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Johan Lyrvall; Zsuzsa Bakk; Jennifer Oser; Roberto Di Mari – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Simulation
Lubke, Gitta; Tueller, Stephen – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent…
Descriptors: Classification, Models, Statistical Analysis, Comparative Analysis
Tracy, Allison J.; Erkut, Sumru; Porche, Michelle V.; Kim, Jo; Charmaraman, Linda; Grossman, Jennifer M.; Ceder, Ineke; Garcia, Heidie Vazquez – Structural Equation Modeling: A Multidisciplinary Journal, 2010
In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from…
Descriptors: Ethnicity, Racial Identification, Adolescents, Item Response Theory
Beauducel, Andre; Herzberg, Philipp Yorck – Structural Equation Modeling: A Multidisciplinary Journal, 2006
This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Classification, Sample Size

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