Comparability of an ADHD Latent Trait Between Groups: Disentangling True Between-Group Differences From Measurement Problems
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate measurement invariance (MI) for an ADHD latent trait across different sociodemographic groups (sex, age, and maternal education), IQs, and co-occurring psychiatric diagnoses. METHOD: Participants were 2,299 children aged 6 to 14 years. ADHD symptoms were assessed by parent report using the Development and Well-Being Assessment (DAWBA). MI was tested through multigroup confirmatory factor analysis and multiple indicators multiple causes models. RESULTS: In a bifactor model including a general ADHD factor and three specific factors (hyperactivity, inattention, and impulsivity), invariance properties were demonstrated and no individual items showed differential functioning. The ADHD general factor was higher in boys and in those with psychiatric disorders. Younger age predicted hyperactivity. Lower IQ and higher level of education of the mother predicted inattention. CONCLUSION: The ADHD trait, as measured by the DAWBA, functions in the same way, and with equivalent scale, revealing true differences in ADHD symptoms based on those.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".