This study investigates the Contingencies of Self-Worth Scale (CSWS) in a sample of 680 university students from a network perspective. We estimated regularized partial correlations among seven... Show moreThis study investigates the Contingencies of Self-Worth Scale (CSWS) in a sample of 680 university students from a network perspective. We estimated regularized partial correlations among seven CSWS domains: family support, competition, appearance, God's love, academic competence, virtue and other's approval. Competition – academic competence and competition – appearance represent the strongest connections in the network. Mean node predictability (shared variance with surrounding nodes) is 0.25. Appearance and academic competence were the most central (i.e., interconnected) domains in the network. Future studies should explore the network structure of self-worth in other healthy adult samples, and also in people with psychopathology. We provide the anonymized dataset as well as the full code in the supplementary materials to ensure complete reproducibility of the results. Show less
The aim of this work is to perform a network analysis on the French adaptation of the interpersonal reactivity index (IRI) scale from a large Belgian database and provide additional information for... Show moreThe aim of this work is to perform a network analysis on the French adaptation of the interpersonal reactivity index (IRI) scale from a large Belgian database and provide additional information for the construct of empathy. We analyze a database of 1973 healthy young adults who were queried on the IRI scale. A regularized partial correlation network is estimated. In the visualization of the model, items are displayed as nodes, edges represent regularized partial correlations between the nodes. Centrality denotes a node's connectedness with other nodes in the network. The spinglass algorithm and the walktrap algorithm are used to identify communities of items, and state-of-the-art stability analyses are carried out. The spinglass algorithm identifies four communities, the walktrap algorithm five communities. Positive edges are found among nodes belonging to the same community as well as among nodes belonging to different communities. Item 14 (“Other people's misfortunes do not usually disturb me a great deal”) shows the highest strength centrality score. The network edges and node centrality order are accurately estimated. Network analysis highlights interesting connections between indicators of empathy; how these results impact empathy models must be assessed in further studies. Show less
