In this video, you will learn to create contingency tables using StatCrunch. The data set I am using is called two categorical variables. This is just a toy data set that contains two columns of data. The var1 column contains 10 values, the value b in four rows and the value of a in 6 rows. The var2 column contains 10 values as well, the value of c in 6 rows and the value of d in 4 rows. Let's begin by creating a contingency table of the data in the var1 column, cross classified with the data in the var2 column. To do so, under the stat menu, choose tables, contingency, with data. I'll select my row variable as var1 and my column variable as var2. And click compute. The resulting contingency table shows the individual, unique values for each column in the first row in the first column of the table. The remaining cells show the frequency for each variable pairing, as well as the row y's totals, column y's totals, and the total number of pairs. The output also shows the results of a standard chi-square test for independence. Since the cell counts are low in this case, a warning message is displayed for these test results. I'll discuss a better alternative for this data later in the video. StatCrunch also allows the user to display additional information in their contingency table. To view the different options, under the options menu, choose edit. This takes us back to the original window where we built our contingency table. In this window, StatCrunch allows the user to customize their contingency table. Under the display options, you will see the options that can be added to the contingency table. For this example, I'll choose row percent and click compute. Now the resulting table shows that 2/3, 66.6% of the a values, are paired with the c values, and 1/3, 33.3% percent, are paired with the d values. The table also shows a 50/50 split between c and d pairings for the b values. As mentioned prior, the chi-square test is not appropriate with this data due to the small cell counts. StatCrunch offers a number of different tests that can be computed from the contingency table output. Back under options, under the box labeled hypothesis test, you will see the different options. Note some of these calculations are restricted to 2x2 tables and are labeled accordingly. I'm going to de-select the chi-squared option and select Fisher's exact test for independence for this example. Also notice, further down in the window, StatCrunch can also calculate confidence intervals. However, for this example, I'm not going to select the confidence interval option. Now click compute. And the resulting output shows the new results for the Fisher's exact test.