Political knowledge research faces a problem, perhaps even a crisis. For two decades, the American National Election Studies asked open-ended questions about political knowledge and coded answers using procedures that are neither reliable nor replicable and that were never shown to be optimally valid. Consequently, conclusions based on these widely used measures of the public's competence are in doubt. This article presents several new and overdue methodological improvements: coding knowledge data using formal and specific coding rules based on a substantive rationale for the validity of the codes, recognizing partially correct answers, using multiple coders working independently, using machine coding, and testing reliability and validity. The new methods are an improvement because they are transparent and replicable and they produce valid and extremely reliable knowledge data. Further, machine coding produces codes nearly identical to those from a team of human coders, at much lower cost.

Valid comparisons of group scores on additive measures such as political knowledge scales require that the conditional response probabilities for individuals on the observed items be invariant across groups after controlling for their overall level of the latent trait of interest. Using a multi-group confirmatory factor analysis of knowledge items drawn from American National Election Studies, we find that the scales used in recent research are not sufficiently invariant for valid comparisons across a host of theoretically important grouping variables. We demonstrate that it is possible to construct valid invariant scales using a subset of items and show the impact of invariance by comparing results from the valid and invalid scales. We provide an analysis of differential item functioning based on grouping variables commonly used in political science research to explore the utility of each item in the construction of valid knowledge scales. An application of the VTT suggests it is more appropriate to conceive of these items as effects of a latent variable rather than cause or formative indicators. These results suggest that models attempting to explain apparent knowledge gaps between subgroups have been unsuccessful because previously constructed scales were validated by fiat.

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention—designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.

Multilevel regression and poststratification (MRP) is a method to estimate public opinion across geographic units from individual-level survey data. If it works with samples the size of typical national surveys, then MRP offers the possibility of analyzing many political phenomena previously believed to be outside the bounds of systematic empirical inquiry. Initial investigations of its performance with conventional national samples produce generally optimistic assessments. This article examines a larger number of cases and a greater range of opinions than in previous studies and finds substantial variation in MRP performance. Through empirical and Monte Carlo analyses, we develop an explanation for this variation. The findings suggest that the conditions necessary for MRP to perform well will not always be met. Thus, we draw a less optimistic conclusion than previous studies do regarding the use of MRP with samples of the size found in typical national surveys.

This article presents a new method for estimating positions of political parties across country- and time-specific contexts by introducing a latent variable model for manifesto data. We estimate latent positions and exploit bridge observations to make the scales comparable. We also incorporate expert survey data as prior information in the estimation process to avoid ex post facto interpretation of the latent space. To illustrate the empirical contribution of our method, we estimate the left-right positions of 388 parties competing in 238 elections across twenty-five countries and over sixty years. Compared to the puzzling volatility of existing estimates, we find that parties more modestly change their left-right positions over time. We also show that estimates without country- and time-specific bias parameters risk serious, systematic bias in about two-thirds of our data. This suggests that researchers should carefully consider the comparability of party positions across countries and/or time.

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

In typical political experiments, researchers randomize a set of households, precincts, or individuals to treatments all at once, and characteristics of all units are known at the time of randomization. However, in many other experiments, subjects “trickle in” to be randomized to treatment conditions, usually via complete randomization. To take advantage of the rich background data that researchers often have (but underutilize) in these experiments, we develop methods that use continuous covariates to assign treatments sequentially. We build on biased coin and minimization procedures for discrete covariates and demonstrate that our methods outperform complete randomization, producing better covariate balance in simulated data. We then describe how we selected and deployed a sequential blocking method in a clinical trial and demonstrate the advantages of our having done so. Further, we show how that method would have performed in two larger sequential political trials. Finally, we compare causal effect estimates from differences in means, augmented inverse propensity weighted estimators, and randomization test inversion.

Unemployment insurance policies are multidimensional objects, with variable waiting periods, eligibility duration, benefit levels, and asset tests, making intertemporal or international comparisons very difficult. Furthermore, labor market conditions, such as the likelihood and duration of unemployment, matter when assessing the generosity of different policies. In this article, we develop a new methodology to measure the generosity of unemployment insurance programs with a single metric. We build a first model with all characteristics of the complex unemployment insurance policy. Our model features heterogeneous agents that are liquidity constrained but can self-insure. We then build a second model, similar in all aspects but one: the unemployment insurance policy is one-dimensional (no waiting periods, eligibility limits, or asset tests, but constant benefits). We then determine which level of benefits in this second model makes society indifferent between both policies. We apply this measurement strategy to the unemployment insurance program of the United Kingdom.