PEARSON'S VERSUS SPEARMAN'S AND KENDALL'S CORRELATION COEFFICIENTS FOR CONTINUOUS DATA

By Nian Shong Chok. 2010

286 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af08d40460c00cc3372fce5378d0bd3fe4eab84f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. J. D. de Winter
2. J. Potter
3. Samuel D. Gosling
4. M. Amani
5. Alireza Ghaderi-Ardakani
6. A. Rostami
7. S. Johnson
8. M. Masoudi

Citations in the corpus

• Academic Engagement and Student Success: Do High-Impact Practices Mean Higher Graduation Rates? (2018, methodology): “Nonparametric tests, such as Spearman, are most accurate with data that are categorically ranked (Chok, 2010).”
• Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. (2016, background): “Moreover, our results are in line with several simulation studies which demonstrated lower variability of rs compared with rp for (severely) non-normal distributions (Bishara & Hittner, 2015; Chok, 2010; Kowalski, 1972).”
• Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis (2016, methodology): “In this section, variable impact analysis was conducted using the Pearson technique [76].”