Estimating Consensus Ideal Points Using Multi-Source Data

2026-01-08

AI Summary

Existing estimates of congressional candidates’ ideal points derive from multiple sources that lack systematic integration, making it difficult to assess whether they reflect a common underlying political dimension and often introducing endogeneity concerns in empirical analyses. This study proposes Consensus Multidimensional Scaling (CoMDS), a novel method that treats diverse ideal point estimates as distinct observations of a shared latent political construct. By integrating multiple data sources, CoMDS extracts their common structural component to construct a unified and interpretable representation of the political space. The approach not only identifies consensus dimensions across disparate measures but also includes diagnostic tools to enhance the reliability and comparability of empirical research in political science.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

In the advent of big data and machine learning, researchers now have a wealth of congressional candidate ideal point estimates at their disposal for theory testing. Weak relationships raise questions about the extent to which they capture a shared quantity -- rather than idiosyncratic, domain-specific factors -- yet different measures are used interchangeably in most substantive analyses. Moreover, questions central to the study of American politics implicate relationships between candidate ideal points and other variables derived from the same data sources, introducing endogeneity. We propose a method, consensus multidimensional scaling (CoMDS), which better aligns with how applied scholars use ideal points in practice. CoMDS captures the shared, stable associations of a set of underlying ideal point estimates and can be interpreted as their common spatial representation. We illustrate the utility of our approach for assessing relationships within domains of existing measures and provide a suite of diagnostic tools to aid in practical usage.

Problem

• ideal points
• consensus estimation
• endogeneity
• multi-source data
• political ideology

Innovation

• Consensus Multidimensional Scaling
• ideal point estimation
• multi-source data integration
• endogeneity
• spatial representation