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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