Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares

Least squares and maximum likelihood

Factor Analysis of Ordinal Variables with Full Information Maximum Likelihood

The basic idea of factor analysis is the following. For a given set of manifest variables x1, . . . , xp one wants to find a set of latent variables ξ1, . . . , ξk, fewer in number than the manifest variables, that contain essentially the same information. The latent variables are supposed to account for the dependencies among the manifest variables in the sense that if the latent variables are...

Least squares methods in maximum likelihood problems

It is well known that the GaussNewton algorithm for solving nonlinear least squares problems is a special case of the scoring algorithm for maximizing log likelihoods. What has received less attention is that the computation of the current correction in the scoring algorithm in both its line search and trust region forms can be cast as a linear least squares problem. This is an important observ...

Weighted Least Squares Estimation with Missing Data

Pairwise likelihood estimation for factor analysis models with ordinal data

Pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and tted both in an exploratory and con rmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and three-stage limited information estimation methods, namely the robust unweighted least squares (3S-RULS) a...