Correlation
1) Correlation is used when you wish to describe the strength and direction of the relationship between two variables (usually continuous).
2) It can also be used when one of the variables is dichotomous—that is, it has only two values (e.g.
sex: males/females).
3) The statistic obtained is Pearson’s product-moment correlation (r). The statistical signifi cance of r is also provided.
Partial correlation
4) Partial correlation is used when you wish to explore the relationship between two variables while statistically controlling for a third variable.
5) This is useful when you suspect that the relationship between your two variables of interest may be infl uenced, or confounded, by the impact of a third variable.
6) Partial correlation statistically removes the infl uence of the third variable, giving a cleaner picture of the actual relationship between your two variables.
Multiple regression
7) Multiple regression allows prediction of a single dependent continuous variable from a group of independent variables.
8) It can be used to test the predictive power of a set of variables and to assess the relative contribution of each individual variable.
Logistic regression
9) Logistic regression is used instead of multiple regression when your dependent variable is categorical.
10) It can be used to test the predictive power of a set of variables and to assess the relative contribution of each individual variable.
Factor analysis
11) Factor analysis is used when you have a large number of related variables (e.g. the items that make up a scale) and you wish to explore the underlying structure of this set of variables.
12) It is useful in reducing a large number of related variables to a smaller, more manageable, number of dimensions or components.
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