Bias and Precision of Continuous Norms Obtained Using Quantile Regression. (September 2021)

• Record Type: Journal Article
• Authors: Crompvoets, Elise A. V.; Keuning, Jos; Emons, Wilco H. M.
• Is Part Of: Assessment. Volume 28:Number 6(2021)
• Page Start: 1735
• Page End: 1750
• Publication Date: 2021-09
• Subjects: bias -- precision -- continuous norming -- regression-based norming -- quantile regression; Psychological tests -- Periodicals; Psychodiagnostics -- Periodicals; Personality tests -- Periodicals; 150.287
• DOI: 10.1177/1073191120910201
• Languages: English
• ISSNs: 1073-1911

Abstract

Continuous norming is an increasingly popular approach to establish norms when the performance on a test is dependent on age. However, current continuous norming methods rely on a number of assumptions that are quite restrictive and may introduce bias. In this study, quantile regression was introduced as more flexible alternative. Bias and precision of quantile regression-based norming were investigated with (age-)group as covariate, varying sample sizes and score distributions, and compared with bias and precision of two other norming methods: traditional norming and mean regression-based norming. Simulations showed the norms obtained using quantile regression to be most precise in almost all conditions. Norms were nevertheless biased when the score distributions reflected a ceiling effect. Quantile regression-based norming can thus be considered a promising alternative to traditional norming and mean regression-based norming, but only if the shape of the score distribution can be expected to be close to normal.