You have a hypothesis involving two variables. This is called a bivariate relationship. Now you need to use statistics to see if the data you have gathered support that hypothesis (or allow us to reject the null!). How you do this depends on the levels of measurement in both the independent and dependent variables. In this unit we will concentrate on the most simple possibility, the case where both independent and dependent variables are nominal or ordinal with just a few values. In this case we use crosstabulations to make the test. But if the variables are at other measurement levels, the test changes. Let me give you a chart to guide you in what can be done. We will look at correlation, regression, and analysis of variance (called ANOVA) later.
 
                                                                            Independent Variable

Dependent	Nominal	Ordinal	Interval/Ratio
Nominal	X-tab	X-tab	Collapse I.V. & X-tab
Ordinal	X-tab or compare medians	X-tab or compare medians	Collapse I.V. & X-tab or compare medians
Interval/Ratio	X-tab & compare means or analysis of variance	X-tab & compare means or analysis of variance	Collapse both variables or scatterplot/regression

 
The Crosstabulation procedure (X-tab) works best when you have no more than three rows in the table. That means no more than three values for the dependent variable. Of course, you can always collapse rows to get this, or you can compare measures of central tendency (medians or means). It really does not matter how many columns you have, but you had better have few enough so that you have a sufficient number of cases in each cell. You can collapse columns to get this. As we have noted earlier, you have a tradeoff here between what is desirable theoretically and what is practical. Theory might tell us that many values for the independent variable are needed for a complete understanding of how the dependent variable reacts to different values of the independent variable. But we may only have enough cases in the sample to have only three columns. Cells with just a few cases in them can radically alter percentage shifts if one or two cases are in one cell rather than in another on that row. So pay attention when cell frequencies get low. You may need to do some collapsing. As you will see later, tests of statistical significance help us to keep a handle on this. If the cell frequencies get too small, the shift will not come out to be statistically significant.

Corbett does a good job in talking about how to set up and read crosstabulations. So read the chapter to get that. We will, of course, go over examples in class. The key is setting it up correctly and then reading acoss each row, looking to see if the trends, if any, fit the relationship expected in the hypothesis.