在使用多层回归和分层后调整的连续规范中调整非代表性
Klazien de Vries1, Marieke E Timmerman1, Anja F Ernst1
1Heymans Institute for Psychological Research, University of Groningen.
Psychological methods
|March 13, 2025
概括
用多层次回归和后分层 (GAMLSS + MRP) 进行位置,规模和形状的通用增值模型有效地减少了心理测试规范中的偏差. 这种方法比目前的技术更有效,特别是在取决于年龄的样本非代表性方面.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 在心理测试中,非代表性的规范样本可以引入偏见.
- 建立样本代表性实际上是具有挑战性的.
- 调整方法对于减轻规范分数估计中的偏差至关重要.
研究的目的:
- 作为偏差调整方法,研究用多层回归和后分层 (GAMLSS + MRP) 来研究位置,规模和形状的通用添加模型.
- 将GAMLSS + MRP与现有的连续规范调整方法进行比较.
- 为GAMLSS + MRP提供一个实用的实施指南.
主要方法:
- 模拟研究将GAMLSS + MRP与GAMLSS + P (分层后) 和cNORM + R () 进行比较.
- 评估不同非代表性场景中的偏见减少和效率.
- 将GAMLSS + MRP应用于来自Schlichting语言测试的现实世界规范数据.
主要成果:
- 与GAMLSS + P和cNORM + R相比,GAMLSS + MRP显示出更高的效率.
- GAMLSS + MRP在减少偏差方面更有效,特别是在依赖年龄的非代表性样本中.
- 该方法被证明是用于连续规范的有效调整技术.
结论:
- GAMLSS + MRP 是一种推的调整方法,用于减轻非代表性规范样本中的偏差.
- 该研究为实施GAMLSS + MRP提供了一种实际的,逐步的方法.
- 提供开源分析代码以促进采用和进一步研究.
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