Spearman’s Rank-Difference Coefficient of Correlation is often referred to as Spearman’s ρ (i.e., the Greek letter rho). It is common to read how Spearman’s Correlation is often viewed as the nonparametric counterpart to the parametric Pearson’s Correlation. However, that comparison may be somewhat misleading given how Spearman’s is used with nonparametric data, whereas Pearson’s is used with data that are more reasonably viewed as parametric. The key point to Spearman’s Correlation is that this test is used to determine if there is an association between two nonparametric variables. However, as a constant reminder, be sure to recall the often used expression Correlation does not imply causation. There may be a correlation (i.e., association) between Variable X and Variable Y, but by no means does that mean that measures for Variable X either cause or influence measures for Variable Y.
Anderson-Darling test Association Bag plot Bar plot (stacked, side-by-side) Box plot Breakout groups Code book Comma-separated values (.csv) Continuous scale Correlation Correlation coefficient Correlation matrix Density plot Descriptive statistics Distribution-free Factor Hinge (lower and upper) Histogram Institutional Review Board (IRB) Interval Kendall’s tau Mean Median Mode Nominal Nonparametric Normal distribution Null hypothesis Ordinal Outlier Parametric Pearson’s product-moment coefficient of correlation Pearson’s r Percentile Probability (p-value) Quantile-Quantile (QQ, Q-Q) Ranking Regression line Sample (quota, convenience) Scatter plot Scatter plot matrix (SPLOM) Spearman’s rank-difference coefficient of correlation Spearman’s rho Statistical significance Trellis graphics Whisker (lower and upper)
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