Module 6: Interpreting data to inform decisions

How do we know something? We can have hunches, pull from our own personal experiences, or even identify patterns by asking those around us what they experience. But are these strategies enough to say we know something? In assessment, the answer is no.

In order to say we know something, we collect data from many respondents so that the conclusions we make from the patterns we observe in the data are trustworthy. In assessment, we can actually measure what is happening in order to better understand the outcomes of student affairs practice. Pulling conclusions from this data, then, allows us to make relevant suggestions for practice.

So, how should we go about drawing conclusions from our data? This question is one to which we could dedicate an entire course, but we will only cover some highlights related to data analysis and interpretation in this module. For reference, Khan Academy Links to an external site. is a tremendous resource for learning how to analyze data. You can find some of their relevant videos here.

As a starting place, consider this table:

This table is called a frequency table, and we use it to understand how many students are represented in relevant categories. This specific table documents a hypothetical number of students and their respective residential statuses, differentiated between first-year students and graduating seniors at the same university.

An appropriate interpretation of this table might be: 35 first-year students live in on-campus housing; or: 10 graduating seniors live in off-campus housing and commute to the university.

We can make comparisons between the two columns, but we have to be careful when we do this. At first glance, it seems that there are more graduating seniors who live in on-campus housing (50) compared to on-campus first-year students (35). However, if you notice the different totals between these two categories, we can see a different pattern between these groups. Consider the following table as a revised version of the first table:

Our frequency table now includes percentages to reflect what proportion of the total students are represented in each cell. This table shows that our total number of first-year students—this is called our sample size—is smaller than our sample of graduating seniors, so the percentages more accurately reflect the patterns in our data than do the cell numbers—these numbers are called frequencies.

Now we can describe our data in this manner: 70% of first-year students live on campus, compared to 50% of graduating seniors. Or: a third (33.3%) of the total sample lives off campus but does not commute to the university.

When in doubt, use percentages. Sample sizes between groups are rarely ever the same in real data, and comparing percentages gives information that comparing frequencies often can’t provide.