Correlation coefficients quantitatively describe the strength and direction of the relationship between two variables. Looks for a linear association between continuous variables. For ordinal or interval data – the correlation coefficient (r) can be used to measure the degree of association between them. ‘0’ indicates the absence of a correlation. ‘-1’ or ‘+1’ indicates a perfect correlation.
Referred to as Pearson’s product moment correlation coefficient when based on the original data or Spearman’s rank correlation coefficient when based on ranks of the original data. Pearson’s correlation is based on the assumption that the data is normally distributed – if not use Spearman’s rank correlation.
Correlations do not imply causation.
Correlations should not be used when:
• The relationship between variables is not linear (check with scatter plot)
• Influential outliers are in the data
• When there is more than one distinct group in the data
• Samples are not independent of one another
Used when variables are dichotomous eg 0 or 1