Multilevel Factor Mixture Modeling
A Tutorial for Multilevel Constructs
Bibliographic Data
| ID | 12424898 |
|---|---|
| Authors | Chunhua Cao (0000-0003-3084-598X, University of Alabama), Yan Wang (0009-0009-9633-0023, University of Massachusetts Lowell), Eunsook Kim (0000-0003-1054-1735, University of South Florida) |
| Year | 2024 |
| Volume | 32 |
| Issue | 1 |
| Pages | 155-171 |
| Publication date | 2024-04-05 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2024.2332257 |
| OpenAlex | W4393999512 |
| Language | EN |
| Citations received | 2 |
| References cited | 45 |
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
Confirmatory factor analysis · Econometrics · Factor (programming language · Multilevel model · Programming language · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Psychology
The multilevel latent covariate model
People are variables too
Construct Meaning in Multilevel Settings
Classroom Climate and Contextual Effects
Model selection and psychological theory
On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA
Reliability estimation in a multilevel confirmatory factor analysis framework.
Latent class and finite mixture models for multilevel data sets
Multilevel Latent Profile Analysis With Covariates
Investigating population heterogeneity with factor mixture models.
Measurement Invariance Testing with Many Groups
Back to Basics with Mixed-Effects Models
Multilevel Latent Class Analysis
Finite Mixture Models
Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters
A new look at the statistical model identification
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling
Estimating the Dimension of a Model
Application of Model-Selection Criteria to Some Problems in Multivariate Analysis
Testing the number of components in a normal mixture
Mixed Effects of Item Parceling on Performance of Factor Mixture Modeling
Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling
Multiple Group Analysis in Multilevel Data Across Within-Level Groups
Testing Latent Mean Differences Between Observed and Unobserved Groups Using Multilevel Factor Mixture Models
Testing Measurement Invariance Across Unobserved Groups
Detecting Social Desirability Bias Using Factor Mixture Models
Multilevel Factor Analysis
Distinguishing Between Latent Classes and Continuous Factors with Categorical Outcomes
H-Work Project
Multilevel Latent Class Models
The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |