The Myth of the Zero-Sum: Rethinking Acculturation in American Politics

30 June 2026, Version 2
This content is an early or alternative research output and has not been peer-reviewed at the time of posting.

Abstract

Political science has often measured acculturation as if heritage and American cultural attachments are mutually exclusive, so that stronger attachment to one implies weaker attachment to the other. This paper tests that assumption directly. Applying comparative cluster analysis to the 2006 Latino National Survey (N = 4,785), with replication in the 2016 Collaborative Multiracial Post-Election Survey, I identify four distinct acculturation orientations among Latino voters. Two are the familiar poles of assimilationist and culture-affirming attachment. The other two are hybrid orientations, bicultural and demicultural, and together they describe more than three-quarters of the sample. These findings show that the limits of binary acculturation are not only theoretical but empirical. The dominant pattern among Latino voters is not movement toward one culture and away from another, but the blending of both.

Keywords

Latinos
Cluster Analysis
Non-Parametric
Political Behavior
Acculturation
Biculturalism
Latino Identity
Cross-Pressures

Supplementary materials

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Appendices
Description
The supplementary appendices provide full methodological documentation for the analyses reported in the manuscript. Appendix A presents a systematic review of 23 acculturation studies in political science (1994–2020), documenting universal reliance on proxy measures and conflation of acculturation with assimilation. Appendix B examines measurement limitations across major Latino political surveys, demonstrating that only the 2006 Latino National Survey captures the independent identity strength measures required to detect hybrid orientations. Appendix C reports full sample characteristics and survey item wording. Appendix D provides complete technical documentation of the cluster analysis methodology, including model selection criteria, validation results across five algorithms, and cluster differentiation statistics.
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