We consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution, implementations of joint MVN MI typically assume that categories of discrete variables are probabilistically constructed from continuous values. We use simulations to examine the implications of these assumptions. For each approach, we assess (1) the accuracy of the imputed values; and (2) the accuracy of coefficients and fitted values from a model fit to completed data sets. These simulations consider continuous, binary, ordinal, and unordered-categorical variables. One set of simulations uses multivariate normal data, and one set uses data from the 2008 American National Election Studies. We implement a less restrictive approach than is typical when evaluating methods using simulations in the missing data literature: in each case, missing values are generated by carefully following the conditions necessary for missingness to be “missing at random” (MAR). We find that in these situations conditional MI is more accurate than joint MVN MI whenever the data include categorical variables.

This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer.” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.

Longitudinal or panel surveys offer unique benefits for social science research, but they typically suffer from attrition, which reduces sample size and can result in biased inferences. Previous research tends to focus on the demographic predictors of attrition, conceptualizing attrition propensity as a stable, individual-level characteristic—some individuals (e.g., young, poor, residentially mobile) are more likely to drop out of a study than others. We argue that panel attrition reflects both the characteristics of the individual respondent as well as her survey experience, a factor shaped by the design and implementation features of the study. In this article, we examine and compare the predictors of panel attrition in the 2008–2009 American National Election Study, an online panel, and the 2006–2010 General Social Survey, a face-to-face panel. In both cases, survey experience variables are predictive of panel attrition above and beyond the standard demographic predictors, but the particular measures of relevance differ across the two surveys. The findings inform statistical corrections for panel attrition bias and provide study design insights for future panel data collections.

Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-n research initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.

This article provides an algorithm to produce a time-series estimate of the political center (or median voter) from aggregate survey data, even when the same questions are not asked in most years. This is compared to the existing Stimson dyad ratios approach, which has been applied to various questions in political science. Unlike the dyad ratios approach, the model developed here is derived from an explicit model of individual behavior—the widely used item response theory model. I compare the results of both techniques using the data on public opinion from the United Kingdom from 1947 to 2005 from Bartle, Dellepiane-Avellaneda, and Stimson. Measures of overall model fit are provided, as well as techniques for testing model's assumptions and the fit of individual items. Full code is provided for estimation with free software WinBUGS and JAGS.

Latent class analysis is used in the political science literature in both substantive applications and as a tool to estimate measurement error. Many studies in the social and political sciences relate estimated class assignments from a latent class model to external variables. Although common, such a “three-step” procedure effectively ignores classification error in the class assignments; Vermunt (2010, “Latent class modeling with covariates: Two improved three-step approaches,” Political Analysis 18:450–69) showed that this leads to inconsistent parameter estimates and proposed a correction. Although this correction for bias is now implemented in standard software, inconsistency is not the only consequence of classification error. We demonstrate that the correction method introduces an additional source of variance in the estimates, so that standard errors and confidence intervals are overly optimistic when not taking this into account. We derive the asymptotic variance of the third-step estimates of interest, as well as several candidate-corrected sample estimators of the standard errors. These corrected standard error estimators are evaluated using a Monte Carlo study, and we provide practical advice to researchers as to which should be used so that valid inferences can be obtained when relating estimated class membership to external variables.

The random utility model (RUM) of voting behavior can account for strategic voting by making use of proxy indicators that measure voter incentives to vote strategically. The contribution of this article is to propose a new method to estimate the RUM in the presence of strategic voters, without having to construct proxy measures of strategic voting incentives. Our method can be used to infer the counterfactual sincere vote of those who vote strategically and provides an estimate of the size of strategic voting. We illustrate the procedure using post-electoral survey data from Spain. Our calculations indicate that strategic voting in Spain is about 2.19%.

Social scientists have increasingly turned to expert judgments to generate data for difficult-to-measure concepts, but getting access to and response from highly expert informants can be costly and challenging. We examine how informant selection and post-survey response aggregation influence the validity and reliability of measures built from informant observations. We draw upon three surveys with parallel survey questions of candidate characteristics to examine the trade-off between expanding the size of the local informant pool and the pool's level of expertise. We find that a “wisdom-of-crowds” effect trumps the benefits associated with the expertise of individual informants when the size of the rater pool is modestly increased. We demonstrate that the benefits of expertise are best realized by prescreening potential informants for expertise rather than post-survey weighting by expertise.

Voting is a fundamental part of any democratic society. But survey-based measures of voting are problematic because a substantial proportion of nonvoters report that they voted. This over-reporting has consequences for our understanding of voting as well as the behaviors and attitudes associated with voting. Relying on the “bogus pipeline” approach, we investigate whether altering the wording of the turnout question can cause respondents to provide more accurate responses. We attempt to reduce over-reporting simply by changing the wording of the vote question by highlighting to the respondent that: (1) we can in fact find out, via public records, whether or not they voted; and (2) we (survey administrators) know some people who say they voted did not. We examine these questions through a survey on US voting-age citizens after the 2010 midterm elections, in which we ask them about voting in those elections. Our evidence shows that the question noting we would check the records improved the accuracy of the reports by reducing the over-reporting of turnout.

The development and proliferation of strategic estimators has narrowed the gap between theoretical models and empirical testing. But despite recent contributions that extend the basic strategic estimator, researchers have continued to neglect a classic social science phenomenon: selection. Compared to nonstrategic estimators, strategic models are even more prone to selection effects. First, external shocks or omitted variables can lead to correlated errors. Second, because the systematic parts of actors' utilities usually overlap on certain key variables, the two sets of explanatory variables are correlated. As a result, both the systematic and the stochastic components can be correlated. However, given that the estimates for the first mover are computed based on the potentially biased predicted probabilities of the second actor, we also generate biased estimates for the first actor. In applied work, researchers neglect the potential shortcomings due to selection bias. This article presents an alternative strategic estimator that takes selection into account and allows scholars to obtain consistent, unbiased, and efficient estimates in the presence of both selection and strategic action. I present a Monte Carlo analysis as well as a real-world application to illustrate the superior performance of this estimator relative to the standard practice.

This article investigates inconsistency and invalid statistical inference that often characterize dynamic panel analysis in international political economy. These econometric concerns are tied to Nickell bias and cross-sectional dependence. First, we discuss how to avoid Nickell bias in dynamic panels. Second, we put forward factor-augmented dynamic panel regression as a means for addressing cross-sectional dependence. As a specific application, we use our methods for an analysis of the impact of terrorism on economic growth. Different terrorism variables are shown to have no influence on economic growth for five regional samples when Nickell bias and cross-dependence are taken into account. Our finding about terrorism and growth is contrary to the extant literature.

Using a scale of child rearing preferences, scholars find that African Americans are far more authoritarian than Whites. We argue that this racial gap in authoritarianism is largely a measurement artifact. The child rearing scale now used to measure authoritarianism is cross-racially invalid because it draws heavily on a metaphor about hierarchy. Akin to someone who favors enforcing conformity in a child, the authoritarian is thought to be inclined toward enforcing conformity in social subordinates. In both cases, one's perspective is drawn from a position of relative power. We believe this metaphor is effective among members of a majority racial group because individual dominance at home meshes with group dominance in society. For members of a racial minority, we believe this metaphor breaks down. Using multi-group confirmatory factor analysis, we establish that Blacks and Whites construe the child rearing items differently. Consequently, authoritarianism correlates highly with the things it should for Whites, but rarely so for Blacks. Using an illegal immigration experiment, we then show divergent patterns of intolerance based on the same child rearing scale. Our results highlight measurement's role in producing large racial gaps in authoritarianism, while illuminating the racial boundaries of the child rearing scale.