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首页> 外文期刊>Journal of applied statistics >Missing data techniques for multilevel data: implications of model misspecification
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Missing data techniques for multilevel data: implications of model misspecification

机译：多级数据缺失的数据技术：模型错误指定的含义

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Department of Statistics, University of Connecticut, Storrs, CT, USA;Department of Statistics, University of Connecticut, Storrs, CT, USA;Department of Educational Psychology, University of Connecticut, Storrs, CT, USA;%When modeling multilevel data, it is important to accurately represent the interdependence of observations within clusters. Ignoring data clustering may result in parameter misestimation. However, it is not well established to what degree parameter estimates are affected by model misspecification when applying missing data techniques (MDTs) to incomplete multilevel data. We compare the performance of three MDTs with incomplete hierarchical data. We consider the impact of imputation model misspecification on the quality of parameter estimates by employing multiple imputation under assumptions of a normal model (MI/NM) with two-level cross-sectional data when values are missing at random on the dependent variable at rates of 10%, 30%, and 50%. Five criteria are used to compare estimates from MI/NM to estimates from MI assuming a linear mixed model (MI/LMM) and maximum likelihood estimation to the same incomplete data sets. With 10% missing data (MD), techniques performed similarly for fixed-effects estimates, but variance components were biased with MI/NM. Effects of model misspecification worsened at higher rates of MD, with the hierarchical structure of the data markedly underrepresented by biased variance component estimates. MI/LMM and maximum likelihood provided generally accurate and unbiased parameter estimates but performance was negatively affected by increased rates of MD.
机译：美国康涅狄格州斯托尔斯市康涅狄格大学统计系;美国康涅狄格州斯托尔斯市康涅狄格大学统计系;美国康涅狄格州斯托尔斯市康涅狄格大学教育心理学系;％在对多层次数据建模时对于准确表示聚类中观测值的相互依赖性很重要。忽略数据聚类可能会导致参数估计错误。但是，当将缺失数据技术（MDT）应用于不完整的多级数据时，参数估计值受模型错误指定影响的程度尚不确定。我们将三种MDT与不完整的分层数据进行比较。当正态模型（MI / NM）具有两级横截面数据时，当因变量随机丢失值时，采用多重归因，我们考虑归因模型错误指定对参数估计质量的影响。 10％，30％和50％。假设线性混合模型（MI / LMM）和对相同不完整数据集的最大似然估计，使用五个标准将MI / NM的估计值与MI的估计值进行比较。缺少10％的数据（MD）时，固定效果估算的技术相似，但方差成分因MI / NM而有偏差。在较高的MD率下，模型错误指定的影响变得更糟，偏倚的方差分量估计值明显不足以表示数据的层次结构。 MI / LMM和最大似然提供了总体上准确且无偏的参数估计，但性能受到MD率增加的负面影响。

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