Imputation of Missing Data for the Pre-Elementary Education Longitudinal Study
نویسندگان
چکیده
In the Pre-Elementary Education Longitudinal Study (PEELS), imputation of item missing data was done using AutoImpute (AI) software, which uses semi-parametric modeling to form imputation classes. In this paper, we summarize PEELS experience with AI, investigate the bias aspect of the imputed data for the PEELS teacher questionnaire data, and study the variance estimation of imputed data using multiple imputation by AI. In the study of variance estimation, we look into the bias issue for the multiple imputation method and the performance of AI multiple imputation on domain estimation.
منابع مشابه
چند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
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تاریخ انتشار 2008