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Article (journal) accessible via
URN: urn:nbn:de:0111-pedocs-126928
DOI: 10.25656/01:12692; 10.1177/2158244016668220
URN: urn:nbn:de:0111-pedocs-126928
DOI: 10.25656/01:12692; 10.1177/2158244016668220
Title |
Multiple imputation of multilevel missing data: An introduction to the R package pan |
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Authors | Grund, Simon ; Lüdtke, Oliver; Robitzsch, Alexander |
Source | SAGE Open 6 (2016) 4, S. 1-17 |
Document | full text (722 KB) |
License of the document | |
Keywords (German) | Fehler; Statistik; Statistische Analyse; Statistisches Modell; Software; Statistische Methode |
sub-discipline | Empirical Educational Research |
Document type | Article (journal) |
ISSN | 2158-2440; 21582440 |
Language | English |
Year of creation | 2016 |
review status | Peer-Reviewed |
Abstract (English): | 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. |
Statistics | Number of document requests |
Checksums | checksum comparison as proof of integrity |
Date of publication | 27.01.2017 |
Citation | Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander: Multiple imputation of multilevel missing data: An introduction to the R package pan - In: SAGE Open 6 (2016) 4, S. 1-17 - URN: urn:nbn:de:0111-pedocs-126928 - DOI: 10.25656/01:12692; 10.1177/2158244016668220 |