Beyond Pearson’s correlation: modern nonparametric independence tests for psychological research

Karch, Julian D., Perez-Alonso, Andres F. and Bergsma, Wicher P. ORCID: 0000-0002-2422-2359 (2024) Beyond Pearson’s correlation: modern nonparametric independence tests for psychological research. Multivariate Behavioral Research, 59 (5). 957 - 977. ISSN 0027-3171

Text (Karch_et_al__Beyond-Pearson-s-correlation--published) - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB)

• Scopus publication
• Author

Abstract

When examining whether two continuous variables are associated, tests based on Pearson’s, Kendall’s, and Spearman’s correlation coefficients are typically used. This paper explores modern nonparametric independence tests as an alternative, which, unlike traditional tests, have the ability to potentially detect any type of relationship. In addition to existing modern nonparametric independence tests, we developed and considered two novel variants of existing tests, most notably the Heller-Heller-Gorfine-Pearson (HHG-Pearson) test. We conducted a simulation study to compare traditional independence tests, such as Pearson’s correlation, and the modern nonparametric independence tests in situations commonly encountered in psychological research. As expected, no test had the highest power across all relationships. However, the distance correlation and the HHG-Pearson tests were found to have substantially greater power than all traditional tests for many relationships and only slightly less power in the worst case. A similar pattern was found in favor of the HHG-Pearson test compared to the distance correlation test. However, given that distance correlation performed better for linear relationships and is more widely accepted, we suggest considering its use in place or additional to traditional methods when there is no prior knowledge of the relationship type, as is often the case in psychological research.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/hmbr20
Additional Information:	© 2024 The Author(s)
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics
Date Deposited:	16 Aug 2024 12:15
Last Modified:	12 Dec 2024 04:25
URI:	http://eprints.lse.ac.uk/id/eprint/124587

Actions (login required)

View Item