Revealing Principal Components, Patterns, and Structural Gaps in Health Security among High-Income Countries: A Comparative Analysis Using PCA and a Multi-Scenario Clustering Approach.

3.1 Principal Component Analysis with Varimax Rotation: Factor extraction and indicator structuring The principal component analysis (PCA) results confirmed the dataset’s suitability for dimensionality reduction and latent structure identification (see Supplementary File: Principal Component Extraction). 39 Bartlett’s test of sphericity was highly significant (χ 2 = 1614.8, df = 36, p < 0.01), indicating sufficient intercorrelations among the indicators to justify factor analysis.

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