Representativeness of autistic samples in studies recruiting through social media
This publication is included in the Autistic Autism Scholarship Project. one author of this publication is identified as autistic in the project.
About the autistic author markerRødgaard, E. M., Jensen, K., Miskowiak, K. W., & Mottron, L. (2022). Representativeness of autistic samples in studies recruiting through social media. Autism Research, 15(8), 1447-1456. https://doi.org/10.1002/aur.2777
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Authors
Abstract
Abstract Survey‐based research with recruitment through online channels is a convenient way to obtain large samples and has recently been increasingly used in autism research. However, sampling from online channels may be associated with a high risk of sampling bias causing findings not to be generalizable to the autism population. Here we examined autism studies that have sampled on social media for markers of sampling bias. Most samples showed one or more indicators of sampling bias, in the form of reversed sex ratio, higher employment rates, higher education level, lower fraction of individuals with intellectual disability, and later age of diagnosis than would be expected when comparing with for example population study results from published research. Findings from many of the included studies are therefore difficult to generalize to the broader autism population. Suggestions for how research strategies may be adapted to address some of the problems are discussed. Lay Summary Online surveys offer a convenient way to recruit large numbers of participants for autism research. However, the resulting samples may not fully reflect the autism population. Here we investigated the samples of 36 autism studies that recruited participants online and found that the demographic composition tended to deviate from what has been reported about the autism population in previous research. The results may thus not be generalizable to autism in general.
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Cited bibliography overview
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: 15 Sep 2026 07:45.
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- 61
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- References with detected author names
- 62 / 64 (96.9%)
- Without detected author names
- 2
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- 2
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- 2
- References with raw author names still to review
- 2
- References with external metadata lookup issues
- 0
- From DOI or external metadata
- 359
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- 0
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- 0
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- 2
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