Multiple Group IRT Measurement Invariance Analysis of the Forms of Self-Criticising/Attacking and Self-Reassuring Scale in Thirteen International Samples
Bibliographic record
Abstract
The purpose of this study was to examine the measurement invariance of the Forms of Self-Criticising/Attacking & Self-Reassuring Scale (FSCRS) in terms of Item Response Theory differential test functioning in thirteen distinct samples (N = 7714) from twelve different countries. We assessed differential test functioning for the three FSCRS subscales, Inadequate-Self, Hated-Self and Reassured-Self separately. 32 of the 78 pairwise comparisons between samples for Inadequate-Self, 42 of the 78 pairwise comparisons for Reassured-Self and 54 of the 78 pairwise comparisons for Hated-Self demonstrated no differential test functioning, i.e. measurement invariance. Hated-Self was the most invariant of the three subscales, suggesting that self-hatred is similarly perceived across different cultures. Nonetheless, all three subscales of FSCRS are sensitive to cross-cultural differences. Considering the possible cultural and linguistic differences in the expression of self-criticism and self-reassurance, future analyses of the meanings and connotations of these constructs across the world are necessary in order to develop or tailor a scale which allows cross-cultural comparisons of various treatment outcomes related to self-criticism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".