correlations

• Correlation is a method used

  to analyse data, to analyse

  the association between two

  variables.
• Types of Correlation

  Correlation illustrates the strength and direction of an

  association between two or more co-variables (things

  that are being measured).

  Correlations are plotted on a

  scattergram. One co-variable forms

  the x-axis and the other the y-axis.

  Each point or dot on the graph is the

  x and y position of each co-variable.
• Types of Correlation

  We might expect to see a

  positive correlation between the

  two variables if we plotted the

  data on a scattergram – a

  positive correlation means the

  more caffeine people drink, the

  higher their level of anxiety.
• Types of Correlation

  Perhaps we could also get these same people to record

  how many hours sleep they have over the same period.

  Drinking a lot of caffeine often disrupts

  sleep patterns, so perhaps the more

  caffeine someone drinks the less sleep

  they have. This would be a negative

  correlation insofar as one variable

  rises the other one falls.
• Types of Correlation

  Finally, we might also persuade our participants to record

  the number of dogs they see in the street within the same

  week. As far as we are aware, there is no relationship

  between the number of caffeine drinks

  someone has and the number of dogs they

  see in the street. For this reason, we might

  expect to find something close to a zero

  correlation between these two variables.
• Correlation;

  A mathematical technique in

  which a researcher investigates

  an association between two

  variables, called co-variables.
• The Difference Between Correlations and

  Experiments

  In an experiment the researcher

  controls or manipulates the

  independent variable (IV) in order to

  measure the effect on the dependent

  variable (DV). As a result of this

  deliberate change in one variable it is

  possible to infer that the IV caused any

  observed changes in the DV.
• The Difference Between Correlations and

  Experiments

  In contrast, in a correlation, there is no

  such manipulation of one variable and

  therefore it is not possible to establish

  cause and effect between one co-variable

  and another. Even if we found a strong

  positive correlation between caffeine and

  anxiety level we cannot assume that

  caffeine was the cause of the anxiety.
• The Difference Between Correlations and

  Experiments

  People may be anxious for all sorts of

  reasons (personality type, a stressful

  job, personal problems) and

  therefore their influence on the

  other variable cannot be

  disregarded.

  These ‘other variables’ are called

  intervening variables.
• Hypothesis in Experiments vs. Correlations

  1. Participants in condition A who sleep 8 hours a

  night will have a higher IQ score, than those in

  condition B who sleep 2 hours a night.

  2. There will be a positive relationship between

  hours of sleep and IQ score.
• Hypothesis in Experiments vs. Correlations

  1. There will be a negative association

  between hours of sleep and IQ score.

  2. There will be an association between

  hours of sleep and IQ score.
• Evaluation of Correlations (AO3)

  Strengths

  Correlations are useful when

  investigating trends. If a

  correlation is significant, then

  further investigation (such as

  experiments) is justified.
• Evaluation of Correlations (AO3)

  Strengths

  The procedures in a

  correlational analysis can

  usually be easily replicated,

  which means that the findings

  can be confirmed.
• Evaluation of Correlations (AO3)

  Strengths

  Correlations are relatively

  quick and economical to

  carry out. There is no need

  for a controlled environment

  and no manipulation of

  variables is required.
• Evaluation of Correlations (AO3)

  Limitations

  In a correlational analysis, no conclusion can be made about

  one co-variable causing the other.

  Correlations cannot demonstrate

  cause and effect between

  variables and therefore we do not

  know which co-variable is causing

  the other to change.
• Evaluation of Correlations (AO3)

  Limitations

  This is additionally a limitation

  because people may assume

  causal conclusions. This is a

  problem because is creates

  misinterpretations of

  correlations.
• Evaluation of Correlations (AO3)

  Limitations

  It may also be the case that another

  untested variable is causing the

  relationship between the two

  co-variables we are interested in –

  an intervening variable. This is

  known as the third variable

  problem.