Effective Approaches for Presenting Data

Ask eAIR invites questions from AIR members about the work of institutional research, careers in the field, and other broad topics that resonate with a large cross-section of readers. Questions may be submitted to eAIR@airweb.org.

This month’s question is answered by Craig This, Interim Director of Institutional Research, Wright State University-Main Campus.

The ideas, opinions, and perspectives expressed are those of the author, and not necessarily AIR. Members are invited to join the discussion by commenting at the end of the article

Craig-This.jpgDear Craig, what are some effective approaches I can use when presenting data to faculty?

Our Office of institutional Research is called upon to make several presentations to faculty each year, whether it be a global setting, such as the faculty senate or new faculty orientation, or individual departments and colleges. Recently, borrowing from an AIR theme “We eat data for breakfast,” we have been inviting faculty to breakfast (yes, we provide it) and leading data presentations for those who attend. Each of these settings provide unique situations for presenting data to faculty.

  • Not all faculty are alike. What I mean by this is that different academic programs have different forms of academic research. Some faculty have a background in quantitative research, while others have a background in qualitative research. As such, not every faculty person may know what a t-test is or a linear regression or statistical significance. We need to keep this in mind when presenting data. We stick to percentages (providing the sample size) and means when reporting to a global audience. The percentages, as much as possible, are whole numbers, especially if adding up to 100 percent, and means are carried out no more than two decimal points. Tests of significance are included and are asterisked and footnoted for those who might be interested.  

  • Do the math. Our presentations, PowerPoints, and charts and graphs are written in such a way that the faculty person can do the math on their own, but we always walk them through it as we conduct the presentation. We try not to have more than two different calculations on one visual. That is, a percentage and a mean could be shown, but not a percentage, mean, and mode. Just as presentations should have no more than two fonts per page, we also keep data calculations to just two.

  • Define data definitions. We define data definitions as we go along and try to stay away from acronyms as much as possible. Furthermore, a consistent color-coding of each definition in a chart or graph helps faculty to follow along. For example, first-time students are green and transfers are yellow throughout the entire presentation. When data definitions require a time sequence, such as retention or graduation rates, we present the data from the starting point and walk the data calculation forward. We also try to compare definitions against what faculty know or believe they know, such as first-time student is the equivalent to new. Finally, we strive to be consistent in using IPEDS definitions for our presentations. It conforms to what they would typically see when pulling data for grants.  

  • Drill Down. As researchers, we love to take data results and drill down. We do the same for faculty when making presentations. We start with an overview and then drill down to more specific groups, such as student classification, race/ethnicity, and gender. We will not do college comparisons in a general audience for fear of possibly showing favor to one college over another. The focus on class, race/ethnicity, and gender is usually enough to get them thinking about what information they want to see within their college.

Ultimately, the goal is to provide concise and clear presentations of data that stimulate faculty thinking about their own colleges and departments.

 

 

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Mike posted on 3/17/2016 1:21 PM
These are some great tips! I was recently astonished that showing time to degree as a box plot was rejected by my faculty. Some did not understand how to read a box plot or want to learn to read a box plot. Your point about keeping in mind that some specialize in qualitative, quantitative, or neither is very important!

Also your tip about consistent coloring for field values is so important! This year I just started assigning unique color codes to fields for consistency across data visualizations.

Thank you Craig for these tips!