Correlations (AQA GCSE Psychology)

• A correlation is not a research method, it is an analysis of the possible relationship between co-variables 
• In correlational research, the variables are not manipulated (i.e. there is no IV), instead two co-variables are measured and compared to look for a relationship
• One or both of the co-variables could be pre-existing, e.g.
• School attendance measured as days present at school in Year 11 (co-variable) and number of GCSEs achieved (co-variable)
• Average temperature in August (co-variable) and number of arrests made for violent behaviour in August in one town
• One or both of the co-variables could be measured for the research itself, e.g.
• Number of takeaway meals consumed in a month (co-variable) and self-reported stress levels for the same month (co-variable)
• Average number of hours sleep in one week (co-variable) and number of cups of caffeinated beverages consumed in the same week (co-variable)
• A correlation uses two scores e.g. number of cups of caffeine and the number of hours of sleep
• In the case of self-reported data there are two scores per participant e.g. an average of 4 hours of sleep per night correlated with 58 cups of caffeine consumed in a week
• In the case of pre-existing data the researcher would simply go to the records e.g. Student X was present for 188 days in Year 11 - they achieved 10 GCSEs 

• There are three types of correlation:
  • Positive correlation: One co-variable increases as does the other (but not necessarily at the same rate) e.g. calories consumed and weight gained
  • Negative correlation: One co-variable increases while the other co-variable decreases  (but not necessarily at the same rate) e.g. hours spent watching television and level of fitness
  • Zero correlation: There is no relationship between the variables e.g. hair colour and IQ
• Correlations are plotted as one point on a scatter diagram and the direction of the ‘line of best fit’ indicates what type of correlation there is between the two variables:

Scatter diagrams and the type of correlation shown on each.

• Analysing the relationship between co-variables can be done either with a scattergraph visually or by calculating the correlation coefficient  which is expressed as a numerical value
• The correlation coefficient represents both the direction (i.e. positive or negative) and strength of the relationship between the variables, expressed as a value between -1 and +1
• A perfect positive correlation is expressed as  +1
• A perfect negative correlation is expressed as  -1 
• No relationship is expressed as 0 
• Both positive and negative coefficient correlations can be described as weak, moderate or strong e.g. a correlation coefficient of 0.03 is a weak positive correlation; a correlation coefficient of - 0.08 is a strong negative correlation

Evaluation of correlations

Strengths

• Correlations show a relationship between variables which can be a good starting point for further research e.g. if take-away meals show a positive correlation with stress then this could inform research projects into combatting the downsides of convenience foods
•  The data can be analysed quickly, particularly if it is pre-existing data

Weaknesses

• Correlations cannot show cause and effect so it is unclear, e.g., why a high consumption of take-away food is linked to high stress
• Extraneous relationships with other variables may affect the co-variables e.g. number of days absence from school may be due to illness rather than to choice