A2 Review article, Literature review, Systematic review
Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2022)

Rönkkö, M., Aalto, E., Tenhunen, H., & Aguirre-Urreta, M. I. (2022). Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects. Organizational Research Methods, 25(1), 48-87. https://doi.org/10.1177/1094428121991907

JYU authors or editors

Publication details

All authors or editors: Rönkkö, Mikko; Aalto, Eero; Tenhunen, Henni; Aguirre-Urreta, Miguel I.

Journal or series: Organizational Research Methods

ISSN: 1094-4281

eISSN: 1552-7425

Publication year: 2022

Publication date: 11/03/2021

Volume: 25

Issue number: 1

Pages range: 48-87

Publisher: SAGE Publications

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1177/1094428121991907

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/74700

Abstract

Transforming variables before analysis or applying a transformation as a part of a generalized linear model are common practices in organizational research. Several methodological articles addressing the topic, either directly or indirectly, have been published in the recent past. In this article, we point out a few misconceptions about transformations and propose a set of eight simple guidelines for addressing them. Our main argument is that transformations should not be chosen based on the nature or distribution of the individual variables but based on the functional form of the relationship between two or more variables that is expected from theory or discovered empirically. Building on a systematic review of six leading management journals, we point to several ways the specification and interpretation of nonlinear models can be improved.

Keywords: organisational research; statistical models; linear models; regression analysis

Free keywords: transformations; generalized linear model; Poisson regression; logistic regression

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Related projects

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 3