Comparing two variables

Sometimes we want to know how the scores in Variable 1 compare to Variable 2. A common situation is comparing your study participants' pre-test and post-test scores. Or you might compare your participants' level of satisfaction with two things such as photos manipulated in Pretty Photo Premier versus Super Snapshot Suite. For these situations, we would use Mr. T-Test again, this time a paired samples t-test.

In the last blog entry, we walked through the example of deciding whether a picture improved sales per visitor on the MowBee site. Using an independent samples t-test, we compared the means and variability of the purchases by Pic vs. No Pic visitors. The less the two pots of scores overlap, the more confident we can be that the Pic condition did statistically increase sales over the No Pic condition. When we use a paired samples t-test, we are also comparing means and variability but of two variables for one group rather than one variable for two groups.

Example: We ran a survey asking participants to rate satisfaction on a scale of 1 to 7 with the results produced in Pretty Photo Premier versus Super Snapshot Suite. We have a data file of 100 cases (participants) and three variables: participant id number, PPP satisfaction, and SSS satisfaction. We want to know whether the higher ratings for SSS are statistically significant. If they were, we would write the results with the t-test score (t) and the level of probability (p) that we falsely found significant difference between the two products. The means and standard deviations for each variable (PPP and SSS) would also be included so you could see which group was high and which was low. Our results could look like this:

Participants were significantly more satisfied, t (99) = 4.85, p < 0.01, with the results of Super Snapshot Suite (M=5.60, SD=1.90) than Pretty Photo Premier (M=3.50, SD = 1.43).