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Direct linkErik-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
Lee, Sik-Yum; Song, Xin-Yuan; Cai, Jing-Heng – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Analysis of ordered binary and unordered binary data has received considerable attention in social and psychological research. This article introduces a Bayesian approach, which has several nice features in practical applications, for analyzing nonlinear structural equation models with dichotomous data. We demonstrate how to use the software…
Descriptors: Bayesian Statistics, Structural Equation Models, Computer Software, Computation
Zhang, Zhiyong; McArdle, John J.; Wang, Lijuan; Hamagami, Fumiaki – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model…
Descriptors: Bayesian Statistics, Computer Software, Monte Carlo Methods, Multiple Regression Analysis
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
