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

The Forgotten Tabs: Correlation Analysis

Next up in the ForgottenTabs series is the Correlation Analysis tab. The Correlation Analysis tab provides a correlation coefficient for any two variables in our dataset. To get these values, simply check the boxes next to the variables you’re interested in correlating. The resulting correlation coefficient can be either positive or negative, and generally if the value is greater than +/- .1, we say that those two variables are significantly correlated. Knowing how different variables are correlated can allow us to understand variable selection and create more accurate models. 

Sometimes a high correlation value can explain why a variable may not have made its way into a final model if a similar variable did. An example of this is the correlation between the variables “SAT Math”, “SAT Verbal”, and “HS GPA”. As indicators of student success, you might guess that these variables have a positive correlation – so, you would expect that a student with a relatively high HS GPA will, in turn also have relatively high SAT scores, and vice versa. If we were to build a model that utilized these variables, however, we would typically get something like the following: 

Here we see an “SAT Math” variable in our final model, but “SAT Verbal” and “HS GPA” are nowhere to be found. 

Looking at these variables in the Correlation Analysis tab will confirm our earlier guess that the variables are correlated, which in turn explains why all three are not included in the model: 

Note that each correlation coefficient is well above the general .1 threshold of significant correlation, meaning that these variables are, in fact, strongly correlated. This correlation is accounted for when we build our predictive models, so that if a change in one generally brings about a change in another, Analytics will pick the stronger predictor of the two and leave the other out.

Another thing to check for in a correlation analysis is for perfect predictors. If a variable pair has a correlation equal to one, you’ll know that those variables are perfect predictors of each other. Some common examples are retention and attrition, and housing deposit and enrollment. These things are perfect predictors of each other because retention is the opposite of attrition, and you typically need to enroll to make a housing deposit. Using the Correlation Analysis tab can tell you if you do have any perfect predictors; if you do, you should be sure to take one of the variables out of the analysis. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight

The Forgotten Tabs: Means Analysis

Continuing with the Forgotten Tabs series, the next tab we’ll be focusing on is the Means Analysis tab. The Means Analysis tab provides the mean, number of observations, maximum values, and minimum values for any of the variables in your dataset. You also have the option to take “means by” and “subclass by” to view the means of multiple subcategories across variable combinations.

 In this case, we’re comparing the attrition rates of legacy and non-legacy students by whether or not they received financial aid. For each of these four possible categories, we are able to see the mean attrition rate, the number of observations, and the min and max values. Doing so allows us to see the differences in attrition rate over a couple of different characteristics.

The Means Analysis tab can be also useful in comparing data from different cohorts or years in order to spot trends. In the example below, we’re comparing attrition rates by year, which allows us to pick up on any trends or changes that are occurring from year to year. If for some reason we were noticing a year that had a much higher or lower attrition rate than the other years, this gives us the opportunity to pick up on that and investigate further as to why that might be. 

You might also note that beyond looking at ‘Attrition’, we are also looking at a variable called ‘Predicted Attrition’. This variable represents the predicted attrition probabilities that we’ve assigned to each student. In this case, we’ve grouped these values by year to get an idea of how well we’re predicting attrition for that year compared to the actual attrition rate. Comparing our predicted values to actual values gives us a sense of any weaknesses from year to year that our predictive model might have. If we do find any weakness in predictive ability, we have the opportunity to go back and further fine-tune our model in order to incorporate our findings. 

-Caitlin Garrett, Statistical Analyst at Rapid Insight

The Forgotten Tabs: Frequency Analysis

During this year’s User Conference, I gave a presentation called “Analytics: The Forgotten Tabs”, which I’ve decided to expand into a blog series. The purpose of this series will be to explain how and why to use four of the lesser-known tabs in Analytics – Frequency Analysis, Means Analysis, Correlation Analysis, and Profiling Analysis. Each entry will focus on one of these tabs and we’ll start with the Frequency Analysis tab.

The Frequency Analysis tab’s output is actually fairly simple; it gives you the frequency of occurrence for any binary or categorical variable. For a single variable, it will output counts and percentages for each value of that variable. It is also capable of creating two-way cross frequencies, which output raw numbers, as well as row, column, and total percentages. 

While Frequency Analysis isn’t actually performing any statistical test – its functions are simple summing and percentage operations – it is providing valuable information about the number and percentage of observations that fall into each sub-category of a binary or categorical variable. Using this tab gives you a quick by-the-numbers glance at variables like “Ethnicity” or “Department”, which allows you to instantaneously compare subcategories without doing any manual addition or division. This is particularly useful when you’re working with a variable such as “Department” that may have a lot of sub-categories.

One other little-known fact about the output from Frequency Analysis (and other tabs) is that you can save it to the Report Bar the same way you would a graph or chart. To do so, click on the ‘Reports’ section of the taskbar and select ‘Launch Report Bar’. 

The report bar will float over your analysis; you can save things to it by dragging the outputs you wish to save into the bar itself. Saving things to the report bar allows you to export them from Analytics in a few different ways. If you select the ‘PPoint’ option before clicking ‘Export’, Analytics will create a PowerPoint such that each of the graphs our outputs you saved will become their own slide in the presentation. The other option you have is to save the information you’re interested in to the Reports tab in Analytics (by selecting the ‘Report’ option on the Report Bar), which allows you to create custom reports within the program and export these reports as Word Documents to be used later on. In any case, there are a number of ways to take the information that you’re getting from Analytics and use it in a presentation or report down the line.  

-Caitlin Garrett, Statistical Analyst at Rapid Insight