Cautionary Note on the Two-Step Transformation to Normality

Templeton and Burney (2017) proposed a two-step normality transformation as a remedy for non-normally distributed data, which are commonly found in AIS research. We argue that, rather than transforming the data toward normality, researchers should first seek to analyze and understand the sources of...

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Bibliographic Details
Published in:The Journal of information systems 2020-03, Vol.34 (1), p.151-166
Main Authors: Rönkkö, Mikko, Aguirre-Urreta, Miguel I.
Format: Article
Language:English
Subjects:
Correlation analysis
Datasets
Information processing
Regression analysis
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Summary:Templeton and Burney (2017) proposed a two-step normality transformation as a remedy for non-normally distributed data, which are commonly found in AIS research. We argue that, rather than transforming the data toward normality, researchers should first seek to analyze and understand the sources of non-normality. Using simulated datasets, we demonstrate three sources of non-normality and their consequences for regression estimation. We then demonstrate that the two-step transformation cannot solve any of these problems and that each source of non-normality can be handled with alternative, existing techniques. We further present two empirical examples to demonstrate these issues with real datasets.
ISSN:0888-7985
1558-7959
DOI:10.2308/isys-52255