Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 3 |
Descriptor
Bayesian Statistics | 3 |
Computation | 3 |
Structural Equation Models | 3 |
Computer Software | 2 |
Comparative Analysis | 1 |
Computer Simulation | 1 |
Diseases | 1 |
Interaction | 1 |
Measurement | 1 |
Sample Size | 1 |
Simulation | 1 |
More ▼ |
Source
Structural Equation Modeling:… | 3 |
Author
Lee, Sik-Yum | 3 |
Song, Xin-Yuan | 3 |
Cai, Jing-Heng | 1 |
Pan, Jun-Hao | 1 |
Tang, Nian-Sheng | 1 |
Xia, Ye-Mao | 1 |
Publication Type
Journal Articles | 3 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider…
Descriptors: Structural Equation Models, Bayesian Statistics, Comparative Analysis, Computation
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
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