As the number of Asian American voters has increased with each election, more research is needed to understand the participation and voting patterns of this diverse electorate. This paper offers an analysis of Asian American political participation and vote choice preferences during the 2016 presidential election. The paper begins by addressing the concerns related to Asian American political incorporation. We disaggregate Asian Americans into three voting types—voters, those who are eligible to vote but are not registered, and those who are ineligible to vote—and compare the demographic differences found across these three groups. The second half of the paper turns to Asian American candidate preferences in the 2016 election. We find that voters who report high levels of media consumption and those with a strong sense of political efficacy were more likely to support Clinton. Our analysis of non-voters suggests that the potential incorporation of these Asian Americans would result in a continued base of support for the Democratic party.
Objective
This study aims to examine the patterns of Asian Americans’ partisanship acquisition by asking why and in what ways Asian Americans develop partisan affiliation with the Democratic and Republican Party.
Methods
Drawing on the 2008 National Asian American Survey data and employing logistic regression analysis, this research situates the analytical framework on socioeconomic attainment and political socialization, and tests the extent to which socioeconomic status, policy preferences, and race‐based political commonality constitute the directional components contributing to partisan orientation.
Results
We find that socioeconomic status cannot efficiently predict Asian Americans’ partisan preferences; instead, Asian Americans’ partisanship acquisition derives mainly from policy preferences and a sense of minority political commonality and racial identity.
Conclusion
Different from white and African Americans, where socioeconomic attainment and a sense of in‐group linked fate tend to work efficiently to predict their partisanship, in the Asian‐American context, partisan orientation is grounded in a combination of liberal policy preferences and race‐based political calculus. Specifically, liberal policy preferences and the sense of political commonality with blacks and Latinos tend to lay a concrete groundwork for partisan identification with the Democratic Party.
This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and GLS statistics are based on large sample statistical theory. However, violation of asymptotic sample size is ubiquitous in real applications of structural equation modeling (SEM), and ML and GLS goodness-of-fit tests in SEM often make incorrect decisions on the true model. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Proposed by Arruda and Bentler (2017), RGLS replaces a GLS weight matrix with a regularized one. Rediscovered by Hayakawa (2019), RLS replaces this weight matrix with one that derives from an ML function. Both of these methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A confirmatory factor analysis Monte Carlo simulation study with normal data was carried out to examine the statistical performance of these two methods at different sample sizes. Based on empirical rejection frequencies and empirical distributions of test statistics, we find that RLS and RGLS have equivalent performance when N≥70; whereas when N<70, RLS outperforms RGLS. Both methods clearly outperform ML and GLS with N≤400.
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