Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by…

A time-saving and space-saving algorithm is presented for computing the sums of squares and estimated cell means under the additive model in a two-way analysis of variance or covariance with unequal numbers of observations in the cells. The procedure is illustrated. (Author/JKS)

Covariance structures associated with linear structural equation models are discussed. Algorithms for computing maximum likelihood estimates (namely, the EM algorithm) are reviewed. An example of using likelihood ratio tests based on complete and incomplete data to improve the fit of a model is given. (SLD)

An implementation of the Gauss-Newton algorithm for the analysis of covariance structure that is specifically adapted for high-level computer languages is reviewed. This simple method for estimating structural equation models is useful for a variety of standard models, as is illustrated. (SLD)

The statistical power of analysis of covariance (ANCOVA) and its advantages over simple analysis of variance are examined in some experimental situations, and an algorithm is proposed for its proper application. In nonrandomized experiments, an ANCOVA is generally not a good approach. (SLD)