title: An Improved Correction for Range Restricted Correlations Under Extreme, Monotonic Quadratic Nonlinearity and Heteroscedasticity.
abstract: Standardized tests are frequently used for selection decisions, and the validation of test scores remains an important area of research. This paper builds upon prior literature about the effect of nonlinearity and heteroscedasticity on the accuracy of standard formulas for correcting correlations in restricted samples. Existing formulas for direct range restriction require three assumptions: (1) the criterion variable is missing at random; (2) a linear relationship between independent and dependent variables; and (3) constant error variance or homoscedasticity. The results in this paper demonstrate that the standard approach for correcting restricted correlations is severely biased in cases of extreme monotone quadratic nonlinearity and heteroscedasticity. This paper offers at least three significant contributions to the existing literature. First, a method from the econometrics literature is adapted to provide more accurate estimates of unrestricted correlations. Second, derivations establish bounds on the degree of bias attributed to quadratic functions under the assumption of a monotonic relationship between test scores and criterion measurements. New results are presented on the bias associated with using the standard range restriction correction formula, and the results show that the standard correction formula yields estimates of unrestricted correlations that deviate by as much as 0.2 for high to moderate selectivity. Third, Monte Carlo simulation results demonstrate that the new procedure for correcting restricted correlations provides more accurate estimates in the presence of quadratic and heteroscedastic test score and criterion relationships.
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Champaign, IL, 61820 , USA. sculpepp@illinois.edu.
authors.forename: Steven Andrew
authors.initials: SA
authors.lastname: Culpepper
mesh_terms: D015982:Bias; D004521:Educational Measurement; D006801:Humans; D009010:Monte Carlo Method; D010560:Personnel Selection; D011594:Psychometrics; D013223:Statistics as Topic
keywords: college admissions; heteroscedasticity; nonlinearity; personnel selection; range restriction