Development, Construct Validation, and Normalization of a New Early Childhood Self-Regulation Assessment Scale
Bibliographic record
Abstract
Abstract Although there are many tools for assessing young children’s self-regulation according to varied conceptual definitions and purposes, the purpose of this study was to develop, validate, and norm a Self-Regulation Assessment Scale for Early Childhood (SASEC) for directly evaluating observed behaviors of young children in naturalistic play experiences within the normal preschool environment. An exploratory sequential mixed methods research design was used. The 315 participants included 153 parents and 15 educators for the qualitative component and 147 children ages 3–5 years for the quantitative component. The analytical steps of a qualitative grounded theory research design were applied to adult participant interviews and focus group discussions, which culminated in 12 scale items for measuring a child’s ability to initiate, modulate, and cease behaviors, tasks, or activities of varied complexities, social configurations, and limiting conditions. Children’s SASEC scores were assessed via video recordings of play behaviors in naturalistic settings. Based on factor analysis results, the SASEC items constitute a single construct. According to the results of hierarchical linear modeling and multiple linear regression, preschool children’s SASEC scores can be compared to the SASEC mean and standard deviation regardless of various demographic variables. Implications and recommendations for future work include having early childhood educators, child and youth care practitioners, counselors, parents and families, social workers, behavioral sciences researchers, and policy makers use the SASEC to measure young children’s self-regulation while developing or monitoring the efficacy of generalized enhancement programs and individualized treatment plans.
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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 | 0.024 | 0.038 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".