Correcting for survey misreports using auxiliary information with an application to estimating turnout

Misreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies. ©2010, Midwest Political Science Association

Do Surveys Overestimate or Underestimate Socioeconomic Differences in Voter Turnout? : Evidence from Administrative Registers

Surveys generally overestimate the overall level of voter turnout in elections due to both the misreporting of voting and nonresponse. It is sometimes argued that socioeconomic differences in turnout are exaggerated in surveys because social desirability has a more pronounced effect on eligible voters in more advantaged socioeconomic positions. However, the contribution of nonresponse bias has not been taken into consideration in these assessments. Using a register-linked survey with information on the education, occupational social class, income, and voting in the 2015 Finnish parliamentary elections of both respondents and nonrespondents, this study shows that nonresponse bias leads to not only a larger overestimation of the overall level of turnout than social desirability, but also an underestimation of educational, social class, and income-related differences in the propensity to vote. Socioeconomic differences in the probability of voting in register-based data were at least two-thirds larger than differences obtained when using standard survey techniques. This finding implies that socioeconomic inequality in electoral participation is a more pressing social problem than previous evidence might indicate.Peer reviewe

Electoral Competitiveness and Turnout in British Elections, 1964-2010

This is the author version accepted for publication in Political Science Research and Methods. The final version is forthcoming and will be available on the Publisher's website via http://journals.cambridge.org/action/displayJournal?jid=RAMAnalyzing the British Election Study from 1964 to 2010, we examine the influence of electoral context on turnout, focusing on the closeness of elections in terms of lagged seat and constituency-level winning margins. Using cross-classified multilevel models to account for individual and contextual factors and disentangle life-cycle, cohort- and election-specific effects, we find that closeness strongly affects voting behavior, particularly among new electors. Widening seat margins in British elections over the last decades have had a persistent impact on turnout. Respondents who faced less competitive environments when young are more likely to abstain in subsequent elections than those reaching voting age after close-fought races. We conclude that variations in competitiveness have had both short- and long-term effects on turnout

Employment, Wages and Voter Turnout

This paper argues that, since activities that provide political information are complementary with leisure, increased labor market activity should lower turnout, but should do so least in prominent elections where information is ubiquitous. Using official county-level voting data and a variety of OLS and TSLS models, we find that increases in wages and employment: reduce voter turnout in gubernatorial elections by a significant amount; have no effect on Presidential turnout; and raise the share of persons voting in a Presidential election who do not vote on a House of Representative election on the same ballot. We argue that this pattern (which contradicts some previous findings in the literature) can be fully accounted for by an information argument, and is either inconsistent with or not fully explicable by arguments based on citizens’ psychological motivations to vote in good or bad times; changes in logistical voting costs; or transitory migration. Using individual-level panel data methods and multiple years’ data from the American National Election Study (ANES) we confirm that increases in employment lead to less use of the media and reduced political knowledge, and present associational individual evidence that corroborates our main argument.

A Unified Approach to Measurement Error and Missing Data: Overview, Sociological Methods and Research

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2014b).Governmen

Solutions to Turnout Over-Reporting: What Is Out There, What Works, and Can We Do Better?

Valid measurement of voter turnout is crucial to electoral studies. One major problem in obtaining valid turnout measurements is over-reporting, i.e. survey respondents who did not vote report having voted. Aiming to identify effective solutions to turnout over-reporting, this doctoral thesis consists of four separate but interrelated papers , plus introductory and concluding chapters. The introductory chapter reviews the causes and consequences of turnout over-reporting, providing the basis for an in-depth research into solutions. Each of the papers then addresses a question about solutions. Paper 1 critically re-examines an influential study of turnout over-reporting. The examination results highlight the need for better solutions to over-reporting. Addressing the question of "What is out there?", Paper 2 conducts a meta-analysis of studies that have experimented on innovative solutions to turnout over-reporting. Addressing the question of "What works?", Paper 3 experimentally compares two promising solutions – item-count and pipeline techniques – and finds that the former is, overall, better than the latter for preventing turnout over-reporting. Addressing the question of "Can we do better?", Paper 4 improves the design and analysis of the item-count technique, making it an even better solution to turnout over-reporting. From the results of these research papers, the concluding chapter considers the implications for developing effective solutions to turnout over-reporting, and laying the foundations for future advances in the measurement of turnout. Furthermore, the concluding chapter also discusses how the results of this doctoral research can contribute beyond election studies, towards scientific studies on a wide range of topics on which people often misreport

Spatial voting across electoral arenas and policy dimensions

This dissertation proposes a novel way to consistently model policy-based voting behavior across multiple electoral levels. Building on the multidimensional model of spatial competition, change in electoral turnout and party vote choice across elections may result from voters reweighing different policy dimension at different levels of government. An estimation strategy that implements the spatial model in the panel conditional logit fixed-effects framework and allows for the modeling of non-separable preferences is developed. This framework is brought to bear on the long-standing debate on the role of voter EU integration preferences in explaining differences in voting behavior between national-level and European Parliamentary elections. Leveraging a uniquely suited panel voter survey from the German state of Bavaria, evidence of voters recalibrating their policy priorities across electoral levels is established

Dirichlet process probit misclassification mixture model for misclassified binary data

Mislabelling or misclassification in binary data refers to incorrectly labelled responses and could arise due to problems in the labelling process or imperfect evidence for labelling. The latent misclassification process could take a variety of forms depending on how it relates to the true labels as well as the associated covariates of each response. Modelling under misclas- sification is challenging because of the inherent identifiability issues and ignoring misclassi- fication could lead to inaccurate inferences. Statistical methods addressing misclassification have appeared in the literature in a variety of contexts, sometimes using di↵erent terminology, and often focusing on a particular application. In this thesis, we first cast existing statistical methods under a unified framework and later propose a new flexible Bayesian mixture model for modelling misclassified binary data - the Dirichlet process probit misclassification mix- ture model. The main idea is to assume a Dirichlet process mixture model over the covariate space and misclassification probabilities. This naturally partitions observations into clusters where di↵erent clusters can possess di↵erent misclassification probabilities. The clustering uses both covariates and observed responses and covariates are approximated using a Dirich- let mixture of multivariate Gaussians. The incorporation of cluster-specific misclassification probabilities takes into consideration of the misclassification in the observed responses. An e cient Gibbs-like algorithm is available based on the truncated approximation of Dirichlet process and the stick-breaking construction. This thesis is motivated by the pervasiveness of label noise in a wide variety of applica- tions, coupled with the lack of unified statistical exposition and comparison of all available methods. The structure of the thesis as follows. Chapter 1 introduces the problem of label misclassification and reviews existing methods for modelling misclassification in binary data. Chapter 2 discusses the basic of Bayesian nonparametrics, Dirichlet process, Dirichlet pro- cess mixture models, and posterior inference procedures for Dirichlet process mixture models, which are essential components of the Dirichlet process probit misclassification mixtures that we propose later. Chapter 3 describes our proposed model for modelling mislabelled binary data. Chapter 4 presents experimental studies on our proposed model using a real dataset. Section 5 wraps up the discussion on the topic and include final remarks such as possible model extension

European Inclusion: Electoral Differences and Individual Participation in European Parliament Elections

This book investigates electoral procedures and their effects on individual participation in different elections within multi–level political systems. My basic research expectation is that electoral differences – i.e. differences in electoral procedures, for example between the 2009 European Parliament (EP) election and the previous national parliamentary elections in the member states of the European Union (EU) – reduce the individual understanding and thus participation in the EP election. As I show, the individual voter knows less about the EU than about her domestic politics, due to the EU’s lower political salience. Instead, the multi–level structure of the EU and its member states enables the individual to resort to political knowledge acquired on the domestic level, using it as a proxy for knowledge of the EU. That is, the individual employs a domestic perspective on the EU. But electoral differences cause this domestic perspective to fail, due to inappropriate reliance on other political knowledge. As a consequence, individual political knowledge about the EU is lower, reducing the individual understanding of the EU and the EP election. On the one hand, this lower understanding implies that political knowledge is more relevant in the context of electoral differences. It increases the individual’s awareness of electoral differences, enabling her to overcome the consequences of such differences for the EU and the EP election. On the other hand, electoral differences also mean that greater political knowledge has a demobilizing effect. If the consequences of the differences are not in line with the individual’s political preferences, they discourage her from casting a ballot for the EP. In short, electoral differences matter, diminishing the individual understanding of the EU and reducing individual participation in the EP election