Two is more valid than one, but is six even better? The factor structure of the Self-Compassion Scale (SCS)

Self-compassion refers to a non-evaluative, interconnected and mindful attitude towards oneself especially when facing difficulties or feelings of personal inadequacies. The Self-Compassion Scale (SCS) is a frequently used instrument designed to measure self-compassion either by using the six subscale scores, or by calculating a total score, averaged across all 26 items.

The purpose of this study is to examine the factor structure of the Self-Compassion Scale, and in particular, whether the widely used six-factor model and the unidimensional model can be confirmed.

The internal structure of the SCS was examined using confirmatory factor analysis (CFA). Six different models (a one-factor model, an oblique six-factor model, a higher-order model, an oblique two-factor model, a bi-factor model with one general factor (bifactor model) and a bi-factor model with two general factors, i.e. two-bifactor model) were tested in a sample of adolescents (n = 1725; 50.3% female; mean age = 16.56, SD = 1.95). All models were replicated using responses collected five months after the first data collection from 1497 students (W2), who were largely, but not completely, the same students involved in W1 data collection.

Fit indices for the two-factor model implied an acceptable fit, but none of the remaining models tested met the criteria for an adequate solution. Although the fit indices for the six-factor model suggested an acceptable fit to the data, in this model the negative components of the SCS were highly correlated with each other, especially with the over-identification factor.

The results of this study provide evidence to support the use of the separate self-compassion- and self-coldness -scores rather than the overall score of the SCS. Although the fit indices supported the six-factor model, the use of six subscale scores cannot be recommended on the basis of our results given the extremely high correlations within this model between some factors.