Publication Date
| In 2025 | 1 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 2 |
Descriptor
| Hierarchical Linear Modeling | 2 |
| Measurement | 2 |
| Statistical Analysis | 2 |
| Data | 1 |
| Elementary School Students | 1 |
| Equations (Mathematics) | 1 |
| Factor Analysis | 1 |
| Mathematics | 1 |
| Models | 1 |
| Nonparametric Statistics | 1 |
| Simulation | 1 |
| More ▼ | |
Source
| Structural Equation Modeling:… | 2 |
Publication Type
| Journal Articles | 2 |
| Reports - Descriptive | 1 |
| Reports - Research | 1 |
Education Level
| Elementary Education | 1 |
| Grade 4 | 1 |
| Grade 5 | 1 |
| Grade 6 | 1 |
| Intermediate Grades | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkChunhua Cao; Yan Wang; Eunsook Kim – Structural Equation Modeling: A Multidisciplinary Journal, 2025
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…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Nonparametric Statistics, Statistical Analysis
Jak, Suzanne; Oort, Frans J.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…
Descriptors: Statistical Bias, Measurement, Structural Equation Models, Hierarchical Linear Modeling
