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Examines the effects of poststratification & inverse-probability weights, both used as corrective measures, for household size in telephone polling. Focus is on how these tools rectify distortions in sample composition produced by nonresponse rates & a lower probability of individuals in large households being included in the surveys using random samples of households. Comparison of responses in 1988 national telephone polls & the National Election Study (N = 2,404 adult respondents to in-person interviews) vs 1990 US census data revealed that probability weighting corrected distortions but overrepresented large households in the telephone polls. It is concluded that poststratification is a more effective tool to reduce the predictable biases due to nonavailability/nonresponse & produce less variable final weights that fit the target population more accurately. 3 Tables, 2 References. T. Arnold

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

AbstractWe build a model of American presidential voting in which the cumulative impression left by political events determines the preferences of voters. The impression varies by voter, depending on their age at the time the events took place. We use the Gallup presidential approval‐rating time series to reflect the major events that influence voter preferences, with the most influential occurring during a voter's teenage and early adult years. Our fitted model is predictive, explaining more than 80% of the variation in voting trends over the last half‐century. It is also interpretable, dividing voters into five meaningful generations: New Deal Democrats, Eisenhower Republicans, 1960s Liberals, Reagan Conservatives, and Millennials. We present each generation in context of the political events that shaped its preferences, beginning in 1940 and ending with the 2016 election.

AbstractWe consider inference from nonrandom samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable and the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We find in simulation studies that the regularized predictions using soft Bayesian additive regression trees yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiologic study.

The so-called "red/blue paradox" is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state -- the difference in the Republican vote share between rich and poor voters was much larger in low-income, conservative, middle-American states like Mississippi than in high-income, liberal, coastal states like Connecticut. We use exit poll and other survey data to assess whether this was still the case for the 2012 Presidential election. Based on this preliminary analysis, we find that, while the red/blue paradox is still strong, the explanation offered by Gelman et al. no longer appears to hold. We explore several empirical patterns from this election and suggest possible avenues for resolving the questions posed by the new data. Adapted from the source document.

We use multilevel modeling to estimate support for health-care reform by age, income, and state. Opposition to reform is concentrated among higher-income voters and those over 65. Attitudes do not vary much by state. Unfortunately, our poll data only go to 2004, but we suspect that much can be learned from the relative positions of different demographic groups and different states, despite swings in national opinion. We speculate on the political implications of these findings.

We use multilevel modeling to estimate support for health-care reform by age, income, and state. Opposition to reform is concentrated among higher-income voters and those over 65. Attitudes do not vary much by state. Unfortunately, our poll data only go to 2004, but we suspect that much can be learned from the relative positions of different demographic groups and different states, despite swings in national opinion. We speculate on the political implications of these findings. Adapted from the source document.

Political campaigns are commonly understood as random walks, during which, at any point in time, the level of support for any party or candidate is equally likely to go up or down. Each shift in the polls is then interpreted as the result of some combination of news and campaign strategies. A completely different story of campaigns is the mean reversion model in which the elections are determined by fundamental factors of the economy and partisanship; the role of the campaign is to give voters a chance to reach their predetermined positions. Using a new approach to analyze individual level poll data from recent presidential elections, we find that the fundamentals predict vote intention increasingly well as campaigns progress, which is consistent with the mean reversion model, at least at the time scale of months. We discuss the relevance of this finding to the literature on persuasion and activation effects.

Objectives.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?Methods.We examine trends from the 1940s through the 2000s in the country as a whole and in the states.Results.We find no clear relation between income inequality and class‐based voting.Conclusions.Factors such as religion and education result in a less clear pattern of class‐based voting than we might expect based on income inequality alone.