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In statistics, Spearman's rank correlation coefficient or Spearman's ρ, named after Charles Spearman [1] and often denoted by the Greek letter (rho) or as , is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables). It assesses how well the relationship between two variables can be described ...
Closely related to Spearman's hypothesis is the hypothesis that the magnitude of certain group differences correlates with within-group heritability estimates. Arthur Jensen and J. Phillippe Rushton , for example, reported in 2010 that the found psychometric meta-analytic correlation between g-loadings and heritability estimates was 1.
Charles Edward Spearman, FRS [1][3] (10 September 1863 – 17 September 1945) was an English psychologist known for work in statistics, as a pioneer of factor analysis, and for Spearman's rank correlation coefficient. He also did seminal work on models for human intelligence, including his theory that disparate cognitive test scores reflect a ...
Dave Kerby (2014) recommended the rank-biserial as the measure to introduce students to rank correlation, because the general logic can be explained at an introductory level. The rank-biserial is the correlation used with the Mann–Whitney U test, a method commonly covered in introductory college courses on statistics. The data for this test ...
g and s. Spearman's two-factor theory proposes that intelligence has two components: general intelligence ("g") and specific ability ("s"). [7] To explain the differences in performance on different tasks, Spearman hypothesized that the "s" component was specific to a certain aspect of intelligence. [2] Regarding g, Spearman saw individuals as ...
Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. [1] It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. [1] Bivariate analysis can be helpful in testing simple hypotheses of association.
The correlation reflects the noisiness and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case, the correlation coefficient is undefined because the variance of Y is zero.
Partial correlation. In probability theory and statistics, partial correlation measures the degree of association between two random variables, with the effect of a set of controlling random variables removed. When determining the numerical relationship between two variables of interest, using their correlation coefficient will give misleading ...