Tilburg University, Tilburg, Netherlands.
Cito Institute for Educational Measurement, Arnhem, Netherlands.
Assessment. 2021 Sep;28(6):1735-1750. doi: 10.1177/1073191120910201. Epub 2020 Jun 2.
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.
连续定标是一种越来越流行的方法，用于在测试表现依赖于年龄时建立规范。然而，目前的连续定标方法依赖于许多相当严格的假设，这些假设可能会引入偏差。在这项研究中，引入了分位数回归作为更灵活的替代方法。研究了以（年龄）组为协变量、不同样本量和分数分布的分位数回归定标中的偏差和精度，并与两种其他定标方法（传统定标和基于均值回归的定标）的偏差和精度进行了比较。模拟结果表明，在几乎所有情况下，使用分位数回归获得的规范都具有最高的精度。然而，当分数分布反映出上限效应时，规范会出现偏差。因此，分位数回归定标可以被认为是传统定标和基于均值回归定标有前途的替代方法，但前提是可以预期分数分布的形状接近正态分布。
