Publication Date
In 2025 | 1 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 8 |
Descriptor
Correlation | 8 |
Multivariate Analysis | 8 |
Structural Equation Models | 6 |
Evaluation Methods | 4 |
Sample Size | 3 |
Bayesian Statistics | 2 |
Computation | 2 |
Error of Measurement | 2 |
Intervals | 2 |
Measures (Individuals) | 2 |
Models | 2 |
More ▼ |
Source
Structural Equation Modeling:… | 8 |
Author
Alexander Robitzsch | 1 |
Beretvas, S. Natasha | 1 |
Blozis, Shelley A. | 1 |
Brennan, Mark | 1 |
Enders, Craig K. | 1 |
Ge, Xiaojia | 1 |
Horowitz, Amy | 1 |
James Ohisei Uanhoro | 1 |
Kaplan, David | 1 |
Keller, Bryan | 1 |
Leve, Leslie D. | 1 |
More ▼ |
Publication Type
Journal Articles | 8 |
Reports - Research | 4 |
Reports - Evaluative | 3 |
Reports - Descriptive | 1 |
Education Level
Secondary Education | 1 |
Audience
Location
Germany | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
James Ohisei Uanhoro – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean…
Descriptors: Bayesian Statistics, Structural Equation Models, Correlation, Monte Carlo Methods
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
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
Raykov, Tenko; Brennan, Mark; Reinhardt, Joann P.; Horowitz, Amy – Structural Equation Modeling: A Multidisciplinary Journal, 2008
A correlation structure modeling method for comparison of mediated effects is outlined. The procedure permits point and interval estimation of differences in mediator effects, and is useful with models postulating 1 or more predictor, intervening, or response variables that may also be latent constructs. The approach allows scale-free evaluation…
Descriptors: Multivariate Analysis, Comparative Analysis, Correlation, Structural Equation Models
Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction