Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data

By Jan Hauke, Tomasz Kossowski. 2011

2,035 citations recorded by the source corpus.

Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data Spearman's rank correlation coefficient is a nonparametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of an association between two variables. It is a measure of a monotone association that is used when the distribution of data makes Pearson's correlation coefficient undesirable or misleading. Spearman's coefficient is not a measure of the linear relationship between two variables, as some "statisticians" declare. It assesses how well an arbitrary monotonic function can describe a relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson's product-moment correlation coefficient, it does not require the assumption that the relationship between the variables is linear, nor does it require the variables to be measured on interval scales; it can be used for variables measured at the ordinal level. The idea of the paper is to compare the values of Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient as well as their statistical significance for different sets of data (original - for Pearson's coefficient, and ranked data for Spearman's coefficient) describing regional indices of socio-economic development.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0c31e084b1bb101aaa9e0db7d8ea4f5eb4318100

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In Artificial Intelligence

1. Bodhisattva Sen
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Citations in the corpus

• Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation (2019, methodology): “Note that applications of Spearman’s rank correlation as a measure of association have been extensively studied in the statistics literature (see, e.g., Hauke and Kossowski 2011; Iman and Conover 1982; Mukaka 2012).”
• Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* (2015, methodology): “Hence, in that context, it is more appropriate to use the Spearman correlation that does not require a linear relationship or the variables to be normally distributed (e.g., Hauke and Kossowski 2011).”
• A Systematic Study of Sustainable Development Goal (SDG) Interactions (2017, methodology): “Unlike Pearson’s correlation analysis, Spearman’s analysis is able to capture nonlinear correlation between the variables and is less sensible to outliers (Hauke & Kossowski, 2011).”
• Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance (2016, neutral): “Pearson correlation coefficient is a measure of the linear relationship, whereas Spearman correlation coefficient is a measure of the monotonic relationship (Hauke and Kossowski 2011).”