This study was conducted to establish cut-off scores for the subscales of the Caregiving System Scale (CSS). Two samples of Italian adults (N's = 682 and 227) completed the CSS. In the first sample, K-means node clustering and ROC curve analyses were conducted. Four caregiving profiles were identified and cut-off scores were calculated for classifying participants into these profiles. In the second sample, participants completed the CSS and the Attachment Style Questionnaire. Findings supported the presence of unique CSS profiles and meaningful connections between them and attachment orientations. This work offers a method for determining cut-off scores when gold-standard measures needed to run ROC curve analyses are unavailable.
Using the K-Means Node Clustering Method and ROC Curve Analysis to Define Cut-Off Scores for the Caregiving System Scale / Colledani, D.; Mikulincer, M.; Shaver, P. R.; Meneghini, A. M.. - In: INTERNATIONAL JOURNAL OF PSYCHOLOGY. - ISSN 0020-7594. - 60:2(2025). [10.1002/ijop.70038]
Using the K-Means Node Clustering Method and ROC Curve Analysis to Define Cut-Off Scores for the Caregiving System Scale
Colledani D.;
2025
Abstract
This study was conducted to establish cut-off scores for the subscales of the Caregiving System Scale (CSS). Two samples of Italian adults (N's = 682 and 227) completed the CSS. In the first sample, K-means node clustering and ROC curve analyses were conducted. Four caregiving profiles were identified and cut-off scores were calculated for classifying participants into these profiles. In the second sample, participants completed the CSS and the Attachment Style Questionnaire. Findings supported the presence of unique CSS profiles and meaningful connections between them and attachment orientations. This work offers a method for determining cut-off scores when gold-standard measures needed to run ROC curve analyses are unavailable.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
