Results matching “R”

In response to my most recent post expressing bafflement over the Erving Goffman mystique, several commenters helped out by suggesting classic Goffman articles for me to read. Naturally, I followed the reference that had a link attached--it was for an article called Cooling the Mark Out, which analogized the frustrations of laid-off and set-aside white-collar workers to the reactions to suckers after being bilked by con artists.

Goffman's article was fascinating, but I was bothered by a tone of smugness. Here's a quote from Cooling the Mark Out that starts on the cute side but is basically ok:

In organizations patterned after a bureaucratic model, it is customary for personnel to expect rewards of a specified kind upon fulfilling requirements of a specified nature. Personnel come to define their career line in terms of a sequence of legitimate expectations and to base their self-conceptions on the assumption that in due course they will be what the institution allows persons to become.

It's always amusing to see white-collar types treated anthropologically, so that's fine. But then Goffman continues:

Sometimes, however, a member of an organization may fulfill some of the requirements for a particular status, especially the requirements concerning technical proficiency and seniority, but not other requirements, especially the less codified ones having to do with the proper handling of social relationships at work.

This seemed naive at best and obnoxious at worst. As if, whenever someone is not promoted, it's either because he can't do the job or he can't play the game. Unless you want to define this completely circularly (with "playing the game" retrospectively equaling whatever it takes to do to keep the job), this just seems wrong. In corporate and academic settings alike, lots of people get shoved aside either for reasons entirely beyond their control (e.g., a new division head comes in and brings in his own people) or out of simple economics.

Goffman was a successful organization man and couldn't resist taking a swipe at the losers in the promotion game. It wasn't enough for him to say that some people don't ascend the ladder; he had to attribute that to not fulfilling the "less codified [requirements] having to do with the proper handling of social relationships at work."

Well, no. In the current economic climate this is obvious, but even back in the 1960s there were organizations with too few slots at the top for all the aspirants at the bottom, and it seems a bit naive to suppose that not reaching the top rungs is necessarily a sign of improper handling of social relationships.

In this instance, Goffman seems like the classic case of a successful person who things that, hey, everybody could be a success where they blessed with his talent and social skills.

This was the only thing by Goffman I'd read, though, so to get a broader perspective I sent a note to Brayden King, the sociologist whose earlier post on Goffman had got me started on this.

King wrote:

People in sociology are mixed on their feelings about Goffman's scholarship. He's a love-him-or-hate-him figure. I lean more toward the love him side, if only because I think he really built up the symbolic interactionist theory subfield in sociology.

I think that one of the problems is that you're thinking of this as a proportion of variance problem, in which case I think you're right that "how you play the game" explains a lot less variance in job attainment than structural factors. Goffman wasn't really interested in explaining variance though. His style was to focus on a kind of social interaction and then try to explain the strategies or roles that people use in those interactions to engage in impression management. So, for him, a corporate workplace was interesting for the same reason an asylum is - they're both places where role expectations shape the way people interact and try to influence the perceptions that others have of them.

It's a very different style of scholarship, but nevertheless it's had a huge influence in sociology's version of social psych. The kind of work that is done in this area is highly qualitative, often ethnographic. From a variance-explanation perspective, though, I see your point. How much does "playing the game" really matter when the economy is collapsing and companies are laying off thousands of employees?

Visual Economics shows statistics on average food consumption in America:

My brief feedback is that water is confounded with these results. They should have subtracted water content from the weight of all dietary items, as it inflates the proportion of milk, vegetable and fruit items that contain more water. They did that for soda (which is represented as sugar/corn syrup), amplifying the inconsistency.

Time Magazine had a beautiful gallery that visualizes diets around the world in a more appealing way.

Well put.

Chris Wiggins writes of an interesting-looking summer program that undergraduate or graduate students can apply to:

The hackNY Fellows program is an initiative to mentor the next generation of technology innovators in New York, focusing on tech startups. Last summer's class of fellows was paired with NYC startups which demonstrated they could provide a mentoring environment (a clear project, a person who could work with the Fellow, and sufficient stability to commit to 10 weeks of compensation for the Fellow). hackNY, with the support of the Kauffman foundation and the Internet Society of New York, provided shared housing in NYU dorms in Union Square, and organized a series of pedagogical lectures.

hackNY was founded by Hilary Mason, chief scientist at bit.ly, Evan Korth, professor of CS at NYU, and Chris Wiggins, professor of applied mathematics at Columbia. Each of us has spent thousands of student-hours teaching and mentoring, and is committed to help build a strong community of technological innovators here in New York.

Applicants should be able to demonstrate proficiency in coding, in any language of their choosing, and a desire to find out more about NYC's rapidly-growing tech startup ecosystem. We strive to find excellent placements in a variety of fields, including front end (e.g., web development), back end (e.g., data science), and UIUX (user interface and user experience). The deadline to apply is 11:59 pm on Sunday December 5 (NYC time).

Students can apply here.

For information about last year's program, we encourage you to skim the list of activities from last year, which includes links to many of the blog entries, or to consult the press page.

for coverage in the media about the summer Fellows program as well as our once-per-semester hackathons.

Please don't hesitate to email info at hackNY.org with any questions, or to contact us individually at {hilary,evan,chris}@hackNY.org.

I just read this article on the treatment of medical volunteers, written by doctor and bioethicist Carl Ellliott.

As a statistician who has done a small amount of consulting for pharmaceutical companies, I have a slightly different perspective. As a doctor, Elliott focuses on individual patients, whereas, as a statistician, I've been trained to focus on the goal of accurately estimate treatment effects.

I'll go through Elliott's article and give my reactions.

Paul Nee sends in this amusing item:

Amusing authorship analysis.

See here for the full story.

I have this great talk on the above topic but nowhere to give it.

Here's the story. Several months ago, I was invited to speak at IEEE VisWeek. It sounded like a great opportunity. The organizer told me that there were typically about 700 people in the audience, and these are people in the visualization community whom I'd like to reach but normally wouldn't have the opportunity to encounter. It sounded great, but I didn't want to fly most of the way across the country by myself, so I offered to give the talk by videolink.

I was surprised to get a No response: I'd think that a visualization conference, of all things, would welcome a video talk.

In the meantime, though, I'd thought a lot about what I'd talk about and had started preparing something. Once I found out I wouldn't be giving the talk, I channeled the efforts into an article which, with the collaboration of Antony Unwin, was completed about a month ago.

It would take very little effort to adapt this graph-laden article into a powerpoint presentation. But now I have nowhere to give it, which is too bad as I'd welcome the feedback. So if anyone is interested in hearing this talk, just let me know. (No planes, though.)

Dan Hopkins sends along this article:

[Hopkins] uses regression discontinuity design to estimate the turnout and election impacts of Spanish-language assistance provided under Section 203 of the Voting Rights Act. Analyses of two different data sets - the Latino National Survey and California 1998 primary election returns - show that Spanish-language assistance increased turnout for citizens who speak little English. The California results also demonstrate that election procedures an influence outcomes, as support for ending bilingual education dropped markedly in heavily Spanish-speaking neighborhoods with Spanish-language assistance. The California analyses find hints of backlash among non-Hispanic white precincts, but not with the same size or certainty. Small changes in election procedures can influence who votes as well as what wins.

Beyond the direct relevance of these results, I find this paper interesting as an example of research that is fundamentally quantitative. The qualitative finding--"Spanish-language assistance increased turnout for citizens who speak little English"--reaches deep into dog-bites-man territory. What makes the paper work is that the results are quantitative (for example, comparing direct effect to backlash).

P.S. I love love love that Hopkins makes his points with graphs that display data and fitted models.

P.P.S. The article is on a SSRN website that advertises "*NEW* One-Click Download." Huh? You click on a pdf and it downloads. That's standard, no? What would be the point of "two-click download"? Screening out the riff-raff?

Suguru Mizunoya writes:

When we estimate the number of people from a national sampling survey (such as labor force survey) using sampling weights, don't we obtain underestimated number of people, if the country's population is growing and the sampling frame is based on an old census data? In countries with increasing populations, the probability of inclusion changes over time, but the weights can't be adjusted frequently because census takes place only once every five or ten years.

I am currently working for UNICEF for a project on estimating number of out-of-school children in developing countries. The project leader is comfortable to use estimates of number of people from DHS and other surveys. But, I am concerned that we may need to adjust the estimated number of people by the population projection, otherwise the estimates will be underestimated.

I googled around on this issue, but I could not find a right article or paper on this.

My reply: I don't know if there's a paper on this particular topic, but, yes, I think it would be standard to do some demographic analysis and extrapolate the population characteristics using some model, then poststratify on the estimated current population.

P.S. Speaking of out-of-date censuses, I just hope you're not working with data from Lebanon!

Chris Wiggins sends along this.

It's a meetup at Davis Auditorium, CEPSR Bldg, Columbia University, on Wed 10 Nov (that's tomorrow! or maybe today! depending on when you're reading this), 6-8pm.

Anthony Goldbloom writes:

For those who haven't come across Kaggle, we are a new platform for data prediction competitions. Companies and researchers put up a dataset and a problem and data scientists compete to produce the best solutions.

We've just launched a new initiative called Kaggle in Class, allowing instructors to host competitions for their students. Competitions are a neat way to engage students, giving them the opportunity to put into practice what they learn. The platform offers live leaderboards, so students get instant feedback on the accuracy of their work. And since competitions are judged on objective criteria (predictions are compared with outcomes), the platform offers unique assessment
opportunities.

The first Kaggle in Class competition is being hosted by Stanford University's Stats 202 class and requires students to predict the price of different wines based on vintage, country, ratings and other information.

Those interested in hosting a competition for their students should visit the Kaggle in Class page or contact daniel.gold@kaggle.com

Looks cool to me. More on Kaggle here.

Greg Kaplan writes:

I noticed that you have blogged a little about interstate migration trends in the US, and thought that you might be interested in a new working paper of mine (joint with Sam Schulhofer-Wohl from the Minneapolis Fed) which I have attached.

Briefly, we show that much of the recent reported drop in interstate migration is a statistical artifact: The Census Bureau made an undocumented change in its imputation procedures for missing data in 2006, and this change significantly reduced the number of imputed interstate moves. The change in imputation procedures -- not any actual change in migration behavior -- explains 90 percent of the reported decrease in interstate migration between the 2005 and 2006 Current Population Surveys, and 42 percent of the decrease between 2000 and 2010.

I haven't had a chance to give a serious look so could only make the quick suggestion to make the graphs smaller and put multiple graphs on a page, This would allow the reader to better follow the logic in your reasoning.

But some of you might be interested in the substance of the paper. In any case, it's pretty scary how a statistical adjustment can have such a large effect. (Not that, in general, there's any way to use "unadjusted" data. As Little and Rubin have pointed out, lack of any apparent adjustment itself corresponds to some strong and probably horrible assumptions.)

P.S. See here for another recently-discovered problem with Census data.

A recent story about academic plagiarism spurred me to some more general thoughts about the intellectual benefits of not giving a damn.

I'll briefly summarize the plagiarism story and then get to my larger point.

Copying big blocks of text from others' writings without attribution

Last month I linked to the story of Frank Fischer, an elderly professor of political science who was caught copying big blocks of text (with minor modifications) from others' writings without attribution.

Brian Mulford writes:

I [Mulford] ran across this blog post and found myself questioning the relevance of the test used.

I'd think Chi-Square would be inappropriate for trying to measure significance of choice in the manner presented here; irrespective of the cute hamster. Since this is a common test for marketers and website developers - I'd be interested in which techniques you might suggest?

For tests of this nature, I typically measure a variety of variables (image placement, size, type, page speed, "page feel" as expressed in a factor, etc) and use LOGIT, Cluster and possibly a simple Bayesian model to determine which variables were most significant (chosen). Pearson Chi-squared may be used to express relationships between variables and outcome but I've typically not used it to simply judge a 0/1 choice as statistically significant or not.

My reply:

I like the decision-theoretic way that the blogger (Jason Cohen, according to the webpage) starts:

If you wait too long between tests, you're wasting time. If you don't wait long enough for statistically conclusive results, you might think a variant is better and use that false assumption to create a new variant, and so forth, all on a wild goose chase! That's not just a waste of time, it also prevents you from doing the correct thing, which is to come up with completely new text to test against.

But I agree with Mulford that chi-square is not the way to go. I'd prefer a direct inference on the difference in proportions. Take that inference--the point estimate and its uncertainty, estimated using the usual (y+1)/(n+2) formulas--and then carry that uncertainty into your decision making. Balance costs and benefits, and all that.

Moving forward, you're probably making lots and lots of this sort of comparison, so put it into a hierarchical model and you'll get inferences that are more reasonable and more precise.

But . . . who knows? Maybe Cohen's advice is a net plus. Ignoring the chi-square stuff, the key message I take away from the above-linked blog is that, with small samples, randomness can be huge. And that's an important lesson--really, one of the key concepts in statistics. Don't overreact to small samples. If the silly old chi-square test is your way of coming to this conclusion, that's not so bad.

After learning of a news article by Amy Harmon on problems with medical trials--sometimes people are stuck getting the placebo when they could really use the experimental treatment, and it can be a life-or-death difference, John Langford discusses some fifteen-year-old work on optimal design in machine learning and makes the following completely reasonable point:

With reasonable record keeping of existing outcomes for the standard treatments, there is no need to explicitly assign people to a control group with the standard treatment, as that approach is effectively explored with great certainty. Asserting otherwise would imply that the nature of effective treatments for cancer has changed between now and a year ago, which denies the value of any clinical trial. . . .

Done the right way, the clinical trial for a successful treatment would start with some initial small pool (equivalent to "phase 1″ in the article) and then simply expanded the pool of participants over time as it proved superior to the existing treatment, until the pool is everyone. And as a bonus, you can even compete with policies on treatments rather than raw treatments (i.e. personalized medicine).

Langford then asks: if these ideas are so good, why aren't they done already? He conjectures:

Getting from here to there seems difficult. It's been 15 years since EXP3.P was first published, and the progress in clinical trial design seems glacial to us outsiders. Partly, I think this is a communication and education failure, but partly, it's also a failure of imagination within our own field. When we design algorithms, we often don't think about all the applications, where a little massaging of the design in obvious-to-us ways so as to suit these applications would go a long ways.

I agree with these sentiments, but . . . the sorts of ideas Langford is talking about have been around in a statistics for a long long time--much more than 15 years! I welcome the involvement of computer scientists in this area, but it's not simply that the CS people have a great idea and just need to communicate it or adapt it to the world of clinical trials. The clinical trials people already know about these ideas (not with the same terminology, but they're the same basic ideas) but, for various reasons, haven't widely adapted them.

P.S. The news article is by Amy Harmon, but Langford identifies it only as being from the New York Times. I don't think this is appropriate to omit the author's name. The publication is relevant but it's the reporter who did the work. I certainly wouldn't like it if someone referred to one of my articles by writing, "The Journal of the American Statistical Association reported today that . . ."

"A statistical model is usually taken to be summarized by a likelihood, or a likelihood and a prior distribution, but we go an extra step by noting that the parameters of a model are typically batched, and we take this batching as an essential part of the model."

Ryan Seals writes:

I'm an epidemiologist at Emory University, and I'm working on a project of release patterns in jails (basically trying to model how long individuals are in jail before they're release, for purposes of designing short-term health interventions, i.e. HIV testing, drug counseling, etc...). The question lends itself to quantile regression; we're interested in the # of days it takes for 50% and 75% of inmates to be released. But being a clustered/nested data structure, it also obviously lends itself to multilevel modeling, with the group-level being individual jails.

So: do you know of any work on multilevel quantile regression? My quick lit search didn't yield much, and I don't see any preprogrammed way to do it in SAS.

My reply:

To start with, I'm putting in the R keyword here, on the hope that some readers might be able to refer you to an R function that does what you want. Beyond this, I think it should be possible to program something in Bugs. In ARM we have an example of a multilevel ordered logit, which doesn't sound so different from what you're doing. I've never done a full quantile regression, but I imagine that you have to take some care in setting up the distributional form.

To start, you could fit some multilevel logistic regressions using different quantiles as cut-off points and plot your inferences to see generally what's going on.

I'm a physicist by training, statistical data analyst by trade. Although some of my work is pretty standard statistical analysis, more often I work somewhere in a gray area that includes physics, engineering, and statistics. I have very little formal statistics training but I do study in an academic-like way to learn techniques from the literature when I need to. I do some things well but there are big gaps in my stats knowledge compared to anyone who has gone to grad school in statistics. On the other hand, there are big gaps in most statisticians' physics and engineering knowledge compared to anyone who has gone to grad school in physics. Generally my breadth and depth of knowledge is about right for the kind of work that I do, I think.

But last week I was offered a consulting job that might be better done by someone with more conventional stats knowledge than I have. The job involves gene expression in different types of tumors, so it's "biostatistics" by definition, but the specific questions of interest aren't specialized biostats ones (there's no analysis of microarray data, for instance). I'm comfortable doing the work, but I'm not the ideal person for the job. I was very clear about that both in writing and on the phone, but the company wanted to hire me anyway: they need a few questions answered very quickly, and their staff is so overworked at the moment that they would rather have me -- I was suggested or at least mentioned by a friend who works at the company -- than have one of their people spend hours trying to track down someone else who can do the work right away, even if that person is better.

I said sure, but then had to decide how much to charge. I've only ever done five small consulting jobs, and I've charged as little as $80/hour (working for some ecologists who didn't have any money) and as much as $250/hour (consortium of insurance companies).

Picking a number out of the air, I'm charging $150/hour. Upon reflection, this feels low to me. Of course one way to think of it is: would I rather have spent three hours last night working on this project for $450, or would I have preferred doing whatever else I would have done instead but not making any money? (My wife is out of town and I hadn't made plans, so I probably just would have read or watched TV). By that standard I am charging a fair rate, I was happy enough working on this last night. But I also have to put in some time this weekend, when I might feel differently: I'll probably be giving up something more enjoyable this weekend. Still, overall I think that if I focus just on my own satisfaction in a limited sense, then $150/hour is OK.

On the other hand, I think that from the company's perspective, at least in this particular instance, they are getting a fantastic deal. Having spoken with the people they've had looking at the data up to now, I am definitely much better at this than they are!

So if the company is thinking "boy, this is absolutely fantastic, that we were able to get this so quickly and for so little money", while I'm thinking "Eh, OK, this isn't too bad and I'm getting enough money to pay for a year of cell phone service [or whatever]", then I feel like I should have asked for more (or should in the future).

I know there are people out there who charge much more. But on the other hand, some universities offer stats consulting for $80-$100/hour, although this is surely not the free-market rate.

For the future it would be good to have a better idea of how to set a rate.

Thoughts?

A lot of people are asking, How could the voters have swung so much in two years? And, why didn't Obama give Americans a better sense of his long-term economic plan in 2009, back when he still had a political mandate? As an academic statistician and political scientist, I have no insight into the administration's internal deliberations, but I have some thoughts based on my interpretation of political science research.

The baseline

As Doug Hibbs and others have pointed out, given the Democrats' existing large majority in both houses of Congress and the continuing economic depression, we'd expect a big Republican swing in the vote. And this has been echoed for a long time in the polls--as early as September, 2009--over a year before the election--political scientists were forecasting that the Democrats were going to lose big in the midterms. (The polls have made it clear that most voters do not believe the Republican Party has the answer either. But, as I've emphasized before, given that the Democrats control the presidency and are still (at the time of this writing) likely to keep the Senate, it's perfectly reasonable for swing voters to swing Republican in congressional voting.)

The Tea Party

What about those new Republican candidates? Radical or merely conservative? How much did they stir up the base and how much did they turn off moderate voters? Based on some of my research with Caltech political scientist Jonathan Katz, I estimate moderation to be worth about 2 percentage points in congressional elections. There's a lot of variation here, but overall people are voting primarily the party and secondarily the candidate. Ideology is a distant third. Sure, it's a good plan to run moderate candidates if you can, but the choice of ideology is really much more of a battle within the party than a concern in the general election. 2010 was not a good year to be a Democrat in any case.

But where did that bad economy come from?

The unemployment rate increased from 6.6% in October 2008 to 8.6% in March 2009: a huge jump before the new administration had a chance to do much at all. So the Democrats were starting in a deep hole.

Thus, one story of the election, as expressed, for example, by journalist Jonathan Chait, is that Obama and congressional Democrats shouldn't be blamed for their 2010 election failure: after all, they did about as well as forecasted. To be fair, Chait is not a pure determinist; he's just using the forecast as a baseline. Still, he's missing a key piece of the picture, which is that economic performance is not fixed. According to Paul Krugman, for example, the economy would've been doing much worse right now had there been no stimulus plan and would be doing much better had a larger stimulus been enacted in 2009. Economists on the right have a different view, but even those who think the government can't do much to repair the economy tend to feel that the government has the ability to make things worse.

To put it another way, nobody's claiming that the correct economic policies (whatever they may have been) would cause the economy to be booming right now, but perhaps the difference between a mild depression, a severe depression, or complete free fall would have some impact on whether the Republicans achieved small gains, large gains, or a landslide in 2010.

Why didn't the Democrats do more?

The next natural question is: Why, in early 2009, seeing the economy sink, did Obama and congressional Democrats not do more? Why didn't they follow the advice of Krugman and others and (a) vigorously blame the outgoing administration for their problems and (b) act more decisively to get Americans spending again?

I offer a few thoughts, but bear in mind that I know nothing about the people involved in these deliberations, so these are all just speculations or, at best, rational reconstructions:

One answer is that Obama wanted to do more but was limited by the preferences of the 60th-most-liberal senator. I buy this argument a bit but not completely. For one thing, all the Democratic senators, even the conservative ones, have an interest in their party remaining in the majority. OK, maybe Joe Lieberman is ready to switch at any time, but most of them are locked in. So they don't gain from a Republican landslide, More to the point, the 55 or so Democratic senators who certainly wanted their party to remain in power could've done more, if they'd really felt it was a good idea.

Now we're getting closer. Several Democratic senators did not favor the big stimulus. Part of this can be attributed to ideology (or, to put it in a more positive way, conservative or free-market economic convictions) or maybe even to lobbyists etc. Beyond this, there was the feeling, somewhere around mid-2009, that government intervention wasn't so popular--that, between TARP, the stimulus, and the auto bailout, voters were getting a bit wary of big government taking over the economy.

Now, from the standpoint of November, 2010, if you're a Democratic senator and can go back in time to mid-2009, you might want to forget about looking like a moderate and go for a stronger, Krugman-approved plan to juice up the economy. Being a compromiser might have seemed like a good idea at the time, but in retrospect it appears that voters care about results, not about what happens to be popular at the time of the vote.

On the other hand, most of the Senate's moderate-to-conservative Democrats were not up for reelection in 2010. Thus they had little personal reason to support policies with immediate effects on the economy and had more motivation to favor a go-slow approach.

On not wanting to repeat the mistakes of the past

OK, so why didn't Obama do a better job of leveling with the American people? In his first months in office, why didn't he anticipate the example of the incoming British government and warn people of economic blood, sweat, and tears? Why did his economic team release overly-optimistic graphs such as shown here? Wouldn't it have been better to have set low expectations and then exceed them, rather than the reverse?

I don't know, but here's my theory. When Obama came into office, I imagine one of his major goals was to avoid repeating the experiences of Bill Clinton and Jimmy Carter in their first two years.

Clinton, you may recall, was elected with less then 50% of the vote, was never given the respect of a "mandate" by congressional Republicans, wasted political capital on peripheral issues such as gays in the military, spent much of his first two years on centrist, "responsible" politics (budgetary reform and NAFTA) which didn't thrill his base, and then got rewarded with a smackdown on heath care and a Republican takeover of Congress. Clinton may have personally weathered the storm but he never had a chance to implement the liberal program.

Carter, of course, was the original Gloomy Gus, and his term saw the resurgence of the conservative movement in this country, with big tax revolts in 1978 and the Reagan landslide two years after that. It wasn't all economics, of course: there were also the Russians, Iran, and Jerry Falwell pitching in.

Following Plan Reagan

From a political (but not a policy) perspective, my impression was that Obama's model was not Bill Clinton or Jimmy Carter but Ronald Reagan. Like Obama in 2008, Reagan came into office in 1980 in a bad economy and inheriting a discredited foreign policy. The economy got steadily worse in the next two years, the opposition party gained seats in the midterm election, but Reagan weathered the storm and came out better than ever.

If the goal was to imitate Reagan, what might Obama have done?

- Stick with the optimism and leave the gloom-and-doom to the other party. Check.
- Stand fast in the face of a recession. Take the hit in the midterms with the goal of bouncing back in year 4. Check.
- Keep ideological purity. Maintain a contrast with the opposition party and pass whatever you can in Congress. Check.

The Democrats got hit harder in 2010 than the Republicans in 1982, but the Democrats had further to fall. Obama and his party in Congress can still hope to bounce back in two years.

Avoiding the curse of Bartels

Political scientist Larry Bartels wrote an influential paper, later incorporated into his book, Unequal Democracy, presenting evidence that for the past several decades, the economy generally has done better under Democratic than Republican presidents. Why then, Bartels asked, have Republicans done so well in presidential elections? Bartels gives several answers, including different patterns at the low and high end of the income spectrum, but a key part of his story is timing: Democratic presidents tend to boost the economy when the enter office and then are stuck watching it rebound against them in year 4 (think Jimmy Carter), whereas Republicans come into office with contract-the-economy policies which hurt at first but tend to yield positive trends in time for reelection (again, think Ronald Reagan).

Overall, according to Bartels, the economy does better under Democratic administrations, but at election time, Republicans are better situated. And there's general agreement among political scientists that voters respond to recent economic conditions, not to the entire previous four years. Bartels and others argue that the systematic differences between the two parties connect naturally to their key constituencies, with new Democratic administrations being under pressure to heat up the economy and improve conditions for wage-earners and incoming Republicans wanting to keep inflation down.

Some people agree with Bartels's analysis, some don't. But, from the point of Obama's strategy, all that matters is that he and his advisers were familiar with the argument that previous Democrats had failed by being too aggressive with economic expansion. Again, it's the Carter/Reagan example. Under this story, Obama didn't want to peak too early. So, sure, he wanted a stimulus--he didn't want the economy to collapse, but he didn't want to turn the stove on too high and spark an unsustainable bubble of a recovery. In saying this, I'm not attributing any malign motives (any more than I'm casting aspersions of conservatives' skepticism of unsustainable government-financed recovery). Rather, I'm putting the economic arguments in a political context to give a possible answer to the question of why Obama and congressional Democrats didn't do things differently in 2009.

Journalism in the age of data is a video report including interviews with many visualization people. It's also a great example of how citations, and further information appear alongside with the video - showing us the future of video content online.

Lei Liu writes:

I've written a lot on polls and elections ("a poll is a snapshot, not a forecast," etc., or see here for a more technical paper with Kari Lock) but had a few things to add in light of Sam Wang's recent efforts. As a biologist with a physics degree, Wang brings an outsider's perspective to political forecasting, which can be a good thing. (I'm a bit of an outsider to political science myself, as is my sometime collaborator Nate Silver, who's done a lot of good work in the past few years.)

But there are two places where Wang misses the point, I think.

I recently had the occasion to reread my review of The Black Swan, from April 2007.

It was fun reading my review (and also this pre-review; "nothing useful escapes from a blackbody," indeed). It was like a greatest hits of all my pet ideas that I've never published.

Looking back, I realize that Taleb really was right about a lot of things. Not that the financial crisis has happened, we tend to forget that the experts who Taleb bashes were not always reasonable at all. Here's what I wrote in my review, three and a half years ago:

On page 19, Taleb refers to the usual investment strategy (which I suppose I actually use myself) as "picking pennies in front of a steamroller." That's a cute phrase; did he come up with it? I'm also reminded of the famous Martingale betting system. Several years ago in a university library I came across a charming book by Maxim (of gun fame) where he went through chapter after chapter demolishing the Martingale system. (For those who don't know, the Martingale system is to bet $1, then if you lose, bet $2, then if you lose, bet $4, etc. You're then guaranteed to win exactly $1--or lose your entire fortune. A sort of lottery in reverse, but an eternally popular "system.")

Throughout, Taleb talks about forecasters who aren't so good at forecasting, picking pennies in front of steamrollers, etc. I imagine much of this can be explained by incentives. For example, those Long-Term Capital guys made tons of money, then when their system failed, I assume they didn't actually go broke. They have an incentive to ignore those black swans, since others will pick up the tab when they fail (sort of like FEMA pays for those beachfront houses in Florida). It reminds me of the saying that I heard once (referring to Donald Trump, I believe) that what matters is not your net worth (assets minus liabilities), but the absolute value of your net worth. Being in debt for $10 million and thus being "too big to fail" is (almost) equivalent to having $10 million in the bank.

So, yeah, "too big to fail" is not a new concept. But as late as 2007, it was still a bit of an underground theory. People such as Taleb screamed about, but the authorities weren't listening.

And then there are parts of the review that make me really uncomfortable. As noted in the above quote, I was using the much-derided "picking pennies in front of a steamroller" investment strategy myself--and I knew it! Here's some more, again from 2007:

I'm only a statistician from 9 to 5

I try (and mostly succeed, I think) to have some unity in my professional life, developing theory that is relevant to my applied work. I have to admit, however, that after hours I'm like every other citizen. I trust my doctor and dentist completely, and I'll invest my money wherever the conventional wisdom tells me to (just like the people whom Taleb disparages on page 290 of his book).

Not long after, there was a stock market crash and I lost half my money. OK, maybe it was only 40%. Still, what was I thinking--I read Taleb's book and still didn't get the point!

Actually, there was a day in 2007 or 2008 when I had the plan to shift my money to a safer place. I recall going on the computer to access my investment account but I couldn't remember the password, was too busy to call and get it, and then forgot about it. A few weeks later the market crashed.

If only I'd followed through that day. Oooohhh, I'd be so smug right now. I'd be going around saying, yeah, I'm a statistician, I read Taleb's book and I thought it through, blah blah blah. All in all, it was probably better for me to just lose the money and maintain a healthy humility about my investment expertise.

But the part of the review that I really want everyone to read is this:

On page 16, Taleb asks "why those who favor allowing the elimination of a fetus in the mother's womb also oppose capital punishment" and "why those who accept abortion are supposed to be favorable to high taxation but against a strong military," etc. First off, let me chide Taleb for deterministic thinking. From the General Social Survey cumulative file, here's the crosstab of the responses to "Abortion if woman wants for any reason" and "Favor or oppose death penalty for murder":

40% supported abortion for any reason. Of these, 76% supported the death penalty.

60% did not support abortion under all conditions. Of these, 74% supported the death penalty.

This was the cumulative file, and I'm sure things have changed in recent years, and maybe I even made some mistake in the tabulation, but, in any case, the relation between views on these two issues is far from deterministic!

Finally, a lot of people bash Taleb, partly for his idosyncratic writing style, but I have fond memories of both his books, for their own sake and because they inspired me to write down some of my pet ideas. Also, he deserves full credit for getting things right several years ago, back when the Larry Summerses of the world were still floating on air, buoyed by the heads-I-win, tails-you-lose system that kept the bubble inflated for so long.

I studied math and physics at MIT. To be more precise, I started in math as default--ever since I was two years old, I've thought of myself as a mathematician, and I always did well in math class, so it seemed like a natural fit.

But I was concerned. In high school I'd been in the U.S. Mathematical Olympiad training program, and there I'd met kids who were clearly much much better at math than I was. In retrospect, I don't think I was as bad as I'd thought at the time: there were 24 kids in the program, and I was probably around #20, if that, but I think a lot of the other kids had more practice working on "math olympiad"-type problems. Maybe I was really something like the tenth-best in the group.

Tenth-best or twentieth-best, whatever it was, I reached a crisis of confidence around my sophomore or junior year in college. At MIT, I started right off taking advanced math classes, and somewhere along the way I realized I wasn't seeing the big picture. I was able to do the homework problems and do fine on the exams, but something was missing. Ultimately I decided the problem was that, in the world of theoretical math, there were the Cauchys, the Riemanns, etc., and there were everybody else. I didn't want to be one of the "everbody else." Unfortunately I didn't know about applied math at the time--at MIT, as elsewhere, I imagine, the best math students did the theory track.

I was also majoring in physics, which struck me as much more important than math, but which I felt I had even less of an understanding of. I did well in my classes--it was MIT, I didn't have a lot of friends and I didn't go on dates, so that gave me lots of time to do my problem sets each week--and reached the stage of applying to physics grad schools. In fact it was only at the very last second in April of my senior year that I decided to go for a Ph.D. in statistics rather than physics.

I had some good experiences in physics, most notably taking the famous Introduction to Design course at MIT--actually, that was a required course in the mechanical engineering department but many physics students took it too--and working for two summers doing solid-state physics research at Bell Labs. We were working on zone-melt recrystallization of silicon and, just as a byproduct of our research, discovered a new result (or, at least it was new to us) that solid silicon could superheat to something like 20 degrees (I think it was that, I don't remember the details) above its melting point before actually melting. This wouldn't normally happen, but we had a set-up in which the silicon wafer was heated in such a way that the center got hotter than the edges, and at the center there were no defects in the crystal pattern for the melting process to easily start. So it had to get really hot for it to start to melt.

Figuring this out wasn't so easy--it's not like we had a thermometer in the inside of our wafer. (If we did, the crystalline structure wouldn't have been pure, and there wouldn't have been any superheating.) We knew the positions and energies of our heat sources, and we had radiation thermometers to measure the exterior temperature from various positions, we knew the geometry of the silicon wafer (which was encased in silicon dioxide), and we could observe the width of the molten zone.

So what did we do? What did I do, actually? I set up a finite-element model on the computer and played around with its parameters until I matched the observations, then looked inside to see what our model said was the temperature at the hottest part of the wafer. Statistical inference, really, although I didn't know it at the time. When I came to Bell Labs for my second summer, I told my boss that I'd decided to go to grad school in statistics. He was disappointed and said that this was beneath me, that statistics was a step down from physics. I think he was right (about statistics being simpler than physics), but I really wasn't a natural physicist, and I think statistics was the right field for me.

Why did I study statistics? I've been trained not to try to answer Why questions but rather to focus on potential interventions. The intervention that happened to me was that I took a data analysis course from Don Rubin when I was a senior in college. MIT had very few statistics classes. I'd taken one of them and liked it, and when I went to a math professor to ask what to take next, he suggested I go over to Harvard and see what they had to offer.

I sat in on two classes: one was deadly dull and the other was Rubin's, which was exciting from Day 1. The course just sparkled with open problems, and the quality of the ten or so students in the class was amazing. I remember spending many hours laboriously working out every homework problem using the Neyman-Pearson theory we'd been taught in my theoretical statistics course. It's only by really taking this stuff seriously that I realized how hopeless it all is. When, two years later, I took a class on Bayesian statistics from John Carlin, I was certainly ready to move to a model-based philosophy.

Anyway, to answer the question posed at the beginning of the paragraph, Don's course was great. I was worried that statistics was just too easy to be interesting, but Don assured me that, no, the field has many open problems and that I'd be free to work on them. As indeed I have.

Why did I start a blog? I realize I'm skipping a few steps here, considering that I started my Ph.D. studies in 1986 and didn't start blogging until nearly two decades later. I started my casual internet reading with Slate and Salon and at some point had followed some links and been reading some blogs. In late 2004 my students, postdocs, and I decided to set up a blog and a wiki to improve communication in our group and to reach out to others. The idea was that we would pass documents around on the wiki and post our thoughts on each others' ideas on the blog.

I figured we'd never run out of material because, if we ever needed to, I could always post links and abstracts of my old papers. (I expect I'm far from unique among researchers in having a fondness for many of my long-forgotten publications.)

What happened? For one thing, after a couple months, the blog and wiki got hacked (apparently by some foreign student with no connection to statistics who had some time on his hands). Our system manager told us the wiki wasn't safe so we abandoned it and switched account names for the blog. Meanwhile, I'd been doing most of the blog posting. For awhile, I'd assign my students and postdocs to post while I was on vacation, but then I heard they were spending hours and hours on each entry so I decided to make it optional, which means that most of my cobloggers rarely post on the blog. Which is too bad but I guess is understandable.

Probably the #1 thing I get from posting on the blog is an opportunity to set down my ideas in a semi-permanent form. Ideas in my head aren't as good as the same ideas on paper (or on the screen). To put it another way, the process of writing forces me to make hard choices and clarify my thoughts. The weakness of my blogging is that it's all in words, not in symbols, so quite possibly the time I spend blogging distracts me from thinking more deeply on mathematical and computational issues. On the other hand, sometimes blogging has motivated me to do some data analyses which have motivated me to do new statistical research.

There's a lot more that I could say about my blogging experiences, but really it all fits in a continuum with the writing of books and articles, meetings with colleagues, and all stages of teaching (from preparation of materials to meetings with students). One thing that blogging has in common with book-writing and article-writing is that I don't really know who my audience is. I can tell you, though, that the different blogs have much different sets of readers. My main blog has an excellent group of commenters who often point out things of which I'd been unaware. At the other blogs where I post, the commenters often don't always understand where I'm coming from, and all I can really do is get my ideas out there and let people use them how they may. In that way it's similar to the frustrating experience of writing for journals and realizing that sometimes I just can't get my message across. In my own blog I can go back and continue modifying my ideas in the light of audience feedback. My model is George Orwell, who wrote on the same (but not identical) topics over and over again, trying to get things just right. (I know that citing Orwell is a notorious sign of grandiosity in an author, but in my defense all I'm saying is that Orwell is my model, not that I have a hope of reaching that level.)

Xian pointed me to this recycling of a classic probability error. It's too bad it was in the New York Times, but at least it was in the Opinion Pages, so I guess that's not so bad. And, on the plus side, several of the blog commenters got the point.

What I was wondering, though, was who was this "Yitzhak Melechson, a statistics professor at the University of Tel Aviv"? This is such a standard problem, I'm surprised to find a statistics professor making this mistake. I was curious what his area of research is and where he was trained.

I started by googling Yitzhak Melechson but all I could find was this news story, over and over and over and over again. Then I found Tel Aviv University and navigated to its statistics department but couldn't find any Melechson in the faculty list. Next stop: entering Melechson in the search engine at the Tel Aviv University website. It came up blank.

One last try: I entered the Yitzhak Melechson into Google Scholar. Here's what came up:

Your search - Yitzhak Melechson - did not match any articles

Computing wrong probabilities for the lottery must be a full-time job! Get this guy on the Bible Code next.

P.S. If there's some part of this story that I'm missing, please let me know. How many statistics professors could there be in Tel Aviv, anyway? Perhaps there's some obvious explanation that's eluding me.

Our apartment is from earlier in the century, so I can't give Tyler Cowen's first answer, but, after that, I follow him in thinking of the several books I have from that decade. Beyond that, lemme think . . . We occasionally play Risk, and our set dates from the 50s. Some kitchen implements (a mixmaster, a couple of cookbooks, who knows which old bowls, forks, etc). Probably some of the furniture, although I don't know which. Probably some of the items in our building (the boiler?) What else, I wonder? There are probably a few things I'm forgetting.

50-60 years is a long time, I guess.

P.S. to the commenters: I'm taking the question to refer to things manufactured in the 1950s and not before!

I think I can best do my civic duty by running this one every Election Day, just like Art Buchwald on Thanksgiving. . . .

With a national election coming up, and with the publicity at its maximum, now is a good time to ask, is it rational for you to vote? And, by extension, wass it worth your while to pay attention to whatever the candidates and party leaders have been saying for the year or so? With a chance of casting a decisive vote that is comparable to the chance of winning the lottery, what is the gain from being a good citizen and casting your vote?

The short answer is, quite a lot. First the bad news. With 100 million voters, your chance that your vote will be decisive--even if the national election is predicted to be reasonably close--is, at best, 1 in a million in a battleground district and much less in a noncompetitive district such as where I live. (The calculation is based on the chance that your district's vote will be exactly tied, along with the chance that your district's electoral vote is necessary for one party or the other to take control of a house of congress. Both these conditions are necessary for your vote to be decisive.) So voting doesn't seem like such a good investment.

But here's the good news. If your vote is decisive, it will make a difference for 300 million people. If you think your preferred candidate could bring the equivalent of a $50 improvement in the quality of life to the average American--not an implausible hope, given the size of the Federal budget and the impact of decisions in foreign policy, health, the courts, and other areas--you're now buying a $1.5 billion lottery ticket. With this payoff, a 1 in 10 million chance of being decisive isn't bad odds.

And many people do see it that way. Surveys show that voters choose based on who they think will do better for the country as a whole, rather than their personal betterment. Indeed, when it comes to voting, it is irrational to be selfish, but if you care how others are affected, it's a smart calculation to cast your ballot, because the returns to voting are so high for everyone if you are decisive. Voting and vote choice (including related actions such as the decision to gather information in order to make an informed vote) are rational in large elections only to the extent that voters are not selfish.

That's also the reason for contributing money to a candidate: Large contributions, or contributions to local elections, could conceivably be justified as providing access or the opportunity to directly influence policy. But small-dollar contributions to national elections, like voting, can be better motivated by the possibility of large social benefit than by any direct benefit to you. Such civically motivated behavior is consistent with both small and large anonymous contributions to charity.

The social benefit from voting also explains the declining response rates in opinion polls. In the 1950s, when mass opinion polling was rare, we would argue that it was more rational to respond to a survey than to vote in an election: for example, as one of 1000 respondents to a Gallup poll, there was a real chance that your response could noticeably affect the poll numbers (for example, changing a poll result from 49% to 50%). Nowadays, polls are so common that a telephone poll was done recently to estimate how often individuals are surveyed (the answer was about once per year). It is thus unlikely that a response to a single survey will have much impact.

So, yes, if you are in a district or state that might be close, it is rational to vote.

For further details, see our articles in Rationality and Society and The Economist's Voice.

I'd like to add one more thing. You've all heard about low voter turnout in America, but, among well-educated, older white people, turnout is around 90% in presidential elections. Some economists treat this as a source of amusement--and, sure, I'd be the first to admit that well-educated, older white people have done a lot of damage to this country--but it's a funny thing . . . Usually economists tend not to question the actions of this particular demographic. I'm not saying that the high turnout of these people (e.g., me) is evidence that voting is rational. But I would hope that it would cause some economists to think twice before characterizing voting as irrational or laughable.

(And, no, it's not true that "the closer an election is, the more likely that its outcome will be taken out of the voters' hands." See the appendix on the last page of this article for a full explanation, with calculus!)

Bruce McCullough writes:

The Sept 2009 issue of Wired had a big article on the increase in the placebo effect, and why it's been getting bigger.

Kaiser Fung has a synopsis.

As if you don't have enough to do, I thought you might be interested in blogging on this.

My reply:

I thought Kaiser's discussion was good, especially this point:

Effect on treatment group = Effect of the drug + effect of belief in being treated

Effect on placebo group = Effect of belief in being treated

Thus, the difference between the two groups = effect of the drug, since the effect of belief in being treated affects both groups of patients.

Thus, as Kaiser puts it, if the treatment isn't doing better than placebo, it doesn't say that the placebo effect is big (let alone "too big") but that the treatment isn't showing any additional effect. It's "treatment + placebo" vs. placebo, not treatment vs. placebo.

That said, I'd prefer for Kaiser to make it clear that the additivity he's assuming is just that--an assumption. Like Kaiser, I don't know much about pharma in particular, but like Kaiser, I feel that the assumption of additivity is a reasonable starting point. I just think it would be clearer to frame this as a battle of assumptions (much as in Rubin's discussion of Lord's Paradox).

I also agree with Kaiser that the scientific questions about placebos are interesting. As in much medical research, it's frustrating how the ground seems to keep shifting and how little seems to be known. Or, to put it another way, a lot is known--lots of studies have been done--but nothing seems to be known with much certainty. There are few pillars of knowledge to hold on to, even in a field such as placebos that has been studied for so many decades.

Also, as Kaiser points out, the waters can be muddied by the huge financial conflicts of interests involved in medical research.

I don't exactly disagree with the two arguments that I reproduce below, but I think they miss the point.

Is "the battle over elitism" really central to this election?

First, the easy one. Peter Baker in the New York Times, under the heading, "Elitism: The Charge That Obama Can't Shake":

For all the discussion of health care and spending and jobs, at the core of the nation's debate this fall has been the battle of elitism. . . . Ron Bonjean, a Republican strategist, said Mr. Obama had not connected with popular discontent. "A lot of people have never been to Washington or New York, and they feel people there are so out of touch," he said. . . . Rather than entertaining the possibility that the program they have pursued is genuinely and even legitimately unpopular, the White House and its allies have concluded that their political troubles amount to mainly a message and image problem.

I think this is misleading for the usual reason that these message-oriented critiques are misleading: When things are going well, your message is going to sound good; when things aren't going so well, it doesn't matter much how you spin things. Baker recognizes this: "the debate has taken on particular resonance in a time of economic distress." To put it another way, I disagree that the battle of elitism is "at the core of the nation's debate this fall." I think it would be more accurate to say that economic policy and outcomes are at the core of the debate, and "elitism" is just being attached to that debate. If it wasn't "elitism," it would be something else.

Is it really true that you can't blame the Democrats for their anticipated poor performance in the upcoming election?

Here's the second story I'm not thrilled with, this time from Jonathan Chait in the New Republic:

Republicans are going to gain a lot of seats in the midterm elections. The big question is why. Political punditry has been saturated with arguments interpreting this result as a verdict of sorts on the Obama administration. Liberals are interpreting the incipient GOP win on poor communication or perhaps timid policies by the Democrats. Conservatives are interpreting it as the natural punishment for a party that moved too far left. . . .

But in order to have that conversation, you need to begin with a baseline expectation. What sort of performance should we expect normally? Clearly, in the current environment, it's not rational to expect the majority party to escape any losses whatsoever. If you want to blame the Democrats' loss on bad messaging or wimpy policies or rampaging socialism, then you need to establish how you'd expect them to do given normal messaging and policies.

Chait then discusses Doug Hibbs's model, featured on this blog before, which predicts midterm election outcomes given incumbency, the current distribution of House seats, and recent economic performance. Given that Hibbs's model--which does not use any polling data--predicts a Republican gain of 45 seats, Chait concludes that you can't blame Obama (or, by extension, congressional Democrats) for what's happening now. In Chait's words, "If you want to have the "what did Obama do wrong" argument, you first need to establish what "wrong" would look like. That's probably a 50 seat-or-more loss."

But I don't think that's right. From Paul Krugman on the left to Casey Mulligan on the right, commenters have been arguing that the governing party can make a difference. Whether it's Kruguman recommending a larger stimulus or Mulligan saying the government should avoid intervention into the financial sector, the claim is that that the economy could be doing better (or worse). In statistical jargon, recent personal income is "endogenous."

Or, to put it another way, I think it's perfectly reasonable for liberals to interpret some of the current state of the economy, and thus the predicted election outcome, to "timid policies by the Democrats." That's what Krugman is saying every day. Similarly, why shouldn't conservatives think that the current economic doldrums are partly explained by the Democrats' policies, from regulation to stimulus to health care? Republicans have been making this argument for awhile, that these activist government policies are counterproductive.

I see where Chait is coming from, and I agree with him that it's silly to say that the Democrats are losing because of bad messaging or any such shallow thing, but I think he's too quick to dismiss the idea that different policies on the part of the Democrats could've led to better (or worse) economic outcomes.

Summary

Baker is following a traditional path in political journalism by focusing on what seem to me to be superficial issues--the kinds of things that voters sometimes tell pollsters but I think are ultimately driven by more fundamental concerns (the economy, security, etc).

Chait is making an opposite mistake. He's right that elections are largely determined by the fundamentals, but he's wrong to think that the governing party has no effect on the economy. (Or, maybe he's right, but if so, he should take this up with Krugman et al., not me.) To say that the fundamentals matter, that the economy is key, is not the same as saying that the president and Congress can't influence elections. The 2010 economy was not predetermined as of January, 2009. Or, if it was, a lot of people were wasting their time arguing about the macroeconomic consequences of the stimulus, the bailouts, the deficit, the tax cuts, etc etc etc.

Maria Wolters writes:

The parenting club Bounty, which distributes their packs through midwives, hospitals, and large UK supermarket and pharmacy chains, commissioned a fun little survey for Halloween from the company OnePoll. Theme: Mothers as tricksters - tricking men into fathering their babies. You can find a full smackdown courtesy of UK-based sex educator and University College London psychologist Petra Boynton here.

Lee Mobley writes:

I recently read what you posted on your blog How does statistical analysis differ when analyzing the entire population rather than a sample?

What you said in the blog accords with my training in econometrics. However I am concerned about a new wrinkle on this problem that derives from multilevel modeling.

Justin Phillips placed some questions on the YouGov Model Politics poll and reports the following:

This came in the spam the other day:

College Station, TX--August 16, 2010--Change and hope were central themes to the November 2008 U.S. presidential election. A new longitudinal study published in the September issue of Social Science Quarterly analyzes suicide rates at a state level from 1981－2005 and determines that presidential election outcomes directly influence suicide rates among voters.

In states where the majority of voters supported the national election winner suicide rates decreased. However, counter-intuitively, suicide rates decreased even more dramatically in states where the majority of voters supported the election loser (4.6 percent lower for males and 5.3 lower for females). This article is the first in its field to focus on candidate and state-specific outcomes in relation to suicide rates. Prior research on this topic focused on whether the election process itself influenced suicide rates, and found that suicide rates fell during the election season.

Richard A. Dunn, Ph.D., lead author of the study, credits the power of social cohesion, "Sure, supporting the loser stinks, but if everyone around you supported the loser, it isn't as bad because you feel connected to those around you. In other words, it is more comforting to be a Democrat in Massachusetts or Rhode Island when George W. Bush was re-elected than to be the lonely Democrat in Idaho or Oklahoma."

Researchers have commonly thought that people who are less connected to other members of society are more likely to commit suicide. The authors of the study first became interested in this concept when studying the effect of job loss and unemployment on suicide risk, which theoretically causes people to feel less connected to society. The authors realized that while previous work had explored whether events that brought people together and reaffirmed their shared heritage such as elections, war, religious and secular holidays lowered suicide rates, researchers had generally ignored how the outcomes of these events could also influence suicide risk.

The study holds implications for public health researchers studying the determinants of suicide risk, sociologists studying the role of social cohesion and political scientists studying the rhetoric of political campaigns.

I want to laugh at this sort of thing . . . but, hey, I have an article (with Lane Kenworthy) scheduled to appear in Social Science Quarterly. I just hope that when they send out mass emails about it, they link to the article itself rather than, as above, generically to the journal.

More seriously, I don't want to mock these researchers at all. In most of my social science research, I'm a wimp, reporting descriptive results and usually making causal claims in a very cagey way. (There are rare exceptions, such as our estimates of the effect of incumbency and the effects of redistricting. But in these examples we had overwhelming data on our side. Usually, as in Red State, Blue State, I'm content to just report the data and limit my exposure to more general claims.) In contrast, the authors of the above article just go for it. As Jennifer says, causal inference is what people really want--and what they should want--and so my timidity in this regard should be no sort of model for social science researchers.

With regard to the substance of their findings, I don't buy it. The story seems too convoluted, and the analysis seems to have too many potential loopholes, for me to have any confidence at all in the claims presented in the article. Sure, they found an intriguing pattern in their data, but the paper does not look to me to be a thorough examination of the questions that they're studying.

P.S. to those who think I'm being too critical here:

Hey, this is just a blog and I'm talking about a peer-reviewed publication in a respectable journal. I'm not saying that you, the reader, should disbelieve Classen and Dunn's claims, just because I'm not convinced.

I'm a busy person (aren't we all) and don't have the time or inclination right now to go into the depths of the article and find out where their mistakes are (or, alternatively, to look at their article closely enough to be convinced by it). So you can take my criticisms as seriously as they deserve to be taken.

Given that I haven't put in the work, and Classen and Dunn have, I think it's perfectly reasonable for you to believe what they wrote. And it would be completely reasonable for them, if they happen to run across this blog, to respond with annoyance to my free-floating skepticism. I'm just calling this one as I see it, while recognizing that I have not put in the effort to look into it in detail. Those readers who are interested in the subject can feel free to study the matter further.

par (mar=c(3,3,2,1), mgp=c(2,.7,0), tck=-.01)

Thank you.

Oof! (if you'll forgive my reference to bowling)

What's funny to me, though, is the phrase, "she's not nearly as smart as she seems to think she is." I mean, doesn't that describe most people? (Link from here.)

P.S. I hate to spell things out, Jeff, but . . . I hope you caught the Douglas Ginsburg reference!

Red State Blue State: How Will the U.S. Vote?

It's the "annual Halloween and pre-election extravaganza" of the Department of Mathematics and Statistics, and they suggested I could talk on the zombies paper (of course), but I thought the material on voting might be of more general interest.

The "How will the U.S. vote?" subtitle was not of my choosing, but I suppose I can add a few slides about the forthcoming election.

Fri 29 Oct 2010, 7pm in Ward I, in the basement of the Ward Circle building.

Should be fun. I haven't been to AU since taking a class there, over 30 years ago.

P.S. It was indeed fun. Here's the talk. I did end up briefly describing my zombie research but it didn't make it into any of the slides.

Jaidev Deshpande writes:

The World Economic Forum recently posed a data visualization problem. The dataset is a survey of experts from the so called "Agenda Councils" of the WEF. Here are the details.

The dataset primarily contains the experts' opinions on which global / regional / industrial agenda council of the WEF they would benefit from by interacting with the most. It occurs to me that this dataset can be thought of as an instance of a social networking dynamics, in that it represents the preferences of individuals towards belonging or not belonging to a particular group within the network. It is these 'groups' that must be identified to solve the problem. Under what conditions would this hypothesis be valid?

I have a hunch that dimensionality reduction will not necessarily help me visualize this data satisfactorily. They also need to be complemented by the way social networks detect cliques amongst their members.

The prize is $3000 plus bragging rights, and submissions are due 15 Nov.

Boris writes:

Chris Blattman writes:

Matching is not an identification strategy a solution to your endogeneity problem; it is a weighting scheme. Saying matching will reduce endogeneity bias is like saying that the best way to get thin is to weigh yourself in kilos. The statement makes no sense. It confuses technique with substance. . . . When you run a regression, you control for the X you can observe. When you match, you are simply matching based on those same X. . . .

I see what Chris is getting at--matching, like regression, won't help for the variables you're not controlling for--but I disagree with his characterization of matching as a weighting scheme. I see matching as a way to restrict your analysis to comparable cases. The statistical motivation: robustness. If you had a good enough model, you wouldn't neet to match, you'd just fit the model to the data. But in common practice we often use simple regression models and so it can be helpful to do some matching first before regression. It's not so difficult to match on dozens of variables, but it's not so easy to include dozens of variables in your least squares regression. So in practice it's not always the case that "you are simply matching based on those same X. To put it another way: yes, you'll often need to worry about potential X variables that you don't have--but that shouldn't stop you from controlling for everything that you do have, and matching can be a helpful tool in that effort.

Beyond this, I think it's useful to distinguish between two different problems: imbalance and lack of complete overlap. See chapter 10 of ARM for further discussion. Also some discussion here.

In the inbox today:

From Jimmy.

From Kieran.

The relevant references are here and, of course, here.

I was recently speaking with a member of the U.S. House of Representatives, a Californian in a tight race this year. I mentioned the fivethirtyeight.com prediction for him, and he said "fivethirtyeight.com? What's that?"

After our recent discussion of semigraphic displays, Jay Ulfelder sent along a semigraphic table from his recent book. He notes, "When countries are the units of analysis, it's nice that you can use three-letter codes, so all the proper names have the same visual weight."

Ultimately I think that graphs win over tables for display. However in our work we spend a lot of time looking at raw data, often simply to understand what data we have. This use of tables has, I think, been forgotten in the statistical graphics literature.

So I'd like to refocus the eternal tables vs. graphs discussion. If the goal is to present information, comparisons, relationships, models, data, etc etc, graphs win. Forget about tables.

But . . . when you're looking at your data, it can often help to see the raw numbers. Once you're looking at numbers, it makes sense to organize them. Even a displayed matrix in R is a form of table, after all. And once you're making a table, it can be sensible to set it up as a semigraphic display. So if there is room for tables in statistics, that's where they go, I think.

Tyler Cowen links to a paper by Bruno Frey on the lack of space for articles in economics journals. Frey writes:

To further their careers, [academic economists] are required to publish in A-journals, but for the vast majority this is impossible because there are few slots open in such journals. Such academic competition maybe useful to generate hard work, however, there may be serious negative consequences: the wrong output may be produced in an inefficient way, the wrong people may be selected, and losers may react in a harmful way.

According to Frey, the consensus is that there are only five top economics journals--and one of those five is Econometrica, which is so specialized that I'd say that, for most academic economists, there are only four top places they can publish. The difficulty is that demand for these slots outpaces supply: for example, in 2007 there were only 275 articles in all these journals combined (or 224 if you exclude Econometrica), while "a rough estimate is that there are around 10,000 academics actively aspiring to publish in A-journals."

I agree completely with Frey's assessment of the problem, and I've long said that statistics has a better system: there are a lot fewer academic statisticians than academic economists, and we have many more top journals we can publish in (all the probability and statistics journals, plus the econ journals, plus the poli sci journals, plus the psych journals, etc), so there's a lot less pressure.

I wonder if part of the problem with the econ journals is that economists enjoy competition. If there were not such a restricted space in top journals, they wouldn't have a good way to keep score.

Just by comparison, I've published in most of the top statistics journals, but my most cited articles have appeared in Statistical Science, Statistica Sinica, Journal of Computational and Graphical Statistics, and Bayesian Analysis. Not a single "top 5 journal" in the bunch.

But now let's take the perspective of a consumer of economics journals, rather than thinking about the producers of the articles. From my consumer's perspective, it's ok that the top five journals are largely an insider's club (with the occasional exceptional article from an outsider). These insiders have a lot to say, and it seems perfectly reasonable for them to have their own journal. The problem is not the exclusivity of the journals but rather the presumption that outsiders and new entrants should be judged based on their ability to conform to the standards of these journals. The tenured faculty at the top 5 econ depts are great, I'm sure--but does the world really need 10,000 other people trying to become just like them??? Again, based on my own experience, some of our most important work is the stuff that does not conform to conventional expectations.

P.S. I met Frey once. He said, "Gelman . . . you wrote the zombies paper!" So, you see, you don't need to publish in the AER for your papers to get noticed. Arxiv is enough. I don't know whether this would work with more serious research, though.

P.P.S. On an unrelated note, if you have to describe someone as "famous," he's not. (Unless you're using "famous" to distinguish two different people with the same name (for example, "Michael Jordan--not the famous one"), but it doesn't look like that's what's going on here.)

I found a $5 bill on the street today.

Hendrik Juerges writes:

I am an applied econometrician. The reason I am writing is that I am pondering a question for some time now and I am curious whether you have any views on it.

One problem the practitioner of instrumental variables estimation faces is large standard errors even with very large samples. Part of the problem is of course that one estimates a ratio. Anyhow, more often than not, I and many other researchers I know end up with large point estimates and standard errors when trying IV on a problem. Sometimes some of us are lucky and get a statistically significant result. Those estimates that make it beyond the 2 standard error threshold are often ridiculously large (one famous example in my line of research being Lleras-Muney's estimates of the 10% effect of one year of schooling on mortality). The standard defense here is that IV estimates the complier-specific causal effect (which is mathematically correct). But still, I find many of the IV results (including my own) simply incredible.

Now comes my question: Could it be that IV is particularly prone to "type M" errors? (I recently read your article on beauty, sex, and power). If yes, what can be done? Could Bayesian inference help?

My reply:

I've never actually done any instrumental variables analysis, Bayesian or otherwise. But I do recall that Imbens and Rubin discuss Bayesian solutions in one of their articles, and I think they made the point that the inclusion of a little bit of prior information can help a lot.

In any case, I agree that if standard errors are large, then you'll be subject to Type M errors. That's basically an ironclad rule of statistics.

My own way of understanding IV is to think of the instrument has having a joint effect on the intermediate and final outcomes. Often this can be clear enough, and you don't need to actually divide the coefficients.

And here are my more general thoughts on the difficulty of estimating ratios.

Shankar Vedantam writes:

Americans distrust the GOP. So why are they voting for it? . . . Gallup tells us that 71 percent of all Americans blame Republican policies for the bad economy, while only 48 percent blame the Obama administration. . . . while disapproval of congressional Democrats stands at 61 percent, disapproval of congressional Republicans stands at 67 percent.

[But] Republicans are heavily tipped to wrest control of one or both houses of Congress from the Democrats in the upcoming midterms.

Hey! I know the answer to that one. As I wrote in early September:

I was flipping through the paper yesterday and noticed something which I think is a bit of innumeracy--although I don't have all the facts at my disposal so I can't be sure. It came in an item by Robert Woletz, society editor of the New York Times, in response to the following letter from Max Sarinsky (click here and scroll down):

I was going through the blog and noticed this note on an article by Mankiw and Weinzierl who implied that the state only has a right to tax things that are "unjustly wrestled from someone else." This didn't make much sense to me--whether it's the sales tax, the income tax, or whatever, I see taxes as a way to raise money, not as a form of punishment. At the time, I conjectured this was a general difference in attitude between political scientists and economists, but in retrospect I realize I'm dealing with n=1 in each case.

See here for further discussion of taxing "justly acquired endowments."

The only reason I'm bringing this all up now is that I think it is relevant to our recent discussion here and here of Mankiw's work incentives. Mankiw objected to paying a higher marginal tax rate, and I think part of this is that he sees taxes as a form of punishment, and since he came by his income honestly he doesn't think it's fair to have to pay taxes on it. My perspective is slightly different, partly because I never thought of taxation as being restricted to funds that have been "unjustly wrestled."

Underlying this is a lot of economics, and I'm not presenting this as any sort of argument for higher (or lower) marginal tax rates. I'm just trying to give some insight into where Mankiw might be coming from. A.lot of people thought his column on this 80% (or 90% or 93%) marginal tax rate was a little weird, but if you start from the position that only unjust income should be taxed, it all makes a lot more sense.

Brayden King points to this page of materials on sociologist Erving Goffman. Whenever I've read about Goffman, it always seems to be in conjunction with some story about his bad behavior--in that respect, King's link above does not disappoint. In the absence of any context, it all seems mysterious to me Once or twice I've tried to read passages in books by Goffman but have never manage to get through any of it. (This is not mean as any kind of criticism, it's just a statement of my lack of knowledge.) I was amused enough by the stories reported by King that I clicked through to the Biographical Materials section of the Goffman page and read a few. I still couldn't really quite get the point, though, perhaps in part because I only know one of the many people on that list.

Nate Silver and Justin Wolfers are having a friendly blog-dispute about momentum in political polling. Nate and Justin each make good points but are also missing parts of the picture. These questions relate to my own research so I thought I'd discuss them here.

Here are my answers to the following questions asked by Pauline Peretz:

1. Many analysts have emphasized that there was a redrawing of the electoral map in 2008. To what extent will the November midterm elections affect this red-blue map? How long will the newly blue states remain blue?

2. Do you think the predictable loss of the Democrats in November definitely disqualifies the hypothesis that Obama's election was the beginning of a realignment in American politics, that is a period of dominance for the Democratic party due to favourable demographics?

3. Some analysts consider that voting patterns are best explained by economic factors, others by values. How do you position yourself in the debate on culture wars vs. economic wars?

4. In your book Red State, Blue State, Rich State, Poor State, you renew the ongoing debate on the correlation between income and vote, showing it is much stronger in poor states. In light of this correlation, would you say that there currently is an increasing economic polarization in American politics?

5. To what extent do you think internal mobility and the geographical concentration of like-minded voters (in some sort of "self-sorting" mechanism) increases the polarization of American politics?

P.S. No, I don't know what was going on with my hair that day.

Alex Hoffman writes:

I am reviewing a article with a whole bunch of tables with likert scale responses. You know, the standard thing with each question on its own line, followed by 5 columns of numbers.

Is there a good way to display this data graphically? OK, there's no one best way, but can you point your readers to a few good examples?

My reply: Some sort of small multiples. I'm thinking of lineplots. Maybe a grid of plots, each with three colored and labeled lines. For example, it might be a grid with 10 rows and 5 columns. To really know what to do, I'd have to have more sense of what's being plotted.

Feel free to contribute your ideas in the comments.

Speakers:

Cyrus Samii, PhD candidate, Department of Political Science, Columbia University:
"Peacebuilding Policies as Quasi-Experiments: Some Examples"

Macartan Humphreys, Associate Professor, Department of Political Science, Columbia University:
"Sampling in developing countries: Five challenges from the field"

Friday 22 Oct, 3-5pm in the Playroom (707 International Affairs Building). Open to all.

Hadley Wickham sent me this, by Keith Baggerly and Kevin Coombes:

In this report we [Baggerly and Coombes] examine several related papers purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response. Patients in clinical trials are currently being allocated to treatment arms on the basis of these results. However, we show in five case studies that the results incorporate several simple errors that may be putting patients at risk. One theme that emerges is that the most common errors are simple (e.g., row or column offsets); conversely, it is our experience that the most simple errors are common.

This is horrible! But, in a way, it's not surprising. I make big mistakes in my applied work all the time. I mean, all the time. Sometimes I scramble the order of the 50 states, or I'm plotting a pure noise variable, or whatever. But usually I don't drift too far from reality because I have a lot of cross-checks and I (or my close collaborators) are extremely familiar with the data and the problems we are studying.

Genetics, though, seems like more of a black box. And, as Baggerly and Coombes demonstrate in their fascinating paper, once you have a hypothesis, it doesn't seem so difficult to keep coming up with what seems like confirming evidence of one sort or another.

To continue the analogy, operating some of these methods seems like knitting a sweater inside a black box: it's a lot harder to notice your mistakes if you can't see what you're doing, and it can be difficult to tell by feel if you even have a functioning sweater when you're done with it all.

See below for the job announcement. It's for Teachers College, which is about 2 blocks from the statistics department and 2 blocks from the political science department. So even though I don't have any official connection with Teachers College (besides occasionally working with them on research projects), I very much would like to have another exciting young applied researcher here, to complement all the people we currently have in stat, poli sci, engineering, etc. In particular, we have zillions of interesting and important social science research projects going on here, and they all need statistics work. A lot of social scientists do statistics, but it's not so easy to find a statistician who does serious social science research.

All this is to say that I hope this job gets some applicants from some people who are serious about applied statistics and the development of new models and methods.

Following up on our discussion of crime rates--surprisingly (to me), Detroit's violent crime rate was only 75% more than Minneapolis's--Chris Uggen pointed me to this warning from Richard Rosenfeld and Janet Lauritsen about comparative crime stats.

Last week, as I walked into Andrew's office for a meeting, he was formulating some misgivings about applying an ideal-point model to budgetary bills in the U.S. Senate. Andrew didn't like that the model of a senator's position was an indifference point rather than at their optimal point, and that the effect of moving away from a position was automatically modeled as increasing in one direction and decreasing in the other.

Executive Summary

The monotonicity of inverse logit entails that the expected vote for a bill among any fixed collection of senators' ideal points is monotonically increasing (or decreasing) with the bill's position, with direction determined by the outcome coding.

The Ideal-Point Model

The ideal-point model's easy to write down, but hard to reason about because of all the polarity shifting going on. To recapitulate from Gelman and Hill's Regression book (p. 317), using the U.S. Senate instead of the Supreme Court, and ignoring the discrimination term, the ideal-point model is

Pr(y[i]=1) = invLogit(alpha[j[i]]-beta[k[i]])

           = 1/(1 + exp(-(alpha[j[i]]-beta[k[i]]))).

y[i] is the vote by senator j[i] on bill k[i]. The value alpha[j] is the ideal point of senator j and the value beta[k] the position of bill k. The probability of a 1 vote versus the difference alpha[j[i]]-beta[k[i]] looks as follows.

But what does a 0 or 1 response mean? In Gelman and Hill's words, the vote is "coded so that a 'yes' response (y[i]=1) is intended to correspond to the politically 'conservative' outcome, with 'no' (y[i]=0) corresponding to a 'liberal' vote."

To identify the parameters, we can center the alphas around 0 and assume that liberal senators will have smaller alpha[j] and conservative senators larger alpha[j] values. On the same scale, more conservative bills will have larger beta[k] and more liberal bills smaller beta[k].

The Problem: Directional Monotonicity

Gelman and Hill correctly state that "if a justice's [senator's] ideal point is near a case's [bill's] position, then the case [bill] could go either way, but if the ideal point is far from the position, then the justice's [senator's] vote is highly predictable." This is easy to see from the graph. It's the specifics of how the votes get more predictable that's problematic.

Given a vote on bill k with position beta[k], the further the senator's position is to the right (i.e., larger alpha[j]), the more likely they are to vote conservative (i.e., y=1), and the further a senator is to the left (i.e., smaller alpha[j]), the more likely they are to vote liberal (i.e., y=0).

Holding a senator's ideal point alpha[j] steady, bills further to the left (i.e. smaller beta[k]) are more likely to get conservative (i.e., y=1) votes and those further to the right more likely to get liberal (i.e., y=0) votes.

Expected Votes Given Position

The obvious thing to plot here is the expected number of votes predicted by the model versus the position of a bill. The following graph is a simulation. I simulated a mixture of 57 Democratic and 43 Republican senators (using Norm(-1.5,1) and Norm(2,1) sampling distributions, respectively). I then centered them so they'd have a mean of zero. The senators' positions are shown on the x axis as circles, colored blue for Democratic and red for Republican senators.

The red curve shows the expected number of 1 votes given the position of a bill. It's colored red because a conservative bill is coded with a vote of y[i]=1 being for the bill. Thus the red curve is the number of votes for a conservative bill based on its position.

Although only 51 votes are required to pass a bill, 60 are required to block an opposing filibuster, so we have drawn a horizontal line at 60 votes. It intersects the curves at the least extreme positions at which a bill is expected to pass even under threat of filibuster.

Things flip for a liberal bill, where outcome y[i]=0 is considered a vote for the bill. The blue curve shows the number of votes for a liberal bill based on its position. Thus the coding of a bill makes a difference to the position at which it'll pass as well as the direction of movement which increases likelihood of passage. Specifically, for a bill coded as conservative, the more liberal its position, the more likeliy it is to pass. For bills coded liberal, the more conservative its position, the more likely it is to pass.

Andrew's right -- the model doesn't make sense. Making a bill more liberal or more conservative has the same effect on senators of both parties, making them either more or less likely to vote for the bill, depending on how the coding is chosen. Flipping the coding actually changes the point at which a bill will pass, and seems redundant given that a bill's position, beta[k], is also coding a bill's liberal/conservative orientation.

A More Concrete Example

For concreteness, consider the US$ 800+ 2009 stimulus bill, a liberal bill which squeaked through the U.S. Senate. We will suppose that voting for it is "liberal", so coded as 0. Presumably larger stimulus packages are also more liberal, corresponding to smaller estimated beta[k] (see what I mean about polarity shifting?).

Now consider any old senator j with any old ideal-point alpha[j], no matter how far right or left. What happens to their voting preferences as the bill beta[k] moves to the left or right? If the bill moves to the left, beta[k] decreases, so alpha[i]-beta[k] inreases, and the vote is more likely to be 1 (against the stimulus). Thus no matter what the senator's ideal point is, they're more likely to vote against the bill as the target amount becomes more liberal (rises). If the bill moves to the right, presumably corresponding to a smaller stimulus package, beta[k] increases, so alpha[i]-beta[k] decreases, so the vote is more likely to be 0 (for the stimulus).

If we recode the outcome so that y[i]=1 rerpesents a vote for the stimulus package, we're left with the same problem in the opposite direction.

Pardon me for whipping out the Latin, but I am back in academia, and this sure feels like a reductio ad absurdum.

A Better Approach?

A better approach would presumably model a senator's preferences for bills based on proximity to their ideal point rather than difference. Then, the further the bill got away from the senator's ideal in either direction, the more likely they'd be to vote against it, rather than assuming their preference for the bill goes up in one direction of change and down in the other relative to their indifference point.

A hypothetical bill with a position to the left (or right) of all the senators will still act monotonically, but now with the expected directional effect, namely increasing expected votes as the bill approaches the senator's ideal points. Points in the middle act in different directions on different senators, moving closer to some and further from others based on their ideal points.

In addition to hierarchical structure on the ideal points and multiplicative discrimination terms (for senator and/or the bill), as Gelman and Hill discuss, we could also add some basic predictors in addition to senator and bill identity, such as for the party of the senator, the party of the senator(s) who introduced the bill, the chance of filibuster, pork going to the senator's state, and all the the other things that make the U.S. senate so much fun.

Jason Roos sends along this article:

On election days many of us see a colorful map of the U.S. where each tiny county has a color on the continuum between red and blue. So far we have not used such data to improve the effectiveness of marketing models. In this study, we show that we should.

We demonstrate the usefulness of political data via an interesting application--the demand for movies. Using boxoffice data from 25 counties in the U.S. Midwest (21 quarters between 2000 and 2005) we show that by including political data one can improve out-of-sample predictions significantly. Specifically, we estimate the improvement in forecasts due to the addition of political data to be around $43 million per year for the entire U.S. theatrical market.

Furthermore, when it comes to movies we depart from previous work in another way. While previous studies have relied on pre-determined movie genres, we estimate perceived movie attributes in a latent space and formulate viewers' tastes as ideal points. Using perceived attributes improves the out-of-sample predictions even further (by around $93 million per year). Furthermore, the latent dimensions that we identify are not only effective in improving predictions, they are also quite insightful about the nature of movies.

My reaction:

I'm too busy to actually read this one, but it's a great title! But--hey!--whassup with all those tables? Let's start by replacing Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 by graphs. (See here for some tips.) Or are you gonna tell me that the reader really needs to know that the standard error for the "Turnout - Midterm" variable in Dimension 4 is "0.108"? I mean, why not just print out a core dump in hex and cut out the middleman?

In all seriousness, the paper looks interesting and I'm sure would hugely benefit by some plots of data and fitted models.

On a more specific note, I wonder if the authors can shed any light on the controversial question of the Brokeback Mountain's popularity in Republican-voting areas in "red states" (search Kaus Brokeback Mountain for more than you could possibly want to read on this topic). I never saw the movie, nor have I followed the debates about its box office, but I recall there being some controversy on the topic.

Xian sends along this link that might be of interest to some of you.

Just was buying my ticket online. Huge amounts of paperwork . . . can't they contract out with Amazon.com? Anyway, at the very end, I got this item:

Recommended: Add Quik-Trip Travel Protection

Get 24/7 protection for your trip with a plan that provides:

* Electronic and Sporting Equipment coverage up to $1,000
* Travel Delay coverage (delays of 6 hrs. or more) up to $150
* 24/7 Travel Emergency Assistance

Yes! For just $8.50 per traveler, I'd like to add Quik-Trip Travel Protection. This is $8.50 total. Restrictions apply, learn more.
No thanks. I decline Quik-Trip Travel Protection.

"Restrictions apply," huh? My favorite part, though, is "Travel Delay coverage (delays of 6 hrs. or more) up to $150." I can just imagine the formula they have: "Your delay is 8 hours and 20 minutes, huh? Let's look that up . . . it looks like you're entitled to $124. And thanks for riding Amtrak!" But if your delay is only 5 hours and 50 minutes, forget about it.

P.S. My most memorable Amtrak experience was several years ago when I found myself sitting next to an elderly gentleman who was reading through some official-looking documents. I gradually realized it was Rep. Mike Castle of Delaware. I started up a conversation and told him our research on political polarization, a topic which he knew all about, of course.

Alban Zeber writes:

Christopher Uggen reports.

I'm surprised the difference is so small. I would've thought the crime rate was something like 5 times higher in Detroit than in Minneapolis. I guess Minneapolis must have some rough neighborhoods. Or maybe it's just that I don't have a good framework for thinking about crime statistics.

Susan points me to this. But I don't really see the point. Simply leaning the bike against the wall seems like a better option to me.

There is live debate that will available this week for those that might be interested. The topic: Can early stopped trials result in misleading results of systematic reviews?

In this article, Oliver Sacks talks about his extreme difficulty in recognizing people (even close friends) and places (even extremely familiar locations such as his apartment and his office).

After reading this, I started to wonder if I have a very mild case of face-blindness. I'm very good at recognizing places, but I'm not good at faces. And I can't really visualize faces at all. Like Sacks and some of his correspondents, I often have to do it by cheating, by recognizing certain landmarks that I can remember, thus coding the face linguistically rather than visually. (On the other hand, when thinking about mathematics or statistics, I'm very visual, as readers of this blog can attest.)

Our "arm" package in R requires Doug Bates's "lme4" which fits multilevel models.

lme4 is currently having some problems on the Mac. But installation on the Mac can be done; it just takes a bit of work.

I have two sets of instructions below.

See page 179 here for Gowa's review from 1986.

And here's my version (from 2008).

Mandelbrot on taxonomy (from 1955; the first publication about fractals that I know of):

Searching for Mandelbrot on the blog led me to Akaike, who also recently passed away and also did interesting early work on self-similar stochastic processes.

For example, this wonderful opening of his 1962 paper, "On a limiting process which asymptotically produces f^{-2} spectral density":

In the recent papers in which the results of the spectral analyses of roughnesses of runways or roadways are reported, the power spectral densities of approximately the form f^{-2} (f: frequency) are often treated. This fact directed the present author to the investigation of the limiting process which will provide the f^{-2} form under fairly general assumptions. In this paper a very simple model is given which explains a way how the f^{-2} form is obtained asymptotically. Our fundamental model is that the stochastic process, which might be considered to represent the roughness of the runway, is obtained by alternative repetitions of roughening and smoothing. We can easily get the limiting form of the spectrum for this model. Further, by taking into account the physical meaning of roughening and smoothing we can formulate the conditions under which this general result assures that the f^{-2} form will eventually take place.

P.S. I've placed this in the Multilevel Modeling category because fractals are a form of multilevel model, although not always recognized as such: fractals are hi-tech physical science models, whereas multilevel modeling is associated with low-grade fields such as education and social science. The connection is clear, though, when you consider Mandelbrot's taxonomy model or the connection between Akaike's dynamic model of roughness to complex models of student, teacher, and classroom effects.

I met Mandelbrot once, about 20 years ago. Unfortunately, at the time I didn't recognize the general importance of multilevel models, so all I could really do in the conversation was to express my awe and appreciation of his work. If I could go back now, I'd have some more interesting things to ask.

I never met Akaike at all.

This one belongs in the statistical lexicon. Kaiser Fung nails it:

In reading [news] articles, we must look out for the moment(s) when the reporters announce story time. Much of the article is great propaganda for the statistics lobby, describing an attempt to use observational data to address a practical question, sort of a Freakonomics-style application.

We have no problems when they say things like: "There is a substantial gap at year's end between students whose teachers were in the top 10% in effectiveness and the bottom 10%. The fortunate students ranked 17 percentile points higher in English and 25 points higher in math."

Or this: "On average, Smith's students slide under his instruction, losing 14 percentile points in math during the school year relative to their peers districtwide, The Times found. Overall, he ranked among the least effective of the district's elementary school teachers."

Midway through the article (right before the section called "Study in contrasts"), we arrive at these two paragraphs (Kaiser's italics):

On visits to the classrooms of more than 50 elementary school teachers in Los Angeles, Times reporters found that the most effective instructors differed widely in style and personality. Perhaps not surprisingly, they shared a tendency to be strict, maintain high standards and encourage critical thinking.

But the surest sign of a teacher's effectiveness was the engagement of his or her students -- something that often was obvious from the expressions on their faces.

At the very moment they tell readers that engaging students makes teachers more effective, they announce "Story time!" With barely a fuss, they move from an evidence-based analysis of test scores to a speculation on cause--effect. Their story is no more credible than anybody else's story, unless they also provide data to support such a causal link.

I have only two things to add:

1. As Jennifer frequently reminds me, we--researchers and also the general public--generally do care about causal inference. So I have a lot of sympathy for researchers and reporters who go beyond the descriptive content of their data and start speculating. The problem, as Kaiser notes, is when the line isn't drawn clearly, in the short time leading the reader astray and in the longer term, perhaps, discrediting social-scientific research more generally.

2. "Story time" doesn't just happen in the newspapers. We also see it in journal articles all the time. It's that all-too-quick moment when the authors pivot from the causal estimates they've proved, to their speculations, which, as Kaiser says, are "no more credible than anybody else's story." Maybe less credible, in fact, because researchers can fool themselves into thinking they've proved something when they haven't.

How am I supposed to handle this sort of thing? (See below.) I just stuck it one of my email folders without responding, but then I wondered . . . what's it all about? Is there some sort of Glengarry Glen Ross-like parallel world where down-on-their-luck Jack Lemmons of public relations world send out electronic cold calls? More than anything else, this sort of thing makes me glad I have a steady job.

Here's the (unsolicited) email, which came with the subject line "Please help a reporter do his job":

Dear Andrew,

As an Editor for the Bulldog Reporter (www.bulldogreporter.com/dailydog), a media relations trade publication, my job is to help ensure that my readers have accurate info about you and send you the best quality pitches. By taking five minutes or less to answer my questions (pasted below), you'll receive targeted PR pitches from our client base that will match your beat and interests. Any help or direction is appreciated. Here are my questions.

We have you listed in our media database as : Andrew Gelman, Editor with Chance Magazine covering Gambling.

1. Which specific beats and topic areas do you cover?
2. What do the best PR people do to grab you, to get your attention and make you want to work with them?
3. On the other hand, what are some inappropriate pitches for your type of coverage (i.e., material that PR keeps sending you that you don't cover or pet peeves you may have about PR people)?
4. Can you briefly tell me about a PR pitch that resulted in a story? What was it about the pitch or PR pro that sparked your interest?

Thanks so much for helping me gather this information.

Sincerely,

Jim Bucci
Research Journalist
Bulldog Reporter
124 Linden Street
Oakland, CA 94607

Frank Fischer, a political scientist at Rutgers U., says his alleged plagiarism was mere sloppiness and not all that uncommon in scholarship.

I've heard about plagiarism but I had no idea it occurred in political science.

I had the following email exchange with Jonathan DePeri, a reader of Bayesian Data Analysis.

I recently saw this article that Stephen Senn wrote a couple of years ago, criticizing Bayesian sensitivity analyses that relied on vague prior distributions. I'm moving more and more toward the idea that Bayesian analysis should include actual prior information, so I generally agree with his points. As I used to say when teaching Bayesian data analysis, a Bayesian model is modular, and different pieces can be swapped in and out as needed. So you might start with an extremely weak prior distribution, but if it makes a difference it's time to bite the bullet and include more information.

My only disagreement with Senn's paper is in its recommendation to try the so-called fixed-effects analysis. Beyond the difficulties with terminology (the expressions "fixed" and "random" effects are defined in different ways by different people in the literature; see here for a rant on the topic which made its way into some of my articles and books), there is the problem that, when a model gets complicated, some of the estimates that are called "fixed effects" get very noisy, especially in sparse data settings such as arise in logistic regression.

Meow!

Wow--economists are under a lot of pressure. Not only do they have to keep publishing after they get tenure; they have to be funny, too! It's a lot easier in statistics and political science. Nobody expects us to be funny, so any little witticism always gets a big laugh.

P.S. I think no one will deny that Levitt has a sense of humor. For example, he ran this item with a straight face, relaying to NYT readers in October 2008 that "the current unemployment rate of 6.1 percent is not alarming."

P.P.S. I think this will keep me safe for awhile.

In the spirit of Dehejia and Wahba:

Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons, by Cook, Shadish, and Wong.

Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments, by Shadish, Clark, and Steiner.

I just talk about causal inference. These people do it. The second link above is particularly interesting because it includes discussions by some causal inference heavyweights. WWJD and all that.