Archive for the ‘mere empirics’ Category
A few days ago, I suggested that sociologists should seriously consider teaming up with computer scientists. Here, I’d like to sketch out the big picture to suggest why we are in a special moment. Basically, computer science has had three major stages of development:
- Stage 1 (1949-1970s): The construction of computers. In this stage, it was all about the engineering. How could you make a machine that (a) could be programmed, as opposed to running one command, and (b) do it in a way that didn’t require a machine the size of a house?
- Stage 2 (1970s-1990s): Learning and theory. Could you make a machine that could, say, solve an algebra equation? Play chess? See things? CS also developed its mathematical side. Does this algorithm find an answer in a reasonable amount of time?
- Stage 3: (1990s-present): Social computers. Can we build machines that will help people, say, trade using e-currency? Operate in secure networks? In other words, instead of making computers mimic people, we make computers extensions of people.
Of course, people still work in all streams of computer science. The issue is that the social computing stream is now huge. That means that computer scientists are building a technical system that integrates human beings and computer networks. In other words, there isn’t going to be real sharp distinction between online behavior and “real world” behavior. They’ll be connected.
A second observation is that social computing is the engineering analog of “social action.” It’s a broad idea that encompasses a lot of behavior. This is a bit different than say, economics, which reduces a lot to price theory, or political science, which focuses on very specific things like voting or legislation. Instead, computer scientists are dealing with something that is extremely broad. That’s why they can entertain all the different types of data: video recording how people use computers, text analysis, online experiments, and plain old vanilla stats.
None of this means that the CS/soc hookup will automatically happen. Rather, this post explains why this opportunity has appeared. It’s up to us to make the most of it. Otherwise, you can bet on a series of Nature and Science articles that are sociological, but lack sociology authors.
Every once in a while, you get a free lunch. About a year and a half ago, sociology got a small free lunch. It was announced that the MCAT would now include sociology material. Awesome.
But there is a seriously huge free lunch coming up – the rise of “big data.” Ignore the nay sayers. Ignore the hand wringers who worry if Facebook is hurting our feelings. Look at the big picture. Silicon Valley has created a new social world that requires analysis. And not just the generic stuff you get from your local management consultant. They need analysis from people who understand human behavior and can build arguments. They don’t want data mining. They want theory and real research designs.
Consider this tweet from Elise Hu, a Washington Reporter, who quoted Joi Ito, director of the MIT media lab:
In other words, the world of computer science has stumbled into social science. As usual, many think that social science is garbage, but that is slowly changing. Many are being hired at Google and Facebook. Others are striking out on their own. Many within the social sciences are using computer science.
The big message? This is a huge opportunity. It can change the discipline – but only if we constructively interact with the computer science discipline. My recommendations:
- Reach out to your colleagues in computer science. Run a seminar or write a grant.
- Reach out to computer science students. Create courses for them, invite them to be on projects.
- Treat “big data” was we would other data. It has strengths and weaknesses, but in being critical we can use it in the correct way and raise the level of discussion.
- Submit to computer science conference. I’ll be honest, computer scientists are not statisticians. There are a lot of fascinating areas of computer science where the stats are very simple or the ideas are basic. We can add a lot of value.
The benefit? CS will get an infusion of good ideas to work through. Sociology will come into contact with some really cool people, create a bigger audience, and get more resources. We can also get answers to some great questions.
So don’t screw it up, people. This doesn’t happen very often.
A recent article in the Atlantic provides some evidence that the tweets/votes correlation holds up in the recent Indian election:
The direct comparison between volumes of tweets mentioning the different parties shows a similar movement: from a somewhat even distribution—particularly in the mid phases of the campaign between January 28 and March 3, before Kejriwal started his road show in Gujarat and his live Facebook talk—but the BJP took over in the final stages of the campaign.
They should do relative tweet measures, which helps with American data.
For previous More Tweets, More Votes – click here.
Gordon Gee, former president of Ohio State, made more than $6 million in FY 2013, including the $1.5 million “release payment” he got in exchange for
not letting the door hit him agreeing not to sue the university on his way out. Now the New York Times is reporting that the 25 public universities with the highest-paid presidents have greater increases in student debt and numbers of adjuncts than other publics.
I had a story for this, an organizational story. Ah, I thought. The NYT is implying that the high pay is taking away money that would be going to the other stuff. But really, this just reflects a new model for flagship publics: limit faculty costs (hence the adjuncts), increase the proportion of out-of-state students paying high tuition (hence the debt), and pursue corporate-style CEOs who can lead us into this brave new world (hence the salaries). The non-flagships can’t pursue this strategy successfully, so we’re seeing a divergence between the two groups.
But it turns out that the data don’t, in fact, support that story. They don’t really support any story. The NYT article is based on a report from the Institute for Policy Studies, a progessive think tank. And as I read it, things didn’t seem quite right. IPS reports on the number of adjunct faculty at these institutions, but I haven’t seen good data anywhere on the number of adjuncts. And administrative spending at publics increased 65% between FYs 2006 and 2012, as states slashed budgets?
Yeah, basically the IPS report is just a mess. IPEDS made some major redefinitions of terms in the middle — like who falls under “Part-time/Instruction, Research and Public Service,” what IPS is calling “Adjunct Labor” — so the years aren’t comparable with each other, and AFT appears to have mislabeled some of the years entirely. The University of Minnesota’s impressively fast PR office has a debunking report up, and while I haven’t checked all the numbers, my impression is that it’s right on target.
That doesn’t disprove my theory that there will be increasing divergence between the model for flagships and the path taken by the rest of the publics. And it’s entirely possible that universities with highly paid presidents have underwhelming outcomes in other areas. But if we’re going to argue over what to do about it, it would be nice if it were based on numbers that actually mean something.
Guest blogger emerita Jenn Lena and Danielle Lindemann have a forthcoming article in Poetics analyzing the self-identity of artists. The issue is that people often question whether they are artists. From the paper “Who is an Artist? New Data for an Old Question:”
Employment in the arts and creative industries is high andgrowing, yet scholars have not achieved consensus on who should be included in these professions. In this study, we explore the ‘‘professionalartist’’ as the outcome of an identity process, rendering it the dependent rather than the independent variable. In their responses to the 2010 Strategic National Arts Alumni Project survey (N=13,581)— to our knowledge, the largest survey ever undertaken of individuals who have pursued arts degrees in the United States—substantial numbers of respondents gave seemingly contradictory answers to questions asking about their artistic labor. These individuals indicated that they simultaneously had been and had never been professional artists, placing them in what we have termed the ‘‘dissonance group.’’An examination of these responses reveals meaningful differences and patterns in the interpretation of this social category. We find significant correlation between membership in this group and various markers of cultural capital and social integration into artistic communities. A qualitative analysis of survey comments reveals unique forms of dissonance over artistic membership within teaching and design careers.
When you get into the nitty gritty, the authors focus on embededness in institutions as decreasing ambiguity. There’s probably an Abbott side of the story where people in specific orgs or art systems successfully getting the high position in the field.
On the Soc Job Rumor Board, there was a discussion of the non-replicability of ethnography. I think this is mistaken. Ethnography is easily replicable, it’s just that ethnographers don’t want to do it. For example, ethnographers could:
- Stop making everything anonymous so others can verify and check. Mitch Duinier is right about this.
- Group ethnography. Have multiple observers and do inter-coder reliability.
- Standardize data collection – how field codes are done and recorded.
- Encourage others to revisit the same population (which is actually done in anthropological ethnography)
Of course, no single study can strive for replication in the same way and some folks do a good job addressing these issues. But still, the anti-positivist framing of much ethnography probably prevents ethnographers from developing intuitive and sensible things to create standards that would move the field away from the solo practitioner model of unique and non-replicable studies.
Jerry Kim and I have an op-ed in Sunday’s New York Times about our new paper on status bias in baseball umpiring. We analyzed over 700,000 non-swinging pitches from the 2008-09 season and found that umpires made numerous types of mistakes in calling strikes-balls. Most notably, we expected that umpires would be influenced by the status and reputation of the pitcher, and this is indeed what we found:
One of the sources of bias we identified was that umpires tended to favor All-Star pitchers. An umpire was about 16 percent more likely to erroneously call a pitch outside the zone a strike for a five-time All-Star than for a pitcher who had never appeared in an All-Star Game. An umpire was about 9 percent less likely to mistakenly call a real strike a ball for a five-time All-Star. The strike zone did actually seem to get bigger for All-Star pitchers and it tended to shrink for non-All-Stars.
An umpire’s bias toward All-Star pitchers was even stronger when the pitcher had a reputation for precise control, as measured by the career percentage of batters walked. We found that pitchers with a track record of not walking batters — like Greg Maddux — were much more likely to benefit from their All-Star status than similarly decorated but “wilder” pitchers like Randy Johnson.
Baseball insiders have long suspected what our research confirms: that umpires tend to make errors in ways that favor players who have established themselves at the top of the game’s status hierarchy. But our findings are also suggestive of the way that people in any sort of evaluative role — not just umpires — are unconsciously biased by simple “status characteristics.” Even constant monitoring and incentives can fail to train such biases out of us.
You can can download the paper, which is forthcoming in Management Science, if you’re interested in learning more about the analyses and their implications for theories about status characteristics and the Matthew Effect.