Political knowledge is a central concept in the study of public opinion and political behavior. Yet what the field collectively believes about this construct is based on dozens of studies using different indicators of knowledge. We identify two theoretically relevant dimensions: atemporaldimension that corresponds to the time when a fact was established and atopicaldimension that relates to whether the fact is policy-specific or general. The resulting typology yields four types of knowledge questions. In an analysis of more than 300 knowledge items from late in the first decade of the 2000s, we examine whether classic findings regarding the predictors of knowledge withstand differences across types of questions. In the case of education and the mass media, the mechanisms for becoming informed operate differently across question types. However, differences in the levels of knowledge between men and women are robust, reinforcing the importance of including gender-relevant items in knowledge batteries.

Political scientists often theorize that an explanatory variable should have "no effect" and support this claim by demonstrating that its coefficient's estimate is not statistically significant. This empirical argument is quite weak, but I introduce applied researchers to simple, powerful tools that can strengthen their arguments for this hypothesis. With several supporting examples, I illustrate that researchers can use 90% confidence intervals to argue against meaningful effects and provide persuasive evidence for their hypothesis.

When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or nonevents. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invariant prior to stabilize the estimates. While Jeffreys’ prior has the advantage of being automatic, I show that it often provides too much prior information, producing smaller point estimates and narrower confidence intervals than even highly skeptical priors. To help researchers assess the amount of information injected by the prior distribution, I introduce the concept of a partial prior distribution and develop the tools required to compute the partial prior distribution of quantities of interest, estimate the subsequent model, and summarize the results.

A growing body of research documents the crucial role played by moral concerns in the formation of attitudes and a wide range of political behaviors. Yet extant models of moral judgment portray a direct linkage between moral intuitions and policy attitudes, leaving little room for the influence of political context. In this article, we argue that political rhetoric plays an important role in facilitating the connection between moral intuitions and political attitudes. Using a unique combination of media content analysis of the stem cell debate and individual-level measures of the public's moral foundations, we examine the role of rhetoric in linking a person's moral foundations to his or her attitudes. Our results show that individuals who are the most likely to have been exposed to political rhetoric have the strongest connection between their moral foundations and their attitudes on the issue. We also find that rhetoric was persuasive on this moralized issue and present suggestive evidence that it was most persuasive among those who endorsed the relevant moral foundation.

In small samples, maximum likelihood (ML) estimates of logit model coefficients have substantial bias away from zero. As a solution, we remind political scientists of Firth's (1993, Biometrika, 80, 27–38) penalized maximum likelihood (PML) estimator. Prior research has described and used PML, especially in the context of separation, but its small sample properties remain under-appreciated. The PML estimator eliminates most of the bias and, perhaps more importantly, greatly reduces the variance of the usual ML estimator. Thus, researchers do not face a bias-variance tradeoff when choosing between the ML and PML estimators—the PML estimator has a smaller bias and a smaller variance. We use Monte Carlo simulations and a re-analysis of George and Epstein (1992, American Political Science Review, 86, 323–337) to show that the PML estimator offers a substantial improvement in small samples (e.g., 50 observations) and noticeable improvement even in larger samples (e.g., 1000 observations).