Correlations

• The difference between correlations and experiments:

  Experimental designs require manipulation of the independent variable and measurement of the change in the dependant variable. Whereas in a correlation study, NO variables are manipulated, TWO co-variables are measured and compared to look for a relationship.
• what are Co-Variables?

  the two factors that are measured/collected by the researcher and compared to each other. e.g. Age, IQ, Reaction time, Bank account balance, hight, hostility level.
• What are Scattergrams ?

  a graph used to plot the measurement of two co-variables. Scattergrams visually display the relationship between co-variables.
• Positive correlation

  As one co-variable increases the other co-variable increases
• Negative correlation

  as one co-variable increases the other co-variable decreases
• Zero correlation

  no relationship between co-variables
• Correlation coefficient:

  Represents both the strength and direction of the relationship between the co-variables as a number between -1 and +1.
• perfect negative co efficient

  -1
• perfect positive

  +1
• How are correlation coefficients calculated?

  By using statistical tests such as spearman's rho Pearson's. Inter-rater and test-retest reliability is assessed in this way.
• How is a strong correlation coefficients judged?

  a corelation coefficient of 0.8 is usually judged as a strong correlation.
• AO3 ~ limitation

  • Correlation does NOT show causation. while a strong correlation may suggest a relationship exists between two variables , it does not show which co-variable led to the change in the other co-variable and there is a possibility that an unknown third variable caused the change in both covariables.
• AO3 ~ support

  correlational studies can highlight potential causal relationships, these can then be tested with experimental methods to discover cause and effect relationships
• AO3 ~ support

  often the covariable data already exists and is easily accessible, this means there's minimal ethical problems in data collection.
• Correlation coefficient

  is a useful tool in describing both the direction and strength of relationships between factors.