Structural Validity By The Method Of Exploratory FactorAnalysis Of Wechsler Intelligence Scale For ChildrenFourth Edition
Bibliographic record
Abstract
The research aimed to verify the structural validity of the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV) on a sample of (149) male and female students of the first stage (from the first to fourth grades) using exploratory factor analysis. The results of the exploratory factor analysis showed six factors, the latent root of each factor reached more than (1), and the sum of its explanatory variance was (63.736), and most of the sub-tests were saturated on the first factor, with an explanatory variance ratio (16,217), and this is what the researcher called the general factor. The second factor was saturated by six tests with a percentage of Explained variance (11.627), and the third factor was saturated with six tests, with an explanatory variance ratio (10.310), while the fourth factor was saturated with three tests, with an explanatory variance ratio (9.567), As for the fifth factor, five tests were saturated with a percentage of explained variance (8.508), and the sixth factor was saturated with five tests with an explained percentage of variance (7.506). In contrast to the first factor, the five factors (from the second to the sixth) could not be named, as they included tests that measured different factors according to the authors of the scale. The results of the research are in agreement with the findings of the Kush & Canivez study (2021), the study of Gary et al. (2020), the study of Muhammad (2013) and the study of Abdullah and Ahmed (2009), and they differ with the findings of the study of Chen et al. (2020), and the study of Canivez, Watkins and McGill( 2019), Koch and Kanitz study (2019
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".