Archive for the ‘mere empirics’ Category
Vox has a nice interview with Dartmouth political scientist Brendan Nyhan about vaccine skeptics. What can be done to convince them? Brendan does research on political beliefs and has shown that in experimental settings, people don’t like to change beliefs even when confronted with correct information. His experiments show that this is true not only for political beliefs, but also controversial health beliefs like believing in the vaccine-autism link.
But there was an additional section in the interview that I found extremely interesting. Nyhan notes that it is easier to be a vaccine skeptic when you don’t actually see a lot of disease: “… many of the diseases that vaccines prevent today are essentially invisible in the US. Vaccines are a victim of their own success here.” This reminded me of a 2002 paper I wrote on STD/HIV transmission. In a model worked out by Kirby Schroeder and myself about people proposing to have risky sex with each other, we wrote that the model has an unusual prediction. If people are proposing risky sex based on how often their friends are infected, you may get unexpected outbreaks of disease:
In the models we have presented, there is no replacement; the population is stable. If we allow for replacement, then we arrive at a novel prediction: as uninfected individuals the population (through birth, migration, etc.) and HIV+ individuals leave (through illness), the proportion of infected individuals will decrease. Once this proportion falls, prior beliefs about the proportion of infected individuals will fall, and if this new prior belief is low enough , then HIV- negative individuals will switch from protected to unprotected sex. The long-term effect of replacement in our model, then, is an oscillation of infection rates… There is some evidence that oscillations in infection rates do occur… An intriguing avenue for research would be to link these patterns in infection rates to the behavior depicted in our model.
In other words, if your model of the world assumes that people take risk based on the infection rates of their buddies, then it is entirely possible, even predictable, that you will see sudden spikes or outbreaks because people “let their guard down.” For HIV, as more people use condoms and other measures, people may engage in more risky sex because few of their friends are infected. For measles and other childhood infections, people who live in very safe places may feel free to deviate from the standard practices that create that safety in their first place. I don’t know how to make vaccine skeptics change their minds, but I do know that movements like vaccine skepticism are some what predictable and we can prepare for it.
James Iveniuk is a doctoral candidate in sociology at the University of Chicago. He recently collected data on professors to understand how people choose their research specialty. He collected data on all professors at 97 ranked sociology doctoral programs in the US News & World Report. Click on this link: Iveniuk Discipline Analysis. Lots of fun results. In my view, this report supports the “Prada Bag hypothesis,” which suggests that the areas of cultural, politics, and historical are luxury items more likely to be found at higher ranked programs. Add your own interpretations in the comments.
Measuring such things is tough, but newly published research reports telling indicators can be found in bursts of 140 characters or less. Examining data on a county-by-county basis, it finds a strong connection between two seemingly disparate factors: deaths caused by the narrowing and hardening of coronary arteries and the language residents use on their Twitter accounts
“Given that the typical Twitter user is younger (median age 31) than the typical person at risk for atherosclerotic heart disease, it is not obvious why Twitter language should track heart disease mortality,” writes a research team led by Johannes Eichstaedt and Hansen Andrew Schwartz of the University of Pennsylvania. “The people tweeting are not the people dying. However, the tweets of younger adults may disclose characteristics of their community, reflecting a shared economic, physical, and psychological environment.”
Not a puzzle to me. I have argued that social media content is often an indicator – a smoke signal – of other trends. Thus, if people are stressed due to environmental conditions (the economy, unemployment), they will have heart attacks and write angry text. The only question is when the correlation holds. For more discussion of the more tweets/more votes/more anything phenomena, click here.
Last year, Nicholas Christakis argued that the social sciences were stuck. Rather that fully embrace the massive tidal wave of theory and data from the biological and physical sciences, the social sciences are content to just redo the same analysis over and over. Christakis’ used the example of racial bias. How many social scientists would be truly shocked to find that people have racial biases? If we already know that (and we do, by the way), then why not move on to new problems?
Christakis’ was recently covered in the media for his views and for attending a conference that tries to push this idea. To further promote this view, I would like to introduce Christakis’ Query, which every researcher should ask:
Think about the major question that you are working on and what you think the answer is. Estimate the confidence in your answer. If you already know the answer with more than 50% confidence, then why are you working on it? Why not move on?
Try it out.
So far, the Patriots have been nailed on two cheating scandals – deflation gate 2015 and the 2006 spying scandal. Each of these is interesting in its own right but there is one implication that few are willing to utter. The Patriots are probably cheating in more ways than we imagine.
The intuition is simple. Cheating incidents are not independent. It is not likely that every person will cheat with equal probability. Rather, people who want to cheat are the most likely to cheat and do so over and over. Also, consider incentives. They have been caught cheating multiple times and that hasn’t seemed to harm them much at all. The conclusion is that it is highly likely the Patriots are cheating in other ways.
I think it would be interesting for the fans of vanquished teams to conduct Levitt style analyses of the Patriots. I would guess that looking at other data in addition to the now famous fumble analysis will yeild some interesting answers.
Question for readers who teach networks: What software should I use for low tech undergrads? So far, I am having some real challenges…
I have an undergrad class where the first major assignment is to download one’s Facebook network and analyze it. I have been using NetVizz, an app inside Facebook, to extract network data. But it suddenly disappeared! One solution is to use the Facebook importer in NodeXL. That works but… Windows 8 is highly allergic to NodeXL. And lots of people have Windows 8 and they have endless installation problems. And the Java version is an issue. Even when it does work, NodeXL gets stuck downloading data from some student accounts. No explanation. It just does.
Then one can try Gephi, which is a whole ball of wax. The issue with Gephi is that it is highly sensitive to OS version. Luckily, there are fixes but they often involve Mac esoterica (e.g., Apple support does weird things in Safari, but not Chrome). Even then, students have all kinds of unexplained Gephi problems (e.g., the visualization pane simply doesn’t work on some Macs).
I need people to download a spreadsheet of data (e.g., centrality scores for people in your network) and not just pictures, so the Wolfram App and others are of limited value. Also, Wolfram seems to stall on some machines (including a Mac I have at home). I tried installing UCINET on Windows 8 as an end run… but had installation problems.
Here are my requirements. I need software that:
- Can be easily used by low-math undergrads
- Low cost/free
- Is very stable in terms of Windows 7, 8 and various Mac OS versions.
- If possible, a way to import Facebook data, and produce spreadsheets of data.
The last time two times I did this course, NetVizz, Gephi and UCINET did the trick. But there is a new generation of operating systems and the usual software hasn’t been upgraded and thoroughly tested. In previous years, I might have only or two students who couldn’t get network software running. This semester, it is a third of the class. Argh.
Any advice is welcome.