🤖 AI Summary
Existing construct clustering methods (CCMs) exhibit insufficient accuracy in identifying construal groups within social survey data, primarily due to their neglect of response polarity (support/oppose) and underlying cognitive mapping mechanisms. To address this, we propose Bipolar Construct Analysis (BCA): (1) a polarity-aware similarity metric quantifying support–oppose response shift patterns; (2) a cognition-driven data generation model that explicitly formalizes the latent attitude–observed response mapping; and (3) a novel evaluation framework specifically designed for construct clustering. Theoretically, BCA breaks from conventional unipolar clustering paradigms by embedding bipolarity into both representation and inference. Empirically, multi-round simulations demonstrate BCA’s statistically significant superiority over state-of-the-art CCMs. When applied to real-world survey data, BCA uncovers previously obscured construal group structures—characterized by greater theoretical coherence and interpretability—thereby advancing both methodological rigor and substantive insight in survey-based social science.
📝 Abstract
Empirical research on extit{construals}--social affinity groups that share similar patterns of meaning--has advanced significantly in recent years. This progress is largely driven by the development of extit{Construal Clustering Methods} (CCMs), which group survey respondents into construal clusters based on similarities in their response patterns. We identify key limitations of existing CCMs, which affect their accuracy when applied to the typical structures of available data, and introduce Bipolar Class Analysis (BCA), a CCM designed to address these shortcomings. BCA measures similarity in response shifts between expressions of support and rejection across survey respondents, addressing conceptual and measurement challenges in existing methods. We formally define BCA and demonstrate its advantages through extensive simulation analyses, where it consistently outperforms existing CCMs in accurately identifying construals. Along the way, we develop a novel data-generation process that approximates more closely how individuals map latent opinions onto observable survey responses, as well as a new metric to evaluate the performance of CCMs. Additionally, we find that applying BCA to previously studied real-world datasets reveals substantively different construal patterns compared to those generated by existing CCMs in prior empirical analyses. Finally, we discuss limitations of BCA and outline directions for future research.