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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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Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying – Structural Equation Modeling: A Multidisciplinary Journal, 2013
The purpose of this study was to explore the influence of the number of targets specified on the quality of exploratory factor analysis solutions with a complex underlying structure and incomplete substantive measurement theory. Three Monte Carlo studies were performed based on the ratio of the number of observed variables to the number of…
Descriptors: Factor Analysis, Monte Carlo Methods, Sample Size, Measurement
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Jackson, Dennis L.; Voth, Jennifer; Frey, Marc P. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Determining an appropriate sample size for use in latent variable modeling techniques has presented ongoing challenges to researchers. In particular, small sample sizes are known to present concerns over sampling error for the variances and covariances on which model estimation is based, as well as for fit indexes and convergence failures. The…
Descriptors: Sample Size, Factor Analysis, Measurement, Models
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Kim, Eun Sook; Yoon, Myeongsun – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This study investigated two major approaches in testing measurement invariance for ordinal measures: multiple-group categorical confirmatory factor analysis (MCCFA) and item response theory (IRT). Unlike the ordinary linear factor analysis, MCCFA can appropriately model the ordered-categorical measures with a threshold structure. A simulation…
Descriptors: Measurement, Factor Analysis, Item Response Theory, Comparative Analysis
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Depaoli, Sarah – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…
Descriptors: Markov Processes, Factor Analysis, Statistical Bias, Evaluation Research
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Johnson, Emily C.; Meade, Adam W.; DuVernet, Amy M. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Confirmatory factor analytic tests of measurement invariance (MI) require a referent indicator (RI) for model identification. Although the assumption that the RI is perfectly invariant across groups is acknowledged as problematic, the literature provides relatively little guidance for researchers to identify the conditions under which the practice…
Descriptors: Measurement, Validity, Factor Analysis, Models
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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
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Flora, David B.; Curran, Patrick J.; Hussong, Andrea M.; Edwards, Michael C. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
A large literature emphasizes the importance of testing for measurement equivalence in scales that may be used as observed variables in structural equation modeling applications. When the same construct is measured across more than one developmental period, as in a longitudinal study, it can be especially critical to establish measurement…
Descriptors: Structural Equation Models, Item Response Theory, Measurement, Scores
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Vlachopoulos, Symeon P. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This study examined the extent of measurement invariance of the Basic Psychological Needs in Exercise Scale responses (BPNES; Vlachopoulos & Michailidou, 2006) across male (n = 716) and female (n = 1,147) exercise participants. BPNES responses from exercise participants attending private fitness centers (n = 1,012) and community exercise programs…
Descriptors: Psychological Patterns, Factor Structure, Measures (Individuals), Measurement
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Noar, Seth M. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Across a variety of disciplines and areas of inquiry, reliable and valid measures are a cornerstone of quality research. This is the case because to have confidence in the findings of our studies, we must first have confidence in the quality of our measures. This article briefly reviews the literature on scale development and provides an empirical…
Descriptors: Measures (Individuals), Factor Analysis, Structural Equation Models, Test Validity