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Chunhua 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
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Daniel McNeish; Patrick D. Manapat – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models.…
Descriptors: Factor Analysis, Goodness of Fit, Hierarchical Linear Modeling, Benchmarking
Strauss, Christian L. L. – ProQuest LLC, 2022
In many psychological and educational applications, it is imperative to obtain valid and reliable score estimates of multilevel processes. For example, in order to assess the quality and characteristics of high impact learning processes, one must compute accurate scores representative of student- and classroom-level constructs. Currently, there…
Descriptors: Scores, Factor Analysis, Models, True Scores
Minju Hong – ProQuest LLC, 2022
Reliability indicates the internal consistency of a test. In educational studies, reliability is a key feature for a test. Researchers have proposed many traditional reliability estimates, such as coefficient alpha and coefficient omega. However, traditional reliability indices do not deal with the data hierarchy, even though the multilevel…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Factor Structure, Test Reliability
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Son, Sookyoung; Hong, Sehee – Educational and Psychological Measurement, 2021
The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Structural Equation Models, Groups
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Ekpenyong, John A.; Owan, Valentine J.; Ogar, Joseph O.; Undie, John A. – Cogent Education, 2022
Research has assessed the diverse characteristics of principals and teachers in analysing students' educational outcomes at various levels. However, these studies often focus on the cognitive domain of learning, ignoring the affective and psychomotor aspects. Bridging this gap, we used hierarchical linear regression to link two inputs of teachers…
Descriptors: Hierarchical Linear Modeling, Outcomes of Education, Secondary School Students, Foreign Countries