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Scaling methods have long been utilized to simplify and clusterhigh-dimensional data. However, the general latent spaces across all predefinedgroups derived from these methods sometimes do not fall into researchers'interest regarding specific patterns within groups. To tackle this issue, weadopt an emerging analysis approach called contrastive learning. We contributeto this growing field by extending its ideas to multiple correspondenceanalysis (MCA) in order to enable an analysis of data often encountered bysocial scientists -- containing binary, ordinal, and nominal variables. Wedemonstrate the utility of contrastive MCA (cMCA) by analyzing two differentsurveys of voters in the U.S. and U.K. Our results suggest that, first, cMCAcan identify substantively important dimensions and divisions among subgroupsthat are overlooked by traditional methods; second, for other cases, cMCA canderive latent traits that emphasize subgroups seen moderately in those derivedby traditional methods.

Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties