Factor structure of the Social Experience Questionnaire across time, sex, and grade among early elementary school children.
Bibliographic record
Abstract
Ample research suggests that peer victimization predicts social and psychological maladjustment, including emotional (e.g., anxiety, low self-esteem, and depression) and behavioral (e.g., aggression) problems among children. Thus, a reliable measure of peer victimization for research with young children is needed. The Social Experience Questionnaire-Self-Report (SEQ-S) has been widely used in existing research to assess children's victimization (Crick & Grotpeter, 1996). However, empirical support for the psychometric properties of the SEQ-S is limited by the methods used to evaluate it (i.e., exploratory as opposed to confirmatory analyses), by the lack of longitudinal data, and by the limited age ranges studied. This study examined the underlying factor structure of SEQ-S ratings across 3 time points in a sample of 830 early elementary school children using confirmatory factor analysis. The hypothesized model included 3 latent factors: overt victimization, relational victimization, and receipt of prosocial acts from peers. This model provided a good fit to the data at each time point. Although it is not clear that there is invariance, results indicate that invariance across time, sex, and grade could be present. Recommendations for continued use of the SEQ-S in future research on peer victimization with young children are discussed. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".