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Psychol Assess ; 31(1): 27-45, 2019 Jan.
Article in English | MEDLINE | ID: mdl-30124303

ABSTRACT

This study examined the factor structure of the Self-Compassion Scale (SCS) using secondary data drawn from 20 samples (N = 11,685)-7 English and 13 non-English-including 10 community, 6 student, 1 mixed community/student, 1 meditator, and 2 clinical samples. Self-compassion is theorized to represent a system with 6 constituent components: self-kindness, common humanity, mindfulness and reduced self-judgment, isolation and overidentification. There has been controversy as to whether a total score on the SCS or if separate scores representing compassionate versus uncompassionate self-responding should be used. The current study examined the factor structure of the SCS using confirmatory factor analyses (CFA) and Exploratory Structural Equation Modeling (ESEM) to examine 5 distinct models: 1-factor, 2-factor correlated, 6-factor correlated, single-bifactor (1 general self-compassion factor and 6 group factors), and 2-bifactor models (2 correlated general factors each with 3 group factors representing compassionate or uncompassionate self-responding). Results indicated that a 1- and 2-factor solution to the SCS had inadequate fit in every sample examined using both CFA and ESEM, whereas fit was excellent using ESEM for the 6-factor correlated, single-bifactor and correlated 2-bifactor models. However, factor loadings for the correlated 2-bifactor models indicated that 2 separate factors were not well specified. A general factor explained 95% of the reliable item variance in the single-bifactor model. Results support use of the SCS to examine 6 subscale scores (representing the constituent components of self-compassion) or a total score (representing overall self-compassion), but not separate scores representing compassionate and uncompassionate self-responding. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


Subject(s)
Empathy , Psychometrics/statistics & numerical data , Self Concept , Adolescent , Adult , Aged , Aged, 80 and over , Factor Analysis, Statistical , Female , Humans , Male , Middle Aged , Psychometrics/instrumentation , Young Adult
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