Impact on Cronbach's alpha of simple treatment methods for missing data

Sebastien Beland*, Francois Pichette, Shahab Jolani

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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 on Cronbach's alpha (Cronbach, 1951). We use simulation studies to investigate the behavior of alpha 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.
Original languageEnglish
Pages (from-to)57-73
JournalThe Quantitative Methods for Psychology
Issue number1
Publication statusPublished - 2016


  • Cronbach's alpha coefficient
  • missing data
  • missing completely at random
  • missing at random
  • simulation study
  • R software

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