andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-162 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: On the basis of two papers and because it is completely obvious, we (meaning me , Justin, and John ) predict that Elena Kagan will get confirmed to be an Associate Justice of the Supreme Court. But we also want to see how close we can come to predicting the votes for and against. We actually have two sets of predictions, both using the MRP technique discussed previously on this blog. The first is based on our recent paper in the Journal of Politics showing that support for the nominee in a senator’s home state plays a striking role in whether she or he votes to confirm the nominee. The second is based on a new working paper extending “basic” MRP to show that senators respond far more to their co-partisans than the median voter in their home states. Usually, our vote “predictions” do not differ much, but there is a group of senators who are predicted to vote yes for Kagan with a probability around 50% and the two sets of predictions thus differ for Kagan more than usual.
sentIndex sentText sentNum sentScore
1 On the basis of two papers and because it is completely obvious, we (meaning me , Justin, and John ) predict that Elena Kagan will get confirmed to be an Associate Justice of the Supreme Court. [sent-1, score-0.061]
2 But we also want to see how close we can come to predicting the votes for and against. [sent-2, score-0.147]
3 We actually have two sets of predictions, both using the MRP technique discussed previously on this blog. [sent-3, score-0.266]
4 The first is based on our recent paper in the Journal of Politics showing that support for the nominee in a senator’s home state plays a striking role in whether she or he votes to confirm the nominee. [sent-4, score-0.703]
5 The second is based on a new working paper extending “basic” MRP to show that senators respond far more to their co-partisans than the median voter in their home states. [sent-5, score-0.519]
6 Usually, our vote “predictions” do not differ much, but there is a group of senators who are predicted to vote yes for Kagan with a probability around 50% and the two sets of predictions thus differ for Kagan more than usual. [sent-6, score-1.165]
7 The other key factors that enter into the models (which build on the work of Cameron, Segal, Songer, Epstein, Lindstadt, Segal, and Westerland) are senator and nominee ideology, party and partisan control, presidential approval, nominee quality, and nomination timing. [sent-7, score-0.875]
8 The older model predicts nine Republican defections (votes for Kagan) but the newer model breaking down opinion by party predicts only five. [sent-9, score-0.714]
9 Ten Republicans straddle or push against the 50% mark for point predictions. [sent-10, score-0.081]
10 Median state-level support for Kagan is approximately that for Alito, and about nine points higher than that for Sotomayor. [sent-11, score-0.287]
11 Median state-level support for Kagan among Republicans is about 12 points higher than for Sotomayor. [sent-12, score-0.164]
12 On the other hand, Obama’s approval is definitely lower. [sent-13, score-0.114]
13 So far, we have only one national poll to work with (which we thank ABC for), but we will update our data and “predictions” later when other poll data become available. [sent-14, score-0.263]
14 We do not yet have Jeff Segal’s official scores for quality and ideology so are currently fudging these a bit (using the same scores as for Sotomayor). [sent-15, score-0.362]
wordName wordTfidf (topN-words)
[('kagan', 0.464), ('segal', 0.282), ('nominee', 0.232), ('senators', 0.202), ('vote', 0.189), ('mrp', 0.181), ('median', 0.172), ('predictions', 0.166), ('party', 0.159), ('votes', 0.147), ('opinion', 0.139), ('predicted', 0.137), ('senator', 0.128), ('nine', 0.123), ('approval', 0.114), ('predicts', 0.112), ('technique', 0.111), ('ideology', 0.101), ('differ', 0.099), ('image', 0.097), ('support', 0.097), ('poll', 0.097), ('scores', 0.092), ('republicans', 0.087), ('state', 0.086), ('sets', 0.084), ('republican', 0.083), ('home', 0.083), ('abc', 0.081), ('straddle', 0.081), ('sotomayor', 0.077), ('elena', 0.077), ('cameron', 0.077), ('quality', 0.077), ('epstein', 0.075), ('using', 0.071), ('graham', 0.071), ('national', 0.069), ('newer', 0.069), ('view', 0.069), ('higher', 0.067), ('nomination', 0.066), ('justice', 0.064), ('supreme', 0.062), ('extending', 0.062), ('confirmed', 0.061), ('extension', 0.061), ('confirmation', 0.06), ('enter', 0.058), ('confirm', 0.058)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 162 andrew gelman stats-2010-07-25-Darn that Lindsey Graham! (or, “Mr. P Predicts the Kagan vote”)
Introduction: On the basis of two papers and because it is completely obvious, we (meaning me , Justin, and John ) predict that Elena Kagan will get confirmed to be an Associate Justice of the Supreme Court. But we also want to see how close we can come to predicting the votes for and against. We actually have two sets of predictions, both using the MRP technique discussed previously on this blog. The first is based on our recent paper in the Journal of Politics showing that support for the nominee in a senator’s home state plays a striking role in whether she or he votes to confirm the nominee. The second is based on a new working paper extending “basic” MRP to show that senators respond far more to their co-partisans than the median voter in their home states. Usually, our vote “predictions” do not differ much, but there is a group of senators who are predicted to vote yes for Kagan with a probability around 50% and the two sets of predictions thus differ for Kagan more than usual.
Introduction: I dodged a bullet the other day, blogorifically speaking. This is a (moderately) long story but there’s a payoff at the end for those of you who are interested in forecasting or understanding voting and public opinion at the state level. Act 1 It started when Jeff Lax made this comment on his recent blog entry: Nebraska Is All That Counts for a Party-Bucking Nelson Dem Senator On Blowback From His Opposition To Kagan: ‘Are They From Nebraska? Then I Don’t Care’ Fine, but 62% of Nebraskans with an opinion favor confirmation… 91% of Democrats, 39% of Republicans, and 61% of Independents. So I guess he only cares about Republican Nebraskans… I conferred with Jeff and then wrote the following entry for fivethirtyeight.com. There was a backlog of posts at 538 at the time, so I set it on delay to appear the following morning. Here’s my post (which I ended up deleting before it ever appeared): Party-Bucking Nelson May Be Nebraska-Bucking as Well Under the head
Introduction: John Kastellec, Jeff Lax, and Justin Phillips write : Do senators respond to the preferences of their states’ median voters or only to the preferences of their co-partisans? We [Kastellec et al.] study responsiveness using roll call votes on ten recent Supreme Court nominations. We develop a method for estimating state-level public opinion broken down by partisanship. We find that senators respond more powerfully to their partisan base when casting such roll call votes. Indeed, when their state median voter and party median voter disagree, senators strongly favor the latter. [emphasis added] This has significant implications for the study of legislative responsiveness, the role of public opinion in shaping the personnel of the nations highest court, and the degree to which we should expect the Supreme Court to be counter-majoritarian. Our method can be applied elsewhere to estimate opinion by state and partisan group, or by many other typologies, so as to study other important qu
4 0.19811262 2061 andrew gelman stats-2013-10-14-More on Mister P and how it does what it does
Introduction: Following up on our discussion the other day, Matt Buttice and Ben Highton write: It was nice to see our article mentioned and discussed by Andrew, Jeff Lax, Justin Phillips, and Yair Ghitza on Andrew’s blog in this post on Wednesday. As noted in the post, we recently published an article in Political Analysis on how well multilevel regression and poststratification (MRP) performs at producing estimates of state opinion with conventional national surveys where N≈1,500. Our central claims are that (i) the performance of MRP is highly variable, (ii) in the absence of knowing the true values, it is difficult to determine the quality of the MRP estimates produced on the basis of a single national sample, and, (iii) therefore, our views about the usefulness of MRP in instances where a researcher has a single sample of N≈1,500 are less optimistic than the ones expressed in previous research on the topic. Obviously we were interested in the blog posts. We found them stimulating
5 0.18667211 2056 andrew gelman stats-2013-10-09-Mister P: What’s its secret sauce?
Introduction: This is a long and technical post on an important topic: the use of multilevel regression and poststratification (MRP) to estimate state-level public opinion. MRP as a research method, and state-level opinion (or, more generally, attitudes in demographic and geographic subpopulation) as a subject, have both become increasingly important in political science—and soon, I expect, will become increasingly important in other social sciences as well. Being able to estimate state-level opinion from national surveys is just such a powerful thing, that if it can be done, people will do it. It’s taken 15 years or so for the method to really catch on, but the ready availability of survey data and of computing power—as well as our increasing comfort level, as a profession, with these techniques, has made MRP become more of a routine research tool. As a method becomes used more and more widely, there will be natural concerns about its domains of applicability. That is the subject of the pres
6 0.18228731 355 andrew gelman stats-2010-10-20-Andy vs. the Ideal Point Model of Voting
9 0.1588158 36 andrew gelman stats-2010-05-16-Female Mass Murderers: Babes Behind Bars
10 0.15164608 2255 andrew gelman stats-2014-03-19-How Americans vote
11 0.14647649 389 andrew gelman stats-2010-11-01-Why it can be rational to vote
12 0.14647649 1565 andrew gelman stats-2012-11-06-Why it can be rational to vote
13 0.13250436 394 andrew gelman stats-2010-11-05-2010: What happened?
14 0.13029018 1027 andrew gelman stats-2011-11-25-Note to student journalists: Google is your friend
15 0.12639043 237 andrew gelman stats-2010-08-27-Bafumi-Erikson-Wlezien predict a 50-seat loss for Democrats in November
16 0.12411351 2062 andrew gelman stats-2013-10-15-Last word on Mister P (for now)
17 0.1147534 286 andrew gelman stats-2010-09-20-Are the Democrats avoiding a national campaign?
18 0.11145569 369 andrew gelman stats-2010-10-25-Misunderstanding of divided government
19 0.11109537 210 andrew gelman stats-2010-08-16-What I learned from those tough 538 commenters
20 0.10986707 1570 andrew gelman stats-2012-11-08-Poll aggregation and election forecasting
topicId topicWeight
[(0, 0.135), (1, -0.031), (2, 0.202), (3, 0.089), (4, -0.069), (5, 0.029), (6, -0.089), (7, -0.061), (8, -0.036), (9, -0.036), (10, 0.116), (11, 0.022), (12, 0.019), (13, -0.065), (14, -0.022), (15, -0.005), (16, 0.012), (17, 0.021), (18, -0.021), (19, -0.016), (20, -0.01), (21, 0.008), (22, 0.003), (23, -0.082), (24, 0.001), (25, -0.015), (26, -0.005), (27, 0.036), (28, 0.012), (29, 0.016), (30, 0.05), (31, -0.055), (32, 0.08), (33, -0.016), (34, 0.003), (35, 0.03), (36, 0.061), (37, -0.044), (38, 0.069), (39, -0.026), (40, 0.013), (41, -0.037), (42, -0.001), (43, -0.02), (44, 0.007), (45, -0.032), (46, 0.018), (47, 0.05), (48, 0.057), (49, 0.007)]
simIndex simValue blogId blogTitle
same-blog 1 0.97065753 162 andrew gelman stats-2010-07-25-Darn that Lindsey Graham! (or, “Mr. P Predicts the Kagan vote”)
Introduction: On the basis of two papers and because it is completely obvious, we (meaning me , Justin, and John ) predict that Elena Kagan will get confirmed to be an Associate Justice of the Supreme Court. But we also want to see how close we can come to predicting the votes for and against. We actually have two sets of predictions, both using the MRP technique discussed previously on this blog. The first is based on our recent paper in the Journal of Politics showing that support for the nominee in a senator’s home state plays a striking role in whether she or he votes to confirm the nominee. The second is based on a new working paper extending “basic” MRP to show that senators respond far more to their co-partisans than the median voter in their home states. Usually, our vote “predictions” do not differ much, but there is a group of senators who are predicted to vote yes for Kagan with a probability around 50% and the two sets of predictions thus differ for Kagan more than usual.
Introduction: The above graph shows the estimated support, by state, for the Employment Nondiscrimination Act, a gay rights bill that the Senate will be voting on this Monday. The estimates were constructed by Kate Krimmel, Jeff Lax, and Justin Phillips using multilevel regression and poststratification. Check out that graph again. The scale goes from 20% to 80%, but every state is in the yellow-to-red range. Support for a law making it illegal to discriminate against gays has majority support in every state. And in most states the support is very strong. And here’s the research paper by Krimmel, Lax, and Phillips, which begins: Public majorities have supported several gay rights policies for some time, yet Congress has responded slowly if at all. We address this puzzle through dyadic analysis of the opinion- vote relationship on 23 roll-call votes between 1993 and 2010, matching members of Congress to policy-specific opinion in their state or district. We also extend the MRP opinion e
Introduction: I dodged a bullet the other day, blogorifically speaking. This is a (moderately) long story but there’s a payoff at the end for those of you who are interested in forecasting or understanding voting and public opinion at the state level. Act 1 It started when Jeff Lax made this comment on his recent blog entry: Nebraska Is All That Counts for a Party-Bucking Nelson Dem Senator On Blowback From His Opposition To Kagan: ‘Are They From Nebraska? Then I Don’t Care’ Fine, but 62% of Nebraskans with an opinion favor confirmation… 91% of Democrats, 39% of Republicans, and 61% of Independents. So I guess he only cares about Republican Nebraskans… I conferred with Jeff and then wrote the following entry for fivethirtyeight.com. There was a backlog of posts at 538 at the time, so I set it on delay to appear the following morning. Here’s my post (which I ended up deleting before it ever appeared): Party-Bucking Nelson May Be Nebraska-Bucking as Well Under the head
4 0.74876505 377 andrew gelman stats-2010-10-28-The incoming moderate Republican congressmembers
Introduction: Boris writes : By nearly all accounts, the Republicans looks set to take over the US House of Representatives in next week’s November 2010 general election. . . . Republicans, in this wave election that recalls 1994, look set to win not just swing districts, but also those districts that have been traditionally Democratic, or those with strong or longtime Democratic incumbents. Naturally, just as in 2008, this has led to overclaiming by jubilant conservatives and distraught liberals-though the adjectives were then reversed-that this portends a realignment in American politics. . . . Republican moderates in Congress are often associated with two factors: 1) a liberal voting record earlier in their career, and 2) a liberal district. Of course, both are related, in the sense that ambitious moderates choose liberal districts to run in, and liberal districts weed out conservative candidates. . . . Given how competitive Republicans are in 2010, even in otherwise unfriendly territory,
Introduction: John Kastellec, Jeff Lax, and Justin Phillips write : Do senators respond to the preferences of their states’ median voters or only to the preferences of their co-partisans? We [Kastellec et al.] study responsiveness using roll call votes on ten recent Supreme Court nominations. We develop a method for estimating state-level public opinion broken down by partisanship. We find that senators respond more powerfully to their partisan base when casting such roll call votes. Indeed, when their state median voter and party median voter disagree, senators strongly favor the latter. [emphasis added] This has significant implications for the study of legislative responsiveness, the role of public opinion in shaping the personnel of the nations highest court, and the degree to which we should expect the Supreme Court to be counter-majoritarian. Our method can be applied elsewhere to estimate opinion by state and partisan group, or by many other typologies, so as to study other important qu
6 0.69496131 292 andrew gelman stats-2010-09-23-Doug Hibbs on the fundamentals in 2010
7 0.69090295 237 andrew gelman stats-2010-08-27-Bafumi-Erikson-Wlezien predict a 50-seat loss for Democrats in November
8 0.68907118 1566 andrew gelman stats-2012-11-07-A question about voting systems—unrelated to U.S. elections!
9 0.68479079 369 andrew gelman stats-2010-10-25-Misunderstanding of divided government
10 0.67825174 362 andrew gelman stats-2010-10-22-A redrawing of the Red-Blue map in November 2010?
11 0.65420568 1227 andrew gelman stats-2012-03-23-Voting patterns of America’s whites, from the masses to the elites
12 0.64463538 934 andrew gelman stats-2011-09-30-Nooooooooooooooooooo!
14 0.63741326 1000 andrew gelman stats-2011-11-10-Forecasting 2012: How much does ideology matter?
15 0.63431662 123 andrew gelman stats-2010-07-01-Truth in headlines
16 0.63425273 44 andrew gelman stats-2010-05-20-Boris was right
18 0.62531626 2061 andrew gelman stats-2013-10-14-More on Mister P and how it does what it does
19 0.62466961 1512 andrew gelman stats-2012-09-27-A Non-random Walk Down Campaign Street
20 0.62284905 654 andrew gelman stats-2011-04-09-There’s no evidence that voters choose presidential candidates based on their looks
topicId topicWeight
[(2, 0.033), (5, 0.019), (15, 0.027), (16, 0.093), (21, 0.048), (24, 0.075), (43, 0.014), (52, 0.013), (58, 0.111), (65, 0.033), (77, 0.025), (86, 0.047), (87, 0.092), (96, 0.013), (99, 0.25)]
simIndex simValue blogId blogTitle
same-blog 1 0.93557853 162 andrew gelman stats-2010-07-25-Darn that Lindsey Graham! (or, “Mr. P Predicts the Kagan vote”)
Introduction: On the basis of two papers and because it is completely obvious, we (meaning me , Justin, and John ) predict that Elena Kagan will get confirmed to be an Associate Justice of the Supreme Court. But we also want to see how close we can come to predicting the votes for and against. We actually have two sets of predictions, both using the MRP technique discussed previously on this blog. The first is based on our recent paper in the Journal of Politics showing that support for the nominee in a senator’s home state plays a striking role in whether she or he votes to confirm the nominee. The second is based on a new working paper extending “basic” MRP to show that senators respond far more to their co-partisans than the median voter in their home states. Usually, our vote “predictions” do not differ much, but there is a group of senators who are predicted to vote yes for Kagan with a probability around 50% and the two sets of predictions thus differ for Kagan more than usual.
2 0.88939321 249 andrew gelman stats-2010-09-01-References on predicting elections
Introduction: Mike Axelrod writes: I [Axelrod] am interested in building a model that predicts voting on the precinct level, using variables such as party registration, age, sex, income etc. Surely political scientists have worked on this problem. I would be grateful for any reference you could provide in the way of articles and books. My reply: Political scientists have worked on this problem, and it’s easy enough to imagine hierarchical models of the sort discussed in my book with Jennifer. I can picture what I would do if asked to forecast at the precinct level, for example to model exit polls. (In fact, I was briefly hired by the exit poll consortium in 2000 to do this, but then after I told them about hierarchical Bayes, they un-hired me!) But I don’t actually know of any literature on precinct-level forecasting. Perhaps one of you out there knows of some references?
3 0.88797921 1788 andrew gelman stats-2013-04-04-When is there “hidden structure in data” to be discovered?
Introduction: Michael Collins sent along the following announcement for a talk: Fast learning algorithms for discovering the hidden structure in data Daniel Hsu, Microsoft Research 11am, Wednesday April 10th, Interschool lab, 7th floor CEPSR, Columbia University A major challenge in machine learning is to reliably and automatically discover hidden structure in data with minimal human intervention. For instance, one may be interested in understanding the stratification of a population into subgroups, the thematic make-up of a collection of documents, or the dynamical process governing a complex time series. Many of the core statistical estimation problems for these applications are, in general, provably intractable for both computational and statistical reasons; and therefore progress is made by shifting the focus to realistic instances that rule out the intractable cases. In this talk, I’ll describe a general computational approach for correctly estimating a wide class of statistical mod
4 0.88349581 119 andrew gelman stats-2010-06-30-Why is George Apley overrated?
Introduction: A comment by Mark Palko reminded me that, while I’m a huge Marquand fan, I think The Late George Apley is way overrated. My theory is that Marquand’s best books don’t fit into the modernist way of looking about literature, and that the gatekeepers of the 1930s and 1940s, when judging Marquand by these standards, conveniently labeled Apley has his best book because it had a form–Edith-Wharton-style satire–that they could handle. In contrast, Point of No Return and all the other classics are a mixture of seriousness and satire that left critics uneasy. Perhaps there’s a way to study this sort of thing more systematically?
5 0.88166666 103 andrew gelman stats-2010-06-22-Beach reads, Proust, and income tax
Introduction: The commenting feature doesn’t work for me on Helen DeWitt’s blog so I’m forced to comment on her entries here. 1. She discusses whether it’s fair to characterize The Last Samurai . I have a slightly different perspective on this: I’ve never really understood the idea that a “beach read” should be something light and fluffy. On the beach, you can relax, you have the time to get into anything. I could see wanting something light on the subway–you have to be able to get into it right away and follow it amid all the jostles. I guess the point is that when you’re at the beach, you’re far from the library. So what you really want for the beach is not necessarily something relaxing or easy to read, but rather a sure thing , a known quantity that you’ll definitely enjoy. No point sitting on the beach reading a book that you hate. 2. In an interesting discussion of translation, DeWitt recommends learning a language by reading great literature in the original tongue. Seems f
7 0.87511611 355 andrew gelman stats-2010-10-20-Andy vs. the Ideal Point Model of Voting
9 0.87418586 1635 andrew gelman stats-2012-12-22-More Pinker Pinker Pinker
11 0.87151593 816 andrew gelman stats-2011-07-22-“Information visualization” vs. “Statistical graphics”
12 0.86972743 1428 andrew gelman stats-2012-07-25-The problem with realistic advice?
13 0.86813265 430 andrew gelman stats-2010-11-25-The von Neumann paradox
14 0.86752743 1574 andrew gelman stats-2012-11-12-How to Lie With Statistics example number 12,498,122
15 0.86730963 73 andrew gelman stats-2010-06-08-Observational Epidemiology
16 0.86723238 154 andrew gelman stats-2010-07-18-Predictive checks for hierarchical models
17 0.86589146 2137 andrew gelman stats-2013-12-17-Replication backlash
18 0.86559665 2301 andrew gelman stats-2014-04-22-Ticket to Baaaaarf
19 0.86541474 127 andrew gelman stats-2010-07-04-Inequality and health
20 0.86456764 100 andrew gelman stats-2010-06-19-Unsurprisingly, people are more worried about the economy and jobs than about deficits