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Alexander Robitzsch; Oliver Lüdtke – Structural Equation Modeling: A Multidisciplinary Journal, 2025
The random intercept cross-lagged panel model (RICLPM) decomposes longitudinal associations between two processes X and Y into stable between-person associations and temporal within-person changes. In a recent study, Bailey et al. demonstrated through a simulation study that the between-person variance components in the RICLPM can occur only due…
Descriptors: Longitudinal Studies, Correlation, Time, Simulation
C. J. Van Lissa; M. Garnier-Villarreal; D. Anadria – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there…
Descriptors: Multivariate Analysis, Structural Equation Models, Open Source Technology, Computation
Walter P. Vispoel; Hyeri Hong; Hyeryung Lee – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM…
Descriptors: Structural Equation Models, Self Concept Measures, Self Esteem, Generalizability Theory
Zsuzsa Bakk; Roberto Di Mari; Jennifer Oser; Jouni Kuha – Structural Equation Modeling: A Multidisciplinary Journal, 2022
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Computation, Measurement
Cheung, Mike
W.-L. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Guidelines, Multivariate Analysis
Peugh, James L.; Enders, Craig K. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Cluster sampling results in response variable variation both among respondents (i.e., within-cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e.,…
Descriptors: Structural Equation Models, Monte Carlo Methods, Multivariate Analysis, Sampling
Sass, Daniel A.; Smith, Philip L. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Structural equation modeling allows several methods of estimating the disattenuated association between 2 or more latent variables (i.e., the measurement model). In one common approach, measurement models are specified using item parcels as indicators of latent constructs. Item parcels versus original items are often used as indicators in these…
Descriptors: Structural Equation Models, Item Analysis, Error of Measurement, Measures (Individuals)

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