Many networks in political and social research are bipartite, connecting two distinct node types. A common example is cosponsorship networks, where legislators are linked through the bills they support. However, most bipartite network analyses in political science rely on statistical models fitted to a “projected” unipartite network. This approach can lead to aggregation bias and an artificially high degree of clustering, invalidating the study of group roles in network formation. To address these issues, we develop a statistical model of bipartite networks theorized to arise from group interactions, extending the mixed-membership stochastic blockmodel. Our model identifies groups within each node type that exhibit common edge formation patterns and incorporates node and dyad-level covariates as predictors of group membership and observed dyadic relations. We derive an efficient computational algorithm to fit the model and apply it to cosponsorship data from the United States Senate. We show that senators who were perfectly split along party lines remained productive and pass major legislation by forming non-partisan, power-brokering coalitions that found common ground through low-stakes bills. We also find evidence of reciprocity norms and policy expertise impacting cosponsorships. An open-source software package is available for researchers to replicate these insights.

In a time of unprecedented displacement, hostility toward refugees is widespread. Two common strategies refugee advocates pursue to counter hostility and promote inclusion are perspective-getting exercises and providing information that corrects misperceptions. In this study, we evaluate whether these strategies are effective across four outcomes commonly used to measure outgroup inclusion: warmth toward refugees, policy preferences, behavior, and beliefs about a common misperception concerning refugees. Using three studies with nearly 15,000 Americans, we find that information and perspective-getting affect different outcomes. We show that combining both interventions produces an additive effect on all outcomes, that neither strategy enhances the other, but that bundling the strategies may prevent backfire effects. Our results underscore the promise and limits of both strategies for promoting inclusion.

Despite admonitions to address attrition in experiments – missingness on Y – alongside best practices designed to encourage transparency, most political science researchers all but ignore it. A quantitative literature search of this journal – where we would expect to find the most conscientious reporting of attrition – shows low rates of discussion of the issue. We suspect that there is confusion on the link between when attrition occurs and the type of validity it threatens when present, and limited connection to and guidance on which estimands are threatened by different attrition patterns. This is all exacerbated by limited tools to identify, investigate, and report patterns attrition. We offer the R package – attritevis – to visualize attrition over time, by intervention, and include a step-by-step guide to identifying and addressing attrition that balances post hoc analytical tools with guidance for revising designs to ameliorate problematic attrition.

Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on long-standing assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature. Critically, our approach is systematic and powerful but also low cost; it requires researchers to enter relationships they observe in prior studies into a simple spreadsheet—a task accessible to new and experienced researchers alike. Our open-source R package enables researchers to leverage powerful network analysis while minimizing software-specific knowledge. We demonstrate this approach by reviewing redistricting literature.

Migrants are often scapegoated during public health crises. Can such crises create opportunities for migrant inclusion instead? As the COVID-19 pandemic unfolds, many refugee organizations have stepped up their outreach with stories of refugees helping out in the crisis. We have partnered with the country’s leading refugee advocate organizations to test whether solidarity narratives increase public engagement with refugee advocates. We employ a Facebook experimental design to evaluate the effectiveness of refugee narratives. We test whether (1) migrant narratives framed in the context of COVID-19, (2) COVID-19 migrant narratives targeted to more or less local communities, and (3) COVID-19 migrant narratives labeled as refugee vs. immigrant efforts enhance public engagement with refugee organizations. Our results indicate that migrant narratives framed in the context of COVID-19 do not motivate greater engagement than those that make no mention of the pandemic. Our results provide suggestive evidence that locally targeted efforts motivate greater engagement. Finally, we find no difference between the “refugee” and “immigrant” label, but we show that both labels can motivate greater engagement than ads that include neither. Importantly, this is true even in the context of COVID-19, an uncertain environment where worries of backlash might be warranted. These results suggest promising strategies for migrant policy organizations to promote engagement during and possibly after the pandemic.

Immigration is a highly polarized issue in the United States, and negative attitudes toward immigrants are common. Yet, almost all Americans are descended from people who originated outside the country, a narrative often evoked by the media and taught in school curricula. Can this narrative increase inclusionary attitudes toward migrants? We draw from scholarship showing that perspective-taking decreases prejudice toward out-groups to investigate whether reminding Americans about their own immigration history increases support for immigrants and immigration. We propose that priming family experiences can indirectly stimulate perspective-taking and induce empathy toward the out-group, which we test with three separate survey experiments conducted over two years. Our findings show that priming family history generates small but consistent inclusionary effects. These effects occur even among partisan subgroups and Americans who approve of President Trump. We provide evidence that increased empathy for immigrants constitutes one mechanism driving these effects.