The Forgotten Tabs: Profiling Analysis


The final installment of the Forgotten Tabs Series is focused on the Profiling Analysis tab. The Profiling Analysis tab allows us to compare the two groups of a binary variable by generating an output of all of the variables in a dataset for which those two groups are significantly statistically different. Once in the tab, simply select the binary variable you’d like to profile, and you’ll get an output like this:


Here you can see that we are comparing students who enrolled at our institution to students who did not enroll to see what the major differences are between the two populations.


If your y-variable is binary, this tab provides great insight into how the populations that fall into the two possible categories of your y-variable differ. This also provides another way to look at your data. One common question I am asked is something along the lines of “Without knowing the scores, how do I know which variables I should be looking at?”, meaning that though the scores are helpful in decision making, sometimes knowing the differences between the variables that make up those scores can be just as helpful. The Profiling Analysis tab directs you to only those variables for which the two populations differ significantly.


One great use of the profiling tab in higher education is to compare the differences between graduates and non-graduates. The following illustrates what this analysis might look like:


Using an output like this, we can see that students who graduated generally lived closer to campus, had a higher HS GPA, applied earlier, and had higher SAT Math scores than non-graduates. Highlighting these differences and placing an average value on each variable for both graduates and non-graduates allows us greater insights into the differences between these two populations. The Profiling Analysis tab is a great resource whenever you want to compare two populations to see how and where they differ statistically.



-Caitlin Garrett, Statistical Analyst at Rapid Insight

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Decentralize analytics. Harness the power of many.