Distinguishing Adding and Averaging Models in a Personnel Selection Task: When Missing Information Matters

Abstract

Two experiments were conducted to determine if the manner in which information is combined to make personnel evaluations might depend on the correlation between the pieces of information. Introductory psychology students evaluated hypothetical employees for promotion to a supervisory position in a computer-systems department based on personnel test scores in management and computer programming. The management scores were from either a high or a low validity test. In addition some employees were missing test scores. The correlation between the test scores varied across groups of subjects (−.84, .00, .84). Responses were consistent with a relative-weight averaging model in the positive-correlation condition, but not in the negative- or zero-correlation conditions. A constant-weight averaging model seemed most appropriate in the zero-correlation condition, but it was not really possible to distinguish an additive from a constant-weight averaging strategy in the negative-correlation condition. The test to distinguish these strategies involves comparing the ratings of single-test employees to those of two-test employees. However, subjects developed various strategies for rating the single-test employees which tended to invalidate the test. Configural strategies were evident in the negative- and zero-correlation conditions for employees with lopsided score combinations. Employees with one test score were penalized particularly in the negative-correlation condition and when the available score was low in validity.