In a balanced design (i.e. a design in which all cells have the same number of observations), if the effects in the linear model are random and normally distributed, the distribution of the ratio of any sum of squares (s.s.) in the ANOVA to the expected value of its mean square (m.s.) has a [chi][superscript 2]-distribution. In this note, we…

This paper describes structural equation modeling (SEM) in comparison with another overarching analysis within the general linear model (GLM) analytic family: canonical correlation analysis. The uninitiated reader can gain an understanding of SEM's basic tenets and applications. Latent constructs discovered via a measurement model are explored and…

The sensitivity analysis of structural equation models when minor perturbation is introduced is investigated. An influence measure based on the general case weight perturbation is derived for the generalized least squares estimation, and an influence measure is developed for the special case deletion perturbation scheme. (Author/SLD)

A computationally efficient three-stage estimator of thresholds and covariance structure parameters is prepared for analysis of structural equation models with polytomous variables. The method is based on partition maximum likelihood and generalized least squares estimation. An analysis of questionnaire responses of 739 young adults illustrates…

A two-stage approach based on the rationale of maximum likelihood and generalized least-squares methods is developed to analyze the general structural equation model for continuous and polytomous variables. Some illustrative examples and a small simulation study (50 replications) are reported. (SLD)

A Monte Carlo study explores the robustness assumption in structural equation modeling of using a full information normal theory generalized least-squares estimation procedure on Type I censored data. The efficacy of the following proposed alternate estimation procedures is assessed: asymptotically distribution free estimator and a latent…