 So many ways for assessing outliers: What really works and does it matter?Joe H. Sullivan, Merrill Warkentin and Linda Wallace Journal of Business Research, 2021, vol. 132, issue C, 530-543 Abstract: Recent research in leading business journals has varied widely in how statistical outliers are identified and handled; many techniques were reported. But most articles with empirical data have not mentioned outliers; many others simply referred to their removal without details. This wide variety of methods and frequent non-disclosure of methods cannot represent the best practice. Proper outlier identification and handling are important issues for any research community that performs quantitative research based on empirical data. We document this diversity of methods by examining articles published in the UTD-24 business journals during a 12-year period, and we explain why almost all methods described in these articles are ill-advised. To achieve an effective assessment of outliers, we propose a process of outlier identification based on testing hypotheses using a controlled significance level. The implementation of our suggested method is feasible using commonly available statistical analysis software. Keywords: Data cleansing; Empirical business research; Winsorizing; Studentized residuals; Cook’s distance; Mahalanobis distance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:132:y:2021:i:c:p:530-543 DOI: 10.1016/j.jbusres.2021.03.066 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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