Rules of thumb for interpreting the size of a correlation coefficient
Correlation is simply the relationship between two variables (x & y), varying between -1 and +1. Correlation is a descriptive measure of a central tendency and does not necessarily indicate causal relationships. When looking at graphs, an upward trend indicates a positive correlation; a downward trend indicates a negative correlation; and a scattered or messy graph indicates no correlation at all.
contributed by Jennifer Blue
| Size of positive correlation | Size of negative correlation | Interpretation |
|---|---|---|
| .90 to 1.00 | -.90 to -1.00 | Very high positive (negative) correlation |
| .70 to .90 | -.70 to -.90 | High positive (negative) correlation |
| .50 to .70 | -.50 to -.70 | Moderate positive (negative) correlation |
| .30 to .50 | -.30 to -.50 | Low positive (negative) correlation |
| .00 to .30 | .00 to -.30 | Little, if any, correlation |
contributed by Frank LaBanca EdD
Interpreting the line of best fit can show outliers. Outliers can lead to different interpretations of data, and an easy method for spotting outliers is through a scatterplot.
contributed by Lauren Moyer
Recall that this is the r value, not the p value, when interpreting the r value. The Pearson correlation does not show causation. For example, if the r value has a high positive correlation between a teacher shortage and the deterioration of the ozone layer, it does not necessarily mean that the teacher shortage caused the deterioration of the ozone layer.
contributed by Lisa Daigle
