Abstract
Zusammenfassung. Fehlende Werte stellen in der empirisch-psychologischen Forschung oftmals ein Problem dar. Häufig verwendete Verfahren wie fallweiser und paarweiser Ausschluss, Regression- und Mean-Imputation sind aus methodischer Sicht defizitär. Alternative Verfahren für die Analyse von Datensätzen mit fehlenden Werten, die in den letzten drei Jahrzehnten entwickelt wurden, werden in der Forschungspraxis noch selten angewendet. Der vorliegende Beitrag führt zunächst in die grundlegende Terminologie fehlender Werte nach Rubin (1976) ein. Im Anschluss daran wird eine Übersicht der in der Literatur diskutierten Ansätze zum Umgang mit fehlenden Werten vorgenommen, wobei drei Typen von Verfahren unterschieden werden: klassische Verfahren (z.B. fallweiser Ausschluss), imputationsbasierte Verfahren, in denen fehlende Werte ersetzt (imputiert) werden, und modellbasierte Verfahren, in denen die Schätzung des Modells und die Behandlung der fehlenden Werte in einem Schritt vorgenommen werden. Anhand eines Datenbeispiels wird dann der Einsatz der Multiplen Imputation veranschaulicht. Abschließend werden Implikationen für die Forschungspraxis diskutiert.
Abstract. Missing data are a pervasive problem in empirical psychological research. From the methodological perspective, traditional procedures such as Casewise and Pairwise Deletion, Regression Imputation, and Mean Imputation have distinct weaknesses. Yet modern statistical methods for the analysis of datasets with missing values that have been developed in the past three decades have not yet gained a significant foothold in research practice. We begin this article by introducing the basic concepts and terminology of missing data, as proposed by Rubin (1976). We then give an overview of the different approaches to handling missing data discussed in the literature, distinguishing between three types of procedures: traditional procedures (e.g., Listwise Deletion), imputation-based procedures, in which missing values are replaced by imputed values, and model-based procedures, in which models are estimated and missing data handled in a single step. In the empirical section of the article, we demonstrate the application of Multiple Imputation using a dataset from a large-scale educational assessment. Implications for research practice are discussed.
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