In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient structural breaks and perform inference on the number of such breaks in a given time series. I demonstrate the usefulness of the model with applications to campaign contributions in the 2012 U.S. Republican presidential primary and incidences of global terrorism from 1970 to 2015.

In Qualitative Comparative Analysis (QCA), empirical researchers use the consistency value as one, if not sole, criterion to decide whether an association between a term and an outcome is consistent with a set-relational claim. Braumoeller (2015) points out that the consistency value is unsuitable for this purpose. We need to know the probability of obtaining it under the null hypothesis of no systematic relation. He introduces permutation testing for estimating the $p$ value of a consistency score as a safeguard against false positives. In this paper, I introduce permutation-based power estimation as a safeguard against false-negative conclusions. Low power might lead to the false exclusion of truth table rows from the minimization procedure and the generation and interpretation of invalid solutions. For a variety of constellations between an alternative and null hypothesis and numbers of cases, simulations demonstrate that power estimates can range from 1 to 0. Ex post power analysis for 63 truth table analyses shows that even under the most favorable constellation of parameters, about half of them can be considered low-powered. This points to the value of estimating power and calculating the required number of cases before the truth table analysis.

Words matter in politics. The rhetoric that political elites employ structures civic discourse. The emergence of social media platforms as a medium of politics has enabled ordinary citizens to express their ideological inclinations by adopting the lexicon of political elites. This avails to researchers a rich new source of data in the study of political ideology. However, existing ideological text-scaling methods fail to produce meaningful inferences when applied to the short, informal style of textual content that is characteristic of social media platforms such as Twitter. This paper introduces the first viable approach to the estimation of individual-level ideological positions derived from social media content. This method allows us to position social media users—be they political elites, parties, or citizens—along a shared ideological dimension. We validate the proposed method by demonstrating correlation with existing measures of ideology across various political contexts and multiple languages. We further demonstrate the ability of ideological estimates to capture derivative signal by predicting out-of-sample, individual-level voting intentions. We posit that social media data can, when properly modeled, better capture derivative signal than discrete scales used in more traditional survey instruments.

Duration analyses in political science often model nonproportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a useful, initial postestimation step, I argue that they are insufficient to identify the overall impact of a time-varying effect and may lead to faulty inference when a coefficient changes its sign. I show how even significant changes of a coefficient’s sign do not imply that the overall effect is reversed over time. In order to enable a correct interpretation of time-varying effects in this context, researchers should visualize their results with survivor functions. I outline how survivor functions are calculated for models with time-varying effects and demonstrate the need for such a nuanced interpretation using the prominent finding of a time-varying effect of mediation on interstate conflict. The reanalysis of the data using the proposed visualization methods indicates that the conclusions of earlier mediation research are misleading. The example highlights how survivor functions are an essential tool to clarify the ambiguity inherent in time-varying coefficients in event history models.

Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditions required for valid instruments. First, when cross-unit dependence exists in the endogenous predictor, other cross-unit relationships—spillovers and interdependence—likely exist as well and risk violations of the exclusion restriction. Second, spatial instruments produce simultaneity in the first-stage equation, as left-hand side outcomes are included as right-hand side predictors. Because the instrument and the endogenous variable are simultaneously determined, the exclusion restriction is, necessarily and by construction, violated. Taken together, these concerns lead us to conclude that spatial instruments are rarely, if ever, valid.

Multiple imputation (MI) is often presented as an improvement over listwise deletion (LWD) for regression estimation in the presence of missing data. Against a common view, we demonstrate anew that the complete case estimator can be unbiased, even if data are not missing completely at random. As long as the analyst can control for the determinants of missingness, MI offers no benefit over LWD for bias reduction in regression analysis. We highlight the conditions under which MI is most likely to improve the accuracy and precision of regression results, and develop concrete guidelines that researchers can adopt to increase transparency and promote confidence in their results. While MI remains a useful approach in certain contexts, it is no panacea, and access to imputation software does not absolve researchers of their responsibility to know the data.

Dion, Sumner, and Mitchell (2018) find that a published article is more likely to cite at least one female-authored paper if that article is itself authored by women. To complement their work, we study the number of times that an article in their data set is cited given that it has at least one female author. We find that articles with at least one female author are cited no more or less often than male-authored articles once we control for the publishing journal and the number of authors. The importance of controlling for author count in our model suggests that spurious correlation and/or self-citation might explain at least some of the gender differences found by Dion, Sumner, and Mitchell (2018).

The sole purpose of the enhanced standard analysis (ESA) is to prevent so-called untenable assumptions in Qualitative Comparative Analysis (QCA). One source of such assumptions can be statements of necessity. QCA realists, the majority of QCA researchers, have elaborated a set of criteria for meaningful claims of necessity: empirical consistency, empirical relevance, and conceptual meaningfulness. I show that once Thiem’s (2017) data mining approach to detecting supersets is constrained by adhering to those standards, no CONSOL effect of Schneider and Wagemann’s ESA exists. QCA idealists, challenging most of QCA realists’ conventions, argue that separate searches for necessary conditions are futile because the most parsimonious solution formula reveals the minimally necessary disjunction of minimally sufficient conjunctions. Engaging with this perspective, I address several unresolved empirical and theoretical issues that seem to prevent the QCA idealist position from becoming mainstream.

This letter compares the performance of multiple imputation and listwise deletion using a simulation approach. The focus is on data that are “missing not at random” (MNAR), in which case both multiple imputation and listwise deletion are known to be biased. In these simulations, multiple imputation yields results that are frequently more biased, less efficient, and with worse coverage than listwise deletion when data are MNAR. This is the case even with very strong correlations between fully observed variables and variables with missing values, such that the data are very nearly “missing at random.” These results recommend caution when comparing the results from multiple imputation and listwise deletion, when the true data generating process is unknown.

In recent years, political and social scientists have made increasing use of conjoint survey designs to study decision-making. Here, we study a consequential question which researchers confront when implementing conjoint designs: How many choice tasks can respondents perform before survey satisficing degrades response quality? To answer the question, we run a set of experiments where respondents are asked to complete as many as 30 conjoint tasks. Experiments conducted through Amazon’s Mechanical Turk and Survey Sampling International demonstrate the surprising robustness of conjoint designs, as there are detectable but quite limited increases in survey satisficing as the number of tasks increases. Our evidence suggests that in similar study contexts researchers can assign dozens of tasks without substantial declines in response quality.

In this comment on Dion, Sumner, and Mitchell’s article “Gendered Citation Patterns across Political Science and Social Science Methodology Fields,” I explore the role of changes in the disparities of citations to work written by women over time. Breaking down their citation data by era, I find that some of the patterns in citations are the result of the legacy of disparity in the field. Citations to more recent work come closer to matching the distribution of the gender of authors of published work. Although the need for more equitable practices of citation remains, the overall patterns are not quite as bad as Dion, Sumner, and Mitchell conclude.

Measuring the polarization of legislators and parties is a key step in understanding how politics develops over time. But in parliamentary systems—where ideological positions estimated from roll calls may not be informative—producing valid estimates is extremely challenging. We suggest a new measurement strategy that makes innovative use of the “accuracy” of machine classifiers, i.e., the number of correct predictions made as a proportion of all predictions. In our case, the “labels” are the party identifications of the members of parliament, predicted from their speeches along with some information on debate subjects. Intuitively, when the learner is able to discriminate members in the two main Westminster parties well, we claim we are in a period of “high” polarization. By contrast, when the classifier has low accuracy—and makes a relatively large number of mistakes in terms of allocating members to parties based on the data—we argue parliament is in an era of “low” polarization. This approach is fast and substantively valid, and we demonstrate its merits with simulations, and by comparing the estimates from 78 years of House of Commons speeches with qualitative and quantitative historical accounts of the same. As a headline finding, we note that contemporary British politics is approximately as polarized as it was in the mid-1960s—that is, in the middle of the “postwar consensus”. More broadly, we show that the technical performance of supervised learning algorithms can be directly informative about substantive matters in social science.