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A review of techniques for treating missing data in OM survey research
Author(s) -
Tsikriktsis Nikos
Publication year - 2005
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2005.03.001
Subject(s) - missing data , data science , survey data collection , data collection , computer science , management science , psychology , statistics , mathematics , engineering , machine learning
The treatment of missing data has been overlooked by the OM literature, while other fields such as marketing, organizational behavior, economics, statistics and psychometrics have paid more attention to the issue. A review of 103 survey‐based articles published in the Journal of Operations Management between 1993 and 2001 shows that listwise deletion, which is often the least accurate technique of dealing with missing data, is heavily utilized by OM researchers. The paper also discusses the research implications of missing data, types of missing data and concludes with recommendations on which techniques should be used under different circumstances in order to improve the treatment of missing data in OM survey research.