title: Testing for negligible interaction: A coherent and robust approach.
abstract: Researchers often want to demonstrate a lack of interaction between two categorical predictors on an outcome. To justify a lack of interaction, researchers typically accept the null hypothesis of no interaction from a conventional analysis of variance (ANOVA). This method is inappropriate as failure to reject the null hypothesis does not provide statistical evidence to support a lack of interaction. This study proposes a bootstrap-based intersection-union test for negligible interaction that provides coherent decisions between the omnibus test and post hoc interaction contrast tests and is robust to violations of the normality and variance homogeneity assumptions. Further, a multiple comparison strategy for testing interaction contrasts following a non-significant omnibus test is proposed. Our simulation study compared the Type I error control, omnibus power and per-contrast power of the proposed approach to the non-centrality-based negligible interaction test of Cheng and Shao (2007, Statistica Sinica, 17, 1441). For 2 × 2 designs, the empirical Type I error rates of the Cheng and Shao test were very close to the nominal α level when the normality and variance homogeneity assumptions were satisfied; however, only our proposed bootstrapping approach was satisfactory under non-normality and/or variance heterogeneity. In general a × b designs, although the omnibus Cheng and Shao test, as expected, is the most powerful, it is not robust to assumption violation and results in incoherent omnibus and interaction contrast decisions that are not possible with the intersection-union approach.
authors.affiliation: Department of Psychology, York University, Toronto, Ontario, Canada.
authors.forename: Robert A
authors.identifier: http://orcid.org/0000-0002-9247-497X
authors.initials: RA
authors.lastname: Cribbie
authors.affiliation: Department of Psychology, York University, Toronto, Ontario, Canada.
authors.forename: Chantal
authors.initials: C
authors.lastname: Ragoonanan
authors.affiliation: Department of Psychology, York University, Toronto, Ontario, Canada.
authors.forename: Alyssa
authors.initials: A
authors.lastname: Counsell
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D013223:Statistics as Topic
keywords: equivalence testing; factorial ANOVA; lack of interaction; negligible interaction