An iterative technique for clustering criteria which retains optimum predictive efficiency

Abstract

In a personnel classification program, maximum predictive efficiency results from applying least-squares weights to a set of predictor tests to produce a separate composite score for each criterion. With a large, complex organization such as the Air Force, it is not feasible to compute composite scores to predict the success of every individual in every criterion situation. This paper describes an iterative technique, programmed for an electronic computer, that at each step reduces the number of criterion clusters and provides optimal weights for the tests. A cost table can be used to determine the appropriate balance between predictive efficiency and number of criterion clusters. Solution of a sample problem illustrates each step of the technique.

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