Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)

Presents a necessary and sufficient condition for the equivalence of structural-equation models that is applicable to models with parameter restrictions and models that may or may not fulfill assumptions of the rules. Illustrates the application of the approach for studying model equivalence. (SLD)

As psychologists increasingly employ more elaborate and comprehensive data collection schemes, sophisticated analytic techniques will play an ever more important role in understanding behavioral data. This paper outlines one such promising technique, Tucker's three-mode factor analysis, which enables the researcher to explore new taxonomic…

In this article, a case-deletion procedure is proposed to detect influential observations in a nonlinear structural equation model. The key idea is to develop the diagnostic measures based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. An one-step pseudo approximation is proposed to reduce the…

The dangers of overlooking time-honored cautions in the making causal interpretations of data analyses from correlational studies when using highly sophisticated computer programs and their associated techniques are discussed. (JKS)

Procedures for simultaneous confirmatory factor analysis in several populations are useful in a variety of problems. This is demonstrated with examples involving missing data, comparison of part correlations between groups, testing the equality of regression weights between groups with multiple indicators of each variable, and the formulation of…

A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent…