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Chuenjai Sukpan; Rebecca M. Kuiper – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Researchers are interested in the question which of the lagged effects is causally dominant receives considerable attention. However, currently used…
Descriptors: Causal Models, Psychological Studies, Multivariate Analysis, Cognitive Mapping
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
Walter P. Vispoel; Hyeryung Lee; Hyeri Hong – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the…
Descriptors: Structural Equation Models, Multivariate Analysis, Generalizability Theory, College Students
Dayoung Lee; Guangjian Zhang; Shanhong Luo – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time series data collected on the same individuals due to the development of new data collection methods such as smartphone applications and wearable sensors.…
Descriptors: Research Methodology, Affective Measures, Family Relationship, Multivariate Analysis
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
Minjung Kim; Christa Winkler; James Uanhoro; Joshua Peri; John Lochman – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Cluster memberships associated with the mediation effect are often changed due to the temporal distance between the cause-and-effect variables in longitudinal data. Nevertheless, current practices in multilevel mediation analysis mostly assume a purely hierarchical data structure. A Monte Carlo simulation study is conducted to examine the…
Descriptors: Hierarchical Linear Modeling, Mediation Theory, Multivariate Analysis, Causal Models
Abar, Beau; Loken, Eric – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Latent class models are becoming more popular in behavioral research. When models with a large number of latent classes relative to the number of manifest indicators are estimated, researchers must consider the possibility that the model is not identified. It is not enough to determine that the model has positive degrees of freedom. A well-known…
Descriptors: Probability, Statistical Bias, Multivariate Analysis, Models
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
Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…
Descriptors: Data, Structural Equation Models, Correlation, Data Analysis
Kaplan, David; Keller, Bryan – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This article examines the effects of clustering in latent class analysis. A comprehensive simulation study is conducted, which begins by specifying a true multilevel latent class model with varying within- and between-cluster sample sizes, varying latent class proportions, and varying intraclass correlations. These models are then estimated under…
Descriptors: Multivariate Analysis, Sample Size, Correlation, Models
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)