Multiple Imputation of Multilevel Missing Data: An Introduction to the R Package pan

SAGE Open

Simon Grund¹ 

关键词: multiple imputation;    missing data;    multilevel;    R;   

DOI  :  10.1177/2158244016668220

学科分类：社会科学、人文和艺术（综合）

来源: Sage Journals

【 摘 要 】

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.

【 授权许可】

CC BY