In recent years, political scientists increasingly have used data-science tools to research political processes, positions, and behaviors. Because both domestic and international politics are grounded in oral and written texts, computerized text analysis (CTA)—typically based on natural-language processing—has become one of the most notable applications of data-science tools in political research. This article explores the promises and perils of using CTA methods in political research and, specifically, the study of international relations. We highlight fundamental analytical and methodological gaps that hinder application and review processes. Whereas we acknowledge the significant contribution of CTA to political research, we identify a dual “engagement deficit” that may distance those without prior background in data science: (1) the tendency to prioritize methodological innovation over analytical and theoretical insights; and (2) the scholarly and political costs of requiring high proficiency levels and training to comprehend, assess, and use advanced research models.

Despite winning the presidency in 2016, Donald Trump alleged “millions of illegal votes” and other election fraud. He continued using this rhetoric throughout his tenure as president and ultimately suggested that if he did not win reelection in 2020, it would be because it somehow was stolen from him. Through an original survey experiment, this article explores how such allegations of fraud influence the public’s attitudes toward the conduct of elections, election outcomes, representation, and democracy as a whole. In doing so, we found that respondents expressed significantly and substantively more negative attitudes toward elections and democracy after being exposed to claims of fraud (even without evidence). Additionally, Republican identifiers were more likely than Democrats or Independents to doubt that their vote was counted fairly. These results bear important implications for our current understanding of politics in the United States.

Former President Donald Trump’s unsubstantiated vote-fraud claims following the 2020 presidential election divided the Republican Party. Numerous Republicans supported Trump’s efforts to overturn the election, others did not. These futile attempts reached a flashpoint during the January 6 attack on the United States Capitol. Even in the wake of such violence, many House Republicans continued to amplify Trump’s baseless claims by voting to exclude the election results from Arizona and Pennsylvania. This article analyzes these roll-call votes to determine the likely motivations for why some House Republicans were still willing to support Trump’s position following the Capitol riot. We then replicate our analysis with the January 13 impeachment and the May 19 vote to establish a bipartisan National Commission to Investigate the January 6 Attack on the United States Capitol Complex (January 6 Commission) to investigate the insurrection. Our findings indicate the relevance of constituent preferences, Trump’s popularity, legislator ideology, and the racial diversity of constituents represented by Republicans.

Political scientists are increasingly using digitized documents from archives. This article is a practical introduction to doing digital archival research. First, it explains when and why political scientists use evidence from archival research. Second, it argues that the remote accessibility of digitized records provides new opportunities for comparative and transnational research. However, digital archival research also risks aggravating five types of biases that pose challenges for qualitative, quantitative, interpretive, and mixed-methods research: survival, transfer, digitization, and reinforcement bias at the level of record collection and source bias at the level of record creation. Third, this article offers concrete strategies for anticipating and mitigating these biases by walking readers through the experience of entering, being in, and leaving an archive, while also underscoring the importance of learning the structure of an archive. The article concludes by addressing the ethical implications to archival research as a type of field research for political scientists.

Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical strategies to make transitioning from Excel, SPSS, or Stata to R or Python for data analytics less challenging and more exciting. First, it discusses two approaches for making computational methods accessible: showing the big picture and walking through the workflow. Second, a step-by-step guide for a typical course is provided using three examples: learning programming fundamentals, wrangling messy data, and communicating data analysis.

There is a substantial amount of research examining bias in the peer-review process and its influence on the quality and content of political science journal articles. However, there is limited research examining how students peer review other undergraduate research for publication. To better understand the predictors of manuscript evaluations and build on prior literature, this study examines seven years of undergraduate peer evaluations submitted to the Pi Sigma Alpha Undergraduate Journal of Politics from 2013 to 2020. Empirical analyses reveal that a peer reviewer’s prior service on the editorial board (i.e., experience) and race are consistently and significantly associated with manuscript evaluations. By examining how undergraduate peer reviewers assess anonymized manuscripts, this research reveals potential biases in the political science peer-review process. Additionally, the benefits of undergraduate students participating in the peer-review process are explored and discussed.

Undergraduate research labs have long been recognized as having educational and professional benefits, but much less attention has been given to how they can promote diversity and inclusion. Without a conscientious effort to promote these goals, labs are likely to replicate and perpetuate existing inequalities. This article discusses our experiences and lessons from launching an undergraduate research lab in a political science department at a research-oriented state university. It concludes with suggestions for other departments interested in starting undergraduate research labs. Promoting diversity and inclusion by working with undergraduates is unlikely if faculty are recruiting students individually outside of a lab, if the burden is on students to approach faculty, or if labs do not take practical steps to make this happen.

The COVID-19 pandemic has forced governments worldwide—many that previously prioritized austerity—to approve large relief packages. Political economy tells us that politicians will try to profit from this electorally, but much remains unknown about precisely how pandemic relief might influence voting intentions. Then-President Donald Trump foregrounded this question early in the pandemic by becoming the first US president to physically place his name on Internal Revenue Service relief checks mailed to citizens. By leveraging a nationally representative survey whose timing achieved quasi-experimental variation in the receipt of payments both with and without Trump’s name physically on them, this study asks: Can a president successfully win support through physical personalization of the payments? Yes, the study finds. Receiving a physically personalized check in the mail is associated with a much greater self-reported likelihood of voting for the president, with gains mainly from partisan outgroups. No clear effect is found for unpersonalized electronic transfers. These findings withstand multiple robustness checks.

The National Science Foundation (NSF) recently replaced its long-standing Political Science Program with two new programs: the Security and Preparedness Program and the Accountable Institutions and Behavior Program. This article evaluates the likely impact of the reform by way of original survey data. The NSF Program Change Survey asked past recipients of the Political Science Program Standard Grant to evaluate their own previously funded proposals according to the new NSF program descriptions. Respondents were asked whether they would apply for the same research project under the new thematic programs and, if they would, whether they believed it would be necessary to change the framing or substance of their proposal. Data from the survey suggest that the new NSF program themes are likely to discourage some political scientists from applying, while encouraging many more applicants to shift the framing or substance of their research to accommodate the new call for proposals. In particular, the new Security and Preparedness Program carries significant consequences for new knowledge production.

With the increase of data availability, knowledge about quantitative methods is more important than ever. Indeed, students need quantitative methods to understand quantitative academic research, to conduct this research themselves, and to increase their employability in the labor market. Yet, students often experience quantitative methods as difficult to learn and, in turn, instructors find the topic difficult to teach. To make both learning and teaching easier, this article provides seven concrete suggestions regarding the teaching of quantitative methods.

Matias López and Juan Luna (2021) challenged my comparative analysis of populism’s threat to democracy, its reliance on institutional factors (coupled with conjunctural opportunities), and especially the inference about US democracy’s immunity to populist suffocation. However, their emphasis on structuralist and culturalist factors, which would suggest the vulnerability of the United States, is strikingly selective, theoretically unconvincing, and empirically problematic. López and Luna’s methodological improvement of my analysis does not alter the substantive findings or overturn my sanguine inference about US democracy’s likely resilience. Only their further modifications yield more pessimistic scenarios, but those adjustments stand on shaky theoretical and empirical ground. Indeed, the experiences of 2020–2021 corroborate my theory and its comparative lessons. The US institutional framework held firm and foiled the insistent attempts of President Trump and his most fervent followers to perpetuate the US populist in power. Consequently, US democracy continues to appear quite safe from populist strangulation.

We demonstrate how men and women political scientists in PhD-granting departments perceive the professional climates there. We find remarkable differences in how men and women perceive the “cultural” climates of their departments, such as the degree to which it is sexist, but not in how they perceive strictly collegial aspects of climate. We also demonstrate that these patterns characterize the perceptions of men and women at both junior and senior ranks. Contrary to some past research, we also find that climate perceptions do not have a general effect on faculty research productivity. Further, perceptions of high departmental sexism by women scholars does not degrade their research productivity.

This article builds on work by Devine and Kopko (2021) and Lacy and Burden (1999) who estimated a probit model of candidate choice from nationally representative survey data to determine the second choice of third-party voters. Using this model on 2020 election data, we show that the Libertarian candidate Jo Jorgenson probably cost Donald Trump victory in at least two states: Arizona and Georgia. Additionally, the popular-vote margin enjoyed by Joe Biden could have been between 260,000 and 525,000 fewer votes, using conservative estimates. The motivation for this article is to provide contrary evidence for two main misconceptions. First, that third-party candidates are “spoiling” elections for the Democrats. Our evidence clearly shows that third parties have the potential to hurt either of the two main parties; however, in 2020, it was Donald Trump who was hurt the most, although not consequentially. Second, some reformers believe that ranked-choice voting benefits the Democrats; again, we show that—all else being equal—in the 2020 presidential election, it was the Republicans who would have benefited by the change in rules because the majority of third-party votes went to the Libertarian candidate, whose voters prefer Republicans over Democrats 60% to 32%.

Most research on diversity within political methodology focuses on gender while overlooking racial and ethnic gaps. Our study investigates how race/ethnicity and gender relate to political science PhD students’ methodological self-efficacy, as well as their general academic self-efficacy. By analyzing a survey of 300 students from the top 50 US-based political science PhD programs, we find that race and ethnicity correlate with quantitative self-efficacy: students identifying as Black/African American and as Middle Eastern/North African express lower confidence in their abilities than white students. These gaps persist after accounting for heterogeneity among PhD programs, professional and socioeconomic status, and preferred methodological approach. However, small bivariate gender gaps disappear in multivariate analysis. Furthermore, gaps in quantitative self-efficacy may explain racial/ethnic disparities in students’ broader academic self-efficacy. We argue that the documented patterns likely lead to continued underrepresentation of marginalized groups in the political methodology student body and professoriate.

This article introduces how to teach an interactive, one-semester-long statistics and programming class. The setting also can be applied to shorter and longer classes as well as introductory and advanced courses. I propose a project-based seminar that also encompasses elements of an inverted classroom. As a result of this combination, the seminar supports students’ learning progress and also creates engaging virtual classes. To demonstrate how to apply a project-based seminar setting to teaching statistics and programming classes, I use an introductory class to data wrangling and management with the statistical software program R. Students are guided through a typical data science workflow that requires data management and data wrangling and concludes with visualizing and presenting first research results during a simulated mini-conference.

This article assesses whether a gender gap in political science, identified in the international literature, also is present in the context of Italian political science. The literature has mostly centered on the comparison of female publication rates in international journals with the academic workforce in the United States, but this raises an issue of data comparability. As an alternative strategy to avoid comparability biases, this study focuses on the analysis of a single national case: Italy. The article evaluates to what extent the “glass-ceiling” effect persists for political scientists who intend to publish their contributions. By analyzing data on articles published between 2015 and 2020 by the three major Italian political science journals, this contribution shows that (1) the proportion of published articles written by female authors is lower than that of male authors; (2) the hypothesis that the lower female proportion depends on a lower female presence in the field of political science is refuted; (3) there is little collaboration between men and women; (4) there is a correlation between the presence of female editors in scientific journals and the proportion of female-authored articles; and (5) gender differences are reflected in women’s academic career progression.

The relationship between digital technology and politics is an important phenomenon that remains poorly understood due to several structural problems. A key issue is the lack of adequate research infrastructures or the lack of access. This article discusses the challenges many social scientists face and presents the infrastructure we built in Switzerland to overcome them, using COVID-19 as an example. We conclude by discussing seven lessons we learned: automatization is key; avoid data hoarding; outsource some parts of the infrastructure but not others; focus on substantive questions; share data in the context of collaborations; engage in targeted public outreach; and collaboration is more promising than competition. We hope that our experience is helpful to other researchers pursuing similar goals.

The response to the coronavirus pandemic in the United States has shown that even a serious public health crisis cannot escape the lens of partisanship. The literature shows that most Republicans have viewed the coronavirus as less serious than their Democratic counterparts. This study demonstrates that this partisan gap extends to the real behavior of the public during, and after, the coronavirus state lockdowns. Using location data from mobile phones, we find that county-level partisanship predicts compliance with state shutdown orders, even when controlling for local COVID-19 intensity. Further, the magnitude of this effect is stronger than that of other explanatory variables, such as age, education, and population density. These results show that partisan beliefs can affect behavior regarding issues that are not overtly political, even behaviors that could put one or others at risk.

This article examines some aspects of campaign strategies that have risen in importance in twenty-first–century US political competition that are highlighted by the events of January 6, 2021.