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Social scientists and statisticians often use aggregate data to predict individual-level behavior because the latter are not always available. Various statistical techniques have been developed to make inferences from one level (e.g., precinct) to another level (e.g., individual voter) that minimize errors associated with ecological inference. While ecological inference has been shown to be highly problematic in a wide array of scientific fields, many political scientists and analysis employ the techniques when studying voting patterns. Indeed, federal voting rights lawsuits now require such an analysis, yet expert reports are not consistent in which type of ecological inference is used. This is especially the case in the analysis of racially polarized voting when there are multiple candidates and multiple racial groups. The eiCompare package was developed to easily assess two of the more common ecological inference methods: EI and EI:R×C. The package facilitates a seamless comparison between these methods so that scholars and legal practitioners can easily assess the two methods and whether they produce similar or disparate findings.

What motivated Latinos to turnout in 2020 in the middle of a global health pandemic that has devastated their community financially, physically and mentally? How might we explain Latino support for each one of the Presidential candidates in the context of these crises? In this paper, we tackle these questions through an investigation of the factors that drove Latino turnout in 2020 and what might explain Latino favorability for Joe Biden and Donald Trump. To contextualize these findings, we compare these results to the 2016 election. We find that the most predictive factors of Latino turnout in 2020 were perceived group discrimination and mobilization efforts by campaigns and other organizations. We also find that Latino candidate preference in 2020 can be best explained by issue prioritization. Latinos for whom the economy was the most important issue were more likely to support Donald Trump. However, Latinos for whom COVID-19 and racism towards the Latino community were the top pressing political priorities were more likely to favor Joe Biden. These findings continue to shed light on the diversity and heterogeneity of the Latino vote and speak to the significance of outreach efforts by political parties, candidates and community organizations.

Scholars and legal practitioners of voting rights are concerned with estimating individual-level voting behavior from aggregate-level data. The most commonly used technique, King’s ecological inference (EI), has been questioned for inflexibility in multiethnic settings or with multiple candidates. One method for estimating vote support for multiple candidates in the same election is called ecological inference: row by columns (R×C). While some simulations suggest that R×C may produce more precise estimates than the iterative EI technique, there has not been a comprehensive side-by-side comparison of the two methods using real election data that analysts and legal practitioners often rely upon in courts. We fill this void by comparing iterative EI and R×C models with a new statistical package—eiCompare—in a variety of R×C combinations including 2 candidates and 2 groups, 3 candidates and 3 groups, and up to 12 candidates and three groups and multiple candidates and four groups. Additionally, we examine the two methods with 500 simulated data sets that differ in combinations of heterogeneity, polarization, and correlation. Finally, we introduce a new model congruence score to aid scholars and voting rights analysts in the substantive interpretation of the estimates. Across all of our analyses, we find that both methods produce substantively similar results. This suggests that iterative EI and R×C can be used interchangeably when assessing precinct-level voting patterns in Voting Rights Act cases and that neither method produces bias in favor or against finding racially polarized voting patterns.