Literature DB >> 22962887

Next steps in Bayesian structural equation models: comments on, variations of, and extensions to Muthén and Asparouhov (2012).

David Rindskopf1.   

Abstract

Muthén and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By keeping parameters restricted to intervals (such as loadings between -.3 and .3 to produce small loadings), frequentists using standard structural equation modeling software can do something similar to what a Bayesian does by putting prior distributions on these parameters.

Mesh:

Year:  2012        PMID: 22962887     DOI: 10.1037/a0027130

Source DB:  PubMed          Journal:  Psychol Methods        ISSN: 1082-989X


  4 in total

1.  Empirical Bayes MCMC estimation for modeling treatment processes, mechanisms of change, and clinical outcomes in small samples.

Authors:  Timothy J Ozechowski
Journal:  J Consult Clin Psychol       Date:  2014-02-10

2.  Testing gender invariance of the hospital anxiety and depression scale using the classical approach and Bayesian approach.

Authors:  Ted C T Fong; Rainbow T H Ho
Journal:  Qual Life Res       Date:  2013-12-04       Impact factor: 4.147

3.  Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA.

Authors:  Huub Hoofs; Rens van de Schoot; Nicole W H Jansen; IJmert Kant
Journal:  Educ Psychol Meas       Date:  2017-05-23       Impact factor: 2.821

4.  Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).

Authors:  Ozlem Ozkok; Michael J Zyphur; Adam P Barsky; Max Theilacker; M Brent Donnellan; Frederick L Oswald
Journal:  Front Psychol       Date:  2019-09-20
  4 in total

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