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Haiyan Liu; Wen Qu; Zhiyong Zhang; Hao Wu – Grantee Submission, 2022
Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested…
Descriptors: Bayesian Statistics, Structural Equation Models, Statistical Inference, Statistical Distributions
MacCallum, Robert C.; Edwards, Michael C.; Cai, Li – Psychological Methods, 2012
Muthen and Asparouhov (2012) have proposed and demonstrated an approach to model specification and estimation in structural equation modeling (SEM) using Bayesian methods. Their contribution builds on previous work in this area by (a) focusing on the translation of conventional SEM models into a Bayesian framework wherein parameters fixed at zero…
Descriptors: Structural Equation Models, Bayesian Statistics, Computation, Expertise
Scheerens, Jaap; Luyten, Hans; van den Berg, Stéphanie M.; Glas, Cees A. W. – Educational Research and Evaluation, 2015
As expectations of the economic impact of educational attainment are soaring (Hanushek & Woessmann, 2009) and conjectures about successful national educational reforms (Mourshed, Chijioke, & Barber, 2010) are welcomed by educational policy-makers in many countries, a careful assessment of the empirical evidence for these kinds of claims is…
Descriptors: Foreign Countries, Educational Attainment, Educational Change, Comparative Education
Edwards, Michael C. – Psychometrika, 2010
Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show…
Descriptors: Structural Equation Models, Markov Processes, Factor Analysis, Item Response Theory
Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a "true" or population path model was then…
Descriptors: Sample Size, Monte Carlo Methods, Structural Equation Models, Markov Processes
Wirth, R. J.; Edwards, Michael C. – Psychological Methods, 2007
The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA)…
Descriptors: Structural Equation Models, Markov Processes, Item Response Theory, Factor Analysis
Hoshino, Takahiro; Shigemasu, Kazuo – Applied Psychological Measurement, 2008
The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…
Descriptors: Monte Carlo Methods, Markov Processes, Factor Analysis, Computation
Goldstein, Harvey; Bonnet, Gerard; Rocher, Thierry – Journal of Educational and Behavioral Statistics, 2007
The Programme for International Student Assessment comparative study of reading performance among 15-year-olds is reanalyzed using statistical procedures that allow the full complexity of the data structures to be explored. The article extends existing multilevel factor analysis and structural equation models and shows how this can extract richer…
Descriptors: Foreign Countries, Structural Equation Models, Markov Processes, Factor Analysis
Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2006
In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…
Descriptors: Structural Equation Models, Bayesian Statistics, Markov Processes, Monte Carlo Methods