In industrial acceptance sampling one frequently makes use of operating characteristic curves to describe the discriminating power of a particular sampling plan. Similarly, it is possible to demonstrate the selective efficiency of a test battery in terms of (a) the Applicant's Operating Characteristic (A.O.C.); (b) the Selector's Operating Characteristic (S.O.C.). The A.O.C. determines the chance of selection by means of a test for any given level of true ability. The S.O.C. connects functionally probability of success on the criterion with the predictor scores of a battery. For the case of a normal bivariate distribution the exact mathematical expressions of the OC curves are derived in terms of the correlation coefficientρ, the cut-off points α andβ, and the predictor and criterion scoresX andY (in standard measures). The Efficiency IndexH is defined as the percentage of successful subjects gained by the use of a test battery, taking chance selection as a yardstick for comparison. Its optimum, for fixedρ and α, is derived. The distribution law of the criterion scores of selectees is deduced and its first four moments are shown to depart little from normality for cases usually encountered in practice. A “Quality-Gain” diagram graphically illustrates the improvements secured. Another simple device, the “Cost-Utility” diagram, explains to management the full implications of selecting personnel by means of a test battery. Neither of the diagrams requires an understanding of the correlation coefficient. The confidence belt of the OC curves, the standard error of the mean criterion score of selectees and the standard error of the predicted number of successful applicants are determined. Finally, the full theory is applied in detail to a real test battery.
KeywordsTest Battery Sampling Plan Full Theory Bivariate Distribution Normal Bivariate Distribution
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- 1.McClelland, W. Selection for secondary education. London: Univ. of London Press, 1942.Google Scholar
- 2.Bittner, R. H., and Wilder, C. E. Expectancy tables: a method of interpreting correlation coefficients.J. exp. Educ., 1946,14, 245–252.Google Scholar
- 3.Bingham, W. V. Great expectations.Personnel Psych., 1949,2, 397–404.Google Scholar
- 4.Taylor, H. C., and Russell, J. T. The relationship of validity coefficients in the practical effectiveness of tests in selection: Tables and Discussions.J. appl. Psych., 1939,23, 565–578.Google Scholar
- 5.Brogden, H. E. When testing pays off.Personnel Psych., 1949,2, 170–183.Google Scholar
- 6.Jarrett, R. F. Per cent increase in output of selected personnel as an index of test efficiency.J. appl. Psych., 1948,32, 135–145.Google Scholar
- 7.Berkson, J. “Cost-utility” as a measure of the efficiency of a test.J. Amer. statist. Ass., 1947,42, 246–55.Google Scholar
- 8.Kendall, M. G. The advanced theory of statistics, Vol. II. London, Charles Griffin and Co., 1946.Google Scholar
- 9.Hald, A. Statistiske metoder, Tabel-Og formelsamling. Copenhagen, 1948, pp. 54–55.Google Scholar