Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation of how multiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examination focuses on research in the major subfield of comparative and international political economy (CIPE) as an illustrative example. Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent five-year period in International Organization and World Politics, two of the leading subfield journals in CIPE. The outcome is striking: in almost half of the studies, key results “disappear” (by conventional statistical standards) when reanalyzed.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well-defined “retrospective intervention effect” based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.

Many political processes consist of a series of theoretically meaningful transitions across discrete phases that occur through time. Yet political scientists are often theoretically interested in studying not just individual transitions between phases, but also the duration that subjects spend within phases, as well as the effect of covariates on subjects’ trajectories through the process's multiple phases. We introduce the multistate survival model to political scientists, which is capable of modeling precisely this type of situation. The model is appealing because of its ability to accommodate multiple forms of causal complexity that unfold over time. In particular, we highlight three attractive features of multistate models: transition-specific baseline hazards, transition-specific covariate effects, and the ability to estimate transition probabilities. We provide two applications to illustrate these features.

The analysis of necessary conditions for some outcome of interest has long been one of the main preoccupations of scholars in all disciplines of the social sciences. In this connection, the introduction of Qualitative Comparative Analysis (QCA) in the late 1980s has revolutionized the way research on necessary conditions has been carried out. Standards of good practice for QCA have long demanded that the results of preceding tests for necessity constrain QCA's core process of Boolean minimization so as to enhance the quality of parsimonious and intermediate solutions. Schneider and Wagemann's Theory-Guided/Enhanced Standard Analysis (T/ESA) is currently being adopted by applied researchers as the new state-of-the-art procedure in this respect. In drawing on Schneider and Wagemann's own illustrative data example and a meta-analysis of thirty-six truth tables across twenty-one published studies that have adhered to current standards of good practice in QCA, I demonstrate that, once bias against compound conditions in necessity tests is accounted for, T/ESA will produce conservative solutions, and not enhanced parsimonious or intermediate ones.

The appropriateness of experiments for studying causal mechanisms is well established. However, the ability of an experiment to isolate the effect of emotion has received less attention, and in this letter we lay out a guide to manipulating and tracing the impact of emotions. Some experimental manipulations are straightforward. Manipulating an emotion like anxiety is less obvious. There is no magic “political anxiety pill” and placebo that can be randomly assigned to participants. While the magic political anxiety pill is still elusive, we advocate using multiple manipulations, extensive pretesting, and mediation models. These approaches have allowed us to situate a discrete emotional experience in a complex political environment.

Political scientists frequently wish to test hypotheses about the effects of specific emotions on political behavior. However, commonly used experimental manipulations tend to have collateral effects on emotions other than the targeted emotion, making it difficult to ascribe outcomes to any single emotion. In this letter, we propose to address this problem using causal mediation analysis. We illustrate this approach using an experiment examining the effect of emotion on dyadic trust, as measured by the trust game. Our findings suggest that negative emotions can decrease trust, but only if those negative emotions make people feel less certain about their current situation. Our results suggest that only anxiety, a low-certainty emotion, has a negative impact on trust, whereas anger and guilt, two emotions that differ in their control appraisals but induce the same high level of certainty, appear to have no effect on trusting behavior. Importantly, we find that failing to use causal mediation analysis would ascribe a positive effect of anxiety on trust, demonstrating the value of this approach.

By adding an informational component to the judicial review, Rogers (2001) argued that an independent court can be created and maintained by a legislature. This influential article, however, has one important mistake in its game-theoretical model that changes the equilibrium results and ultimately undermines the theoretical contribution to the discipline. The legislature no longer enjoys informational benefits by having an independent court.