The psychometric properties of the Quantitative-Checklist for Autism in Toddlers (Q-CHAT) as a measure of autistic traits in a community sample of Singaporean infants and toddlers
Bibliographic record
Abstract
BACKGROUND: There is growing research evidence that subclinical autistic traits are elevated in relatives of individuals with autism spectrum disorder (ASD), continuously distributed in the general population and likely to share common etiology with ASD. A number of measures have been developed to assess autistic traits quantitatively in unselected samples. So far, the Quantitative-Checklist for Autism in Toddlers (Q-CHAT) is one of very few measures developed for use with toddlers as young as 18 months, but little is known about its measurement properties and factor structure. METHODS: The present study examined internal consistency, factor structure, test-retest stability, and convergent validity of the Q-CHAT in a sample of toddlers in Singapore whose caregivers completed the Q-CHAT at 18 (n = 368) and 24 months (n = 396). RESULTS: Three factors were derived accounting for 38.1 % of the variance: social/communication traits, non-social/behavioral traits, and a speech/language factor. Internal consistency was suboptimal for the total and speech/language scores, but acceptable for the social/communication and non-social/behavioral factor scores. Scores were generally stable between 18 and 24 months. Convergent validity was found with the Pervasive Developmental Disorders subscale of the Child Behavior Checklist (CBCL) completed by caregivers when their children were 24 months. Q-CHAT total scores in this sample were higher than those reported in other unselected samples from the UK. CONCLUSIONS: The Q-CHAT was found to have a three-factor structure, acceptable internal consistency for its two main factor scores (social/communication and non-social/behavioral), normally distributed scores in an unselected sample, and similar structure and measurement properties as those reported in other published studies. Findings are discussed in relation to existing literature and future directions for the validation of the Q-CHAT.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".