Correlations

• Correlational Studies - Strengths:

  • Finding a correlation between 2 co-variables may suggest the need for further research.
  • Can use secondary data so it is fairly easy to conduct.
  • No need to manipulate variables or a controlled environment.
• Correlational Studies - Weaknesses:

  • Studies can only tell us if there is a relationship but not why.
  • Another variable may actually be causing the relationship.
• Correlation does not mean one variable caused the other to change.

  There are 3 possible explanations:

  1. Causality - One variable caused the other to change.
  2. Chance - Variables just happen to be related.
  3. Third factor involved - Another variable is causing the relationship.
• No Correlation:

  • The two variables are not related at all.
  • Anything measured close to 0 on the correlation coefficient shows zero correlation.

  
  

  r = - 0.02 shows no correlation

  r = +0.06 shows no correlation
• Negative Correlation:

  • As one variable increases, the other variable being measured decreases.
  • Anything measured below 0 on the correlation coefficient shows a negative correlation.

  r = - 0.2 shows a weak negative correlation.

  r - - 0.09 shows a strong negative correlation.
• Positive Correlation:

  • Means both variables move in the same direction.
  • As one increases, the other value also increases (or they both decrease).
  • Anything measured above 0 in the correlation coefficient shows a positive correlation.

  r = +/- 0.2 is a weak positive correlation.

  r = +/- 0.9 is a strong positive correlation.
• Correlation Coefficient:

  • Correlations can be represented mathematically as a correlation coefficient.
  • This is a number between +1 and -1 and is represented by the letter r.
• • A correlation is a technique that measures whether or not there is a relationship between two variables.
  • The independent variable is not manipulated.
  • It can be a research method in its own right or can be used to analyse data gathered from another method.
  • Correlation is typically shows graphically and in a numerical representation (correlation coefficient).