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Zsuzsa Bakk – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A standard assumption of latent class (LC) analysis is conditional independence, that is the items of the LC are independent of the covariates given the LCs. Several approaches have been proposed for identifying violations of this assumption. The recently proposed likelihood ratio approach is compared to residual statistics (bivariate residuals…
Descriptors: Goodness of Fit, Error of Measurement, Comparative Analysis, Models
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Emma Somer; Carl Falk; Milica Miocevic – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and…
Descriptors: Scores, Structural Equation Models, Comparative Analysis, Sample Size
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Li, Xin; Beretvas, S. Natasha – Structural Equation Modeling: A Multidisciplinary Journal, 2013
This simulation study investigated use of the multilevel structural equation model (MLSEM) for handling measurement error in both mediator and outcome variables ("M" and "Y") in an upper level multilevel mediation model. Mediation and outcome variable indicators were generated with measurement error. Parameter and standard…
Descriptors: Sample Size, Structural Equation Models, Simulation, Multivariate Analysis
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Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
Descriptors: Correlation, Models, Vertical Organization, Predictor Variables
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Chen, Qi; Kwok, Oi-Man; Luo, Wen; Willson, Victor L. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Growth mixture modeling (GMM) is a relatively new technique for analyzing longitudinal data. However, when applying GMM, researchers might assume that the higher level (nonrepeated measure) units (e.g., students) are independent from each other even though it might not always be true. This article reports the results of a simulation study…
Descriptors: Longitudinal Studies, Data Analysis, Models, Monte Carlo Methods
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Raykov, Tenko; Penev, Spiridon – Structural Equation Modeling: A Multidisciplinary Journal, 2010
A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…
Descriptors: Reliability, Evaluation, Models, Intervals
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Forero, Carlos G.; Maydeu-Olivares, Alberto; Gallardo-Pujol, David – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally…
Descriptors: Factor Analysis, Models, Least Squares Statistics, Computation