In most studies in the social sciences we collect information on more than just two variables. Although it would be possible and simpler to examine the relationships between these variables just two at a time, there are serious disadvantages to restricting oneself to this approach, as we shall see. It is preferable initially to explore these data with multivariate rather than bivariate tests. The reasons for looking at three or more variables vary according to the aims and design of a study. Consequently, we will begin by outlining four design features that only involve three variables at a time. Obviously these features may include more than three variables and the features themselves can be combined to form more complicated designs, but we shall discuss them largely as if they were separate designs. However, as has been done before, we will use one set of data to illustrate their analysis, all of which can be carried out with a general statistical model called the general linear model. Although the details of the model are difficult to understand and to convey simply (and so this will not be attempted here), its basic principles are similar to those of other parametric tests we have previously discussed such as the t test, one-way analysis of variance and simple regression.