A Meta-Analysis of Measures of Self-Esteem for Young Children: A Framework for Future Measures
Bibliographic record
Abstract
The objective of this study was to synthesize information from literature on measures of the self in young children to create an empirical framework for developing future methods for measuring this construct. For this meta-analysis, all available preschool and early elementary school self-esteem studies were reviewed. Reliability was used as the criterion variable and the predictor variables represented different aspects of methodology that are used in testing an instrument: study characteristics, method characteristics, subject characteristics, measure characteristics, and measure design characteristics. Using information from two analyses, the results indicate that the reliability of self-esteem measures for young children can be predicted by the setting of the study, number of items in the scale, the age of the children being studied, the method of data collection (questionnaires or pictures), and the socioeconomic status of the children. Age and number of items were found to be critical features in the development of reliable measures for young children. Future studies need to focus on the issues of age and developmental limitations on the complicated problem of how young children actually think about the self and what methods and techniques can aid in gathering this information more accurately.
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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 | 0.077 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.023 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".