andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1699 knowledge-graph by maker-knowledge-mining
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Introduction: Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. James O’Malley and Christakis and Fowler present the positive case. Andrew Thomas and Tyler VanderWeele present constructive criticism. Christakis and Fowler reply . Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. P.S. The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models.” Wow! I had no idea people still did this sort of thing. How horrible. But I guess that’s what half-life is all about. These ideas last forever, they just become less and less relevant to people.
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1 Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. [sent-1, score-0.408]
2 James O’Malley and Christakis and Fowler present the positive case. [sent-2, score-0.214]
3 Andrew Thomas and Tyler VanderWeele present constructive criticism. [sent-3, score-0.27]
4 Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. [sent-5, score-0.951]
5 The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models. [sent-8, score-0.667]
6 These ideas last forever, they just become less and less relevant to people. [sent-13, score-0.429]
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same-blog 1 1.0 1699 andrew gelman stats-2013-01-31-Fowlerpalooza!
Introduction: Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. James O’Malley and Christakis and Fowler present the positive case. Andrew Thomas and Tyler VanderWeele present constructive criticism. Christakis and Fowler reply . Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. P.S. The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models.” Wow! I had no idea people still did this sort of thing. How horrible. But I guess that’s what half-life is all about. These ideas last forever, they just become less and less relevant to people.
2 0.40813312 757 andrew gelman stats-2011-06-10-Controversy over the Christakis-Fowler findings on the contagion of obesity
Introduction: Nicholas Christakis and James Fowler are famous for finding that obesity is contagious. Their claims, which have been received with both respect and skepticism (perhaps we need a new word for this: “respecticism”?) are based on analysis of data from the Framingham heart study, a large longitudinal public-health study that happened to have some social network data (for the odd reason that each participant was asked to provide the name of a friend who could help the researchers locate them if they were to move away during the study period. The short story is that if your close contact became obese, you were likely to become obese also. The long story is a debate about the reliability of this finding (that is, can it be explained by measurement error and sampling variability) and its causal implications. This sort of study is in my wheelhouse, as it were, but I have never looked at the Christakis-Fowler work in detail. Thus, my previous and current comments are more along the line
3 0.36673856 756 andrew gelman stats-2011-06-10-Christakis-Fowler update
Introduction: After I posted on Russ Lyons’s criticisms of the work of Nicholas Christakis and James Fowler’s work on social networks, several people emailed in with links to related articles. (Nobody wants to comment on the blog anymore; all I get is emails.) Here they are: Political scientists Hans Noel and Brendan Nyhan wrote a paper called “The ‘Unfriending’ Problem: The Consequences of Homophily in Friendship Retention for Causal Estimates of Social Influence” in which they argue that the Christakis-Fowler results are subject to bias because of patterns in the time course of friendships. Statisticians Cosma Shalizi and AT wrote a paper called “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies” arguing that analyses such as those of Christakis and Fowler cannot hope to disentangle different sorts of network effects. And Christakis and Fowler reply to Noel and Nyhan, Shalizi and Thomas, Lyons, and others in an article that begins: H
4 0.25544265 1412 andrew gelman stats-2012-07-10-More questions on the contagion of obesity, height, etc.
Introduction: AT discusses [link broken; see P.P.S. below] a new paper of his that casts doubt on the robustness of the controversial Christakis and Fowler papers. AT writes that he ran some simulations of contagion on social networks and found that (a) in a simple model assuming the contagion of the sort hypothesized by Christakis and Fowler, their procedure would indeed give the sorts of estimates they found in their papers, but (b) in another simple model assuming a different sort of contagion, the C&F; estimation would give indistinguishable estimates. Thus, if you believe AT’s simulation model, C&F;’s procedure cannot statistically distinguish between two sorts of contagion (directional and simultaneous). I have not looked at AT’s paper so I can’t fully comment, but I don’t fully understand his method for simulating network connections. AT uses what he calls a “rewiring” model. This makes sense: as time progresses, we make new friends and lose old ones—but I am confused by the details
Introduction: The other day, Nicholas Christakis wrote an article in the newspaper criticizing academic social science departments: The social sciences have stagnated. . . . This is not only boring but also counterproductive, constraining engagement with the scientific cutting edge and stifling the creation of new and useful knowledge. . . . I’m not suggesting that social scientists stop teaching and investigating classic topics like monopoly power, racial profiling and health inequality. But everyone knows that monopoly power is bad for markets, that people are racially biased and that illness is unequally distributed by social class. There are diminishing returns from the continuing study of many such topics. And repeatedly observing these phenomena does not help us fix them. I disagreed , saying that Christakis wasn’t giving social science research enough credit: I’m no economist so I can let others discuss the bit about “monopoly power is bad for markets.” I assume that the study by
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Introduction: Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. James O’Malley and Christakis and Fowler present the positive case. Andrew Thomas and Tyler VanderWeele present constructive criticism. Christakis and Fowler reply . Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. P.S. The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models.” Wow! I had no idea people still did this sort of thing. How horrible. But I guess that’s what half-life is all about. These ideas last forever, they just become less and less relevant to people.
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Introduction: After I posted on Russ Lyons’s criticisms of the work of Nicholas Christakis and James Fowler’s work on social networks, several people emailed in with links to related articles. (Nobody wants to comment on the blog anymore; all I get is emails.) Here they are: Political scientists Hans Noel and Brendan Nyhan wrote a paper called “The ‘Unfriending’ Problem: The Consequences of Homophily in Friendship Retention for Causal Estimates of Social Influence” in which they argue that the Christakis-Fowler results are subject to bias because of patterns in the time course of friendships. Statisticians Cosma Shalizi and AT wrote a paper called “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies” arguing that analyses such as those of Christakis and Fowler cannot hope to disentangle different sorts of network effects. And Christakis and Fowler reply to Noel and Nyhan, Shalizi and Thomas, Lyons, and others in an article that begins: H
3 0.64707541 1949 andrew gelman stats-2013-07-21-Defensive political science responds defensively to an attack on social science
Introduction: Nicholas Christakis, a medical scientist perhaps best known for his controversial claim (see also here ), based on joint work with James Fowler, that obesity is contagious, writes : The social sciences have stagnated. They offer essentially the same set of academic departments and disciplines that they have for nearly 100 years: sociology, economics, anthropology, psychology and political science. This is not only boring but also counterproductive, constraining engagement with the scientific cutting edge and stifling the creation of new and useful knowledge. . . . I’m not suggesting that social scientists stop teaching and investigating classic topics like monopoly power, racial profiling and health inequality. But everyone knows that monopoly power is bad for markets, that people are racially biased and that illness is unequally distributed by social class. There are diminishing returns from the continuing study of many such topics. And repeatedly observing these phenomen
4 0.61909175 1128 andrew gelman stats-2012-01-19-Sharon Begley: Worse than Stephen Jay Gould?
Introduction: Commenter Tggp links to a criticism of science journalist Sharon Begley by science journalist Matthew Hutson. I learned of this dispute after reporting that Begley had received the American Statistical Association’s Excellence in Statistical Reporting Award, a completely undeserved honor, if Hutson is to believed. The two journalists have somewhat similar profiles: Begley was science editor at Newsweek (she’s now at Reuters) and author of “Train Your Mind, Change Your Brain: How a New Science Reveals Our Extraordinary Potential to Transform Ourselves,” and Hutson was news editor at Psychology Today and wrote the similarly self-helpy-titled, “The 7 Laws of Magical Thinking: How Irrational Beliefs Keep Us Happy, Healthy, and Sane.” Hutson writes : Psychological Science recently published a fascinating new study on jealousy. I was interested to read Newsweek’s 1300-word article covering the research by their science editor, Sharon Begley. But part-way through the article, I
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Introduction: I had a couple of email exchanges with Jan-Emmanuel De Neve and James Fowler, two of the authors of the article on the gene that is associated with life satisfaction which we blogged the other day. (Bruno Frey, the third author of the article in question, is out of town according to his email.) Fowler also commented directly on the blog. I won’t go through all the details, but now I have a better sense of what’s going on. (Thanks, Jan and James!) Here’s my current understanding: 1. The original manuscript was divided into two parts: an article by De Neve alone published in the Journal of Human Genetics, and an article by De Neve, Fowler, Frey, and Nicholas Christakis submitted to Econometrica. The latter paper repeats the analysis from the Adolescent Health survey and also replicates with data from the Framingham heart study (hence Christakis’s involvement). The Framingham study measures a slightly different gene and uses a slightly life-satisfaction question com
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Introduction: Russ Lyons points us to a discussion in Statistics in Medicine of the famous claims by Christakis and Fowler on the contagion of obesity etc. James O’Malley and Christakis and Fowler present the positive case. Andrew Thomas and Tyler VanderWeele present constructive criticism. Christakis and Fowler reply . Coincidentally, a couple weeks ago an epidemiologist was explaining to me the differences between the Framingham Heart Study and the Nurses Health Study and why Framingham got the postmenopausal supplement risks right while Nurses got it wrong. P.S. The journal issue also includes a comment on “A distribution-free test of constant mean in linear mixed effects models.” Wow! I had no idea people still did this sort of thing. How horrible. But I guess that’s what half-life is all about. These ideas last forever, they just become less and less relevant to people.
2 0.97366583 1194 andrew gelman stats-2012-03-04-Multilevel modeling even when you’re not interested in predictions for new groups
Introduction: Fred Wu writes: I work at National Prescribing Services in Australia. I have a database representing say, antidiabetic drug utilisation for the entire Australia in the past few years. I planned to do a longitudinal analysis across GP Division Network (112 divisions in AUS) using mixed-effects models (or as you called in your book varying intercept and varying slope) on this data. The problem here is: as data actually represent the population who use antidiabetic drugs in AUS, should I use 112 fixed dummy variables to capture the random variations or use varying intercept and varying slope for the model ? Because some one may aruge, like divisions in AUS or states in USA can hardly be considered from a “superpopulation”, then fixed dummies should be used. What I think is the population are those who use the drugs, what will happen when the rest need to use them? In terms of exchangeability, using varying intercept and varying slopes can be justified. Also you provided in y
3 0.97173274 1525 andrew gelman stats-2012-10-08-Ethical standards in different data communities
Introduction: I opened the paper today and saw this from Paul Krugman, on Jack Welch, the former chairman of General Electric, who posted an assertion on Twitter that the [recent unemployment data] had been cooked to help President Obama’s re-election campaign. His claim was quickly picked up by right-wing pundits and media personalities. It was nonsense, of course. Job numbers are prepared by professional civil servants, at an agency that currently has no political appointees. But then maybe Mr. Welch — under whose leadership G.E. reported remarkably smooth earnings growth, with none of the short-term fluctuations you might have expected (fluctuations that reappeared under his successor) — doesn’t know how hard it would be to cook the jobs data. I was curious so I googled *general electric historical earnings*. It was surprisingly difficult to find the numbers! Most of the links just went back to 2011, or to 2008. Eventually I came across this blog by Barry Ritholtz that showed this
4 0.96695262 1603 andrew gelman stats-2012-12-03-Somebody listened to me!
Introduction: Several months ago, I wrote : One challenge, though, is that uncovering the problem [of scientific fraud] and forcing the retraction is a near-thankless job. That’s one reason I don’t mind if Uri Simonsohn is treated as some sort of hero or superstar for uncovering multiple cases of research fraud. Some people might feel there’s something unseemly about Simonsohn doing this . . . OK, fine, but let’s talk incentives. If retractions are a good thing, and fraudsters and plagiarists are not generally going to retract on their own, then somebody’s going to have to do the hard work of discovering, exposing, and confronting scholarly misconduct. If these discoverers, exposers, and confronters are going to be attacked back by their targets (which would be natural enough) and they’re going to be attacked by the fraudsters’ friends and colleagues (also natural) and even have their work disparaged by outsiders who think they’re going too far, then, hey, they need some incentives in the othe
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