Psychometric Properties of the Autism-Spectrum Quotient for Assessing Low and High Levels of Autistic Traits in College Students.
Likert scoring makes the college AQ more reliable, but still give it right before your study task and treat the trait label as temporary.
01Research in Context
What this study did
The team tested two ways to score the Autism-Spectrum Quotient in college students. They compared the usual yes-or-no format with a 1-to-4 Likert scale.
Participants took the survey twice, weeks apart. Researchers checked which scoring method gave steadier results.
What they found
Likert scoring won. It produced higher internal consistency and better test-retest reliability.
Yet both methods still wobbled when labeling the same student as high or low on autistic traits over time.
How this fits with other research
Schanding et al. (2012) also tweaked cut-offs, but on teacher forms for younger kids. They lowered SRS and SCQ thresholds to catch more true cases in elementary and middle-school classrooms.
Liu et al. (2022) and Nwokolo et al. (2024) likewise fine-tuned SCQ cut-offs, one for Chinese children and one for Nigerian teens. All three papers agree: small scoring changes boost accuracy.
Sappok et al. (2017) warn that one fixed SCQ cut-off fails across cultures and IDD severity levels. Together these studies show that questionnaire tweaks matter at every age.
Why it matters
If you use the Autism-Spectrum Quotient in college research, switch to Likert scoring and give it the same day as your experimental task. The scale becomes more reliable, but remember that trait labels can still shift over time, so avoid treating one score as a permanent diagnosis.
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02At a glance
03Original abstract
The current study systematically investigated the effects of scoring and categorization methods on the psychometric properties of the Autism-Spectrum Quotient. Four hundred and three college students completed the Autism-Spectrum Quotient at least once. Total scores on the Autism-Spectrum Quotient had acceptable internal consistency and test-retest reliability using a binary or Likert scoring method, but the results were more varied for the subscales. Overall, Likert scoring yielded higher internal consistency and test-retest reliability than binary scoring. However, agreement in categorization of low and high autistic traits was poor over time (except for a median split on Likert scores). The results support using Likert scoring and administering the Autism-Spectrum Quotient at the same time as the task of interest with neurotypical participants.
Journal of autism and developmental disorders, 2017 · doi:10.1007/s10803-017-3109-1