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Next steps in Bayesian structural equation models: comments on, variations of, and extensions to Muthén and
1Program in Educational Psychology, City University of New York Graduate Center, 365 Fifth Avenue, New York, NY 10016, USA. drindskopf@gc.cuny.edu
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.
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