Gender bias in autism screening: measurement invariance of different model frameworks of the Autism Spectrum Quotient

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Belcher, H., Uglik-Marucha, N., Vitoratou, S., Ford, R. M., & Morein-Zamir, S. (2023). Gender bias in autism screening: measurement invariance of different model frameworks of the Autism Spectrum Quotient. BJPsych Open, 9(5), e173-e173. https://doi.org/10.1192/bjo.2023.562

Publication date: 1 Sep 2023 Added to AutiHub: 8 Sep 2026 Type: Article Article language: English
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Authors

Publication authors
5
Publication authors identified as autistic
1 / 5 (20.0%)
  1. Hannah Belcher *
  2. Nora Uglik-Marucha
  3. Silia Vitoratou
  4. Ruth M. Ford
  5. Sharon Morein-Zamir

Abstract

Background The Autism Spectrum Quotient is a popular autism screening tool recommended for identifying potential cases of autism. However, many women with autism demonstrate a different presentation of traits to those currently captured by screening measures and assessment methods, such as the Autism Spectrum Quotient. Aims Different models of the Autism Spectrum Quotient have been proposed in the literature, utilising different items from the original 50-item scale. Within good-fitting models, the current study aimed to explore whether these items assess autistic traits similarly across men and women. Method Seventeen Autism Spectrum Quotient models were identified from the literature. Using the responses of a large sample of adults from the UK general population (5246 women, 1830 men), confirmatory factor analysis was used to evaluate the fit of each model. Measurement invariance with respect to gender, adjusting for age, was explored in the 11 model frameworks that were found to have satisfactory fit to our data. Results It emerged that only two items were gender invariant (non-biased), whereas for the remaining items, the probability of endorsement was influenced by gender. In particular, women had a higher probability of endorsing items relating to social skills and communication. Conclusions If the items of the Autism Spectrum Quotient indeed reflect autism-related traits, those items should be rephrased to ensure they do not present a gender-related bias. This is vital for ensuring more timely diagnoses and support for all people with autism.

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These indicators describe the bibliography cited by this publication. An author name is counted each time it appears in one cited reference, so the same person can be counted more than once. Names not yet linked to an author already present in AutiHub are treated as unknown, not as non-autistic. Last computed: 12 Sep 2026 01:04.

Cited references
65
With a DOI
60
Without a DOI, from raw bibliography text
5
1 / 65 (1.5%) cited references include at least one author identified as autistic.
References with data to complete
3 / 65 (4.6%)
References with detected author names
62 / 65 (95.4%)
Without detected author names
3
Without a structured title
1
Without a stable identifier
4
References with raw author names still to review
1
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
296
From DOI or external metadata
296
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
3
92 / 296 (31.1%) names are linked to an author already present in AutiHub. 204 / 296 (68.9%) names are not yet linked.
Names linked to a person identified as autistic
1 / 296 (0.3%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 1 / 92 (1.1%). Distinct people identified as autistic: 1 / 236 (0.4%).
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