Learning from polls

Voters’ expectations of party strengths are a central part of many foundational political science theories that posit a strategic act by the voter. But how do voters develop these beliefs and how is this belief formation affected by polling reports? In this article, we present a dynamic Bayesian learning model that serves as a baseline for how beliefs are formed. We use survey experiments to estimate parameters of the dynamic learning process and analyze how and when belief formation deviates from theoretical model. We find that respondents update closely to new arriving poll results, they judge the polls to be two times more imprecise as the actual sample error and that this makes the induced differences in prior beliefs about a race vanish over time. We further apply the experiment to the study of partisan bias and the quality of the polls

Eliciting beliefs as distributions in online surveys

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter, we ask if there is an effective way of eliciting beliefs as distributions in the context of online surveys. Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It measures average citizens’ subjective belief distributions reliably and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs

Replication Data for: What is Islamophobia? Disentangling Citizens’ Feelings Towards Ethnicity, Religion and Religiosity Using a Survey Experiment

Replication data set (STATA format) and R code to reproduce analyses and figures in the paper. Abstract: What citizens think about Muslim immigrants is of great importance for some of the most pressing challenges facing Western democracies. To advance our understanding of what “Islamophobia” really is – i.e. whether it is a dislike based on immigrants` ethnic background, their religious identity or their specific religious behaviour – we fielded a representative online survey experiment in the UK in the summer 2015. Our results suggest that Muslims are not per se viewed more negatively than Christian immigrants. Instead, we provide evidence that citizens’ uneasiness with Muslim immigration is first and foremost the result of a rejection of fundamentalist forms of religiosity. This suggests that com-mon explanations, which are based on simple dichotomies between liberal supporters and conservative critics of immigration need to be re-evaluated. While the politically left and culturally liberal have more positive attitudes towards immigrants than right leaning and conservatives, they are also far more critical towards religious groups. We conclude that a large part of the current political controver-sy over Muslim immigration has to do with this double opposition. Importantly, the current political conflict over Muslim immigration is not so much about immigrants versus natives or even Muslim versus Christians as it is about political liberalism versus religious fundamentalism

Replication Data for: The Sensitivity of Sensitivity Analysis

This article evaluates the reliability of sensitivity tests (Leamer 1978). Using Monte Carlo methods we show that, first, the definition of robustness exerts a large influence on the robustness of var¬iables. Second and more importantly, our results also demonstrate that inferences based on sen¬sitivity tests are most likely to be valid if determinants and confounders are almost uncorrelated and if the variables included in the true model exert a strong influence on outcomes. Third, no definition of robustness reliably avoids both false positives and false negatives. We find that for a wide variety of data-generating processes, rarely used definitions of robustness perform better than the frequently used model averaging rule suggested by Sala-i-Martin. Fourth, our results also suggest that Leamer’s extreme bounds analysis and Bayesian model averaging are extremely un¬likely to generate false positives. Thus, if based on these inferential criteria a variable is robust, it is almost certain to belong into the empirical model. Fifth and finally, we also show that research¬ers should avoid drawing inferences based on lack of robustness

Eliciting beliefs as distributions in online surveys

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter we ask whether there is an effective way to elicit beliefs as distributions in the context of (online) surveys? Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It reliably measures the subjective belief distribution of average citizens and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs

Replication data for: Modeling Latent Information in Voting Data with Dirichlet Process Priors

N/

Learning from Polls During Electoral Campaigns

Voters’ beliefs about the strength of political parties are a central part of many foundational political science theories. In this article, we present a dynamic Bayesian learning model that allows us to study how voters form these beliefs by learning from pre-election polls over the course of an election campaign. In the model, belief adaptation to new polls can vary due to the perceived precision of the poll or the reliance on prior beliefs. We evaluate the implications of our model using two experiments. We find that respondents update their beliefs assuming that the polls are relatively imprecise but still weigh them more strongly than their priors. Studying implications for motivational learning by partisans, we find that varying adaptation works through varying reliance on priors and not necessarily by discrediting a poll’s precision. The findings inform our understanding of the consequences of learning from polls during political campaigns and motivational learning in general

Learning from polls

Voters’ expectations of party strengths are a central part of many foundational political science theories that posit a strategic act by the voter. But how do voters develop these beliefs and how is this belief formation affected by polling reports? In this article, we present a dynamic Bayesian learning model that serves as a baseline for how beliefs are formed. We use survey experiments to estimate parameters of the dynamic learning process and analyze how and when belief formation deviates from theoretical model. We find that respondents update closely to new arriving poll results, they judge the polls to be two times more imprecise as the actual sample error and that this makes the induced differences in prior beliefs about a race vanish over time. We further apply the experiment to the study of partisan bias and the quality of the polls

Learning from polls

Voters’ expectations of party strengths are a central part of many foundational political science theories that posit a strategic act by the voter. But how do voters develop these beliefs and how is this belief formation affected by polling reports? In this article, we present a dynamic Bayesian learning model that serves as a baseline for how beliefs are formed. We use survey experiments to estimate parameters of the dynamic learning process and analyze how and when belief formation deviates from theoretical model. We find that respondents update closely to new arriving poll results, they judge the polls to be two times more imprecise as the actual sample error and that this makes the induced differences in prior beliefs about a race vanish over time. We further apply the experiment to the study of partisan bias and the quality of the polls

Adversarial Collaboration: Freedom of Speech on Campus

We present the results of a study on free expression at the university that emerged from an adversarial collaboration. Specifically, we scrutinize the claim of widespread intolerance of controversial speakers on campus by presenting a novel vignette experiment