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Erik-Jan van Kesteren; Daniel L. Oberski – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this paper, we introduce…
Descriptors: Structural Equation Models, Computation, Graphs, Algorithms
van de Schoot, Rens; Hoijtink, Herbert; Hallquist, Michael N.; Boelen, Paul A. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Researchers in the behavioral and social sciences often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model resulting in an informative hypothesis. The questions they would like an answer to are "Is the hypothesis Correct" or "Is the hypothesis…
Descriptors: Bayesian Statistics, Structural Equation Models, Hypothesis Testing, Computer Software
Evermann, Joerg – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Multiple-group analysis in covariance-based structural equation modeling (SEM) is an important technique to ensure the invariance of latent construct measurements and the validity of theoretical models across different subpopulations. However, not all SEM software packages provide multiple-group analysis capabilities. The sem package for the R…
Descriptors: Structural Equation Models, Computer Software, Sample Size
A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates
Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…
Descriptors: Models, Statistical Analysis, Structural Equation Models, Factor Analysis
Leite, Walter L.; Zuo, Youzhen – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Among the many methods currently available for estimating latent variable interactions, the unconstrained approach is attractive to applied researchers because of its relatively easy implementation with any structural equation modeling (SEM) software. Using a Monte Carlo simulation study, we extended and evaluated the unconstrained approach to…
Descriptors: Monte Carlo Methods, Structural Equation Models, Evaluation, Researchers
Henseler, Jorg; Chin, Wynne W. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article…
Descriptors: Interaction, Least Squares Statistics, Computation, Prediction
van de Schoot, Rens; Hoijtink, Herbert; Dekovic, Maja – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Researchers often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model. It is currently not possible to test these so-called informative hypotheses in structural equation modeling software. We offer a solution to this problem using M"plus." The hypotheses are…
Descriptors: Structural Equation Models, Computer Software, Hypothesis Testing, Statistical Analysis
Stapleton, Laura M. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural…
Descriptors: Structural Equation Models, Simulation, Sampling, Longitudinal Studies
Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…
Descriptors: Structural Equation Models, Simulation, Computer Software, Least Squares Statistics
Song, Xin-Yuan; Lee, Sik-Yum – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Structural equation models are widely appreciated in behavioral, social, and psychological research to model relations between latent constructs and manifest variables, and to control for measurement errors. Most applications of structural equation models are based on fully observed data that are independently distributed. However, hierarchical…
Descriptors: Psychological Studies, Life Satisfaction, Job Satisfaction, Structural Equation Models
Lee, Sik-Yum; Song, Xin-Yuan; Tang, Nian-Sheng – Structural Equation Modeling: A Multidisciplinary Journal, 2007
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a…
Descriptors: Interaction, Structural Equation Models, Bayesian Statistics, Computation
Stapleton, Laura M.; Leite, Walter L. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
With increases in the use of structural equation modeling (SEM) in the social sciences, graduate course offerings in this statistical technique can be expected to increase. Knowledge of the content of current SEM course offerings may provide ideas to instructors developing new courses or enhancing current courses. This article discusses results…
Descriptors: Course Descriptions, Course Content, Social Sciences, Structural Equation Models

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