Research

In the past two decades, a growing number of ranked-choice voting (RCV) elections have been conducted in various jurisdictions across the United States. However, tabulated results of RCV have been reported and stored in widely different styles across places and years, making it infeasible for researchers to perform systematic analyses of vote tabulations. We introduce ARCHIVE: a dataset of standardized tabulated results for over 7600 round-level candidate vote counts from 514 American RCV elections, 2004-2024. To construct the dataset, we develop a methodological procedure based on large language models that semi-automatically collect, standardize, and store candidate vote counts while instantly validating the resulting information. Our dataset releases multiple levels of data, including election metadata, round-level attributes, and candidate-level information with consistent election identifiers, allowing users to address key questions in electoral competition under RCV. Our project and user interface are also available on our corresponding website.

Social scientists often use ranking questions to study people’s opinions and preferences. However, little is understood about the general nature of measurement errors in such questions, let alone their statistical consequences and what researchers can do about them. We introduce a statistical framework to improve ranking data analysis by addressing measurement errors in ranking questions. First, we characterize measurement errors from random responses—arbitrary and meaningless responses based on a wide range of random patterns. We then quantify bias due to random responses, show that the bias may change our conclusion in any direction, and clarify why item order randomization alone does not solve the statistical issue. Next, we introduce our methodology based on two key design-based considerations: item order randomization and the addition of an “anchor” ranking question with known correct answers. They allow researchers to learn about the direction of the bias and estimate the proportion of random responses, enabling our bias-corrected estimators. We illustrate our methods by studying the relative importance of people’s partisan identity compared to their racial, gender, and religious identities in American politics. We find that about 30% of respondents offered random responses and that these responses may affect our substantive conclusions.

How does candidate order on the ballot affect voting behavior when voters rank candidates? I extend the analysis of ballot order effects to electoral systems with ordinal ballots, where voters rank candidates, including ranked-choice voting (RCV). First, I discuss two types of ballot order effects, including “position effects”—voters vote for specific candidates because of their ballot positions—and “pattern ranking”—voters rank candidates geometrically given their grid-style ballots. Next, I discuss experimental designs for identifying and estimating these effects based on ballot order randomization. Moreover, I illustrate the proposed methods by using survey and natural experiments based on mayoral and congressional RCV elections in 2022. I find that while voters seem less susceptible to specific ballot positions, ballot design can still impact voters’ ranking behavior via pattern ranking. This work has implications for ballot design, survey research, and ranking data analysis.

The crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies point out that in the presence of inattentive respondents, the conventional estimator of the prevalence of a sensitive attribute is biased toward 0.5. To remedy this problem, we propose a simple design-based bias correction using an anchor question that has a sensitive item with known prevalence. We demonstrate that we can estimate and correct for the bias arising from inattentive respondents without measuring individual-level attentiveness. We also offer extensions including sensitivity analysis, weighting, multivariate regressions, and tools for power analysis and parameter selection. Our method can be implemented through the open-source software cWise.

Understanding when and why minority candidates emerge and win in particular districts has important implications for redistricting and the Voting Rights Act. I introduce a quantitatively predictive logical model of minority candidate emergence and electoral success. I show that the model can predict about 90% of minority candidate emergence and 95% of electoral success using data on Louisiana mayoral elections and state legislative general elections. The model can answer questions about minority representation in redistricting and voting rights cases and can be implemented through the open-source software logical.