3 research outputs found
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties
Scaling methods have long been utilized to simplify and cluster
high-dimensional data. However, the latent spaces derived from these methods
are sometimes uninformative or unable to identify significant differences in
the data. To tackle this common issue, we adopt an emerging analysis approach
called contrastive learning. We contribute to this emerging field by extending
its ideas to multiple correspondence analysis (MCA) in order to enable an
analysis of data often encountered by social scientists -- namely binary,
ordinal, and nominal variables. We demonstrate the utility of contrastive MCA
(cMCA) by analyzing three different surveys of voters in Europe, Japan, and the
United States. Our results suggest that, first, cMCA can identify substantively
important dimensions and divisions among (sub)groups that are overlooked by
traditional methods; second, for certain cases, cMCA can still derive latent
traits that generalize across and apply to multiple groups in the dataset;
finally, when data is high-dimensional and unstructured, cMCA provides
objective heuristics, above and beyond the standard results, enabling more
complex subgroup analysis.Comment: Both authors contributed equally to the paper and listed
alphabetically. This manuscript is currently under revie