贝叶斯线性和非线性交叉随机效应模型的识别和估计能力
Corissa T Rohloff1, Nidhi Kohli1, Eric F Lock2
1Quantitative Methods in Education, Department of Educational Psychology, University of Minnesota, Minneapolis, Minnesota, USA.
The British journal of mathematical and statistical psychology
|January 24, 2024
概括
这项研究介绍了贝叶斯的零碎交叉随机效应模型 (CREM) 对非线性增长. 在纵向数据分析中,足够的重复测量对于准确估计组效应至关重要.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
背景情况:
- 交叉随机效应模型 (CREM) 对具有动态组成员身份的纵向数据至关重要.
- 现有的CREM不能模拟内在的非线性增长.
- 识别纵向CREM的数据条件,特别是群体效应,是未知的.
研究的目的:
- 开发一个贝叶斯的零碎CREM,以实现内在非线性增长.
- 评估数据条件以识别线性和非线性纵向CREM.
- 评估功能形式复杂性对数据要求的影响.
主要方法:
- 开发了一个贝叶斯的断片交叉随机效应模型 (CREM).
- 进行了三项模拟研究,以评估识别条件.
- 将零碎CREM应用到现实世界的纵向数据.
主要成果:
- 每组重复测量的数量显著影响到组效应恢复.
- 增加的功能形式复杂性需要更多的数据来准确估计.
- 确定了用于估计线性,二次性和零碎CREM的关键数据条件.
结论:
- 开发的贝叶斯分片CREM有效地模拟了非线性纵向增长.
- 数据收集策略必须考虑重复测量的数量和模型的复杂性.
- 这些发现为将CREM应用到纵向数据提供了必要的指导.
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