The structure of information matrices in latent-variable models is explicated, and the degree to which missing information can be recovered by exploring collateral variables for respondents is characterized. Results are illustrated in the context of item-response-theory models, and practical implications are discussed. (SLD)

This article shows how a multivariate approximate degrees of freedom procedure based on the Welch-James procedure as simplified by S. Johansen (1980) can be applied to the analysis of repeated measures designs without assuming covariance homogeneity. A Monte Carlo study illustrates the approach. (SLD)

A five by five covariance matrix representing longitudinal measurements at five occasions is used to illustrate that markedly different types of learning curves may generate indistinguishable covariance structures. An excellent fit of a simplex structure can be misleading. Common uses of covariance structure models for growth studies are…

Combining information to estimate standardized partial regression coefficients in a linear model is discussed. A combined estimate obtained from the pooled correlation matrix is proposed, and its large sample distribution is obtained. The method is generalized to handle a random effects model in which correlation parameters vary across studies.…

The EM algorithm is used to derive maximum likelihood estimates for item parameters of the two-parameter logistic item response curves. The observed information matrix is then used to approximate the covariance matrix of these estimates. Simulated data are used to compare the estimated and actual item parameters. (Author/BW)

When repeated observations are taken at equal time intervals, a simple form of a stationary time series structure may be fitted to the observations. Use of correction factors is discussed. A computer simulation method is used to investigate power advantages of fitting a serial correlation pattern to repeated observations. (TJH)