andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1369 knowledge-graph by maker-knowledge-mining
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Introduction: Jay Livingston points to an excellent rant from Peter Moskos, trashing a study about “food deserts” (which I kept reading as “food desserts”) in inner-city neighborhoods. Here’s Moskos: From the Times: There is no relationship between the type of food being sold in a neighborhood and obesity among its children and adolescents. Within a couple of miles of almost any urban neighborhood, “you can get basically any type of food,” said Roland Sturm of the RAND Corporation, lead author of one of the studies. “Maybe we should call it a food swamp rather than a desert,” he said. Sure thing, Sturm. But I suspect you wouldn’t think certain neighborhoods are swamped with good food if you actually got out of your office and went to one of the neighborhoods. After all, what are going to believe: A nice data set or your lying eyes? “Food outlet data … are classifıed using the North American Industry Classifıcation System (NAICS)” (p. 130). Assuming validity and reliability of NAICS
sentIndex sentText sentNum sentScore
1 Here’s Moskos: From the Times: There is no relationship between the type of food being sold in a neighborhood and obesity among its children and adolescents. [sent-2, score-0.667]
2 “Maybe we should call it a food swamp rather than a desert,” he said. [sent-4, score-0.448]
3 But I suspect you wouldn’t think certain neighborhoods are swamped with good food if you actually got out of your office and went to one of the neighborhoods. [sent-6, score-0.391]
4 It means that if something is coded “445110,” then — poof — it’s a grocery store! [sent-11, score-0.196]
5 And notice the sections titled “residents personal reflections on their local food environment” and “food store owners’ attitudes regarding stocking healthy food. [sent-23, score-0.624]
6 More generally, categorizing food outlets by type tends to be insufficient to reflect the heterogeneity of outlets, and it is possible that more detailed measures, such as store inventories, ratings of food quality, and measuring shelf space, would be more predictive for health outcomes. [sent-33, score-1.176]
7 Unfortunately, such data are very costly and time consuming to collect and may never exist on a national scale. [sent-34, score-0.535]
8 And because getting good data may be “very costly and time consuming to collect,” we’ll simply settle for what we have at hand? [sent-36, score-0.459]
9 You know, perhaps we never will have good data on a national level about what produce is sold in each and every store in America. [sent-38, score-0.434]
10 But it is neither very costly nor time consuming to simply go into every store in any one neighborhood and see what is there. [sent-40, score-0.696]
11 And if a corner store sells three moldy heads of iceburg lettuce and some rotting root vegetables, it is not the same as Wholefoods simply because they’re both coded 445! [sent-45, score-0.379]
12 Connoisseurs of multilevel models will appreciate this bit from Moskos: And if you have bad data, it doesn’t matter what fancy quantitative methods you use. [sent-50, score-0.14]
13 It’s putting lipstick on the damn pig of correlation. [sent-51, score-0.133]
14 , counts of food consumption) are regressed on the explanatory variables using negative binomial regression models, a generalization of Poisson models that avoids the Poisson restriction on the mean-variance equality. [sent-54, score-0.601]
15 Negative binomial Poisson regression models to avoid the mean-variance equality restriction. [sent-56, score-0.304]
16 To put it another way, the researchers should just use the damn overdispersed Poisson regression and don’t make such a big deal about it. [sent-62, score-0.2]
17 You should never be doing a non-overdispersed Poisson regression anyway; it shouldn’t even be an option. [sent-64, score-0.187]
18 Moskos also wrote a book called In Defense of Flogging which, as you might imagine from the title, recommends flogging or caning as an alternative to prison as a punishment for convicted criminals. [sent-68, score-0.239]
19 I’ve thought for a long time that the flogging alternative is a good one, but when I mentioned it to my friends who were law professors, they said it would never fly. [sent-70, score-0.301]
20 In truth, you may be committing some crimes you don’t even know about. [sent-80, score-0.166]
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Introduction: Jay Livingston points to an excellent rant from Peter Moskos, trashing a study about “food deserts” (which I kept reading as “food desserts”) in inner-city neighborhoods. Here’s Moskos: From the Times: There is no relationship between the type of food being sold in a neighborhood and obesity among its children and adolescents. Within a couple of miles of almost any urban neighborhood, “you can get basically any type of food,” said Roland Sturm of the RAND Corporation, lead author of one of the studies. “Maybe we should call it a food swamp rather than a desert,” he said. Sure thing, Sturm. But I suspect you wouldn’t think certain neighborhoods are swamped with good food if you actually got out of your office and went to one of the neighborhoods. After all, what are going to believe: A nice data set or your lying eyes? “Food outlet data … are classifıed using the North American Industry Classifıcation System (NAICS)” (p. 130). Assuming validity and reliability of NAICS
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Introduction: I just wrote this, and I realized it might be useful more generally: The article looks reasonable to me–but I just did a shallow read and didn’t try to judge whether the conclusions are correct. My main comment is that if they’re doing a Poisson regression, they should really be doing an overdispersed Poisson regression. I don’t know if I’ve ever seen data in my life where the non-overdispersed Poisson is appropriate. Also, I’d like to see a before-after plot with dots for control cases and open circles for treatment cases and fitted regression lines drawn in. Whenever there’s a regression I like to see this scatterplot. The scatterplot isn’t a replacement for the regression, but at the very least it gives me intuition as to the scale of the estimated effect. Finally, all their numbers should be rounded appropriately. Feel free to cut-and-paste this into your own referee reports (and to apply these recommendations in your own applied research).
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Introduction: A link from the comments here took me to the wonderfully named Barfblog and a report by Don Schaffner on some reporting. First, the background: A university in England issued a press release saying that “Food picked up just a few seconds after being dropped is less likely to contain bacteria than if it is left for longer periods of time . . . The findings suggest there may be some scientific basis to the ‘5 second rule’ – the urban myth about it being fine to eat food that has only had contact with the floor for five seconds or less. Although people have long followed the 5 second rule, until now it was unclear whether it actually helped.” According to the press release, the study was “undertaken by final year Biology students” and led by a professor of microbiology. The press release hit the big time, hitting NPR, Slate, Forbes, the Daily News, etc etc. Some typical headlines: “5-second rule backed up by science” — Atlanta Journal Constitution “Eating food off the floo
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Introduction: Jay Livingston points to an excellent rant from Peter Moskos, trashing a study about “food deserts” (which I kept reading as “food desserts”) in inner-city neighborhoods. Here’s Moskos: From the Times: There is no relationship between the type of food being sold in a neighborhood and obesity among its children and adolescents. Within a couple of miles of almost any urban neighborhood, “you can get basically any type of food,” said Roland Sturm of the RAND Corporation, lead author of one of the studies. “Maybe we should call it a food swamp rather than a desert,” he said. Sure thing, Sturm. But I suspect you wouldn’t think certain neighborhoods are swamped with good food if you actually got out of your office and went to one of the neighborhoods. After all, what are going to believe: A nice data set or your lying eyes? “Food outlet data … are classifıed using the North American Industry Classifıcation System (NAICS)” (p. 130). Assuming validity and reliability of NAICS
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Introduction: Mark Palko explains why a penalty for getting the wrong answer on a test (the SAT, which is used in college admissions and which is used in the famous 8 schools example) is not a “penalty for guessing.” Then the very next day he catches this from Todd Balf in the New York Times Magazine: Students were docked one-quarter point for every multiple-choice question they got wrong, requiring a time-consuming risk analysis to determine which questions to answer and which to leave blank. Ugh! That just makes me want to . . . ok, I won’t go there. Anyway, Palko goes to the trouble to explain: While time management for a test like the SAT can be complicated, the rule for guessing is embarrassingly simple: give your best guess for questions you read; don’t waste time guessing on questions that you didn’t have time to read. The risk analysis actually becomes much more complicated when you take away the penalty for guessing. On the ACT (or the new SAT), there is a positive
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Introduction: Austin Kelly writes: While reading your postings [or here ] on the subject of testing your model by running fake data I was reminded of the fact that I got one of these kinds of tests actually published in a GAO report back in the day. Reading your posts on Unz and political vs. economic discourse made me think of that work again. I thought I’d actually drop you a line on the subject. Back in 2003 GAO was asked to look at Farmer Mac, including a look at the Farm Credit Agency’s regulation of Farmer Mac. As the resident mortgage econometrician back then I was asked to look at FCA’s risk based capital stress test for Farmer Mac. The work was pretty easy. I found a lot of oddities, but the biggest one was that they were using a discrete choice set up (loan goes bad or doesn’t) instead of a hazard model (loan goes bad this period or survives to the next). Not necessarily a problem – lots of mortgage models run that way. But you have to be really careful with your independe
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Introduction: Skirant Vadali writes: I am writing to seek your help in building a community driven Q&A; website tentatively called called ‘Statistics Analysis’. I am neither a founder of this website nor do I have any financial stake in its success. By way of background to this website, please see Stackoverflow (http://stackoverflow.com/) and Mathoverflow (http://mathoverflow.net/). Stackoverflow is a Q&A; website targeted at software developers and is designed to help them ask questions and get answers from other developers. Mathoverflow is a Q&A; website targeted at research mathematicians and is designed to help them ask and answer questions from other mathematicians across the world. The success of both these sites in helping their respective communities is a strong indicator that sites designed along these lines are very useful. The company that runs Stackoverflow (who also host Mathoverflow.net) has recently decided to develop other community driven websites for various other topic are
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