Limited Information Parameter Estimates for Latent or Mixed Manifest and Latent Variable Models

Abstract

We argue for separate analyses of the measurement and structural portions of latent or mixed manifest and latent variable models. We present limited information (single equation) procedures for estimating parameters in the structural portion of these models. These include parameter estimation procedures for recursive or nonrecursive relations, and procedures for testing zero-effect hypotheses. We then compare full and limited information estimates in a Monte Carlo analysis of sample correlation matrices that contained structural model misspecifications. Both full and limited information estimates identified misspecified nonzero effects reasonably well. However, limited information estimates were far superior in detecting misspecified zero-effect hypotheses. We recommend limited information parameter estimation procedures over full information techniques for (a) testing specific causal hypotheses and (b) locating specific structural model misspecifications.