Investigating the Theoretical Structure of the Differential Ability Scales—Second Edition Through Hierarchical Exploratory Factor Analysis
Bibliographic record
Abstract
When the Differential Ability Scales–Second Edition (DAS-II) was developed, the instrument’s content, structure, and theoretical orientation were amended. Despite these changes, the Technical Handbook did not report results from exploratory factor analytic investigations, and confirmatory factor analyses were implemented using selected subtests across the normative age groups from the total battery. To address these omissions, the present study investigated the theoretical structure of the DAS-II using principal axis factoring followed by the Schmid–Leiman procedure with participants from the 5- to 8-year-old age range to determine the degree to which the DAS-II theoretical structure proposed in the Technical Handbook could be replicated. Unlike other age ranges investigated where at most 14 subtests were administered, the entire DAS-II battery was normed on participants aged 5 to 8 years, making it well suited to test the full instrument’s alignment with theory. Results suggested a six-factor solution that was essentially consistent with the Cattell–Horn–Carroll (CHC)-based theoretical structure suggested by the test publisher and simple structure was attained. The only exception involved two subtests (Picture Similarities and Early Number Concepts) that did not saliently load on a group factor. Implications for clinical practice are discussed.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".