Correlations (OCR GCSE Psychology): Revision Note

What is a correlation?

• A correlation is not a research method but a way of analysing data to see if there is a relationship between two co-variables

• Unlike experiments, no variables are manipulated — instead, both are measured and compared to see if changes in one are linked to changes in the other

• Correlations use quantitative data, which can come from questionnaires, observations, or pre-existing records

Co-variables

• The two variables being measured are called co-variables

• Neither is an IV or DV—both are measured as they naturally occur or are 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 slept in one week (co-variable) and number of cups of caffeinated beverages consumed in the same week (co-variable)

• Each participant or data point provides two scores, one for each co-variable (e.g. hours of sleep and stress score)

Types of correlation & scatter diagrams

• There are three types of correlation:

  • Positive correlation: As one variable increases, so does the other, e.g. calories consumed and weight gained

  • Negative correlation: As one variable increases, the other decreases, 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 shown on scatter diagrams, with one dot per participant

  • The direction of the line of best fit shows the type of correlation

Correlation coefficient

• A numerical value that shows the strength and direction of a correlation, ranging from: –1 to +1

  • +1 = perfect positive correlation

  • –1 = perfect negative correlation

  • 0 = no relationship

• Both positive and negative correlations can be weak, moderate, or strong, depending on how close the value is to ±1

  • E.g. a correlation coefficient of 0.2 is a weak positive correlation; a correlation coefficient of -0.8 is a strong negative correlation

Evaluation of correlations

Strengths

• Correlations identify relationships between variables that can be explored further through experiments

• They allow analysis of existing data quickly, often without needing new data collection

• Correlations are useful when manipulating variables would be unethical (e.g. studying links between stress and illness)

Weaknesses

• Correlations cannot show cause and effect — we can’t tell which variable causes the change

• Other variables may influence the results (e.g. illness could affect both attendance and grades)

• Use only quantitative data, which may lack depth or validity