Political scientists have long been interested in citizens' support level for such actors as ethnic minorities, militant groups, and authoritarian regimes. Attempts to use direct questioning in surveys, however, have largely yielded unreliable measures of these attitudes as they are contaminated by social desirability bias and high nonresponse rates. In this paper, we develop a statistical methodology to analyze endorsement experiments, which recently have been proposed as a possible solution to this measurement problem. The commonly used statistical methods are problematic because they cannot properly combine responses across multiple policy questions, the design feature of a typical endorsement experiment. We overcome this limitation by using item response theory to estimate support levels on the same scale as the ideal points of respondents. We also show how to extend our model to incorporate a hierarchical structure of data in order to uncover spatial variation of support while recouping the loss of statistical efficiency due to indirect questioning. We illustrate the proposed methodology by applying it to measure political support for Islamist militant groups in Pakistan. Simulation studies suggest that the proposed Bayesian model yields estimates with reasonable levels of bias and statistical power. Finally, we offer several practical suggestions for improving the design and analysis of endorsement experiments.

Following David Lee's pioneering work, numerous scholars have applied the regression discontinuity (RD) design to popular elections. Contrary to the assumptions of RD, however, we show that bare winners and bare losers in U.S. House elections (1942–2008) differ markedly on pretreatment covariates. Bare winners possess large ex ante financial, experience, and incumbency advantages over their opponents and are usually the candidates predicted to win by Congressional Quarterly's pre-election ratings. Covariate imbalance actually worsens in the closest House elections. National partisan tides help explain these patterns. Previous works have missed this imbalance because they rely excessively on model-based extrapolation. We present evidence suggesting that sorting in close House elections is due mainly to activities on or before Election Day rather than postelection recounts or other manipulation. The sorting is so strong that it is impossible to achieve covariate balance between matched treated and control observations, making covariate adjustment a dubious enterprise. Although RD is problematic for postwar House elections, this example does highlight the design's advantages over alternatives: RD's assumptions are clear and weaker than model-based alternatives, and their implications are empirically testable.

In this paper, we introduce an innovative method to diagnose electoral fraud using vote counts. Specifically, we use synthetic data to develop and train a fraud detection prototype. We employ a naive Bayes classifier as our learning algorithm and rely on digital analysis to identify the features that are most informative about class distinctions. To evaluate the detection capability of the classifier, we use authentic data drawn from a novel data set of district-level vote counts in the province of Buenos Aires (Argentina) between 1931 and 1941, a period with a checkered history of fraud. Our results corroborate the validity of our approach: The elections considered to be irregular (legitimate) by most historical accounts are unambiguously classified as fraudulent (clean) by the learner. More generally, our findings demonstrate the feasibility of generating and using synthetic data for training and testing an electoral fraud detection system.

The study of Latino public opinion has renewed interest in the relationship between language and survey response. However, extant research generally relies on statistical methods that cannot distinguish between two related yet distinct types of language effects in Latino surveys: (1) differences in attitude and (2) differences in measures of attitude. The former reflects varied levels of a latent attitude between English and Spanish interviewees. The latter—formally known as Differential Item Functioning (DIF)—refers to linguistic differences in the interpretation of survey items, which lead Latino respondents to misreport their level of attitude. This paper proposes Multiple Indicators Multiple Causes models to decouple these two types of language effects. Using this modeling framework, I examine language differences in measures of subjective and factual political attitudes from the Latino National Survey (2006). I find that the language of interview systematically colors Latinos' interpretation of survey items, even after controlling for measurement error and individual differences in the latent variable being assessed. I then show through an applied analysis how ignoring language DIF can yield misleading inferences about hypothesized relationships between variables. Together, these findings highlight a need for greater theoretical work on the psychological origins of language effects in multilingual political surveys.

Measures of constituency preferences are of vital importance for the study of political representation and other research areas. Yet, such measures are often difficult to obtain. Previous survey-based estimates frequently lack precision and coverage due to small samples, rely on questionable assumptions or require detailed auxiliary information about the constituencies' population characteristics. We propose an alternative Bayesian hierarchical approach that exploits minimal geographic information readily available from digitalized constituency maps. If at hand, social background data are easily integrated. To validate the method, we use national polls and district-level results from the 2009 German Bundestag election, an empirical case for which detailed structural information is missing.

Researchers in comparative research are increasingly relying on individual level data to test theories involving unobservable constructs like attitudes and preferences. Estimation is carried out using large-scale cross-national survey data providing responses from individuals living in widely varying contexts. This strategy rests on the assumption of equivalence, that is, no systematic distortion in response behavior of individuals from different countries exists. However, this assumption is frequently violated with rather grave consequences for comparability and interpretation. I present a multilevel mixture ordinal item response model with item bias effects that is able to establish equivalence. It corrects for systematic measurement error induced by unobserved country heterogeneity, and it allows for the simultaneous estimation of structural parameters of interest.

Spatial statistical methods in political science provide a tool to deal with spatial and other forms of interdependence in observational data. In this article, I derive a statistical model from a game of impure public goods provision. The resulting strategic autoregressive model (StratAM) allows the researcher to systematically explore the sources of free-riding behavior in the provision of public goods. The StratAM model is tightly related to the well-known spatial autoregressive (SAR) model and can be estimated in a maximum likelihood framework. I demonstrate the use of the StratAM model by analyzing free riding in the provision of foreign aid. Indicators of developmental needs and good governance strongly increase free-riding during the 1990s. Free-riding patterns in the 2000s are more similar to Cold War patterns.

This paper extends models of micro- and macropartisanship in two ways. It first develops a model of individual partisanship that accommodates changes in partisan utilities. Achen's Bayesian partisan updating is a special case of our model. This more general micromodel is then aggregated to create a model of macropartisanship. This macromodel is a more general version of the models of macropartisanship estimated by various authors. The less restricted version incorporates possible individual and temporal heterogeneity. We present an example using real data that offers a possible way to estimate the parameters in the full aggregate model.