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Incorporating Prior Theory in Covariance Structure Analysis: A Bayesian Approach

Published online by Cambridge University Press:  01 January 2025

Claes Fornell and
Roland T. Rust
Claes Fornell*
Affiliation:
The Universty of Michigan
Roland T. Rust
Affiliation:
Vanderbilt University
*
Requests for reprints should be sent to Claes Fornell, Graduate School of Business Administration, University of Michigan, Ann Arbor, M1 48109.
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Abstract

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.

Keywords

Akaike's Information criterion (AIC)model compariosnBayesian cross-validated likelihood (BCVL)

Information

Type
Original Paper
Information
Psychometrika , Volume 54 , Issue 2 , June 1989, pp. 249 - 259
Copyright
Copyright © 1989 The Psychometric Society

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