Problems of sampling error and accumulated rounding error in canonical variate analysis are discussed. A new technique is presented which appears to be superior to canonical variate analysis when the ratio of variables to sampling units is greater than one to ten. Examples are presented. (Author/JKS)

Two potentially useful tests for homogeneity of variance--the jackknife test and the Box test--are described and compared. Recommendations for the use of these techniques and computational examples of each are provided. (Author/JKS)

This paper presents the generalized symmetric means approach to the estimation of population covariances, complete with derivations and examples. Particular attention is paid to the problem of missing data, which is handled very naturally in the incidence sampling framework. (CTM)

Six behavioral statistics textbooks listed by their publishers as "bestsellers" during 1982 were reviewed by the author. The intent was to detect the presence of and to discuss the nature of some theoretical inferential inaccuracies, misinterpretations and errors. (Author/LMO)

Using data from the High School and Beyond, this article presents statistical procedures to estimate the effect of dropping out of high school on cognitive achievement test scores. Each sampled dropout is matched to a student remaining in the same school. Methods for addressing the possible omission of covariates are described. (BS)

It is shown that using formulae for the estimation of sampling errors based on simple random sampling, when a design actually involves cluster sampling, can lead to serious underestimation of error. Jackknife and balanced repeated replication are recommended as techniques for dealing with this problem. (Author/CTM)

The effects of certain characteristics (e.g., sample design) of National Assessment of Educational Progress (NAEP) data on statistical analysis techniques are considered. Ignoring special features of NAEP data and proceeding with a standard analysis can produce inferences that underestimate the true variability and overestimate the true degrees of…

In studies involving matrix sampling on items, it is often the case that there are too few responses per individual to use latent trait item response models. A model is formulated wherein individual level variability appears as independent error within the cells of a cross classification of demographic variables. (JKS)