Examining the Factor Structure of the Self-Compassion Scale in Four Distinct Populations: Is the Use of a Total Scale Score Justified?

ABSTRACT

This study examined the factor structure of the Self-Compassion Scale (SCS) using a bifactor model, a higher order model, a 6-factor correlated model, a 2-factor correlated model, and a 1-factor model in 4 distinct populations: college undergraduates (N = 222), community adults (N = 1,394), individuals practicing Buddhist meditation (N = 215), and a clinical sample of individuals with a history of recurrent depression (N = 390). The 6-factor correlated model demonstrated the best fit across samples, whereas the 1- and 2-factor models had poor fit. The higher order model also showed relatively poor fit across samples, suggesting it is not representative of the relationship between subscale factors and a general self-compassion factor. The bifactor model, however, had acceptable fit in the student, community, and meditator samples. Although fit was suboptimal in the clinical sample, results suggested an overall self-compassion factor could still be interpreted with some confidence. Moreover, estimates suggested a general self-compassion factor accounted for at least 90% of the reliable variance in SCS scores across samples, and item factor loadings and intercepts were equivalent across samples. Results suggest that a total SCS score can be used as an overall mesure of self-compassion.

Acknowledgments

We are grateful to Willem Kuyken for his comments on a draft of this article and the trial team for allowing us to use the data.

Funding

The clinical sample examined in this study was drawn from the PREVENT Trial, a project funded by the National Institute for Health Research Health Technology Assessment Programme (Project Number 08/56/01). This trial is reported in full in the Lancet (doi:http://dx.doi.org/10.1016/S0140-6736(14)62222-4).

Notes

¹ Normal theory maximum likelihood (ML) estimation has been shown to produce accurate parameter estimates for CFAs with ordered variables having five or more categories (Beauducel & Herzberg, 2006; Rhemtulla, Brosseau-Liard, & Savalei, 2012). Nonetheless, MLR estimation was used to adequately correct for underestimated standard errors and inaccurate test statistics that tend to occur with ordered categorical variables when using ML estimation (Rhemtulla et al., 2012).

² Statistical significance was assessed using z scores associated with each of the loadings. The z scores are calculated by dividing the loading value by its respective standard error.