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 editorsRönkkö, Mikko; Aalto, Eero; Tenhunen, Henni; Aguirre-Urreta, Miguel I.

Journal or seriesOrganizational Research Methods

ISSN1094-4281

eISSN1552-7425

Publication year2022

Publication date11/03/2021

Volume25

Issue number1

Pages range48-87

PublisherSAGE Publications

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1177/1094428121991907

Publication open accessOpenly available

Publication channel open accessPartially 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.


Keywordsorganisational researchstatistical modelslinear modelsregression analysis

Free keywordstransformations; generalized linear model; Poisson regression; logistic regression


Contributing organizations


Related projects


Ministry reportingYes

Reporting Year2022

JUFO rating3


Last updated on 2024-03-04 at 20:06