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

Autor/inn/en	Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander
Titel	Multiple imputation of multilevel missing data: An introduction to the R package pan.
Quelle	In: Sage open, 6 (2016) 4, S. 1-17Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	2158-2440
DOI	10.1177/2158244016668220
URN	urn:nbn:de:0111-pedocs-126928
Schlagwörter	Statistisches Modell; Fehler; Statistik; Statistische Analyse; Statistische Methode
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 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.
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2017/2