Nonrecursive Latent Variable Models Under Misspecification

• In Event: Measurement and Modeling Issues in Structural Equation Modeling

Abstract

A problem central to structural equation modeling is measurement model specification error and its propagation within nonrecursive latent variable models. Full information estimation techniques such as maximum likelihood are consistent when the model is correctly specified and the sample size large enough; however, any misspecification within the model can affect parameter estimates in other parts of the model. The goal of this study was to compare the accuracy and efficiency of (a) Jöreskog and Sörbom’s (2007) TSLS estimator (JS-TSLS), (b) Bollen’s (1996a; 1996b; 2001) TSLS estimator (KB-2SLS), (c) Bayesian, (d) Maximum Likelihood (ML), and the Latent Variable Score (LVS; Croon, 2002; Jöreskog and Sörbom,1999) approaches in nonrecursive latent variable models in small to moderate sample size conditions.

Author

• Larry R. Price, Texas State University