Correlation

Correlation

1. Refers to a measure of how strongly two or more variables are related to each other

   • If there is no correlation between two variables they are said to be uncorrelated.

2. A positive correlation means that high values of one variable are associated with high values of the other; the variables increase together.

3. A negative correlation means that high values of one variable are associated with low values of the other; as one variable increases the other decreases. Like a positive correlation, a negative correlation still indicates that some kind of relationship exists.

4. A correlation coefficient refers to a number between -1 and +1 and states how strong a correlation is.

   • If the number is close to +1 then there is a positive correlation.
     • +1.0 - perfect positive correlation
     • +0.8 - strong positive correlation
     • +0.5 - moderate positive correlation
     • +0.3 - weak positive correlation
     • + 0.1 - very weak positive correlation
   • If the number is close to -1 then there is a negative correlation.
     • -0.1 - very weak correlation
     • -0.3 - weak negative correlation
     • -0.5 - moderate negative correlation
     • -0.8 - strong negative correlation
     • -1.0 - perfect negative correlation
   • If the number is close to 0 then the variables are uncorrelated.
     • 0 - no correlation

5. Correlations are good for showing possible relationships between variables and some times are the only practical or ethical way of carrying out an investigation.

   • Researchers may use correlational analysis as a starting point in their research and if a relationship between variables is found they can then investigate this further

6. Correlational analysis cannot demonstrate a cause and effect relationship between variables.

7. Correlational analysis always involves quantitative data.

8. When carrying out correlational analysis the data is summarised by presenting the data in a scattergram (or scattergraph).

   • It is important that the scattergram has a title and both axes are labelled.
   • From the scattergram we may be able to say whether there is a strong positive correlation, a weak positive correlation, no correlation, a weak negative correlation or a strong negative correlation but we can not make a conclusion about the hypothesis.