Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the…

The purpose of this study is to develop statistical tests for within cluster homogeneity when objects are scored on binary variables. (DEP)

Simulated test data (N=864 artificial data sets) with four different error conditions were used to study the recovery characteristics of the beta-flexible clustering method. Conditions under which the beta-flexible method provides good recovery are discussed. (SLD)

A clustering technique is described, the objective of which is to detect deviant subpopulations which deviate from a primary subpopulation in a well defined direction. (Author/DEP)

Five external criteria were used to evaluate the extent of recovery of the true structure in a hierarchical clustering solution. The results of the study indicated that the Hubert and Arabie adjusted Rank index was best suited to the task of comparison across hierarchy levels. (Author/LMO)

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…

A method of typological analysis was applied to computer-generated 96-item questionnaire data for 100 cases, under a variety of conditions to analyze both the item-level and score-level. The results showed a considerable advantage of score-level approach in the number, size, and replicability of clusters recovered. (DEP)

When performing a classification study, it is often useful to leave a residue of unclassified entities to be analyzed separately. Using an interactional paradigm, theoretical reasoning for this approach is outlined. A procedure--RESIDAN--for conducting a classification analysis using a residue is described, and empirical data are provided. (TJH)

A Monte Carlo study evaluated the effectiveness of three rules of classifying objects into clusters: nearest neighbor classification; nearest centroid assignment; and quadratic discriminant analysis. Results suggest that the nearest neighbor rule is a useful tool for assessing the validity of the clustering procedure of J. H. Ward (1963). (SLD)

Two versions of a decision rule for determining the most appropriate number of clusters on the basis of a correlation matrix are presented, applied, and compared with three other decision rules. The new rule is efficient for determining the number of clusters on the surface level for multilevel data. (SLD)

A Monte Carlo study was made of the recovery of cluster structure in binary data by five hierarchical techniques, with a view to finding which data structure factors influenced recovery and to determining differences between clustering methods with respect to these factors. (LMO)

Compares two basic variants of the sorting method: single-sort and multiple sort. The nature of individual differences in sorting, as well as sex differences, were also investigated. Stimulus materials were the 15 mutually exclusive kinship terms selected by Wallace and Atkins (1960). (RC)