My research examines a central question in American politics: How do marginalized groups achieve political voice in systems shaped by exclusion? I address this by building new theoretical frameworks and computational methods that reveal patterns traditional approaches miss.
My work is organized around three interconnected strands that bridge American political development, democratic theory, and political behavior.
My work is organized around three interconnected strands that bridge American political development, democratic theory, and political behavior.
1. Democratic Theory & Measurement: The Herrenvolk Framework
This project provides a macro-level theory of American political development, arguing that the U.S. has functioned as a herrenvolk democracy—a system where democratic institutions operate for a dominant group while systematically excluding others.
Key Components:
The Herrenvolk State (Book Project): Under advanced review with major university presses. Develops a new Power-Sharing Index showing 130 years of near-zero multi-group power sharing in the U.S.
The Power-Sharing Index: A novel methodological approach that captures whether political power can actually transfer between demographic groups, revealing what standard democracy measures miss.
Grant Leadership: Supported by a $100,000 Democracy Initiative grant where I serve as Co-Principal Investigator, leading an interdisciplinary faculty working group.
This work bridges American political development, democratic theory, and the study of racial and ethnic politics.
Key Components:
The Herrenvolk State (Book Project): Under advanced review with major university presses. Develops a new Power-Sharing Index showing 130 years of near-zero multi-group power sharing in the U.S.
The Power-Sharing Index: A novel methodological approach that captures whether political power can actually transfer between demographic groups, revealing what standard democracy measures miss.
Grant Leadership: Supported by a $100,000 Democracy Initiative grant where I serve as Co-Principal Investigator, leading an interdisciplinary faculty working group.
This work bridges American political development, democratic theory, and the study of racial and ethnic politics.
2. Identity & Political Behavior: The Latino Case
I ground my theoretical work in empirical studies of how individuals navigate complex political identities, with a focus on the diverse Latino electorate.
Current Projects:
Beyond Binary Acculturation: Introducing the Dynamic Acculturation Model (DAM) through comparative cluster analysis, demonstrating that traditional frameworks misclassify 75% of Latinos who occupy hybrid cultural positions.
Navigating Political Cross-Pressures: Examining how different acculturation orientations shape political attitudes, particularly on issues like immigration where cultural identities intersect.
The Shifting Latino Electorate: Applying machine learning with SHAP interpretation to model evolving vote choice across the 2016-2024 elections and understand potential partisan realignment.
This research challenges simplistic models of political behavior by centering the complexity of identity formation and its political consequences.
Current Projects:
Beyond Binary Acculturation: Introducing the Dynamic Acculturation Model (DAM) through comparative cluster analysis, demonstrating that traditional frameworks misclassify 75% of Latinos who occupy hybrid cultural positions.
Navigating Political Cross-Pressures: Examining how different acculturation orientations shape political attitudes, particularly on issues like immigration where cultural identities intersect.
The Shifting Latino Electorate: Applying machine learning with SHAP interpretation to model evolving vote choice across the 2016-2024 elections and understand potential partisan realignment.
This research challenges simplistic models of political behavior by centering the complexity of identity formation and its political consequences.
3. Methodological Innovation: New Tools for Complex Questions
I develop and apply computational methods to answer questions that traditional approaches struggle to address.
Methodological Contributions:
Comparative Cluster Analysis: Formalizing a framework for moving beyond descriptive clustering to rigorous theory testing in political science.
Machine Learning Applications: Using Random Forest algorithms with SHAP interpretability to model complex, non-linear relationships in political behavior.
Survey Design & Measurement: Advanced training in psychometrics, scaling, and categorical data analysis through ICPSR.
Technical Expertise: R, Python, , Git Hub, LaTeX
Methodological Contributions:
Comparative Cluster Analysis: Formalizing a framework for moving beyond descriptive clustering to rigorous theory testing in political science.
Machine Learning Applications: Using Random Forest algorithms with SHAP interpretability to model complex, non-linear relationships in political behavior.
Survey Design & Measurement: Advanced training in psychometrics, scaling, and categorical data analysis through ICPSR.
Technical Expertise: R, Python, , Git Hub, LaTeX