Those of you who do add a quantitative piece to your research will be picking and choosing between statistical tests.
The simplest test to measure differences between two groups is the t test.
If we randomly assigned people in an agency to a treatment and a control group to test a marital therapy intervention, we would use the t test in several different ways.
First we might decide to see if the two groups differ much. Just because we've assigned randomly doesn't mean that the characteristics of the two groups will be very well matched. So we could compare age, number of years married, and number of children. If the means tests on any of these variables found one of them to be significantly different, we might want to redo the assignment, perhaps use matching.
Then we might do a pretest of all of the subjects of the experiment to see how both groups fared on the dependent variable. Let's make that marital satisfaction.
Then after the intervention was performed we would see if the pre-test/post-test scores for each group made any significant changes. Then we could compare the changes between the groups to see if there was a significant difference between them.
Very elegant, no?
If we're testing nominal data then we frequently use a contingency table and test for significance with the chi-square test. Instead of comparing means, as the t test does, the chi-square compares frequencies. The greater the difference in observed frequencies from an expected chance value, the greater the chi-square value for each cell.
Above is a table and results of a significant chi-square test
If you tested three groups against a control group, used three different types of marital therapy and tried to determine which was the best, then the t test wouldn't work for you. You would need to use an f test, perform Anova (analysis of variance) or regression.
We'll discuss this in a different post, the one below.
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