Estimation Problems with Type I Censored Response Distributions in Structural Equation Modeling

Abstract

The use of simultaneous linear structural equation modeling has become a popular method for creating and verifying theoretical models. This paper explores, through the use of a Monte Carlo study, the robustness assumption in structural equation modeling of using a full information normal theory generalized least-squares estimation procedure (NTGLS) on Type I censored distributed data. The paper then assesses the efficacy of two proposed alternate estimation procedures; (1) Asymptotically Distribution Free estimator (ADF), and (2) a latent projection Tobit estimator (TOBIT) as a means of providing better estimates when distributions are asymmetrical because of censoring. Results of the Monte Carlo studies indicate that when asymmetry increases in distributions, because of censoring of the distributions, both NTGLS and ADF estimators provide less than optimal model estimates, while the TOBIT estimator afforded efficient and stable estimates even with large asymmetrical distributions.

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