Multiple Imputation of Missing Data for Multilevel Models.

Autor/inn/en	Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander
Titel	Multiple Imputation of Missing Data for Multilevel Models.
Quelle	In: Organizational research methods, 21 (2018) 1, S. 111-149 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1094-4281; 1552-7425
DOI	10.1177/1094428117703686
Schlagwörter	Empirische Forschung; Mehrebenenanalyse; Vergleich; Psychologie; Datenanalyse; Datengewinnung; Softwaretechnologie; Variable; Zufallsgröße; Koeffizient; Messgenauigkeit; Kollektiv; Anleitung; Implementationsforschung; Modell; Statistische Methode; Struktur; Theorie-Praxis-Beziehung; Wechselwirkung; Gruppe (Soz)
Abstract	Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present paper, based on theoretical arguments and computer simulations, [the authors] provide guidance using MI in the context of several classes of multilevel models, including models with random intercepts, random slopes, cross-level interactions (CLIs), and missing data in categorical and group-level variables. [the] findings suggest that, oftentimes, several approaches to MI provide an effective treatment of missing data in multilevel research. Yet [the authors] also note that the current implementations of MI still have room for improvement when handling missing data in explanatory variables in models with random slopes and CLIs. [The authors] identify areas for future research and provide recommendations for research practice along with a number of step-by-step examples for the statistical software R. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/2