Mediation Analysis with Missing Data Through Multiple Imputation and Bootstrap

被引：14

作者：

Zhang, Zhiyong ^{[1
]}

Wang, Lijuan ^{[1
]}

Tong, Xin ^{[2
]}

机构：

[1] Univ Notre Dame, Notre Dame, IN 46556 USA

[2] Univ Virginia, Charlottesville, VA 22904 USA

来源：

Quantitative Psychology Research | 2015年 / 140卷

关键词：

Mediation analysis; Missing data; Multiple imputation; Bootstrap; MATHEMATICS ACHIEVEMENT; MODELS; VARIABLES; DISTINCTION; INFERENCE;

D O I：

10.1007/978-3-319-19977-1_24

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

A method using multiple imputation and bootstrap for dealing with missing data in mediation analysis is introduced and implemented in both SAS and R. Through simulation studies, it is shown that the method performs well for both MCAR and MAR data without and with auxiliary variables. It is also shown that the method can work for MNAR data if auxiliary variables related to missingness are included. The application of the method is demonstrated through the analysis of a subset of data from the National Longitudinal Survey of Youth. Mediation analysis with missing data can be conducted using the provided SAS macros and R package bmem.

引用

页码：341 / 355

页数：15

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