Is Self-Compassion Universal? Support for the Measurement Invariance of the Self-Compassion Scale Across Populations
Bibliographic record
Abstract
The Self-Compassion Scale (SCS) is a widely used measure to assess the trait of self-compassion, and, so far, it has been implicitly assumed that it functions the same way across different groups. This assumption needs to be explicitly tested to ascertain that no measurement biases exist. To address this issue, the present study sought to systematically examine the generalizability of the bifactor exploratory structural equation modeling operationalization of the SCS via tests of measurement invariance across a wide range of populations, varying according to features such as student or community status, gender, age, and language. Secondary data were used for this purpose and included a total of 18 samples and 12 different languages ( N = 10,997). Multigroup analyses revealed evidence for the configural, weak, strong, strict, and latent variance–covariance of the bifactor exploratory structural equation modeling operationalization of the SCS across different groups. These findings suggest that the SCS provides an assessment of self-compassion that is psychometrically equivalent across groups. However, findings comparing latent mean invariance found that levels of self-compassion differed across groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".