Additive and non-additive models for an individual trend curve are examined, and five methods for fitting these to a set of individuals are described. It is suggested that classical fitting methods are more informative than latent curve methods, and commonly preferable. A limited study of the effect of time-structure is reported, and results on…

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of this model include: repeated measures designs; multiple matrix samples; multilevel latent variable models; multiple time series and variance and covariance component models. (Author)

A general model for two-level multivariate data, with responses possibly missing at random, is described. The model combines regressions on fixed explanatory variables with structured residual covariance matrices. The likelihood function is reduced to a form enabling computational methods for estimating the model to be devised. (Author)

It is shown that goodness-of-fit criteria developed for the evaluation of multivariate structural models can be applied to assist in evaluating the dimensionality of a test consisting of binary items, and correlative methods regularly used in factor analysis can be employed to diagnose causes of misfit. (Author)

Within a framework of general theory governing relations between models for structural analysis, the research activities of General Research On Understanding Psychometrics (GROUP) of the Ontario Institute for Studies in Education are discussed. (MT)

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)