Feature reduction
To determine a reduced number of features preserving the composition of clusters, we selected features with maximal amount of variance between clusters in Study 2’s dataset (due to its larger sample size) that had the largest effect sizes from the one-way ANOVAs using cluster as a between-group factor on questionnaire data items’ z-scores. We conducted a cluster analysis on the smaller set of features by applying a PCA to reduce the data to 2 dimensions, followed by a hierarchical agglomerative clustering and k-means analysis (the same set of analysis steps as in Studies1 and 2). This was conducted on half, one third, and one quarter of items within each scale. The solution with the lowest number of features that preserved cluster composition for the majority of features and had the highest mean silhouette value (0.570) was that for one-third of items from each scale (total of 19 items), which yielded comparable mean silhouette values (0.573) when applied to data from Study1 (using this reduced set of 19 features identified in Study 2 from between-cluster effect sizes). These items are highlighted in Table S3.