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Political scientists have been using individuals' self-reported efforts to try to influence the votes of others as one indicator of political activism for more than a half-century. However, in spite of this widespread use, very little is known about the motivations of interpersonal persuasion. This dissertation examines why some people try to influence the votes of others during the course of their everyday political conversations, while others are content to discuss politics without trying to persuade. Although attempts to persuade are often treated as a form of campaign participation with a goal of influencing the outcome of the election, I find that the motivations for persuasion are more internal and interpersonal than are the motivations for other forms of campaign involvement. I argue that interpersonal persuasion should be treated as a form of discursive participation, with consequences for our understanding of public opinion and deliberation.I use three large-scale survey datasets to examine interpersonal persuasion as a distinct form of political participation: the 2012 American National Election Studies (Ch. 3), the Youth-Parent Political Socialization Panel (Ch. 4), and the 2008 National Annenberg Election Studies Phone Survey (Ch. 5). Each dataset has distinct advantages in design and content that allow for examination of particular features of the motivations of persuasion. Using a variety of statistical methods and data sources, the overall argument is that attempts at persuasion have relatively little to do with campaigns and elections, and much more to do with individual orientations towards politics and social norms about political discussion. In particular, I find that organizational membership and campaign mobilization efforts do not provide the driving force for attempts to persuade that characterize other forms of political activity. Additionally, persuasive behavior does not ebb and flow in expected ways relative to the campaign cycle or the competitiveness of races. Rather, internal (but not external) efficacy, political capital (e.g., political interest, attention to political news), and exposure to disagreement and attempts to persuade by other social contacts are highly predictive of persuasive behavior. Persuasive behavior also declines over the life cycle, unlike many other forms of participation, and it is not a stepping stone to other more costly or time-intensive forms of participation. Therefore, persuasive behavior is equally or better understood within a framework of discursive participation, where it is a mechanism for people to develop their political identities, form political opinions, and process political events within the context of their social networks.

We show that a common method of predicting individuals' race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter's neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%.

ABSTRACTWomen earn approximately half of all bachelor's degrees in political science but they comprise only 22% of full professors. Scholars have offered various likely explanations and proposed many interventions to improve women's advancement. This article reviews existing research regarding the effectiveness of these interventions. We find that many of the proposed interventions have yet to be fully evaluated. Furthermore, some of the policies that have been evaluated turn out to be ineffective. Women's mentoring and networking workshops are the most promising of the fully tested interventions. The potential for failure underscores the need for additional evaluation of any proposed intervention before widespread implementation.

Polarization and participation are often connected in the political science literature, though sometimes the causality runs participation to polarization and sometimes the causality runs in the reverse direction. In some accounts there is an expectation that increasing participation and increasing polarization generate an ongoing spiral effect. In this paper we evaluate the over-time relationships between polarization and participation by assessing evidence in existing panel and aggregate data. We find that people with more extreme attitudes are more likely to participate in politics. However, only one particular form of participation—persuading others—appears to predict later levels of polarization. Therefore, only persuasion has the necessary correlation and temporal ordering for a feedback loop with more extreme ideology. The implication is that the discipline should pay more attention to interpersonal persuasion as a form of participation in American politics.

AbstractLow political support for religious minority groups in the United States is often explained as a matter of social distance or unfamiliarity between religious traditions. Observable differences between beliefs and behaviors of religious minority groups and the cultural mainstream are thought to demarcate group boundaries. However, little scholarship has examined why some practices become symbolic boundaries that reduce support for religious accommodation in public policy, while nearly identical practices are tolerated. We hypothesize that politics is an important component of the process by which some religious practices are transformed into demarcations between "us" and "them." We conduct an original survey experiment in which people are exposed to an identical policy demand—women-only swim times at a local public pool—attributed to three different religious denominations (Muslim, Jewish, and Pentecostal). We find that people are less supportive of women-only swim times when the requesting religion is not a part of their partisan coalition.

AbstractIdeal point models have become a powerful tool for defining and measuring the ideology of many kinds of political actors, including legislators, judges, campaign donors, and members of the general public. We extend the application of ideal point models to the public using a novel data source: real-time reactions to statements by candidates in the 2012 presidential debates. Using these reactions as inputs to an ideal point model, we estimate individual-level ideology and evaluate the quality of the measure. Debate reaction ideal points provide a method for estimating a continuous, individual-level measure of ideology that avoids survey response biases, provides better estimates for moderates and the politically unengaged, and reflects the content of salient political discourse relevant to viewers' attitudes and vote choices. As expected, we find that debate reaction ideal points are more extreme among respondents who strongly identify with a political party, but retain substantial within-party variation. Ideal points are also more extreme among respondents who are more politically interested. Using topical subsets of the debate statements, we find that ideal points in the sample are more moderate for foreign policy than for economic or domestic policy.

AbstractWe propose and explore the possibility that language models can be studied as effective proxies for specific human subpopulations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the "algorithmic bias" within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this propertyalgorithmic fidelityand explore its extent in GPT-3. We create "silicon samples" by conditioning the model on thousands of sociodemographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and sociocultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.

Social media sites are often blamed for exacerbating political polarization by creating "echo chambers" that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats' attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online.