Bias and precision of continuous norms obtained using quantile regression

Elise Crompvoets*, Jos Keuning, Wilco Emons

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Number of pages16
JournalAssessment
DOIs
Publication statusE-pub ahead of print - 2020

Keywords

  • AGE
  • SAMPLE-SIZE REQUIREMENTS
  • bias
  • continuous norming
  • precision
  • quantile regression
  • regression-based norming

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