Testing Measurement Invariance across Groups of Children with and without Attention-Deficit/ Hyperactivity Disorder: Applications for Word Recognition and Spelling Tasks
Bibliographic record
Abstract
Although studies have consistently demonstrated that children with attention-deficit/hyperactivity disorder (ADHD) perform significantly lower than controls on word recognition and spelling tests, such studies rely on the assumption that those groups are comparable in these measures. This study investigates comparability of word recognition and spelling tests based on diagnostic status for ADHD through measurement invariance methods. The participants (n = 1,935; 47% female; 11% ADHD) were children aged 6 to 15 with normal IQ (≥ 70). Measurement invariance was investigated through Confirmatory Factor Analysis and Multiple Indicators Multiple Causes models. Measurement invariance was attested in both methods, demonstrating the direct comparability of the groups. Children with ADHD were 0.51 s.d. lower in word recognition and 0.33 s.d. lower in spelling tests than controls. Results suggest that differences in performance on word recognition and spelling tests are related to true mean differences based on ADHD diagnostic status. Implications for clinical practice and research 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 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".