Correlations

Correlations look for

A relationship or association between two co variables

This is not to say that one thing causes another simply that one thing varies in accordance with another

There is no manipulation of the variables

Positive correlation

As one co variable changes the other changes in the same direction

So as one variable increases the other also increases

Negative correlation

As one co variable changes the other variable changes in the other direction

So as one variable increases the other decreases

Zero correlation

No relationship between the co variables

Correlations

Can’t establish cause and effect

Correlation coefficient

The correlation coefficient is measured from +1 to -1

A correlation can be positive negative or no correlation

The higher the number the stronger the relationship

If we find that there is a relationship between co variables X and Y there are three possible explanations

X caused Y

Y caused X

A third variable (Z) caused the change to both X and Y

How to use correlations

Decide what co variables you will be measuring and operationalise your variables

Measure each participant on both co variables

Plot the value on the scatter graph to see if there is a relationship

Carry out a statistical test to see if the relationship is significant or due to chance

Strength

Precise information on the degree of relationship between variables is available in the form of the correlation coefficient - if a significant relationship is found it can suggest ideas for experimental studies to determine cause and effect relationships

Can be used when it would be impractical or unethical to manipulate variables

Strengths

It can make use of existing data and so often can be a quick and easy way to carry out research

Often little manipulation of behaviour is required- all the researcher does is measure existing variables- therefore it is often high in mundane realism and ecological validity

Limitations

No cause and effect relationships can be inferred - one variable cannot be said to cause an increase or decrease in the other variable as the relationship could be caused by a third variable - so correlations are open to misinterpretations

As with experiments the correlations may lack internal/ external validity - for example the methods used may lack validity or the sample may lack generalisability