数: 0引用数: 0
h-index: 0
机构:
Univ Massachusetts Lowell, Lowell, MA USAUniv Alabama, Tuscaloosa, AL USA
Wang, Yan
[2
]
Kim, Eunsook
论文数: 0引用数: 0
h-index: 0
机构:
Univ S Florida, Tampa, FL USAUniv Alabama, Tuscaloosa, AL USA
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
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.
机构:
Univ Toronto, Dept Stat Sci, Ontario Power Bldg, 700 Univ Ave, 9th Floor, Toronto, ON M5G 1Z5, CanadaGeorgia State Univ, Maurice R Greenberg Sch Risk Sci, 35 Broad St NW, Atlanta, GA 30303 USA