How to move away from using symmetric tests, net effects, and p<0.05: Overcoming barriers to good science practices

Abstract

Purpose

The purpose of this paper is to describe how and why to shift away from bad science practices now dominant in research in marketing to good science practices.

Design/methodology/approach

The essay includes details in theory construction and the use of symmetric tests to illustrate bad science practices. In contrast, the essay includes asymmetric case-based asymmetric theory construction and testing to illustrate good science practices.

Findings

Researchers in marketing science should not report null hypothesis significance tests. They should report somewhat precise outcome tests, avoid using multiple regression analysis (MRA) and do use Boolean-algebra-based algorithms to predict cases of interest.

Research limitations/implications

Given the widespread dominance of bad science practices (e.g. MRA and structural equation modeling), the inclusion of both bad and good science practices may be necessary during the transition years of 2015–2025 (e.g. Ordanini et al., 2014).

Practical implications

Good science practices fit reality much closer than bad science practices. Asymmetric modeling includes recognizing the separate models are necessary for positive vs negative outcomes because the antecedents of each often differ.

Originality/value

This essay presents details of why and how researchers need to embrace a new research paradigm that is helpful for ending bad science practices that are now dominant in research in marketing.

Citation

Woodside, A.G. (2018), "How to move away from using symmetric tests, net effects, and p<0.05: Overcoming barriers to good science practices", Journal of Contemporary Marketing Science, Vol. 1 No. 1, pp. 176-187. https://doi.org/10.1108/JCMARS-09-2018-0012