1. Understanding the 'Russian Mortality Paradox' in Central Asia: Evidence from Kyrgyzstan
Short answer: alcohol and suicide.
2. Lumberjacks as a counterexample to the idea of a "risk premium"
They take lots of risks and don't get paid well for it.
This is a visualization you won't want to miss.
5. The political philosophy of the private eye
A genre that was rendered obsolete in 1961 (but nobody realizes it).
Tyler Cowen writes:
Andrew Gelman will have a second blog. I don't yet understand the forthcoming principle of individuation across the two blogs.
I don't like the term "risk aversion" (see here and here). For a long time I've been meaning to write something longer and more systematic on the topic, but every once in awhile I see something that reminds me of the slipperiness of the topic.
For example, Alex Tabarrok asks, "Why are Americans more risk averse about medicine than Europeans?" It's a good question, and it's something I've wondered about myself. But I don't know what he's talking about when he says that "the stereotype is that Americans are more risk-loving" than Europeans. Huh? Americans are notorious for worrying about risks, with car seats, bike helmets, high railings on any possible place where someone could fall, Purell bottles everywhere, etc etc. The commenters on Alex's blog are all talking about drug company regulations, but it seems like a broader cultural thing to me.
But I'm bothered by the term "risk aversion." Why exactly is it appropriate to refer to strict rules on drug approvals as "risk averse"? In a general English-language use of the words, I understand it, but it gets slippery when you try to express it more formally.
Asa writes:
I took your class on multilevel models last year and have since found myself applying them in several different contexts. I am about to start a new project with a dataset in the tens of millions of observations. In my experience, multilevel modeling has been most important when the number of observations in at least one subgroup of interest is small. Getting started on this project, I have two questions:1) Do multilevel models still have the potential to add much accuracy to predictions when n is very large in all subgroups of interest?
2) Do you find SAS, STATA, or R to be more efficient at handling multilevel/"mixed effects" models with such a large dataset (wont be needing any logit/poisson/glm models)?
My reply:
Regarding software, I'm not sure, but my guess is that Stata might be best with large datasets. Stata also has an active user community that can help with such questions.
For your second question, if n is large in all subgroups, then multilevel modeling is typically not needed. But if n is large in all subgroups, you can simply fit a separate model in each group. That is equivalent to a full-interaction model. At that point you might be interested in details within subgroups, and then you might want a multilevel model.
Asa then wrote:
Yes, a "full interaction" model was the alternative I was thinking of. And yes, I can imagine the results from that model raising further questions about whats going on within groups as well.My previous guess was that SAS would be the most efficient for multilevel modeling with big data. But I just completely wrecked my (albeit early 2000's era) laptop looping proc mixed a bunch of times with a much smaller dataset.
I don't really know on the SAS vs. Stata issue. In general, I have warmer feelings toward Stata than SAS, but, on any particular problem, who knows? I'm pretty sure that R would choke on any of these problems.
On the other hand, if you end up breaking the problem into smaller pieces anyway, maybe the slowness of R wouldn't be so much of a problem. R does have the advantage of flexibility.
Aleks sends along this amusing news article by Jennifer Levitz:
A new study found that rates of marriage outside the faith were sharply curbed among young Jews who have taken "birthright" trips to Israel . . . Over the past decade, Taglit-Birthright Israel, a U.S. nonprofit founded by Jewish businessmen, has sponsored nearly 225,000 young Jewish adults for free 10-day educational tours of Israel as a way to foster Jewish identity. . . .A study [by Brandeis University researcher Leonard Saxe and partly funded by Taglit-Birthright] showed that 72% of those who went on the trip married within the faith, compared with 46% of people who applied for the trip but weren't selected in a lottery. . . . The Brandeis study looked at 1,500 non-Orthodox Jewish adults who took Taglit trips or applied for one between 2001 and 2004. . . . The Brandeis study looked at 1,500 non-Orthodox Jewish adults who took Taglit trips or applied for one between 2001 and 2004.
The article also said that 10,000 people participated in these trips last summer, which suggests that the 1,500 people in the research study represent a very small fraction of the participants from 2001-2004. I have no idea if this is a random sample, or what. Also I wonder about the people who participated in the lottery, were selected, but didn't go on the trip. Excluding these people (if there are many of them) could bias the results. The news article unfortunately doesn't link to any research report.
Philip Stark sent along this set of calculations on the probability that the hidden message in Gov. Schwartzenegger's message could've occurred by chance. The message, if you haven't heard, is:
The questions are no big deal, but what I find interesting is that medical school do personal interviews at all. No place where I've ever worked has interviewed grad school applicants. It's hard for me to see what you get from it, that it would be worth the cost. I guess there must be quite a bit of psychology literature on this question.
Christiaan de Leeuw writes:
I write to you with a question about the construction of informative priors in Bayesian analysis. Since most Bayesians at the statistics department here are more of the 'Objective' Bayes persuasion, I wanted some outside opinions as well.
Jay Kaufman writes:
I received the following email:
Hello, my name is Lauren Schmidt, and I recently graduated from the Brain & Cognitive Sciences graduate program at MIT, where I spent a lot of time doing online research using human subjects. I also spent a lot of time being frustrated with the limitations of various existing online research tools. So now I am co-founding a start-up, HeadLamp Research, with the goal of making online experimental design and data collection as fast, easy, powerful, and painless as can be. But we need your help to come up with an online research tool that is as useful as possible!We have a short survey (5-10 min) on your research practices and needs, and we would really appreciate your input if you are interested in online data collection.
I imagine they're planning to make money off this start-up and so I think it would be only fair if they pay their survey participants. Perhaps they can give them a share of the profits, if any exist?
Guilherme Rocha writes:
Here. Official opening is Monday but youall get to see it earlier.
Matt Stephenson writes:
Tuesday 3 Nov, 4-5:30pm in Room R505, Department of Government, LSE.
Culture wars, voting and polarization: divisions and unities in modern American politics
On the night of the 2000 presidential election, Americans sat riveted in front of their televisions as polling results divided the nation's map into red and blue states. Since then the color divide has become a symbol of a culture war that thrives on stereotypes--pickup-driving red-state Republicans who vote based on God, guns, and gays; and elitist, latte-sipping blue-state Democrats who are woefully out of touch with heartland values. But how does this fit into other ideas about America being divided between the haves and the have-nots? Is political polarization real, or is the real concern the perception of polarization?
This work is joint with David Park, Boris Shor, Joseph Bafumi, Jeronimo Cortina, and Delia Baldassarri.
(Here's a video version of the talk, from when I gave it at Google.)
I'll be interested to see if people can explain to me the relevance (or lack thereof) of this work to politics in Britain and other countries.
P.S. I'm speaking at LSE on Monday also (on a different topic).
P.P.S. I'll be speaking again a couple times in London later in the academic year, but on other topics. All my talks there will be different.
Monday 2 Nov, 5-6:30pm at the Methodology Institute, LSE. No link to the seminar on the webpage, so I'll give you the information here:
Why we (usually) don't worry about multiple comparisons
Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise.
Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the $p$-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.
This work is joint with Jennifer Hill and Masanao Yajima.
(Here's a video version of a related talk that I gave at a meeting on statistics and neuroscience.)
P.S. My talk briefly touches upon some work done by a researcher at the London School of Economics!
P.P.S. I'm speaking at LSE on Tuesday also (on a different topic).
P.P.P.S. I'll be speaking again a couple times in London later in the academic year, but on other topics. All my talks there will be different.
I just today learned about an organization called SourceWatch--they have an article on the tobacco connections of the well-known sociologist Peter Berger. Beyond the inherent interest of the topic, I was fascinated by the way that the Sourcewatch webpage mimicked Wikipedia:
This is a smart move, I think: for better or worse, Wikipedia is generally considered to be authoritative.
But then I was thinking . . . is this the beginning of the end for Wikipedia. I don't know anything about Sourcewatch, if they're good guys or bad guys or whatever--but if they can mimic Wikipedia, I'm sure lots of other organizations could do so too. And, when they do it, all of a sudden there will be a lot of authoritative-looking Wikipedia-like pages floating around, a sort of counterfeit money devaluing the "real" 'pedia, which will then have to respond by branding itself--"100% real Wikipedia, accept no imitations"--and so on. Not a bad thing, perhaps, but not what we have now.
From Aaron Swartz, a link stating that famous sociologist Peter L. Berger was a big-time consultant for the Tobacco Insitute:
Peter L. Berger is an academic social philosopher and sociologist who served as a consultant to the tobacco industry starting with the industry's original 1979 Social Costs/Social Values Project (SC/SV). According to a 1980 International Committee on Smoking Issues/Social Acceptability Working Party (International Committee on Smoking Issues/SAWP) progress report, Berger's primary assignment was "to demonstrate clearly that anti-smoking activists have a special agenda which serves their own purposes, but not necessarily the majority of nonsmokers."
Kavalier and Clay impressed me right away, even before I ever even held a copy in my hand, because for a year or so after it came out, I kept seeing people reading it. In the subway, in the park, everywhere. This was a book that people really wanted to read. So I bought it and read it and was duly impressed. It's a great book: DeLillo without the irony, if you will. The book Don DeLillo might have written had he been Michael Chabon and had the interests Chabon had instead of the interests that DeLillo has. Whatever.
More recently, I read (most of) Chabon's first book of stories, A Model World. I was into it for about a story and a half, and then I realized that these were all John Updike stories. Don't get me wrong here--Updike is my hero, and it's pretty impressive to me that he continues to write stories even after he's no longer around. And i don't really hold it against him (Updike) that he, like Gore Vidal, couldn't come up with good book titles. Chabon, though, he's a good titler. But I couldn't get into his book. It was just too weird that he was writing Updike stories.
But then I read Chabon's second book of stories, Werewolves in their Youth. Much better. Good to see someone getting better at what he does.
Lane Kenworthy, Yu-Sung Su, and I write:
Income inequality in the United States has risen during the past several decades. Has this produced an increase in partisan voting differences between rich and poor? We examine trends from the 1940s through the 2000s in the country as a whole and in the states. We find no clear relation between income inequality and class-based voting.
This article will appear in a special issue of Social Science Quarterly on the topic of "Inequality and Poverty: American and International Perspectives." We have some pretty graphs, some of which appeared in the Red State, Blue State book and some of which didn't.
P.S. "We find no clear relation . . .": That works great in an academic article but I don't think we'll be grabbing the headlines anytime soon.
After reading what Christian wrote about the solutions manual that he and his collaborator wrote for their Bayesian Core book, I'm reminded of my own efforts with the Bayesian Data Analysis solutions. Not long after the first edition of the book came out, nearly fifteen years ago, I wrote up solutions to fifty of the homework problems (out of a total of about 150). People sometimes ask for more solutions, but the difficulty is that, once you have official solutions, you want them to be correct, you want them to be clear, and you want them to illustrate good statistical practice. It's a lot of work. Somehow I was able to write up those fifty, back when I had more time on my hands, but, really, writing up another fifty would almost be the equivalent of writing a (short) book! Originally I thought I could quickly put together a complete or nearly complete set by gathering solutions from students, or people just emailing them in, but I quickly realized that this wouldn't work. I think it would be ok to post scanned-in versions of student solutions, but once I start typing them up, I need them to be cleaner, and that takes work. That's one reason I didn't even try to write a solution set for ARM.
In suggesting "a socially responsible method of announcing associations," AT points out that, as much as we try to be rigorous about causal inference, assumptions slip in through our language:
The trouble is, causal claims have an order to them (like "aliens cause cancer"), and so do most if not all human sentences ("I like ice cream"). It's all too tempting to read a non-directional association claim as if it were so -- my (least) favourite was a radio blowhard who said that in teens, cellphone use was linked with sexual activity, and without skipping a beat angrily proclaimed that giving kids a cell phone was tantamount to exposing them to STDs. . . . So here's a modest proposal: when possible, beat back the causal assumption by presenting an associational idea in the order least likely to be given a causal interpretation by a layperson or radio host.
Here's AT's example:
A random Google News headline reads: "Prolonged Use of Pacifier Linked to Speech Problems" and strongly implies a cause and effect relationship, despite the (weak) disclaimer from the quoted authors. Reverse that and you've got "Speech Problems linked to Prolonged Use of Pacifier" which is less insinuating.
It's an interesting idea, and it reminds me of something that really bugs me.
Phil Turk wrote:
Bill Browne sends in this interesting job possibility. Closing date for applications is 30 Oct 2009, so if you're interested, let him know right away!
Song Qian writes:
I am very pleased to see your comment on not analyzing data without context. Would you please elaborate the reasons on your blog? I have been teaching an intro data analysis class to our professional masters students since 2005. One thing I have emphasized is the understanding of the underlying scientific problem before conducting any data analysis. This point is not always well-taken. Thanks.
My response: From a Bayesian point of view, it's pretty clear: no context = no prior information. It's really more than that, though, since the context structures the model itself, not just the numerical information that you use to regularize parameter estimates. For the climate change example, Bill Jefferys provides a good discussion here on what you can get from substantive knowledge.
Richard Morey asks:
I was wondering if you or your blog readers have any experience using cloud computing to do simulations or analyses. The idea of packaging a simulation and having many copies of it running on a cloud (like, say, Amazon's EC2) is appealing. And using it for storage would be nice too.I'd like to get the opinion of statisticians who might have used this approach before spending time figuring it out.
I have no idea. Any suggestions are welcome.
Thanks for all the suggested titles. My current favorite remains, "If You Don't Buy This Magazine, We'll Kill This Blog." Although, I have to admit, "Super-Duper-Freakanomics" [sic] wasn't bad either. And, as much as I like the idea of calling it "Mister P," I can't quite pull the trigger on that one.
To respond to some of your comments:
1. No, I can't just post the general-interest entries at the new blog. That would take a lot of the fun out of the current blog. And the Science Blog people don't want me to cross-post more than 4 items per month. I will, of course, link to the new items from the current blog, but it's not as good if I can't cross-post them.
2. I agree that Science Blogs isn't the same as what I'm doing here, that's why I just wanted to post some stuff there, to reach the different audience, without losing what we have here.
3. I don't plan to be doing anything extra with this new blog; I see it more as a place to post a few things that I was going to post somewhere anyway.
4. Someone commented that it's strange for me to ask for a title before deciding on a topic. I thought it was implicit that, by asking for a title, I'm also asking for suggestions on a topic. I guess I'll try two or three posts a week and see how it goes.
Finally, in all seriousness, if nobody comes up with a better title, I'm going to call it "Applied Statistics." And I'll kick it off with a few posts about literature. Consider yourselves warned.
This news article has made a bit of a splash: Seth Borenstein sent around a temperature time series to four statisticians--just sending the numbers without saying where they came from--and the statisticians uniformly concluded that there were no consistent temperature declines over time:
"If you look at the data and sort of cherry-pick a micro-trend within a bigger trend, that technique is particularly suspect," said John Grego, a professor of statistics at the University of South Carolina.
I don't have anything to add on the temperature series--there's only so much you can learn from a context-free data analysis, and I don't think anyone would want to take this particular set of blind statistical analyses as being at all informative about the science. But there's more going on here.
I'd heard this before, but good advice is typically worth repeating. For those of you who program in R, I'd also recommend that scripts be written so it can run from scratch in an empty R environment. Many many times I've found my R scripts and environments to be palimpsests whose meanings are difficult to unravel. (The official recommendation, I guess, is to put everything in R packages, but I've never actually learned how to do this.)
When I search Gelman in Google, I'm right up there at #2. With Bing, I'm not even on the front page. Heck, I'm not even on the second page! Or the third, or the fourth, or the fifth, or the sixth, . . . OK, enough already! I know, I know, I shouldn't be searching myself anyway, but I had a legitimate reason . . . I had to find my talk on the web today from someone else's computer.
P.S. OK, I take it all back about Bing. I searched my name on Yahoo, and again my homepage did not appear in the first seven pages of search listings. So, really, I shouldn't be blaming Bing, I should be thanking Google for being so nice to me.
Thanks, Google!
David Park sent this along. I haven't really been following basketball statistics lately, but some of you might find it interesting.
Gustaf Granath writes:
I am an ecologist. I have been struggling with a problem for some time now and even asked some statisticians about this. It would be interesting for me (and maybe other people reading your blog) to hear your opinion. So far, I have not received a satisfying answer from anyone.I am doing a meta-analysis (in ecology with normal dist. data) using two different apporaches. My first approach is a frequentist mixed-model, assuming independence of each sample. The second approach is a hierarchical Bayesian model, modelling the dependence structure in the data set (e.g multiple outcomes from each study). I want to investigate if my covariates are important, and since I have many candidate covariates, I need to do some kind of model selection. My questions is then: is there a model selection tool that can be applied on both approaches??
We've been invited to start a blog at Science Blogs. This seemed like a good idea, a way to reach a new set of readers. At the same time, I didn't want to abandon the Mother Blog right here. Recently we've been overflowing with entries, so we decided to start a new blog at Science Blogs and just link back and forth between this blog and that one. Those of you with RSS can just get both feeds. (The Monkey Cage, 538, and New Majority are less of an issue since I can just crosspost.)
Anyway, we have two things to decide. First, what should the new blog be called; second, what sorts of things should we be posting there. Any suggestions? Thanks in advance for your help.
P.S. Yes, I know it would be logical to just move the entire blog over to the Science Blogs platform. But I just can't bring myself to do that. Science Blogs is a bunch of bloggers, which is fine, but I'd like my own blog to be centered on my research and teaching, which is here.
Lorraine Denby and Colin Mallows write:
It is usual to choose to make the bins in a histogram all have the same width. One could also choose to make them all have the same area. These two options have complementary strengths and weaknesses--the equal-width histogram oversmooths in regions of high density and is poor at identifying sharp peaks; the equal-area histogram oversmooths in regions of low density and so does not identify outliers. We describe a compromise approach which avoids both of these defects. We argue that relying on asymptotics of the Integrated Mean Square Error leads to inappropriate recommendations.
I'm so glad they wrote this article (it appeared recently in the Journal of Computational and Graphical Statistics)! I've thought for a long time that (a) histogram bars are typically too wide (for example, as set by default in software packages such as S and R), and (b) that the underlying problem was that people think of the goal of the histogram as to closely approximate the density function.
A key benefit of a histogram is that, as a plot of raw data, it contains the seeds of its own error assessment. Or, to put it another way, the jaggedness of a slightly undersmoothed histogram performs a useful service by visually indicating sampling variability. That's why, if you look at the histograms in my books and published articles, I just about always use lots of bins. I also almost never like those kernel density estimates that people sometimes use to display one-dimensional distributions. I'd rather see the histogram and know where the data are.
Denby and Mallows go far beyond my vague thoughts by considering histograms with varying widths and coming up with a particular algorithm. I'd like to try out their method on my own problems. Is there R package out there?
Margarita Alegría, Glorisa Canino, Patrick Shrout, Meghan Woo, Naihua Duan, Doryliz Vila, Maria Torres, Chih-nan Chen, and Xiao-Li Meng, write:
Although widely reported among Latino populations, contradictory evidence exists regarding the generalizability of the immigrant paradox, i.e., that foreign nativity protects against psychiatric disorders. The authors examined whether this paradox applies to all Latino groups by comparing estimates of lifetime psychiatric disorders among immigrant Latino subjects, U.S-born Latino subjects, and non-Latino white subjects.The authors combined and examined data from the National Latino and Asian American Study and the National Comorbidity Survey Replication, two of the largest nationally representative samples of psychiatric information.
In the aggregate, risk of most psychiatric disorders was lower for Latino subjects than for non-Latino white subjects. Consistent with the immigrant paradox, U.S.-born Latino subjects reported higher rates for most psychiatric disorders than Latino immigrants. However, rates varied when data were stratified by nativity and disorder and adjusted for demographic and socioeconomic differences across groups. The immigrant paradox consistently held for Mexican subjects across mood, anxiety, and substance disorders, while it was only evident among Cuban and other Latino subjects for substance disorders. No differences were found in lifetime prevalence rates between migrant and U.S.-born Puerto Rican subjects.
Winston Churchill said that sometimes the truth is so precious, it must be attended by a bodyguard of lies. Similarly, for a model to be believed, it must, except in the simplest of cases, be accompanied by similar models that either give similar results or, if they differ, do so in a way that can be understood.
In statistics, we call these extra models "scaffolding," and an important area of research (I think) is incorporating scaffolding and other tools for confidence-building into statistical practice. So far we've made progress in developing general methods for building confidence in iterative simulations, debugging Bayesian software, and checking model fit.
My idea for formalizing scaffolding is to think of different models, or different versions of a model, as living in a graph, and to consider operations that move along the edges of this graph of models, both as a way to improve fitting efficiency and as a way to better understand models by making informative comparisons. The graph of models connects to some fundamental ideas in statistical computation, including parallel tempering and particle flitering.
P.S. I want to distinguish scaffolding from model selection or model averaging. Model selection and averaging address the problem of uncertainty in model choice. The point of scaffolding is that we would want to compare our results to simpler models, even if we know that our chosen model is correct. Models of even moderate complexity can be extremely difficult to understand on their own.
My colleague Boris Shor has performed some analysis (jointly with Nolan McCarty) on the ideological positions of state legislators. The estimates are based on state legislative voting, which might make you wonder how you could possibly compare legislators in one state with those in another. The trick is that some state representatives (for example, Barack Obama) also end up in Congress. There are enough of these overlap cases that you can put legislators from all 50 states on a common scale.
Boris and Nolan most recently applied their method to compare Deirdre Scozzofava, a state assemblywoman running on the Republican ticket in special election in New York's 23rd congressoinal district. Boris writes:
Keith points me to this article by Gretchen Chapman and Jingjing Liu:
Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
Sounds interesting. Unfortunately the article has no killer graph to make the point. In psychology, the killer graph often takes the form of a plot with two lines that cross, thus demonstrating the interaction of interaction of interest. Maybe Chapman and Liu could do this for their next article.
P.S. I gotta say, it would be pretty cool to be named "Jingjing." Sort of a Boutros Boutros or Mike Michaelson thing going on here.
Bob writes:
I've been meaning to follow up two comments you made in passing about priors:1. You said you didn't like Dirichlet priors for multinomials because they didn't model covariance. What alternative do you suggest?
2. When I told you I was using the prior from the hierarchical binomial survival example from [page 128 of] your BDA book, you said you didn't like that prior any more. Why and what would you suggest as an alternative?
The book model reparameterized Beta(a,b) in terms of mean a/(a+b) which got a uniform prior, and scale a+b with a Pareto(1.5) prior [p(a+b) proportional to (a+b)**-2.5].
It works fairly well in practice, though it does lead to a fair number of large scale (a+b) samples.
I used your prior for baseball batting average estimation; the post includes the raw data (2006 AL position players) in tsv form, BUGS code, and the R calling harness.
I also use your prior for a hierarchical model of diagnostic test accuracy in epidemiology (or other data coding tasks).
I have longer versions of that paper with more analysis, simulations, data, alternative item-response type models, and pointers to all the code and data.
The basic epidemiology model keeps getting rediscovered. I'm still the only one who's drunk
enough of your Kool-Aid to go the full Bayesian hierarchical model route.
My reply:
Some computational and modeling issues for hierarchical models
How can we fit a complex statistical model and have confidence in our results? There are several challenges, including (a) setting up models that are complicated enough to reflect the aspects of reality that we want to study, (b) regularization or partial pooling to get stable estimates for the resulting large number of parameters, (c) actually fitting the model (in Bayesian terms, getting a point estimate or posterior simulations, (d) checking the fit of the model to data, (e) attaining confidence that the fitting procedure is bug-free, and (f) understanding the fitted model. We discuss these in the context of nonnested varying-intercept, varying-slope multilevel logistic regression models that we have been using to estimate public opinion in demographic and geographic subgroups of the U.S. population.
Mon 26 Oct, 9.30 on the ground floor of the Latarjet building at International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, Lyon. This is where Martyn Plummer (the JAGS guy) works.
[No, we didn't write it this way; it was translated for us. I still like our original title, "Of Beauty, Sex, and Power," but maybe it doesn't make so much sense in other languages.]
The sequel is already assured of box-office success, so now's the time to start thinking about what's gonna be in volume 3. Here are a few models that Levitt and Dubner could consider, in no particular order:
Robin Blackburn in Port-au-Prince:
The conference was opened by the prime minister, Michele Pierre-Louis, who was appointed despite a scurrilous campaign by opposition forces, who argued that appointing a lesbian to such a prominent position was a violation of Haitian manhood. Pierre-Louis had been the director of an NGO known as Fokal (Fondasyon Konesans ak Libete). In choosing her, Preval was thought to have made an adroit move, pleasing the NGO and donor communities: Fokal is supported by George Soros and various Canadian charities.
Carl Bialik reports on a website called the Book of Odds (really, as Carl points out, these are probabilities, not odds, but that's not such a problem because, at least to me, probabilities are much more understandable than odds anyway). It's pretty cool. I could give some examples here but I encourage you to just go to the site yourself and check it out. One thing I really like is that it gives the source of every number: right on the page it gives the country and date of the information, then you can click to get the details. Awesome.
The only thing that bothers me a little bit about the site is that it is almost too professional. When something's that slick, I worry about whether I can trust them.
In contrast, Nate Silver's website is respected but not particularly attractive. And the NameVoyager is just the coolest thing in the world, and, yes, it's professional and it's commercial--that's fine--but it doesn't have the suspicion-inducing hyper-professionalism of the Book of Odds. Seeing the all-so-appealing photo of the bright-eyed oldsters illustrating the "Will you live to be 100?" item that's currently featured on the site's main page, I just think--this is too slick to be trusted. (In case you're wondering, their data say that a randomly-chosen 90-year-old has only a 1-in-9 chance of living to 100. Actually, they say 1 in 8.85, but you know what I think about extra decimal points.)
In some way I prefer the charmingly and unabashedly commercial OK Cupid site to the Book of Odds, which looks so, so commercial but claims only purely altruistic goals. I just don't know what to think.
Anyway, whatever the true story happens to be, it's great stuff. Fun to browse, and a great teaching tool too, I'd think. Enjoy.
Dubner defends himself here. No word on the drunk driving advice, but he has some backstory on the interviews that he and Levitt did regarding global warming. It seems pretty clear that their approach to writing Freakonomics 2 was much different than the original book: the first Freakonomics was all about Levitt's work, whereas the most prominent part of the sequel is a discussion of the ideas of others. As I noted yesterday, this creates a huge selection issue--how did they decide whom to interview?--which is much less present in the first book. I'm also still confused that Dubner describes global warming as "a very difficult problem to solve," given that on his blog the other day he seemed to be endorsing the view that future trends are "virtually assuring us of about 30 years of global cooling."
My guess is that Levitt/Dubner's views on the topic are not completely coherent (by which I mean, not that Levitt and Dubner disagree with each other, but that between them they have a bunch of partly conflicting attitudes on the topic). As a political scientist, I'm the last person to criticize attitudes for being incoherent, and given that neither Levitt nor Dubner is an expert on climate change, it's probably a good thing that their attitudes are fluid and not so easy to pin down. The difficulty comes when they feel the need to defend everything that they've written so far. Again, this is tougher to do here than in the Freakonomics 1 examples, partly because Levitt was much more of an expert on his own research than on others' research, and partly, I suppose, because you'll get a lot more flak in the major news media if you question global warming than if you write about the beneficial consequences of abortion.
P.S. But see the second blurb here!
The above title is a joke. I haven't actually seen the book. As a big-time blogger, I get some books in the mail to review, but maybe this one is sitting in my NYC office. Anyway, the backlash has begun, so maybe this is the right time to buy low and be the first to offer the contrarian claim that, despite what everybody's saying, the book is awesome.
From a short-term economics standpoint, the controversy has gotta be good for the book. So far, Levitt and Dubner have put the words "GLOBAL COOLING" on the cover of their book, they've endorsed a report saying that future trends are "virtually assuring us of about 30 years of global cooling," and that "even if man is warming the planet, it is a small part compared with nature," and they've written that "we believe that rising global temperatures are a man-made phenomenon and that global warming is an important issue to solve." That last bit should do the job of ticking off anybody who was with them so far! (I was actually surprised when reading the comments on that last quote--where Levitt assures us that they do believe in global warming--that pretty much all the global-warming-skeptics among the commenters still seem to think that Levitt is on their side. I guess half a loaf is better than none at all, politically speaking, but I'm surprised that more of them didn't get angry at Levitt for saying that.)
I don't want to get into the substance of climate models, a subject on which I've worked on only a little bit. (The paper we wrote a few years ago never got published--actually, we never finished it enough to submit it anywhere--and our current work on the topic is still in the research-and-writing-up stage.) But I do want to speculate a bit on the political angle.
As I'm sure you know by now, I'm interested in differences between rich and poor. Higher-income people are more likely to vote Republican, and we've seen this in many different subgroups of the population. Among whites, among blacks, among religious attenders, etc., the poorer voters among these subgroups are more Democratic and the richer ones are more Republican.
This got me wondering: What are the subgroups of the population for which this isn't true? Or, more generally, how do rich and poor differ in their voting patterns, in different subgroups of the population.
Here's we found, courtesy of the 2000 and 2004 Annenberg surveys. For each group, we're looking at Republican share of the two-party vote intention among people in the upper third of family income, minus Republican share ... among people in the lower third of family income:
(Click on any of these graphs to see larger versions.)
A striking pattern. The differences between rich and poor are much larger among conservative, Republican groups than among liberal, Democratic groups. At the very bottom of the graph above, you see a few groups where richer people are more likely to vote Democratic. All of these are groups that are mostly liberal and Democratic.
Harker Rhodes writes:
I'm looking for a chapter in someone's textbook titled "Bayesian analysis of case-control studies". Or a chapter with any title covering that subject.....
Here's his longer story:
Just following up . . . this time Dr. McWilliams includes many qualifiers: "I suggested . . . I also suggested . . . Of course, this is only a possibility. I have no numbers to draw on. . . . In any case, it's just a thought."
This helps. As I said before, I have no problem with this sort of op-ed-style reasoning; it just seems out of place on Freakonomics. Anyway, this was part 3 of 3, so I'll have no more to say on the topic.
Kathy Dopp pointed me to this analysis she did regarding the Afghan election. I don't have the time/energy to look into this myself right now but I thought I'd pass this along so that others can comment if they'd like.
Stephen Kershaw writes:
Dan Lakeland writes:
Apropos your recent posting of the Churchill/Roosevelt poster, there has been a bit of a controversy over the effect of smoking bans in terms of heart attack rates. Recent bans in the UK have given researchers some plausible "experiments" to study the effect on a larger scale than the famous "Helena Montana" study. For example, this.On the other hand, when looking for info about this to follow up your poster I found a variety of usually rather obviously biased articles such as this one. But that's no reason to ignore a point of view if it can be backed up by data. The second link at least attempts (poorly) to display some data which suggests that an existing downward trend could be responsible for the reductions, and if poorly done the statistical research could have missed this.
Have you looked at the statistical methodology of any smoking ban studies? it seems like an area ripe for Bayesian modeling, and could be a subject along the lines of the fertility and beauty more girls/more boys research that you recently meta-analyzed.
My reply:
Yes, I imagine that some people have looked into this. I would guess that a smoking ban would reduce smoking and thus save lives, but of course it would be good to see some evidence.
Smoking behavior is a funny thing: It can be really hard to quit, and I've been told that the various anti-smoking programs out there really don't work. It's really hard to make a dent in smoking rates by working with smokers one at a time. On the other hand, rates of smoking vary a huge amount between countries and even between U.S. states:
And smoking bans might work too. Thus, smoking appears to be an individual behavior that is best altered through societal changes.
Aleks points me to this blog by Neuroskeptic, who reports on some recent research studying the placebo effect:
Someone who knows that I hate the so-called Fisher exact test asks:
I was hoping you could point to a Bayesian counterpart or improvement to "Fisher's exact test" - for 2 x 2 categorical, contigency tables with possibly very small numbers (too small to do a chi-square.) I see that you had a blog post on it before (1) but there are several issues i'm unclear about:(i) What would a full applied Bayesian analysis look like of this type of problem, in general? I have seen one beta-binomial like analysis but never in practical/applied examples. Any practical examples you may have for this, e.g. papers or code examples you've used in teaching, would be great.
(ii) What if we add the twist that the data from the two populations for our 2 x 2 test is paired? e.g. we have several male and several female patients, and the two conditions are drug / no drug. But, each male and female are paired as they are twins (which breaks the independence of the samples obviously.) How is this modeled from a Bayesian perspective?
(iii) Less important: when in practice is it ok to use Fisher's exact test if you're open to Bayesian analysis? 'Never' is a reasonable answer but i'd like to understand practical reasons why you think this. Finally, if all of our data counts are greater then 10, do you think its legitimate to use a chi-square?
My reply:
(i) The basic analysis is pretty simple, it goes like this:
y1 ~ Binomial (n1, p1)
y2 ~ Binomial (n2, p2)
We need a prior distribution on (p1,p2), and we usually assume that n1,n2 provide no information about p1,p2. (This latter point depends on the design of the study, but I'm keeping it simple here.) What's a good prior distribution depends on the problem, but in many cases, a simple uniform distribution on (p1,p2) will be fine. Whatever your prior is, you then throw in the likelihood and you get posterior inference for (p1,p2). Draw 1000 simulations and then use these to get inference for p1-p2. That's it. With moderate or large sample sizes, this is basically equivalent to the standard t-test.
If you have many tables, you can set up a hierarchical model for the p's. We have an example near the end of chapter 5 of Bayesian Data Analysis.
(ii) With paired data, you can fit a logistic regression. Call the data y_ij, where i=1 or 2 and j is the index for the pairing. Then you can model Pr(y_ij=1) = invlogit (a_i + b_j), with a hierarchical model for the b_j's, something like b_j ~ N (mu_b, sigma_b^2), with weakly informative or flat prior distributions on mu_b, sigma_b.
(iii) The only case I could even imagine using the so-called Fisher exact test is if the data were collected so that the row and column margins were both pre-specified. The only example I can think of with this design is Fisher's tea-tasting experiment. In all cases I've seen, at most one margin is preset by design. Also, I'd never do a chi-squared test in this setting. See chapter 2 of ARM for an example where I thought a chi-squared test was OK.
Fernando Hoces De La Guardia writes:
Last night we did the traditional first year econ phd student's skit nite @ Penn.One particular thing that I noticed was that we had less public that what the upper years told us to be prepared for.
Somebody suggested that it was due to Passover and Good Friday. My immediate reaction was "science & religion don't go usually together". By this I meant a prior of mine that the fraction of religious people is a lot less within a scientific discipline than among the rest of the population.
Two things pop out of my head this morning:
- in which data base can I check that prior?
- if true, are economists more religious than other scientists?
My reply: Usually people look these things up at the General Social Survey, which has a convenient web interface. Good luck!
Richard Morey writes:
I don't know if you are into this sort of thing, but I came across it on the web and thought it was entertaining. Essentially, it is a music video made up of visualizations of quantitative information. It follows a day in a workers life. I suspect some of the data is real. Anyway, it is a creative use of data visualization. I don't know anything about the artist(s).
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