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AbstractWe present a hierarchical Dirichlet regression model with Gaussian process priors that enables accurate and well-calibrated forecasts for U.S. Senate elections at varying time horizons. This Bayesian model provides a balance between predictions based on time-dependent opinion polls and those made based on fundamentals. It also provides uncertainty estimates that arise naturally from historical data on elections and polls. Experiments show that our model is highly accurate and has a well calibrated coverage rate for vote share predictions at various forecasting horizons. We validate the model with a retrospective forecast of the 2018 cycle as well as a true out-of-sample forecast for 2020. We show that our approach achieves state-of-the art accuracy and coverage despite relying on few covariates.

AbstractIn principle, experiments offer a straightforward method for social scientists to accurately estimate causal effects. However, scholars often unwittingly distort treatment effect estimates by conditioning on variables that could be affected by their experimental manipulation. Typical examples include controlling for posttreatment variables in statistical models, eliminating observations based on posttreatment criteria, or subsetting the data based on posttreatment variables. Though these modeling choices are intended to address common problems encountered when conducting experiments, they can bias estimates of causal effects. Moreover, problems associated with conditioning on posttreatment variables remain largely unrecognized in the field, which we show frequently publishes experimental studies using these practices in our discipline's most prestigious journals. We demonstrate the severity of experimental posttreatment bias analytically and document the magnitude of the potential distortions it induces using visualizations and reanalyses of real‐world data. We conclude by providing applied researchers with recommendations for best practice.

AbstractTopic models, as developed in computer science, are effective tools for exploring and summarizing large document collections. When applied in social science research, however, they are commonly used for measurement, a task that requires careful validation to ensure that the model outputs actually capture the desired concept of interest. In this paper, we review current practices for topic validation in the field and show that extensive model validation is increasingly rare, or at least not systematically reported in papers and appendices. To supplement current practices, we refine an existing crowd-sourcing method by Chang and coauthors for validating topic quality and go on to create new procedures for validating conceptual labels provided by the researcher. We illustrate our method with an analysis of Facebook posts by U.S. Senators and provide software and guidance for researchers wishing to validate their own topic models. While tailored, case-specific validation exercises will always be best, we aim to improve standard practices by providing a general-purpose tool to validate topics as measures.

It is conventional to speak of voting as "habitual." But what does this mean? In psychology, habits are cognitive associations between repeated responses and stable features of the performance context. Thus, "turnout habit" is best measured by an index of repeated behavior and a consistent performance setting. Once habit associations form, the response can be cued even in the absence of supporting beliefs and motivations. Therefore, variables that form part of the standard cognitive-based accounts of turnout should be more weakly related to turnout among those with a strong habit. We draw evidence from a large array of ANES surveys to test these hypotheses and find strong support. Adapted from the source document.

Political scientists have long disagreed about the nature of individual-level change in party identification. While some scholars conclude that party identification is a stable identity—attributing changes in individual responses to measurement error—others show that aggregate party identification responds systematically to short-term forces such as presidential approval. In this article, we use a unique long-term panel measuring party identification twenty times in the 2011–2016 period to support a subtle compromise between these competing claims. We show that individual-level party identification changes systematically over time even after accounting for measurement error and that this change is related to short-term evaluations of the parties and the president. However, although such change exists, it is modest in the medium term and more common among specific subsets of respondents. Finally, we show that that these findings are robust to numerous alternative modeling strategies. We believe that our analysis provides the most systematic examination to date of individual-level changes in party identification.

Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty.

Peffley and Hurwitz's article "Persuasion and resistance: Race and the death penalty in America" is an influential study demonstrating the effects of race on death penalty attitudes. White respondents were found to increase their approval for capital punishment when informed that it disproportionately affects African-Americans. We present results from two studies, including one conducted on a nationally representative sample, that fail to find support for this finding. Our first study, which was conducted on Amazon Mechanical Turk, consists of an exact replication as well as an additional manipulation that strengthens the treatment by adding information about a specific black (versus a white) defendant to the stimulus. However, we fail to elicit the backlash effect found in the original study using either manipulation despite having nearly three times the sample size. These findings are mirrored by replication data from a Time-sharing Experiments for the Social Sciences survey that closely replicates Peffley and Hurwitz's race framing treatment. The results from these studies suggest that the relationship between racial stimuli and death penalty support has changed since the original study, that racial backlash effects in this policy domain are not as robust as previously assumed, or both.

Many observers are concerned that campaign contributions could affect the decisions of elected judges. However, the empirical correlation between contributions and judicial decisions is consistent with two different explanations of judicial behavior: (a) money influences judges or (b) contributors choose to support candidates with a similar philosophical or legal perspective. In this article, we take advantage of North Carolina's shift to a voluntary public finance system for state Supreme Court candidates to obtain more credible estimates of the contributions–behavior relationship. Applying a difference-in-differences research design, we provide evidence that justices who opted into public financing became relatively less favorable toward attorney donors. We also find partial support for our hypothesis that participating justices became more moderate in their voting patterns. Taken together, these findings suggest that public financing reduced responsiveness to donors among participating justices.

In this article, we challenge the conclusion that the preferences of members of Congress are best represented as existing in a low-dimensional space. We conduct Monte Carlo simulations altering assumptions regarding the dimensionality and distribution of member preferences and scale the resulting roll call matrices. Our simulations show that party polarization generates misleading evidence in favor of low dimensionality. This suggests that the increasing levels of party polarization in recent Congresses may have produced false evidence in favor of a low-dimensional policy space. However, we show that focusing more narrowly on each party caucus in isolation can help researchers discern the true dimensionality of the policy space in the context of significant party polarization. We re-examine the historical roll call record and find evidence suggesting that the low dimensionality of the contemporary Congress may reflect party polarization rather than changes in the dimensionality of policy conflict.

Perhapsthemajor story in forecasting the 2012 election is the growing awareness of the benefits of aggregating multiple sources of data for improving prediction. Most prominently, polling analysts including Simon Jackman, Drew Linzer, and Nate Silver made the strong case, ultimately validated by the election results, that combining information from multiple polls gives a better picture of the electorate than any poll analyzed in isolation.

For more than two decades, political scientists have created statistical models
aimed at generating out-of-sample predictions of presidential elections. In 2004 and
2008, PS: Political Science and Politics published symposia of the
various forecasting models prior to Election Day. This exercise serves to validate
models based on accuracy by garnering additional support for those that most
accurately foretell the ultimate election outcome. Implicitly, these symposia assert
that accurate models best capture the essential contexts and determinants of
elections. In part, therefore, this exercise aims to develop the "best" model of the
underlying data generating process. Scholars comparatively evaluate their models by
setting their predictions against electoral results while also giving some attention
to the models' inherent plausibility, parsimony, and beauty.