Validation of the State Self-Compassion Scale in a German Sample and its Relations to Psychological Well-being and Mental Health

Abstract Objectives This study aimed to translate and validate the State Self-Compassion Scale in its long (SSCS-L, 18 items) and short form (SSCS-S, 6 items) for German-speaking samples and to investigate its associations with psychological well-being and mental health. Method An online sample ( n = 1,436) completed the translated SSCS-L and other psychological state and trait measures. Factor structures were examined using Exploratory Structural Equation Modeling (ESEM). Associations between subscales of SSCS-L and other constructs were investigated using partial correlational network models. Results A 6-factor ESEM based on 16 items showed the best fit for the SSCS-L; a global self-compassion factor—and thus using a total score—was not supported. Subscales self-kindness and self-judgment showed acceptable to good internal consistency, all others only marginally acceptable or fair internal consistency. With the SSCS-S, a 2-factor ESEM fits best, representing positive compassionate and negative non-compassionate self-responding. The network model showed positive unique links between positive subscales of SSCS-L and predictors and indicators of well-being; and negative unique links between negative subscales and these indicators. Negative subscales of SSCS-L were positively related to mental distress, while positive subscales showed inverse associations. Conclusions We present the 16-item SSCS-L and 6-item SSCS-S as useful tools for assessing state self-compassion as a multidimensional construct in research and interventions. We recommend using the SSCS-L with its six and the SSCS-S with its two subscales, and advise researchers to check factor structure and reliability in their samples due to potential variability across contexts. Preregistration The study was preregistered in PsychArchives ( https://doi.org/10.23668/psycharchives.6665 ), with deviations reported in the Online Resources.