Impact on Cronbach’s α of simple treatment methods for missing data [r-libre/847]

Béland, Sébastien; Pichette, François et Jolani, Shahab (2016). Impact on Cronbach’s α of simple treatment methods for missing data. The Quantitative Methods for Psychology, 12 (1), 57-73.

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now important to better understand how various common methods for dealing with missing data can affect widely-used psychometric coefficients. The purpose of this study is to compare the impact of ten common fill-in methods and multiple imputation on Cronbach’s α (1951). We use simulation studies to investigate the behavior of α in various situations. Our results show that multiple imputation is the most effective method. Furthermore, simple imputation methods like Winer imputation, item mean, and total mean are interesting alternatives for specific situations. These methods can be easily used by non-statisticians such as teachers and school psychologists.
Adresse de la version officielle :	http://www.tqmp.org/RegularArticles/vol12-1/p057/p...
Déposant:	Pichette, François
Responsable :	François Pichette
Dépôt :	18 janv. 2016 14:44
Dernière modification :	18 janv. 2016 14:44

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