The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative…

A new algorithm is used to test and describe the set of all possible solutions for any linear model of an empirical ordering derived from techniques such as additive conjoint measurement, unfolding theory, general Fechnerian scaling, and ordinal multiple regression. The algorithm is computationally faster and numerically superior to previous…

This paper shows that redundancy maximization with J. K. Johansson's extension can be accomplished via a simple iterative algorithm based on H. Wold's Partial Least Squares. The model and the iterative algorithm for the least squares approach to redundancy maximization are presented. (TJH)

This paper presents the results of three experiments studying routine problem-solving tasks in simple addition and subtraction. Indications are that children tend to solve such problems by internalized counting procedures which may be learned independently as a consequence of practice in problem solving. Brief descriptions of exploratory studies…

The absence of suitable external criteria is a recurrent problem for test, battery, and inventory developers in selecting items or tests for inclusion in final operational instruments. This report presents a computing algorithm developed for use when no adequate external selection criterion is available. The algorithm uses a multiple linear…

Contrary to Anderson (EJ 187 936), his rule for equation identification is a necessary but not sufficient condition; furthermore, the choice of two-stage or ordinary least squares depends on results and not on methodological properties of estimators. Modification of Anderson's rule and a means for choosing between estimates is offered. (Author/CP)

Canonical analysis of two convex polyhedral cones involves looking for two vectors whose square cosine is a maximum. New results about the properties of the optimal solution to this problem are presented. The convergence of an alternating least squares algorithm and properties of limits of calculated sequences are discussed. (SLD)

An empirical method called Predicted Error Sum of Squares (PRESS) is advanced and studied. This method is used to examine the cross-validated prediction accuracies of some popular algorithms for weighted predictor variables. The weighting methods that were considered were ordinary least squares, ridge regression, regression on principal…

The results of an algorithm which is designed to take a set of commonality coefficients, either real or manipulated, and, if possible, produce one or more sets of regressor correlations that are consistent with them are examined. A number of different ways of resolving the higher order commonality values into their lower orders were tried and the…

Commonality analysis is an attempt to understand the relative predictive power of the regressor variables, both individually and in combination. The squared multiple correlation is broken up into elements assigned to each individual regressor and to each possible combination of regressors. The elements have the property that the appropriate sums…

This report documents the theory, structure, and implementation of a performance processor (written in FORTRAN IV) that can accept performance demonstration data representing various levels of operator's skill and, under user control, analyze data to provide candidate performance measures and validation test results. The processor accepts two…

The technical problems involved in obtaining Bayesian model estimates for the regression parameters in m similar groups are studied. The available computer programs, BPREP (BASIC), and BAYREG, both written in FORTRAN, require an amount of computer processing that does not encourage regular use. These programs are analyzed so that the performance…