Kennison Robert F, Zelinski Elizabeth M
Andrus Gerontology Center, University of Southern California, Los Angeles, CA 90089-0191, USA.
Psychol Aging. 2005 Sep;20(3):460-75. doi: 10.1037/0882-7974.20.3.460.
Average change in list recall was evaluated as a function of missing data treatment (Study 1) and dropout status (Study 2) over ages 70 to 105 in Asset and Health Dynamics of the Oldest-Old data. In Study 1 the authors compared results of full-information maximum likelihood (FIML) and the multiple imputation (MI) missing-data treatments with and without independent predictors of missingness. Results showed declines in all treatments, but declines were larger for FIML and MI treatments when predictors were included in the treatment of missing data, indicating that attrition bias was reduced. In Study 2, models that included dropout status had better fits and reduced random variance compared with models without dropout status. The authors conclude that change estimates are most accurate when independent predictors of missingness are included in the treatment of missing data with either MI or FIML and when dropout effects are modeled.
在“高龄老人资产与健康动态”数据中，评估了70至105岁年龄段列表回忆的平均变化，该变化是缺失数据处理（研究1）和退出状态（研究2）的函数。在研究1中，作者比较了完全信息最大似然法（FIML）和多重填补法（MI）在有和没有缺失值独立预测因子情况下处理缺失数据的结果。结果显示所有处理方式下均出现下降，但在处理缺失数据时纳入预测因子时，FIML和MI处理方式的下降幅度更大，这表明损耗偏倚有所降低。在研究2中，与不包含退出状态的模型相比，包含退出状态的模型拟合度更好且随机方差更小。作者得出结论，当使用MI或FIML处理缺失数据并对退出效应进行建模时，纳入缺失值独立预测因子，变化估计最为准确。
