Although I haven’t seen it phrased that way before, colleges do admit students they feel will be the most successsful. The better they are at making that prediction, the higher their retention and graduation rates will be. The question then is whether you want to use retention and graduation rates as criteria for judging a school since it encourages schools to be more selective rather than more inclusive. What would be nice is a measure of how successful a school is with students they expect to fail. This is why sub-group analysis is important.
Graduation Rates by Selectivity: Freshmen, 2007 http://highereddatastories.blogspot.com/2016/02/graduation-rates-by-selectivity.html
(And yes, this is a post from the blog I posted about a few weeks ago.)
The following is a quick demonstration of a way that a custom map can be used to display course enrollment by building for a selected day and time on a college campus. Because the campus in question is rather wide, it created a situation where the visualization had to be wider than I would normally recommend. The most difficult part of this process is actually creating a table that contains the coordinates of each of the buildings on campus. The visualizaton appears after the break…
Continue reading Using a Custom Map for Enrollment
As I mentioned in a previous post, I spend a fair amount of time working with data in the Accountability System produced by the Texas Higher Education Coordinating Board (THECB). One of the things I always found missing in this tool is a way to compare one institution to another or to a set of other institutions. For example, at Texas State we often compare ourselves to the other emerging research universities (ERUs) in the state. Although the accountability system allows us to pull the data on the institutions, we then have to reformat the data and make comparisons manually. So, in an attempt to simplify this process I put together the following Tableau visualization of the data. It doesn’t include all of the measures from the accountability system (being able to download all the data is another of its limitations), but I did include many of the variables that are commonly used with a focus on undergraduates.
Continue reading THECB Accountability Comparisons
This post was inspired by a question on the Tableau forums. Basically the goal was to display a combination of measures in a single table. Some measures were percentages and others whole numbers. Added to this was a desire to highlight change (up or down) for percentages but not for the whole number values. The highlight table seemed to be the best solution, but by default it applies the highlight to all values. So, it took some tweaking to get to trick Tableau into not highlighting the changes in whole numbers.
Continue reading Highlighting change for a subset of measures
After a year of playing around and developing things in Tableau, we have finally been approved to release the new tools we’ve developed at work. Of course, by “we” I really mean “me”. To be fair, other people in the office have been responsible for gathering most of the data up to this point, and a few offered comments and suggestions on layout and colors. Still, it feels like I’ve been the lead on getting these tools developed in Tableau up to this point.
One thing I should mention is that Tableau is really designed as a way to visualize data (hence the term data visualization). In these cases we are actually using it as a tool to develop an interface to university data. The goal with many of these tools, especially the University Enrollment Explorer, is to allow people to find the information they are after, and at the same time provide ways of drilling down to answer additional questions once they have that information at hand. So far Tableau has proven to be very useful for doing this and considerably more user friendly than the pivot tables in MS Excel that we were using. One nice feature is that with Tableau you can export the data behind the tables, graphs, charts, etc. to create a pivot table if someone wants to do more with the data than the visualization allows. Anyway, here’s a link to the “Self-Service Tools” on our website.
IR Self-Service Tools
I should also mention that we’ve moved our “Facts and Highlights” into Tableau as well. It doesn’t allow for the interactivity provided by the other tools, but it’s more convenient than creating all the graphs and charts in Excel or Powerpoint like we were doing. That section of our website can be found here:
Texas State University Facts and Highlights
UPDATE 3/10/2016 – Coming soon there will be a link from the “Facts and Highlights” pages which will take you to the appropriate self-service tool in case you want to drill down and explore the data further. Well, most of them will have the link since we don’t have tools that deal with everything.
Rather than posting another tip and trick about using Tableau, I decided that this week I would take a moment to acknowledge one of the sites that I try to visit on at least a semi-regular basis. The site in question is:
Higher Ed Data Stories
As the name suggests, this blog tells stories about Higher Education using data. Although I don’t always agree with some of the conclusions when the leap is made from the data to underlying causes or meanings, I do find the site useful and informative. He is also a big user of Tableau (which is actually how I found the site). I encourage you to go check it out.
One of the many things I find myself doing at work is looking at enrollment trends at colleges and universities in Texas. Most of this data is pulled from the Accountability System produced by the Texas Higher Education Coordinating Board. Anyone has used that system knows that it isn’t the most user-friendly tool. So, one of the first things I did with Tableau was put together a visualization looking at demographic characteristics of students in the state over the past several years. It’s pretty basic, but it does show a picture of how things have changed as enrollment has increased. By default it shows total enrollment at all colleges and universities, but you do have the option of drilling down to an individual institution. The visualization appears after the break…
Continue reading University Demographics in Texas
By default Tableau uses a sequential color scheme to display the full range of values (e.g. from white to green) for a measure. But a problem comes up when the data is highly skewed (e.g. when displaying the states that students come from with over 97% being from one state and the next closest value being less than 0.5% of students). The end result of this skew in the data is that the dominate category appears in one color and all the other states show up in the same color. The trick to getting around this is to re-define how the colors are scaled across the distribution. Steps on how to do this and an example of the trick in action is available after the break… (I use a map in the example, but the same principle can be applied to any situation where color shading is used to represent values.)
Continue reading Skewed Data and Heat Maps in Tableau
In one of the data visualizations I was working on I encountered a situation where I wanted to be able to switch between a display of the headcount and the percentage of people in categories. In researching the issue, I discovered that what I wanted to achieve could be done with the use of a parameter. The trick to get this to work was the use of custom formatting a measure to display positive and negative values differently, and then using a parameter to set the value being displayed.
Continue on to see the steps and an example of the feature in use…
Continue reading Switching between Count and Percent with Parameter in Tableau
Hello and welcome to the latest version of my blog. It has served many purposes over the years, but beginning this year I decided to redesign the site and focus on things more “work” related. My goal is to post something at least every other week related to Institutional Research (broadly defined) or tips and tricks for using Tableau. There will also be some posts related to technology from time to time. My guess is that starting out, most of the posts will likely be more focused on Tableau, but the examples I use will probably be related to the work I do in institutional research. The first real post should be available in the next few days.
For now, I’ve limited comments to only registered users in an effort to prevent too much spam from showing up. If you find any of my posts useful or informative, feel free to link to them. And I do welcome your comments and feedback via email even if you choose not to register.