Internal Consistency, Test–Retest Reliability and Measurement Error of the Self-Report Version of the Social Skills Rating System in a Sample of Australian Adolescents
Bibliographic record
Abstract
The social skills rating system (SSRS) is used to assess social skills and competence in children and adolescents. While its characteristics based on United States samples (US) are published, corresponding Australian figures are unavailable. Using a 4-week retest design, we examined the internal consistency, retest reliability and measurement error (ME) of the SSRS secondary student form (SSF) in a sample of Year 7 students (N = 187), from five randomly selected public schools in Perth, western Australia. Internal consistency (IC) of the total scale and most subscale scores (except empathy) on the frequency rating scale was adequate to permit independent use. On the importance rating scale, most IC estimates for girls fell below the benchmark. Test-retest estimates of the total scale and subscales were insufficient to permit reliable use. ME of the total scale score (frequency rating) for boys was equivalent to the US estimate, while that for girls was lower than the US error. ME of the total scale score (importance rating) was larger than the error using the frequency rating scale. The study finding supports the idea of using multiple informants (e.g. teacher and parent reports), not just student as recommended in the manual. Future research needs to substantiate the clinical meaningfulness of the MEs calculated in this study by corroborating them against the respective Minimum Clinically Important Difference (MCID).
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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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".