An individual differences model for multidimensional scaling is outlined in which individuals are assumed differentially to weight the several dimensions of a common “psychological space”. A corresponding method of analyzing similarities data is proposed, involving a generalization of “Eckart-Young analysis” to decomposition of three-way (or higher-way) tables. In the present case this decomposition is applied to a derived three-way table of scalar products between stimuli for individuals. This analysis yields a stimulus by dimensions coordinate matrix and a subjects by dimensions matrix of weights. This method is illustrated with data on auditory stimuli and on perception of nations.

Measures of test parsimony and factor parsimony are defined. Minimizing their weighted sum produces a general rotation criterion for either oblique or orthogonal rotation. The quartimax, varimax and equamax criteria are special cases of the expression. Two new criteria are developed. One of these, the parsimax criterion, apparently gives excellent results. It is argued that one of the most important factors bearing on the choice of a rotation criterion for a particular problem is the amount of information available on the number of factors that should be rotated.

An explicit solution is given to the problem of assigning relative lengths to the subtests of a test so as to maximize the correlation of the unit weight composite with a specified criterion when the total testing time is fixed. This solution is valid and unique whenever it specifies nonnegative times for all variables. A step-down procedure is suggested for cases in which some of the testing times are zero. This procedure does not necessarily provide an optimal allocation. However in examples studied it is found to provide near optimum results. Algorithms are also developed for the determination of the least total testing time required to attain specified multiple and composite correlations. A numerical example is given illustrating the use of the unit weight procedure in combination with the regression weight algorithm.

The problem of locating two sets of points in a joint space, given the Euclidean distances between elements from distinct sets, is solved algebraically. For error free data the solution is exact, for fallible data it has least squares properties.

Latent partition analysis enables an examination of the modal population categorizations of a set of items. The initial model proposed by Wiley is reformulated, extended and generalized. Firstly, the effects of judges are regarded as random, rather than fixed. In a second model, several latent partitions, rather than one, are allowed, resulting in a type of multidimensional approach. Finally, both the single and multiple partition models are shown to be specializations of a less restrictive model, called the category focusing model. In each of these three cases, properties implicit in the model are used to derive appropriate solution procedures.

Measures of individual differences are considered, and the application of the procedure to experimental work in concept formation is discussed. Several sets of data are used to demonstrate the characteristics of the techniques evolved.