Correlations

• Correlation illustrates the strength and direction of an association between two or more co-variables (things that can be measured). Correlations are plotted on a scattergram: one co-variable forms the x-axis, and the other the y-axis. Each point on the graph is the x and y position of each co-variable.
• Positive Correlation


  As one co-variable increases, the other increases, is seen by a positive coefficient e.g. +0.73

  Alternatively, if one co-variable decreases, the other decreases, is seen by a positive coefficient.
• Negative correlation


  When one co-variable increases, the other co-variable decreases, is represented by a negative coefficient, e.g. -0.91

  Note that a negative coefficient may not be weak, a coefficient close to 0 is weak and closer to ±1 is stronger
• Zero correlation


  There is no relationship between variables. The coefficient is 0 or is extremely close to 0.
• What is the difference between correlation and experiment

  In an experiment the researcher controls or manipulates the IV in order to measure the effect on the DV. As a result of this deliberate change in one variable it is possible to infer that the IV has caused changes to the DV.

  An experiment does yield a cause and effect relationship, whereas a correlation does not. Just because there is a relationship doesn't mean we can assume one variable is effecting the other, or vice versa. They may be intervening variables causing the relationship which are overlooked.
• How do you write a hypothesis for a correlation

  Hypothesis written for correlations are not the same as those for experiments. The hypothesis for a correlation still has to clearly state the expected relationship between the co-variables and must be clearly operationalised.

  Directional: There is a positive correlation between the price of a chocolate bar and its tastiness rating (out of 20)

  Non-Directional: There is a correlation between the price of a chocolate bar and its tastiness rating (out of 20)