The absolute value of the correlation coefficient indicates the strength, with larger absolute values indicating stronger relationships. The sign of the correlation coefficient indicates the direction of the relationship (positive or negative). The values of the correlation coefficient range from 0 to 1.
The Pearson correlation coefficient is a measure of linear association between two variables. Otherwise, select Two-tailed.Ĭorrelation coefficients significant at the 0.05 level are identified with a single asterisk, and those significant at the 0.01 level are identified with two asterisks. If the direction of association is known in advance, select One-tailed. You can select two-tailed or one-tailed probabilities. If your data are not normally distributed or have ordered categories, choose Kendall's tau-b or Spearman, which measure the association between rank orders. Pearson correlation coefficients assume the data are normally distributed.įor quantitative, normally distributed variables, choose the Pearson correlation coefficient. Two variables can be perfectly related, but if the relationship is not linear, Pearson's correlation coefficient is not an appropriate statistic for measuring their association. Pearson's correlation coefficient is a measure of linear association. Ordinal: A variable can be treated as ordinal when its values represent categories with some intrinsic ranking for example, levels of service satisfaction from highly dissatisfied to highly satisfied.īefore calculating a correlation coefficient, screen your data for outliers (which can cause misleading results) and evidence of a linear relationship. Scale: A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Pairwise: When computing a measure of association between two variables in a larger set, cases are included in the computation when the two variables have non-missing values, irrespective of the values of the other variables in the set. It is useful for determining the strength and direction of the association between two scale or ordinal variables. …procedure computes the pair-wise associations for a set of variables and displays the results in a matrix.
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