Multiple imputation of multilevel missing data: An introduction to the R package pan

Volume: 6, Issue: 4, Pages: 1 - 17
Published: Oct 1, 2016
Abstract
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...
Paper Details
Title
Multiple imputation of multilevel missing data: An introduction to the R package pan
Published Date
Oct 1, 2016
Journal
Volume
6
Issue
4
Pages
1 - 17
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.