In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff determines a treatment that affects the mean-regression function. This paper generalizes this usual “one-score mean RD” in three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This generalization is motivated by (i) many multiple-score RD cases, (ii) the full-effect identification needing the partial effects to be separated, and (iii) informative quantile/mode regression functions. We establish identification for multiple-score RD (MRD), and propose simple estimators that become “local difference in differences” in case of double scores. We also provide an empirical illustration where partial effects exist.

The popularity of online surveys has increased the prominence of using sampling weights to enhance claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in survey experiment analysis: should they be used? If so, which estimators are preferred? We offer practical advice, rooted in the Neyman–Rubin model, for researchers working with survey experimental data. We examine simple, efficient estimators, and give formulas for their biases and variances. We provide simulations that examine these estimators as well as real examples from experiments administered online through YouGov. We find that for examining the existence of population treatment effects using high-quality, broadly representative samples recruited by top online survey firms, sample quantities, which do not rely on weights, are often sufficient. We found that sample average treatment effect (SATE) estimates did not appear to differ substantially from their weighted counterparts, and they avoided the substantial loss of statistical power that accompanies weighting. When precise estimates of population average treatment effects (PATE) are essential, we analytically show poststratifying on survey weights and/or covariates highly correlated with outcomes to be a conservative choice. While we show substantial gains in simulations, we find limited evidence of them in practice.

Network analysis has typically examined the formation of whole networks while neglecting variation within or across networks. These approaches neglect the particular roles actors may adopt within networks. While cross-sectional approaches for inferring latent roles exist, there is a paucity of approaches for considering roles in longitudinal networks. This paper explores the conceptual dynamics of temporally observed roles while deriving and introducing a novel statistical tool, the ego-TERGM, capable of uncovering these latent dynamics. Estimated through an Expectation–Maximization algorithm, the ego-TERGM is quick and accurate in classifying roles within a broader temporal network. An application to the Kapferer strike network illustrates the model’s utility.

Accumulated evidence identifies discernible gender gaps across many dimensions of professional academic careers including salaries, publication rates, journal placement, career progress, and academic service. Recent work in political science also reveals gender gaps in citations, with articles written by men citing work by other male scholars more often than work by female scholars. This study estimates the gender gap in citations across political science subfields and across methodological subfields within political science, sociology, and economics. The research design captures variance across research areas in terms of the underlying distribution of female scholars. We expect that subfields within political science and social science disciplines with more women will have smaller gender citation gaps, a reduction of the “Matthew effect” where men’s research is viewed as the most central and important in a field. However, gender citation gaps may persist if a “Matilda effect” occurs whereby women’s research is viewed as less important or their ideas are attributed to male scholars, even as a field becomes more diverse. Analysing all articles published from 2007–2016 in several journals, we find that female scholars are significantly more likely than mixed gender or male author teams to cite research by their female peers, but that these citation rates vary depending on the overall distribution of women in their field. More gender diverse subfields and disciplines produce smaller gender citation gaps, consistent with a reduction in the “Matthew effect”. However, we also observe undercitation of work by women, even in journals that publish mostly female authors. While improvements in gender diversity in academia increase the visibility and impact of scholarly work by women, implicit biases in citation practices in the social sciences persist.

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).

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.

This note offers an introduction to electromagnetic signal propagation models, which can be used to model terrestrial radio and television signal strength across space. Such data are useful to social scientists interested in identifying the effects of mass media broadcasts when (i) individual-level data on media exposure do not exist or when (ii) media exposure, while observed, is not exogenous. We illustrate the use of electromagnetic signal propagation models by creating a signal strength measure of military-controlled radio stations during the 2012 coup in Mali.