Crosstabulation tables (contingency tables) display the relationship between two or more categorical (nominal or ordinal) variables. The size of the table is determined by the number of distinct values for each variable, with each cell in the table representing a unique combination of values. The purpose of a crosstabulation is to show the relationship (or lack thereof) between two variables. In SPSS, numerous statistical tests are available to determine whether there is a relationship between the variables in a table. The crosstab function is the Statistics Base option in SPSS.
To build crosstab in SPSS, to got the menus choose:
Analyze > Descriptive Statistics > Crosstabs
From there you can pickup two variables to examine the relationship between them. It is often difficult to analyze a crosstabulation simply by looking at the simple counts in each cell, thus a number of tests are available to determine if the relationship between two crosstabulated variables is significant. One of the more common tests is chi-square. One of the advantages of chi-square is that it is appropriate for almost any kind of data.
Pearson chi-square tests the hypothesis that the row and column variables are independent. The actual value of the statistic isn't very informative. The significance value (Asymp. Sig.) has the information we're looking for. The lower the significance value, the less likely it is that the two variables are independent (unrelated). In this case, the significance value is so low that it is displayed as .000, which means that it would appear that the two variables are, indeed, related.
In SPSS, you can add a layer variable to create a three-way table in which categories of the row and column variables are further subdivided by categories of the layer variable. This variable is sometimes referred to as the control variable because it may reveal how the relationship between the row and column variables changes when you "control" for the effects of the third variable.