Evaluating the psychometrics of the SSIS SEL in a socio‐economically diverse sample
Bibliographic record
Abstract
Abstract The increasing interest in implementing social‐emotional learning (SEL) interventions within schools calls for more reliable and valid assessment tools to measure the effectiveness of SEL programs. We investigated the factor structure and psychometric properties of the Social Skills Improvement System – Social‐Emotional Learning Edition (SSIS SEL) with a group of ethnically and socio‐economically diverse grade 3 students in Ontario, Canada (n = 427). The SSIS SEL is an age‐normed measure used for the evaluation of interventions and prevention initiatives. Data collected included both teacher‐report and student self‐report measures. The factor structure proposed by the scale developers was not replicated using this smaller single grade sample. However, the study demonstrates evidence of good construct and convergent validity supporting the novel factor structure. Results from this study suggest that more research on the SSIS SEL's application in real‐world intervention research programs is needed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".