期刊名称：Practical Assessment, Research and Evaluation
印刷版ISSN：1531-7714
电子版ISSN：1531-7714
出版年度：2014
卷号：19
出版社：ERIC: Clearinghouse On Assessment and Evaluation
摘要：When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type of missingness (MCAR, MAR), degree of missingness (10%, 25%, and 50%), and where missingness occurs (one predictor, two predictors, or two predictors with overlap). Using a real dataset, cells are systematically deleted to create various scenarios of missingness so that parameter estimates from listwise deletion and multiple imputation may be compared to the “true” estimates from the full dataset. Results suggest the multiple imputation works well, even when the imputation model itself is missing data.
