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Bias and precision of continuous norms obtained using quantile regression
Crompvoets,Elise Keuning,Jos Emons,Wilco
Crompvoets,Elise
Keuning,Jos
Emons,Wilco
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.
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Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
Date
2021
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Crompvoets_2020.pdf
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AGE, SAMPLE-SIZE REQUIREMENTS, bias, continuous norming, precision, quantile regression, regression-based norming
Citation
Crompvoets, E, Keuning, J & Emons, W 2021, 'Bias and precision of continuous norms obtained using quantile regression', Assessment, vol. 28, no. 6, pp. 1735-1750. https://doi.org/10.1177/1073191120910201