correlation

• Correlation


  A mathematical technique in which a researcher investigated an association between two variables, called co-variables
• Co-variables


  The variables investigated within a correlation, for example height and weight. They rate not referred to as the independent variables because a correlation investigates the association between the variables, rather than trying to show a cause and effect relationship
• Positive correlation


  As one co variable increases so does the other. For example, the number of people in a room and noise are positively correlated.
• Negative correlation


  As one co variable increases the other decreases. For example, the following two co variables: number of people in a room and amount of personal space are negatively correlated
• Zero correlation


  When there is no relationship between the co variables. For example, the association between the number of people in a room in Manchester and the total daily rainfall in Peru
• Correlations


  A quick and easy way of showing a possible relationship between two co-variables ( no I.v. and D.v., nothing is manipulated by the researcher )
• Positive Correlation

  
• Negative Correlation

  
• No Correlation

  
• Perfect Positive Correlation

  
• Perfect Negative Correlation

  
• Perfect Positive Correlation =

  +1.0
• Perfect Negative Correlation =

  -1.0
• Disadvantage of Correlations


  Can't tell cause and effect
• Advantage of Correlations


  Sometimes it is the only ethical way to study something
• Time Advantage of Correlations


  Quick and Easy to do
• Positive correlations describe


  A relationship in which both variables increase together
• Negative correlations describe


  The relationship in which one variable increases as the other decreases
• Correlations are plotted on graphs called

  Scattergrams / Scattergraphs
• Strong Correlation >


  0.8
• Moderate Correlation>


  0.4-0.7
• Weak Correlation<


  0.4
• Terms used to describe what correlations show

  Related

  Association

  Relationship

  Linked
• It is quick and easy to see

  Whether or not there is a relationship between two variables