Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties
This work addresses the need for social scientists to uncover specific patterns within groups in high-dimensional data, though it is incremental as it adapts an existing method to a new domain.
The authors tackled the problem of identifying latent subgroups in political parties by extending contrastive learning to multiple correspondence analysis (cMCA), enabling analysis of binary, ordinal, and nominal data. Their results show that cMCA can identify important dimensions and divisions among subgroups overlooked by traditional methods, and derive latent traits that emphasize subgroups seen moderately in traditional analyses.
Scaling methods have long been utilized to simplify and cluster high-dimensional data. However, the general latent spaces across all predefined groups derived from these methods sometimes do not fall into researchers' interest regarding specific patterns within groups. To tackle this issue, we adopt an emerging analysis approach called contrastive learning. We contribute to this growing field by extending its ideas to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists -- containing binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing two different surveys of voters in the U.S. and U.K. Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among subgroups that are overlooked by traditional methods; second, for other cases, cMCA can derive latent traits that emphasize subgroups seen moderately in those derived by traditional methods.