Correlation is a statistical technique used to quantify the strength of relationship between two variables.

Used a lot in psychology investigations, for example Murstein (1972) carried out a correlation analysis of ratings of attractiveness in partners ('computer dance' study).

Strengths:	Weaknesses
Calculating the strength of a relationship between variables.	Cannot assume cause and effect, strong correlation between variables may be misleading.
Useful as a pointer for further, more detailed research.	Lack of correlation may not mean there is no relationship, it could be non-linear.

For a correlational study, the data can be plotted as points on a scattergraph. A line of best fit is then drawn through the points to show the trend of the data.

If both variables increase together, this is a positive correlation.

If one variable increases as other decreases this is a negative correlation.

If no line of best fit can be drawn, there is no correlation.

Correlation can be quantified by using a correlation coefficient - a mathematical measure of the degree of relatedness between sets of data.

Once calculated, a correlation coefficient will have a value from -1 to +1.

+1 = perfect positive correlation all points on straight line, as x increases y increases. A value close to one indicates a strong positive correlation.

0 = no correlation points show differing degrees of correlation.

Note: A correlation around zero may disguise a non-linear relationship.

-1 = perfect negative correlation all points on straight line, as x increases y decreases. A value close to -1 indicates a strong negative relationship.

Note: In real life human situations, or psychology experiments you will not find perfect correlation between variables, life is just like that.

What psychologists do is calculate a correlation coefficient, then, using statistical tables (thought up by brilliant mathematicians) work out the probability that their results could have occurred at random.

If they can say there is a 95% chance of their results really showing a strong correlation, then they accept that there is one.