Deal with the Item 8 of Rosenberg Self Esteem Scale (1965) and Revalidate the Factor Structure: Based on Measuring Groups of Middle School Students
Bibliographic record
Abstract
Using Rosenberg Self Esteem Scale (1965), we have measured 1889 students in schools. Through correlation analysis, item analysis, exploratory factor analysis and confirmatory factor analysis, we study two different ways in dealing with the item 8 of Rosenberg Self Esteem Scale (Rosenberg, 1965), namely the score is counted according to the positive item method and deleting the item 8, explore factor models of the scale and verify the goodness of fit of different models. Our results show: (a) the item 8 should be reserved. It should adopt that the score is counted according to the positive item method. The score correlating with the total score is 0.33 (P < 0.01); (b)if the factor analysis uses two factors，the two factor correlation model has better goodness of fit (χ2/df=6.12, CFI=0.95, TLI=0.93, RMSEA=0.06), namely the two factor model can be used in the scale.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".