贝叶斯 (非) 线性随机效应调解模型:评估遗漏混因子的影响
Ziwei Zhang1, Nidhi Kohli1, Eric F Lock2
1Department of Educational Psychology, University of Minnesota.
研究人员开发了贝叶斯的非线性随机效应调解模型 (B(N) REMM) 来直接估计线性和非线性纵向调解. 省略混因子会对这些模型中的参数恢复产生负面影响,特别是对细分趋势.
科学领域:
- 心理测量和教育测量方法
- 统计建模 统计建模
- 纵向数据分析 纵向数据分析
背景情况:
- 纵向介导模型通常依赖于结构方程建模,限制了对非线性函数的直接估计.
- 现有的方法需要重新参数化,以处理重复测量数据中的内在非线性关系.
- 在非线性纵向介导中省略混因子的影响尚未先前评估.
研究的目的:
- 开发一个贝叶斯框架,B(N) REMM,用于直接建模内在线性和非线性纵向介导.
- 引入两种特定的模型:线性 (L-BREMM) 和分片式 (P-BREMM) 纵向调解模型.
- 调查遗漏混因子对模型估计和趋同的影响.
主要方法:
- 为纵向数据开发贝叶斯式 (非线性) 随机效应调解模型 (B(N) REMM).
- 实施了线性 (L-BREMM) 和零碎线性 (P-BREMM) 模型,并未知随机变化点.
- 利用经验数据集 (幼儿园对象) 和蒙特卡洛模拟来评估模型性能和遗漏的混因子的影响.
主要成果:
- 经验示例强调了包含混因子的必要性,以避免模型的错误规范.
- 蒙特卡洛模拟表明,省略混因子对L-BREMM和P-BREMM的参数恢复产生负面影响.
- 遗漏的混因素影响了模型的趋同,特别是在P-BREMM模型中.
结论:
- B(N) REMM框架为估计线性和非线性纵向介导提供了直接方法.
- 由于遗漏的混因素导致模型错误规范,可能导致偏差的参数估计和融合问题,特别是在细分的纵向模型中.
- 该研究提供了实施L-BREMM和P-BREMM的R脚本,以促进更广泛的应用.
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