Recovering Latent Dimensions in Cross-National Democracy Measurements Through Robust Exploratory Graph Analysis

In this paper we extend the Exploratory Graph Analysis (EGA) framework by replacing the Pearson correlation matrix with a Nonparanormal (NPN) estimator derived from Spearman rank correlations, which recovers the latent Gaussian dependence under a broader model that accomodates for skewed and heavy-tailed marginals. We further enhance robustness and interpretability by combining bootstrap EGA with Non-Euclidean Fuzzy Relational Clustering on a dissimilarity matrix derived from consensus over output partitions, producing fuzzy rather than crisp assignments that capture overlaps and ambiguity. We apply the full pipeline to 37 indicators from the Varieties of Democracy (V-Dem) dataset covering 179 countries in 2024, coming from five different theoretical democracy indices. The recovered structure validates the theoretical indices while revealing some meaningful discrepancies.