A Bayesian Approach for Multigroup Nonlinear Factor Analysis

Abstract

The main purpose of this article is to develop a Bayesian approach for a general multigroup nonlinear factor analysis model. Joint Bayesian estimates of the factor scores and the structural parameters subjected to some constraints across different groups are obtained simultaneously. A hybrid algorithm that combines the Metropolis-Hastings algorithm and the Gibbs sampler is implemented to produce these joint Bayesian estimates. It is shown that this algorithm is computationally efficient. The Bayes factor approach is introduced for comparing models under various degrees of invariance across groups. The Schwarz criterion (BIC), a simple and useful approximation of the Bayes factor, is calculated on the basis of simulated observations from the Gibbs sampler. Efficiency and flexibility of the proposed Bayesian procedure are illustrated by some simulation results and a real-life example.