Reducing the Bias of Norm Scores in Non-Representative Samples: Weighting as an Adjunct to Continuous Norming Methods

Abstract

We investigated whether the accuracy of normed test scores derived from non-demographically representative samples can be improved by combining continuous norming methods with compensatory weighting of test results. To this end, we introduce Raking, a method from social sciences, to psychometrics. In a simulated reference population, we modeled a latent cognitive ability with a typical developmental gradient, along with three demographic variables that were correlated to varying degrees with the latent ability. We simulated five additional populations representing patterns of non-representativeness that might be encountered in the real world. We subsequently drew smaller normative samples from each population and used an one-parameter logistic Item Response Theory (IRT) model to generate simulated test results for each individual. Using these simulated data, we applied norming techniques, both with and without compensatory weighting. Weighting reduced the bias of the norm scores when the degree of non-representativeness was moderate, with only a small risk of generating new biases.