Correlations

• A correlation is a statistical technique used to measure the relationship between two variables.

  • Unlike an experiment, a correlation does not involve the manipulation of variables—it simply measures whether a relationship exists.
• Correlations are plotted on a scattergram. One co-variable forms the x-axis and the other the y-axis.

  • Co-variables are the variables investigated within a correlation.
• A positive correlation is when as one variable increases so does the other.
• A negative correlation is when as one variable increases, the other decreases.
• A zero correlation is when there is no relationship between the variables.
• Correlations and experiments are different ways of finding the relationship between two variables.

  • In an experiment, the independent variable is manipulated whereas in correlations no variables are manipulated.
  • In an experiment, cause and effect can be determined which is not possible in a correlation.
  • In a correlation, other variables may explain the relationship while in experiments, extraneous variables are controlled.
• Strengths of correlations:

  • Useful for identifying relationships → Allows researchers to see if two variables are linked, this may suggest ideas for future research.
  • Ethically viable → Can study relationships that would be unethical to manipulate in an experiment (e.g., smoking and lung disease).
  • Allows for predictions → If two variables are strongly correlated, one can be used to predict the other (e.g., high stress levels predicting low sleep quality).
  • Quick and economical → No need for a controlled experiment and secondary data can be used.
• Weaknesses of correlations:

  • Does not establish causation → Just because two variables are correlated does not mean one causes the other.
  • Third variables may be involved → A separate variable might be influencing both factors.