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摘要翻译：
纵向数据跟踪对个体的重复测量是非常有价值的研究，因为它们为未测量的个体异质性提供了控制，否则这些异质性可能会导致结果偏差。随机效应或混合模型方法将个体异质性作为模型误差项的一部分，并使用广义最小二乘估计模型参数，由于未观察到的个体效应与其他模型变量之间的相关性会导致参数估计有偏差和不一致，因此经常受到批评。本文从检验标准未观测效应模型中随机效应与固定效应估计量之间的关系入手，通过分析和仿真，证明了混合模型方法在一般的个体异质性模型下具有“偏差压缩”特性，可以缓解由于个体间不受控制的差异而产生的偏差。一般模型的动机是纵向学生成绩测量的复杂性，但结果对纵向建模有广泛的适用性。
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英文标题：
《Controlling for individual heterogeneity in longitudinal models, with
applications to student achievement》
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作者：
J.R. Lockwood, Daniel F. McCaffrey
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
Longitudinal data tracking repeated measurements on individuals are highly valued for research because they offer controls for unmeasured individual heterogeneity that might otherwise bias results. Random effects or mixed models approaches, which treat individual heterogeneity as part of the model error term and use generalized least squares to estimate model parameters, are often criticized because correlation between unobserved individual effects and other model variables can lead to biased and inconsistent parameter estimates. Starting with an examination of the relationship between random effects and fixed effects estimators in the standard unobserved effects model, this article demonstrates through analysis and simulation that the mixed model approach has a ``bias compression'' property under a general model for individual heterogeneity that can mitigate bias due to uncontrolled differences among individuals. The general model is motivated by the complexities of longitudinal student achievement measures, but the results have broad applicability to longitudinal modeling.
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PDF链接：
https://arxiv.org/pdf/706.1401
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