Iterative Intercolumnar Correlation Classification (IICC) computes the correlation coefficients for the entries of every column of a matrix with those of every other column of the matrix. Iteration increases the size and validity of the object indices, reduces error in the indices, and increases homogeneity amongst them. (Author/BW)

A rapid method for hierarchically clustering the n objects of a matrix which portrays the interrelation of every object to every other object, where n equals any number up to 1,000 and even larger, is developed and discussed. Results compare favorably with those from other excellent methods. (Author/BJG)

Develops and illustrates a method for clustering hierarchically the interrelationships between many persons, as represented in a matrix of a thousand by a thousand. (RC)

A relatively reliable and valid hierarchy of clusters of objects is plotted from the highest column entries, exclusively, of a matrix of interassociations between the objects. Having developed out of a loose definition of types, the method isolates both loose and highly definitive types, and all those in between. (Author/RC)

A method of analysis is developed which accounts for whatever variance is present in the responses to test items, additive, configural or the two combined. The method is applied to successive column matrices which report frequencies or endorsements by categories of subjects to answer alternative sets of test items. Approaches are included which…