Correlations

• Correlations measure the relationship between two or more variables.
• Correlations calculate coefficients to show the type and strength of the relationship between the variables.
• A positive correlation has a coefficient between 0 and +1, the closer it is to +1 the stronger the correlation.
• A negative correlation has a coefficient between 0 and -1, the closer it is to -1 the stronger the correlation.
• Correlations can be weak (closer to 0) or strong (closer to 1).
• Correlations can be perfect if the coefficients are either +1 (perfect positive) or -1 (perfect negative).
• Correlations are displayed in scattergrams.
• When no correlation is seen a coefficient of zero will show.
• A positive correlation is found as one variable increases so does the other.
• A negative correlation is found as one variable increases, the other decreases.
• A perfect positive correlation is +1 and a perfect negative correlation is -1.
• No correlation means there is no relationship between the variables.
• Correlations can be seen when a line of best fit is drawn on a scattergram. The closer the points are to the line of best fit, the stronger the correlation.
• The direction of the line of best fit on a scattergram, tells us the type of correlation.

  
• Correlations are calculated using a Spearman's Rho test or Pearson's Product-Moment correlation to get a coefficient between -1 and +1.
• Correlations investigate the relationship between co-variables.
• Correlations cannot infer cause and effect.
• Correlations are shown on scattergrams, which plot one variable against another.
• Correlation is a non experimental method.
• This measures the strength of the relationship between two or more co-variables.
• Positive correlation-

  High values of one variable are associated with high values of the other.

  EG- Height and Weight
• No Correlation-

  There is no relationship between the two variables.

  EG- Number of freckles and shoe size.
• Negative Correlation-

  High values of one variable are associated with low values of another.

  EG- Smoking and Poor health.
• Correlation coefficients-

  Expressed by the strength.

  The correlation coefficient is always between -1 and +1.

  +1 represents positive

  -1 represents negative

  0 represents no correlation

  + or - represents the direction of correlation.
• Strength

  Provide a strength and direction of a relationship between variables and can be used as a starting point to assess the relationship between variables before committing to an experimental study.
• Strength

  Allow researchers to look at the relationship between variables that you would not be able to experimentally investigate.
• Strength

  Quite quick and economical to carry out because there is no need for a controlled environment and no manipulation of variables is required, so they are less time consuming than the planning and execution of setting up an experiment.
• Strength

  Can also use secondary data which means it is less time consuming.
• Weakness

  Correlations don't provide a cause and effect relationship; we cannot conclude that one variable is causing the other to change. This can sometimes lead to correlations being misinterpreted or misused.
• Weakness

  Although correlations can tell us the strength and direction of variables, they cannot tell us why the variables are related.
• Weakness

  It may also be the case that another untested variable is causing the relationship between the two co-variables. This is known as the third variable problem.