正常和非正常的多项式回归混合模型对差分对等效应:一个模拟和教程.
Eunsook Kim1, Radhika Sundar2, Emma Evudottir2
1Department of Educational and Psychological Studies, University of South Florida, 4202 E. Fowler Avenue, EDU 105, Tampa, FL, 33620, USA. ekim3@usf.edu.
Behavior research methods
|October 10, 2025
概括
多项式回归混合分析 (PRMix) 识别了不同的对等效应类,在效应变化时优于标准方法. PRMix准确地检测隐藏类,特别是与非正常数据,提供改进的一致性研究见解.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 一致性研究研究 一致性研究
背景情况:
- 多项式回归与响应表面分析 (PRRSA) 在对应性研究中很常见.
- PRRSA假设个体之间具有同质的一致性效应,这可能不是真的.
- 多项式回归混合分析 (PRMix) 通过识别具有差异一致性效应的潜在类来解决异质性.
研究的目的:
- 检查PRRSA参数中的偏差,当差异性一致性效应存在但未建模时.
- 评估PRMix和非正常的PRMix在检测潜在类的一致性效应方面的性能.
- 为使用PRMix.ix的应用研究人员提供指导和示例.
主要方法:
- 蒙特卡洛模拟以评估PRRSA下的响应表面参数偏差.
- 评估PRMix用于检测具有差异一致效应的两个潜在类.
- 对处理类内非正常残余分布的非正常PRMix的评估.
主要成果:
- 在PRRSA参数中的偏差随着忽略的潜在类的比例而增加.
- 当残留正常性得到满足时,PRMix成功检测了两个隐藏类.
- 当残余正常性被违反时,PRMix导致过度提取类;非正常的PRMix在 skew t残余下进行了充分的执行.
结论:
- PRMix是分析对等效应异质性的宝贵工具.
- 在应用PRMix时,仔细考虑剩余分布至关重要.
- 非正常PRMix为与非正常数据的一致性研究提供了可行的解决方案,提高了分析准确性.
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