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The Self-Compassion Scale: Validation and Psychometric Properties within the Exploratory Structural Equation Modeling

Arman Rakhimov1, Anu Realo1,2, Nicole K Y Tang1

  • 1Department of Psychology, University of Warwick.

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The Self-Compassion Scale (SCS) total score effectively identifies depression, anxiety, and wellbeing. This study supports using the SCS total score in research and practice for measuring compassionate self-attitude.

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Area of Science:

  • Psychological assessment
  • Mental health research
  • Psychometrics

Background:

  • The Self-Compassion Scale (SCS) is widely used to measure compassionate self-attitude.
  • Controversy exists regarding the SCS's underlying factor structure.
  • Previous exploratory structural equation modeling (ESEM) studies favored a single-bifactor model.

Purpose of the Study:

  • To replicate and extend ESEM studies on SCS bifactor models in a UK adult sample.
  • To examine the validity and dimensionality of different bifactor models.
  • To determine if one or two SCS scores better predict mental health outcomes.

Main Methods:

  • Exploratory structural equation modeling (ESEM) was used to analyze bifactor models.
  • Two estimators (maximum likelihood and weighted least square mean and variance adjusted) were employed.
  • The predictive accuracy of SCS scores for depression, anxiety, and mental wellbeing was investigated.

Main Results:

  • Both single- and two-bifactor models demonstrated good fit to the data.
  • Only the single-bifactor model exhibited satisfactory convergent validity, criterion validity, and unidimensionality.
  • The total SCS score more accurately identified depression, anxiety, and high mental wellbeing.

Conclusions:

  • The single-bifactor model provides a valid representation of the SCS.
  • The total SCS score is recommended for use in future research and clinical practice.
  • This finding enhances the reliable application of the SCS in psychological assessment.