correlation

• correlation is a way to measure the strength between two or more co-variables
• a positive correlation is where one co-variable increases and so does the other.
• a negative correlation is where on co-variable increases and the other decreases
• no correlation is when the variables have no relationship
• correlational hypothesis predicts a relationship between two variables
• A directional hypothesis for a correlation states whether the relationship will be a positive or negative correlation
• A non-directional hypothesis simply states that there will be a correlation.
• correlations are designed to investigate the strength and direction of a relationship between two variables.
• The strength of the correlation is expressed by the correlation coefficient
• The correlation coefficient is always a figure between +1 and -1 where +1 represents a perfect positive correlation and -1 represents a perfect negative correlation and 0 means there is no correlation.
• the closer the correlation coefficient is to 0, the weaker the correlation.
• the closer the correlation coefficient is to 1 (-1) the stronger the correlation.
• correlations are useful as a tool of research as they provide a strength and direction of a relationship between variables and can be used a starting point to assess the relationship between variables before committing to an experimental study.
• Correlation allows researchers to look at the relationship between variables that you would not be able to experimental investigate e.g obesity and junk food consumption
• Correlation are 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 that the planning and execution of setting up an experiment
• correlation can use secondary data which means they are less time consuming.
• 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.
• Although correlations can tell us the strength and direction of variables, they cannot tell us why the variables are related
• third variable problem

  another untested variable is causing the relationship between the two co-variables