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Academic Commons Search Resultsen-usProtecting minorities in binary elections: A test of storable votes using field data
https://academiccommons.columbia.edu/catalog/ac:125276
Casella, Alessandra M.; Ehrenberg, Shuky; Gelman, Andrew E.; Shen, Jie10.7916/D8BR9021Fri, 02 Jun 2017 13:07:29 +0000Democratic systems are built, with good reason, on majoritarian principles, but their legitimacy requires the protection of strongly held minority preferences. The challenge is to do so while treating every voter equally and preserving aggregate welfare. One possible solution is storable votes: granting each voter a budget of votes to cast as desired over multiple decisions. During the 2006 student elections at Columbia University, we tested a simple version of this idea: voters were asked to rank the importance of the different contests and to choose where to cast a single extra "bonus vote," had one been available. We used these responses to construct distributions of intensities and electoral outcomes, both without and with the bonus vote. Bootstrapping techniques provided estimates of the probable impact of the bonus vote. The bonus vote performs well: when minority preferences are particularly intense, the minority wins at least one of the contests with 15-30 percent probability; and, when the minority wins, aggregate welfare increases with 85-95 percent probability. When majority and minority preferences are equally intense, the effect of the bonus vote is smaller and more variable but on balance still positive.Political science, Mathematical statistics, Statisticsac186, ag389StatisticsReportsGoing beyond the book: Toward critical reading in statistics teaching
https://academiccommons.columbia.edu/catalog/ac:125240
Gelman, Andrew E.10.7916/D8CF9WTVWed, 31 May 2017 19:34:18 +0000We can improve our teaching of statistical examples from books by collecting further data, reading cited articles, and performing further data analysis. This should not come as a surprise, but what might be new is the realization of how close to the surface these research opportunities are: even influential and celebrated books can have examples where more can be learned with a small amount of additional effort. We discuss three examples that have arisen in our own teaching: an introductory textbook that motivated us to think more carefully about categorical and continuous variables; a book for the lay reader that misreported a study of menstruation and accidents; and a monograph on the foundations of probability that overinterpreted statistically insignificant fluctuations in sex ratios.Political science, Statistics, Left- and right-handedness, Menstruation, Traffic accidentsag389StatisticsReportsBayesian Combination of State Polls and Election Forecasts
https://academiccommons.columbia.edu/catalog/ac:125228
Lock, Kari; Gelman, Andrew E.10.7916/D8WD4698Wed, 31 May 2017 19:34:16 +0000A wide range of potentially useful data are available for election forecasting: the results of previous elections, a multitude of pre-election polls, and predictors such as measures of national and statewide economic performance. How accurate are different forecasts? We estimate predictive uncertainty via analysis of data collected from past elections (actual outcomes, pre-election polls, and model estimates). With these estimated uncertainties, we use Bayesian inference to integrate the various sources of data to form posterior distributions for the state and national two-party Democratic vote shares for the 2008 election. Our key idea is to separately forecast the national popular vote shares and the relative positions of the states. More generally, such an approach could be applied to study changes in public opinion and other phenomena with wide national swings and fairly stable spatial distributions relative to the national average.Political science, Statistics, Bayesian statistical decision theoryag389StatisticsArticlesOne vote, many Mexicos: Income and vote choice in the 1994, 2000, and 2006 presidential elections
https://academiccommons.columbia.edu/catalog/ac:125237
Cortina, Jeronimo; Gelman, Andrew E.10.7916/D8H70NJ4Wed, 31 May 2017 19:34:16 +0000Using multilevel modeling of state-level economic data and individual-level exit poll data from the 1994, 2000 and 2006 Mexican presidential elections, we find that income has a stronger effect in predicting the vote for the conservative party in poorer states than in richer states -- a pattern that has also been found in recent U.S. elections. In addition (and unlike in the U.S.), richer states on average tend to support the conservative party at higher rates than poorer states. Our findings raise questions regarding the role that income polarization and region play in vote choice. The electoral results since 1994 reveal that collapsing multiple states into large regions entails significant loss of information that otherwise may uncover sharper and quiet revealing differences in voting patterns between rich and poor states as well as rich and poor individuals within states.Political science, Statisticsag389StatisticsArticlesWhat does "Do campaigns matter?" mean?
https://academiccommons.columbia.edu/catalog/ac:125249
Bafumi, Joseph; Gelman, Andrew E.; Park, David K.10.7916/D8057NNSWed, 31 May 2017 19:34:16 +0000Scholars disagree over the extent to which presidential campaigns activate predispositions in voters or create vote preferences that could not be predicted. When campaign related information flows activate predispositions, election results are largely predetermined given balanced resources. They can be accurately forecast well before a campaign has run its course. Alternatively, campaigns may change vote outcomes beyond forcing predispositions to some equilibrium level. We find most evidence for the former: opinion poll data are consistent with Presidential campaigns activating predispositions, with fundamental variables increasing in importance as a presidential election draws near.Political science, Statistics, Presidents--Electionjb878, ag389StatisticsArticlesWhat will we know on Tuesday at 7pm?
https://academiccommons.columbia.edu/catalog/ac:125231
Gelman, Andrew E.; Silver, Nate10.7916/D8RR24XZWed, 31 May 2017 19:34:16 +0000Political science, Statisticsag389StatisticsArticlesImproving the Presentation of Quantitative Results in Political Science
https://academiccommons.columbia.edu/catalog/ac:125095
Kastellec, John; Gelman, Andrew E.10.7916/D8NZ8FBGWed, 31 May 2017 19:34:10 +0000Political science, Statisticsag389StatisticsPresentations (Communicative Events)Culture wars, voting, and polarization: divisions and unities in modern American politics
https://academiccommons.columbia.edu/catalog/ac:125089
Gelman, Andrew E.10.7916/D8SN0GPGWed, 31 May 2017 19:34:09 +0000Political science, Statisticsag389StatisticsPresentations (Communicative Events)Rich state, poor state, red state, blue state: What's the matter with Connecticut?
https://academiccommons.columbia.edu/catalog/ac:125297
Gelman, Andrew E.; Shor, Boris; Bafumi, Joseph; Park, David K.10.7916/D8WD45S4Thu, 13 Apr 2017 15:46:17 +0000For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling of individual-level survey data and county- and state-level demographic and electoral data, we reconcile these patterns. Furthermore, we find that income matters more in red America than in blue America. In poor states, rich people are much more likely than poor people to vote for the Republican presidential candidate, but in rich states (such as Connecticut), income has a very low correlation with vote preference.Political science, Mathematical statistics, Statisticsag389, jb878StatisticsArticlesBayes, Jeffreys, Prior Distributions and the Philosophy of Statistics
https://academiccommons.columbia.edu/catalog/ac:125279
Gelman, Andrew E.10.7916/D8J38ZTDThu, 13 Apr 2017 15:46:16 +0000I actually own a copy of Harold Jeffreys's Theory of Probability but have only read small bits of it, most recently over a decade ago to confirm that, indeed, Jeffreys was not too proud to use a classical chi-squared p-value when he wanted to check the misfit of a model to data (Gelman, Meng and Stern, 2006). I do, however, feel that it is important to understand where our probability models come from, and I welcome the opportunity to use the present article by Robert, Chopin and Rousseau as a platform for further discussion of foundational issues. In this brief discussion I will argue the following: (1) in thinking about prior distributions, we should go beyond Jeffreys's principles and move toward weakly informative priors; (2) it is natural for those of us who work in social and computational sciences to favor complex models, contra Jeffreys's preference for simplicity; and (3) a key generalization of Jeffreys's ideas is to explicitly include model checking in the process of data analysis.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesDiscussion of the Article "Website Morphing"
https://academiccommons.columbia.edu/catalog/ac:125288
Gelman, Andrew E.10.7916/D88K7G9VThu, 13 Apr 2017 15:46:16 +0000The article under discussion illustrates the trade-off between optimization and exploration that is fundamental to statistical experimental design. In this discussion, I suggest that the research under discussion could be made even more effective by checking the fit of the model by comparing observed data to replicated data sets simulated from the fitted model.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesRejoinder: Struggles with survey weighting and regression modeling
https://academiccommons.columbia.edu/catalog/ac:125312
Gelman, Andrew E.10.7916/D8CC15WBThu, 13 Apr 2017 15:46:16 +0000I was motivated to write this paper, with its controversial opening line, "Survey weighting is a mess," from various experiences as an applied statistician.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesPartisans without constraint: Political polarization and trends in American public opinion
https://academiccommons.columbia.edu/catalog/ac:125291
Baldassarri, Delia; Gelman, Andrew E.10.7916/D84T6QK4Thu, 13 Apr 2017 15:46:16 +0000Public opinion polarization is here conceived as a process of alignment along multiple lines of potential disagreement and measured as growing constraint in individuals' preferences. Using NES data from 1972 to 2004, the authors model trends in issue partisanship--the correlation of issue attitudes with party identification--and issue alignment--the correlation between pairs of issues--and find a substantive increase in issue partisanship, but little evidence of issue alignment. The findings suggest that opinion changes correspond more to a resorting of party labels among voters than to greater constraint on issue attitudes: since parties are more polarized, they are now better at sorting individuals along ideological lines. Levels of constraint vary across population subgroups: strong partisans and wealthier and politically sophisticated voters have grown more coherent in their beliefs. The authors discuss the consequences of partisan realignment and group sorting on the political process and potential deviations from the classic pluralistic account of American politics.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesStruggles with survey weighting and regression modeling
https://academiccommons.columbia.edu/catalog/ac:125309
Gelman, Andrew E.10.7916/D8H41XN4Thu, 13 Apr 2017 15:46:16 +0000The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesThe playing field shifts: Predicting the seats-votes curve in the 2008 U.S. House election
https://academiccommons.columbia.edu/catalog/ac:125285
Kastellec, Jonathan P.; Gelman, Andrew E.; Chandler, Jamie P.10.7916/D8DB873GThu, 13 Apr 2017 15:46:16 +0000The 2008 U.S. House elections mark the first time since 1994 that the Democrats will seek to retain a majority. With the political climate favoring Democrats this year, it seems almost certain that the party will retain control, and will likely increase its share of seats. In five national polls taken in June of this year, Democrats enjoyed on average a 13-point advantage in the generic congressional ballot; as Bafumi, Erikson, and Wlezien (2007) point out, these early polls, suitably adjusted, are good predictors of the November vote. As of late July, bettors at intrade.com put the probability of the Democrats retaining a majority at about 95% (Intrade.com 2008). Elsewhere in this symposium, Klarner (2008) predicts an 11-seat gain for the Democrats, while Lockerbie (2008) forecasts a 25-seat pickup. In this paper we document how the electoral playing field has shifted from a Republican advantage between 1996 and 2004 to a Democratic tilt today. In an earlier article (Kastellec, Gelman, and Chandler 2008), we predicted the seats-votes curve in the 2006 election, showing how the Democrats faced an uphill battle in their effort to take control of the House and, their victory notwithstanding, ended up winning a lower percentage of seats than their average district vote nationwide. We follow up on this analysis by using the same method to predict the seats-votes curve in 2008. Due to the shift in incumbency advantage from the Republicans to the Democrats, compounded by a greater number of retirements among Republican members, we show that the Democrats now enjoy a partisan bias, and can expect to win more seats than votes for the first time since 1992. While this bias is not as large as the advantage the Republicans held in 2006, it will likely help the Democrats increase their share of seats.Political science, Mathematical statistics, Statisticsjpk2004, ag389StatisticsArticlesPredicting and dissecting the seats-votes curve in the 2006 U.S. House election
https://academiccommons.columbia.edu/catalog/ac:125294
Kastellec, Jonathan P.; Gelman, Andrew E.; Chandler, Jamie P.10.7916/D8125ZW5Thu, 13 Apr 2017 15:46:14 +0000The 2008 U.S. House elections mark the first time since 1994 that the Democrats will seek to retain a majority. With the political climate favoring Democrats this year, it seems almost certain that the party will retain control, and will likely increase its share of seats. In five national polls taken in June of this year, Democrats enjoyed on average a 13-point advantage in the generic congressional ballot; as Bafumi, Erikson, and Wlezien (2007) point out, these early polls, suitably adjusted, are good predictors of the November vote. As of late July, bettors at intrade.com put the probability of the Democrats retaining a majority at about 95% (Intrade.com 2008). Elsewhere in this symposium, Klarner (2008) predicts an 11-seat gain for the Democrats, while Lockerbie (2008) forecasts a 25-seat pickup. In this paper we document how the electoral playing field has shifted from a Republican advantage between 1996 and 2004 to a Democratic tilt today. In an earlier article (Kastellec, Gelman, and Chandler 2008), we predicted the seats-votes curve in the 2006 election, showing how the Democrats faced an uphill battle in their effort to take control of the House and, their victory notwithstanding, ended up winning a lower percentage of seats than their average district vote nationwide. We follow up on this analysis by using the same method to predict the seats-votes curve in 2008. Due to the shift in incumbency advantage from the Republicans to the Democrats, compounded by a greater number of retirements among Republican members, we show that the Democrats now enjoy a partisan bias, and can expect to win more seats than votes for the first time since 1992. While this bias is not as large as the advantage the Republicans held in 2006, it will likely help the Democrats increase their share of seats.Political science, Mathematical statistics, Statisticsjpk2004, ag389StatisticsArticlesComment: Bayesian Checking of the Second Levels of Hierarchical Models
https://academiccommons.columbia.edu/catalog/ac:125303
Gelman, Andrew E.10.7916/D8RN3F38Thu, 13 Apr 2017 15:46:13 +0000Bayarri and Castellanos (BC) have written an interesting paper discussing two forms of posterior model check, one based on cross-validation and one based on replication of new groups in a hierarchical model. We think both these checks are good ideas and can become even more effective when understood in the context of posterior predictive checking. For the purpose of discussion, however, it is most interesting to focus on the areas where we disagree with BC.Political science, Mathematical statistics, Statisticsag389StatisticsArticlesBayes: Radical, liberal, or conservative?
https://academiccommons.columbia.edu/catalog/ac:125306
Gelman, Andrew E.10.7916/D8MW2PCJThu, 13 Apr 2017 15:46:12 +0000Political science, Mathematical statistics, Statisticsag389StatisticsArticles