Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation.

Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up nonresponse rates. Typically, investigators select a subset of available scale items rather than asking the full battery. Reduced batteries, however, can sharply reduce measurement precision and introduce bias. In this article, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and precision. CAT algorithms respond to individuals' previous answers to select subsequent questions that most efficiently reveal respondents' positions on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and empirically comparing dynamic and static measures of political knowledge.

Political scientists are often interested in estimating causal effects. Identification of causal estimates with observational data invariably requires strong untestable assumptions. Here, we outline a number of the assumptions used in the extant empirical literature. We argue that these assumptions require careful evaluation within the context of specific applications. To that end, we present an empirical case study on the effect of Election Day Registration (EDR) on turnout. We show how different identification assumptions lead to different answers, and that many of the standard assumptions used are implausible. Specifically, we show that EDR likely had negligible effects in the states of Minnesota and Wisconsin. We conclude with an argument for stronger research designs.

This article presents a new method of reconstructing actors' political positions from coded political texts. It is based on a model that combines a dynamic perspective on actors' political positions with a probabilistic account of how these positions are translated into emphases of policy topics in political texts. In the article it is shown how model parameters can be estimated based on a maximum marginal likelihood principle and how political actors' positions can be reconstructed using empirical Bayes techniques. For this purpose, a Monte Carlo Expectation Maximization algorithm is used that employs independent sample techniques with automatic Monte Carlo sample size adjustment. An example application is given by estimating a model of an economic policy space and a noneconomic policy space based on the data from the Comparative Manifesto Project. Parties' positions in policy spaces reconstructed using these models are made publicly available for download.

How a treatment causes a particular outcome is a focus of inquiry in political science. When treatment data are either nonrandomly assigned or missing, the analyst will often invoke ignorability assumptions: that is, both the treatment and missingness are assumed to be as if randomly assigned, perhaps conditional on a set of observed covariates. But what if these assumptions are wrong? What if the analyst does not know why—or even if—a particular subject received a treatment? Building on Manski, Molinari offers an approach for calculating nonparametric identification bounds for the average treatment effect of a binary treatment under general missingness or nonrandom assignment. To make these bounds substantively more informative, Molinari's technique permits adding monotonicity assumptions (e.g., assuming that treatment effects are weakly positive). Given the potential importance of these assumptions, we develop a new Bayesian method for performing sensitivity analysis regarding them. This sensitivity analysis allows analysts to interpret the assumptions' consequences quantitatively and visually. We apply this method to two problems in political science, highlighting the method's utility for applied research.

An increasing number of analyses in various subfields of political science employ Boolean algebra as proposed by Ragin's qualitative comparative analysis (QCA). This type of analysis is perfectly justifiable if the goal is to test deterministic hypotheses under the assumption of error-free measures of the employed variables. My contention is, however, that only in a very few research areas are our theories sufficiently advanced to yield deterministic hypotheses. Also, given the nature of our objects of study, error-free measures are largely an illusion. Hence, it is unsurprising that many studies employ QCA inductively and gloss over possible measurement errors. In this article, I address these issues and demonstrate the consequences of these problems with simple empirical examples. In an analysis similar to Monte Carlo simulation, I show that using Boolean algebra in an exploratory fashion without considering possible measurement errors may lead to dramatically misleading inferences. I then suggest remedies that help researchers to circumvent some of these pitfalls.