By published 2008
Correlation means association - more precisely it is a measure of the extent to which two variables are related.
If an increase in one variable tends to be associated with an increase in the other then this is known as a positive correlation. An example would be height and weight. Taller people tend to be heavier.
If an increase in one variable tends to be associated with a decrease in the other then this is known as a negative correlation. An example would be height above sea level and temperature. As you climb the mountain (increase in height) it gets colder (decrease in temperature).
When there is no relationship between two variables this is known as a zero correlation. For example their is no relationship between the amount of tea drunk and level of intelligence.
A correlation can be expressed visually. This is done by drawing a scattergram - that is one can plot the figures for one variable against the figures for the other on a graph.
When you draw a scattergram it doesn't matter which variable goes on the x-axis and which goes on the y-axis. Remember, in correlations we are always dealing with paired scores, so the values of the 2 variables taken together will be used to make the diagram. Decide which variable goes on each axis and then simply put a cross at the point where the 2 values coincide.
Strictly speaking correlation is not a research method but a way of analysing data gathered by other means. This might be useful, for example, if we wanted to know if there were an association between watching violence on T.V. and a tendency towards violent behaviour in adolescence (Variable B = number of incidents of violent behaviour observed by teachers).
Another area where correlation is widely used is in the study of intelligence where research has been carried out to test the strength of the association between the I.Q. levels of identical and non-identical twins.
Some uses of Correlations
- If there is a relationship between two variables, we can make predictions about one from another.
- Concurrent validity (correlation between a new measure and an established measure).