Bias and precision of continuous norms obtained using quantile regression

Resumen

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.

Áreas y palabras clave

Cómo citar (APA 7)

Crompvoets, E. A. V., Keuning, J., & Emons, W. (2019). Bias and precision of continuous norms obtained using quantile regression. https://doi.org/10.31234/osf.io/stc2b