Comparison of computational methods for high dimensional item factor analysis
Tihomir Asparouhov, Bengt Muth · 2012
In this article we conduct a simulation study to compare several methods for estimating conrmatory and exploratory item factor analysis using the software programs Mplus and IRTPRO. When the number of factors is bigger than three or four the standard numerical integration methodology used for computing the maximum-likelihood estimates is intractable due to the exponentially large number of integration points needed to compute the likelihood. Several methods have been developed recently to overcome these computational problems however they have not been directly compared previously. In this paper we present a simulation study to compare maximum likelihood estimation based on Montecarlo integration, maximum likelihood estimation based on Metropolis-Hastings Robbins-Monro algorithm, maximum likelihood estimation based on two-tier integration, Bayesian estimation and the weighted least square estimation.