Cautionary Note on the Two-Step Transformation to Normality

ABSTRACT 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 wit…

Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects

Jyväskylästä kirjoitettiin: Käyn läpi Extra-Vipusessa ristiriitaisiksi luokitettuja yhteisjulkaisuja. Julkaisu " Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects" on meillä laitettu A2 ja teillä A1. Meillä varmaan päädytty tuohon A2:een kun tiivistelmässä sanotaan "Building on a systematic review of six leading management journals..". Mutta mitä mieltä olette, kumpi olisi parempi? 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 rec…

Polynomial Regression and Measurement Error

Many of the phenomena of interest in information systems (IS) research are nonlinear, and it has consequently been recognized that by applying linear statistical models (e.g., linear regression), we may ignore important aspects of these phenomena. To address this issue, IS researchers are increasingly applying nonlinear models to their datasets. One popular analytical technique for the modeling and analysis of nonlinear relationships is polynomial regression, which in its simplest form fits a "U-shaped" curve to the data. However, the use of polynomial regression can be problematic when the independent variables are contaminated with measurement error, and the implications of error can be m…

A cautionary note on the finite sample behavior of maximal reliability.

Several calls have been made for replacing coefficient α with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock's H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted di…

Supplemental Material, sj-pdf-1-orm-10.1177_1094428121991907 - Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects

Supplemental Material, sj-pdf-1-orm-10.1177_1094428121991907 for Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects by Mikko Rönkkö, Eero Aalto, Henni Tenhunen and Miguel I. Aguirre-Urreta in Organizational Research Methods

Omission of Causal Indicators: Consequences and Implications for Measurement – A Rejoinder

Supplemental Material, sj-r-1-orm-10.1177_1094428121991907 - Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects

Supplemental Material, sj-r-1-orm-10.1177_1094428121991907 for Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects by Mikko Rönkkö, Eero Aalto, Henni Tenhunen and Miguel I. Aguirre-Urreta in Organizational Research Methods

Supplemental Material, sj-do-1-orm-10.1177_1094428121991907 - Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects

Supplemental Material, sj-do-1-orm-10.1177_1094428121991907 for Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects by Mikko Rönkkö, Eero Aalto, Henni Tenhunen and Miguel I. Aguirre-Urreta in Organizational Research Methods

Supplemental Material, sj-xlsx-1-orm-10.1177_1094428121991907 - Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects

Supplemental Material, sj-xlsx-1-orm-10.1177_1094428121991907 for Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects by Mikko Rönkkö, Eero Aalto, Henni Tenhunen and Miguel I. Aguirre-Urreta in Organizational Research Methods

Is it really gender? An empirical investigation into gender effects in technology adoption through the examination of individual differences

A recent development in the technology acceptance literature is the inclusion of gender as a moderator of the relationships between intention and its antecedents, such that some are stronger for men than women, and vice versa. While the effects have been well established, the mechanisms by which they operate, that is, which specific gender differences are in operation and how they affect intention to adopt, have not been thoroughly explored. In this research, psychological constructs with established gender differences, such as core self-evaluations, computer self-efficacy and anxiety, psychological gender-role, and risk-taking propensity, are examined. In addition, this research introduces…