The Monarchist of LSU

One hears about liberal bias in education on a regular basis – but is it true? Just how liberal *is* LSU?

ImageNot very, as it turns out.

After running every name from the Reveille’s database of faculty and staff to retrieve their political party affiliations, I found that the LSU faculty is actually about 10 percentage points less Democrat-leaning than the surrounding Parish. The data says that LSU is no hotbed of liberalism – and it also showed we had a Monarchist. That one came as a surprise.

Working with Fernanda Zamudio-Suarez, my favorite reporter at the Reveille, we put together a great package showcasing the results and the implications of those results. Fern even knew the Monarchist – Faculty Senate President Kevin Cope – and got him to open up about his party affiliation.

http://www.lsureveille.com/news/politics/faculty-senate-president-opens-up-about-political-affiliation/article_216dc6b8-ad73-11e3-ad3d-001a4bcf6878.html

The data was a blast, and it showed all sorts of fascinating things. The most liberal department on campus, unsurprisingly, was Music & Dramatic Arts, where 71.79 percent of the faculty & staff are registered Democrats (of those I was able to retrieve party affiliation for). The least liberal department, was UC Advising & Counseling, where 20 percent of the staff are Democrats, and 46.67 percent are Republicans.

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To me, this is the perfect example of how data journalism should be done. We took a dataset we owned – the salary database for the university – and leveraged it into a brand new story, and a brand new insight, simply by running it through a new source of information. Data is all about recombinant information – the mutational evolution of your understanding of the world.

Plus, we found a Monarchist. I don’t think that’s ever going to get old.

Now, I just need to find a new, fun data project to feed Fern – I’m sure she’s disappointed I haven’t kicked anything good her way in quite some time.

One final addendum – after publishing, a friend of a friend asked about what would happen if we had zeroed in on the faculty alone – rather than faculty & staff together. I can’t do that breakdown perfectly, but I can approach it, and when I do it shifts the numbers by a bit less than 3% toward Democrat affiliation. The new numbers, if I’m trying to just grab them for faculty without staff, run 40.77% Democrat, 25.47% Republican, and 33.03% Independent. Don’t let the apparent precision of those percentages fool you — they’re my best attempt at faculty isolation, but they’re not perfect. Even these isolated numbers, however, fail to achieve a strong liberal bias. If you want strong bias, your best bet remains looking at different departments. Music & Dramatic arts, I’m looking at  you.

Special thanks to professor Rosanne Scholl for pointing out that whatever my personal feelings on the matter, the major political parties remain proper nouns and deserving of capitalization. Thanks as well go out to Barbara Clark, who asked the question I failed to consider: How do the numbers change if you look at faculty separate from staff?

Seminoles Most Overhyped CFB Team since 2000

over- and underrated teams by decade

As college football wraps up with all its bowl-ey goodness, it seemed a good time to share my all-time overrated and underrated tool.

http://public.tableausoftware.com/views/overrated3/OverratedDash?:embed=y&:display_count=no

The tool shows that for the “decade” since 2000, Florida State has been dramatically overrated – at least by this measure.

Feel free to explore. Here are some tool options:

  • Click on any of the teams in the list to pull up their detailed results, and see *how* they got their score.
  • You can also click on the drop-down to see a team’s results.
  • Select a different decade – or look at the all-time results by choosing “all” decades.
  • Sensitivity helps choose how detailed the ranking list is. Adjust it to fit your tolerance for list crowding.

The Electoral Boogieman: Voter Suppression Dirty Tricks

Lexis/nexis search results for "voter suppression", graphed over time.

Lexis/nexis search results for “voter suppression”, graphed over time.

I set out to look into voter suppression in terms of political communication, and ended up finding something a bit odd — while systematic disenfranchisement is very real, the kind of “I can’t believe they did that!” suppression we hear about in the news appears to be largely a made-up problem. Something which a handful of amateur would-be election riggers engage in, and which professional communicators then seize upon as a resource for fundraising and partisan mobilization.

My storify: http://storify.com/jwkendall/keeping-out-the-vote

How do we know? A simple Google search. When you hunt for “voter suppression” and, say, “flyers”, you get a handful of results, and most of what you find is a lot like the three examples shown here: http://www.solarbus.org/stealyourelection/voter-suppression-flyers.html

Quite honestly, those all seem like the sorts of things some bored, pissed-off crackpot would come up with on a Sunday afternoon. What’s more, I suspect their largest impact on elections comes not through vote suppression, but through the indignation such efforts inspire. Anger is a valuable commodity in the polarized political world.

Turkeys Over the Years

turkeyAs we sit down to feast and avoid picking a fight with Uncle Pete, why not take a moment to reflect on all the great turkeys to go before? America hasn’t always eaten so much turkey, but after a mad dash to the top, the bird has apparently hit some sort of glass ceiling… Or glass coop, at least. Domestic consumption has leveled off, and only increases in exports appear to show much hope for turkey sales to really take flight.

http://public.tableausoftware.com/views/TurkeyFeast/ATurkeyFeast?:embed=y&:display_count=no

A_Turkey_Feast

The Definition of Big Data

Everyone knows big data is green, but what *else* is it? Graphic linked from http://orangutanswing.com/wp-content/uploads/2013/01/big-data.jpeg.

Big Data, noun: The re-purposing of one or more comprehensive datasets to generate a new, comprehensive dataset describing information not available in the original dataset(s).

An example would be taking voter records for a locality, and cross-referencing them with gun registrations to arrive at gun ownership rates by party affiliation. This new dataset was not the purpose of either source set. Both have been repurposed, and the result, if comprehensive, is an example of big data. Big data also implies a responsibility to include as much data as possible. The same project, if it chose to discard Republican and Democratic gun owners while keeping independents, would not meet the standard of big data. Big data is comprehensive. Anything else is just data. And “just data” doesn’t tell us nearly enough.

Definitions are important. I’ve heard a lot of definitions of big data, and I’ve never heard one that I thought nailed it. In fact, most definitions sound like they’re frustrated, themselves, with the state of affairs. They’re cobbled together descriptions of symptoms, not a fundamental set of rules that tell us what this new thing truly is. I may be wrong about what big data is, but I suspect I’m on the right track.

Big data needs to be big, and it needs to change the way we investigate information. With data being generated at such breakneck speed, the world can’t afford to look at it a tiny bit at a time. We need to gorge on it, digest vast chunks, and aggregate the small truths you can’t see if you only look at part of the story.

Big data is about seeing the trees *because* you see the forest, and it implies that if you *don’t* see the forest, there’s no way you can truly understand the trees.

California vs. The World

The Reveille put up our latest update on the Associated Press college football rankings ( http://bit.ly/1cFcYIq ), and beyond showing that one voter pegged the Tigers as the 20th-best team in the country on his ballot this week, I found some interesting results in the pollster sentiment maps for Stanford, Ohio State and Baylor. The sentiment map is simple — it averages the ballot ranks for pollsters in a given state, then compares the states to one another to see where pollsters live who love a team, and where the ones live who hate a team.

This week, the most interesting thing I found in the weekly sentiment maps is the impression they can give of a California championing Stanford against a nation more keen on Ohio State or Baylor.Image

After checking out Stanford and their west coast support, compare that to the map for Ohio State:

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Or you could look at Baylor, which is quite similar (at least at first glance) to the map of sentiment generated for Ohio State:

Now, sentiment maps like this aren’t proof of regional bias… But if regional bias *does* exist, we would certainly expect to see it in the map. More updates as the season continues!Image