In psychological test norming, nonrepresentativeness in background variables in the normative sample can lead to bias in the normed score estimates. Because representativeness is difficult to establish in practice, adjustment methods are needed to combat this bias. As a candidate adjustment method, we investigated generalized additive models for location, scale, and shape with multilevel regression and poststratification (GAMLSS + MRP), the combination of MRP and continuous norming with GAMLSS. This adjustment method was then compared to current adjustment methods in continuous norming using weighted regression: GAMLSS + P (with poststratification) and cNORM + R (with raking). The results of our simulation showed that GAMLSS + MRP was generally more efficient than GAMLSS + P and cNORM + R. Furthermore, GAMLSS + MRP was better than the current methods at reducing bias in samples where the nonrepresentativeness was age-dependent. We argue that GAMLSS + MRP is a valid adjustment method in continuous norming and recommend this adjustment method to mitigate bias in nonrepresentative normative samples. To facilitate the use of GAMLSS + MRP in practice, we provide a step-wise approach for the implementation of GAMLSS + MRP. We illustrate this approach by deriving normed scores from the normative data of the third Schlichting language test. All analysis code for this illustration is provided. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

中文翻译：

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使用多级回归和后分层调整连续规范中的非代表性。


在心理测试规范中，规范样本中背景变量的非代表性会导致规范分数估计的偏差。由于在实践中很难建立代表性，因此需要调整方法来对抗这种偏差。作为一种候选调整方法，我们研究了具有多级回归和后分层 （GAMLSS + MRP） 的位置、比例和形状的广义加法模型，MRP 和与 GAMLSS 的连续规范相结合。然后使用加权回归将这种调整方法与连续规范中的当前调整方法进行比较：GAMLSS + P（带后分层）和 cNORM + R（带耙）。我们的模拟结果表明，GAMLSS + MRP 通常比 GAMLSS + P 和 cNORM + R 更有效。此外，GAMLSS + MRP 在减少非代表性与年龄相关的样本中的偏差方面优于当前方法。我们认为 GAMLSS + MRP 是连续规范中的一种有效调整方法，并推荐这种调整方法来减轻非代表性规范样本中的偏差。为了促进 GAMLSS + MRP 在实践中的使用，我们提供了一种实施 GAMLSS + MRP 的分步方法。我们通过从第三次 Schlichting 语言测试的规范数据中得出规范分数来说明这种方法。提供了此图的所有分析代码。（PsycInfo 数据库记录 （c） 2025 APA，保留所有权利）。