Spearman's rank correlation coefficient

About: Spearman's rank correlation coefficient is a research topic. Over the lifetime, 1043 publications have been published within this topic receiving 37599 citations. The topic is also known as: Spearman rank correlation coefficient & Spearman's correlation coefficient.

Rank Correlation Methods

Abstract: Rank correlation coefficients are statistical indices that measure the degree of association between two variables having ordered categories. Some well-known rank correlation coefficients are those proposed by Goodman and Kruskal (1954, 1959), Kendall (1955), and Somers (1962). Rank correlation methods share several common features. They are based on counts and are defined such that a coefficient of zero means “no association” between the variables and a value of +1.0 or -1.0 means “perfect agreement” or “perfect inverse agreement,” respectively.

Correlation Coefficients: Appropriate Use and Interpretation.

Abstract: Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.

Interpretation of the Correlation Coefficient: A Basic Review

Abstract: A basic consideration in the evaluation of professional medical literature is being able to understand the statistical analysis presented. One of the more frequently reported statistical methods involves correlation analysis where a correlation coefficient is reported representing the degree of linear association between two variables. This article discusses the basic aspects of correlation analysis with examples given from professional journals and focuses on the interpretations and limitations of the correlation coefficient. No attention was given to the actual calculation of this statistical value.