The factor structure of the Broad Autism Phenotype Questionnaire in a non-clinical sample of Canadian undergraduate students
Bibliographic record
Abstract
Over the last several decades there has been increasing recognition that symptoms of Autism Spectrum Disorder (ASD) vary along a continuum from absent to present and mild to severe. Relatives of those with autism may present with subclinical traits that do not meet diagnostic thresholds, but may nevertheless reflect challenges like those of people diagnosed with ASD. The Broad Autism Phenotype (BAP) reflects a dimensional approach to ASD that includes characteristics of the ‘milder but qualitatively similar’ (Hurley et al, 2007) presentation that is often seen in family members of those with ASD (Happé & Frith, 2020; Robinson, Koenen et al., 2011) as well as the general population (Constantino & Todd, 2003). Understanding the characteristics of this subclinical group may be important for research and clinical contexts, including identifying affected individuals and selecting interventions that may be helpful for functional impairments. As such, accurate measurement of the BAP is critical. Various questionnaires have been used for assessing the BAP; however, the Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al., 2007) is one of the most widely used (Beaver, 2018). Despite its wide use, the limited existing psychometric research on the BAPQ is mixed. Some research suggests problems with its internal structure and construct validity, particularly with its Pragmatic Language subscale (e.g., Godoy-Gimenez et al., 2018; Sasson et al., 2013a). This study examines the factor structure and construct validity of the BAPQ in a sample of 899 Canadian undergraduate students who participated for course credit. Participants completed a series of online measures assessing the BAP, pragmatic language difficulties, executive functioning, and personality. Confirmatory factor analytic methods were used to examine its internal structure. Validity evidences based on relationships between BAPQ scores and other variables were examined. Four out of five fit indices examined suggested that the 3-factor model had unacceptable fit, with Pragmatic Language items contributing to misfit. Revision and/or deletion of certain Pragmatic Language items may improve model fit. Results of this study support the convergent validity of the BAPQ. The findings of this research add to the existing literature and may have implications for the use of the BAPQ.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".