Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One study by Whelpley et al. (2026), commonly used selection tools including general mental ability tests, personality inventories, and situational judgment tests produced subgroup differences unfavorable to autistic applicants. The primary keyword for this post is qualitative analysis of hiring tests; this post explains what Whelpley et al. measured (June 6-9, 2022), what they found (published August 18, 2026), and how AI-enabled qualitative research can translate those numeric signals into better, job-relevant hiring designs.
Key Takeaways
According to the PLOS One study (Whelpley et al., 2026), commonly used hiring tests can reproduce the same adverse outcomes for autistic applicants that earlier work attributed to interviews.
- Whelpley et al., 2026 collected responses from June 6-9, 2022, with 107 autistic and 99 neurotypical respondents.
- Whelpley et al., 2026 reported GMA means of 5.96 for neurotypical and 4.73 for autistic respondents in June 2022 (Cohen’s d = 0.52).
- Whelpley et al., 2026 reported SJT means of 4.24 for neurotypical and 0.57 for autistic respondents in June 2022 (Cohen’s d = 0.84).
- Whelpley et al., 2026 concluded that "commonly used selection tools... may nonetheless result in adverse impact for autistic applicants if used in hiring decisions, " calling for assessment redesign rather than blind tool substitution.
What happened and how the study measured it
Direct answer: Whelpley et al., 2026 compared autistic and neurotypical adults on three standard selection tools and found consistent subgroup differences unfavorable to autistic respondents.
Whelpley et al., 2026 recruited two samples via Amazon Mechanical Turk, one neurotypical sample (n = 99, mean age 37.8) and one autistic sample (n = 107, mean age 35.1), with data collected from June 6, 2022, to June 9, 2022, as reported in PLOS One.
Whelpley et al., 2026 administered a 50-item Five-Factor personality inventory, the 16-item ICAR-16 general mental ability (GMA) measure, and a 12-stem situational judgment test (SJT) validated for customer service; the study reports internal reliabilities and mean differences in PLOS One.
Whelpley et al., 2026 used standardized mean comparisons and multivariate regressions to test whether SJT differences persisted after controlling for GMA, personality, gender, and age; the regression showed autism remained a significant negative predictor of SJT score in the PLOS One results.
Limitations noted by Whelpley et al., 2026 in PLOS One include lower-than-expected reliability for personality scales in the autistic sample and the SJT being developed and validated in a predominantly neurotypical workforce.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| June 6-9, 2022 | Sample sizes | Autistic n = 107, Neurotypical n = 99 | Study approximated an applicant-like pool (Whelpley et al., 2026) |
| June 2022 | GMA mean score | Neurotypical 5.96, Autistic 4.73 (d = 0.52) | Moderate-large difference on ICAR-16 (Whelpley et al., 2026) |
| June 2022 | SJT mean score | Neurotypical 4.24, Autistic 0.57 (d = 0.84) | Large effect favoring neurotypical respondents (Whelpley et al., 2026) |
| August 18, 2026 | Publication | PLOS One (Whelpley et al., 2026) | Conclusions and data publicly available via PLOS One |
Implications for HR teams and selection researchers
Direct answer: Organizations should not assume standard tests automatically increase equity for autistic applicants; instead, selection design must target job-relevant skills and avoid conflating social inference with task ability.
Whelpley et al., 2026 in PLOS One show that personality inventories, GMA tests, and SJTs were each associated with subgroup differences unfavorable to autistic applicants, indicating that replacing interviews with these tools can reproduce adverse impact.
Whelpley et al., 2026 recommend emphasizing task-based assessments such as work samples and assessment centers for roles where interpersonal inference is not central, because those approaches evaluate observable job performance rather than normative social judgments.
Practitioner testimony cited in Whelpley et al., 2026 (for example, SAP’s hiring programs) highlights why companies frame neurodiverse talent as strategic: SAP reportedly said “innovation comes from the edges, ” which organizations cited as part of a project-based evaluation approach.
How Evidano helps teams translate these findings into inclusive hiring
Problem: Test scores show disparities but not why
Answer: AI-enabled qualitative analysis can surface the evaluative cues and question framings that drive subgroup differences.
Whelpley et al., 2026 show numeric gaps (for example SJT d = 0.84), but the PLOS One data and OSF repository require qualitative unpacking to see whether scenarios, response wording, or assumed social norms cause misalignment for autistic respondents.
Solution: Thematic and cross-segment analysis with Evidano
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano can ingest SJT item text, candidate open responses, and interview transcripts, then produce thematic codes, frequency counts, and cross-segment comparisons (e.g., autistic vs neurotypical) to reveal which items disproportionately trigger confusion or negative scoring.
Evidano’s features such as transcription and AI chat over documents let teams iterate on prompt wording and simulate candidate interpretations; see the Evidano features page for relevant capabilities.
Outcome: Design fairer, job-relevant assessments
Answer: Using AI-enabled qualitative insights lets teams redesign SJTs and personality items to remove irrelevant social inference and align items to core tasks.
Evidano users can move from 'tool substitution' to 'assessment design' by combining quantitative disparities (as reported in PLOS One) with qualitative evidence about candidate interpretation, enabling work-sample and simulation formats that Whelpley et al., 2026 recommend.
FAQ: qualitative analysis of hiring tests
Do SJTs inherently disadvantage autistic applicants?
Direct answer: Not inherently, but SJTs that rely on social judgment or interpersonal inference can disadvantage autistic applicants, according to Whelpley et al., 2026 in PLOS One.
Whelpley et al., 2026 report that SJT group differences persisted after controlling for GMA and personality, suggesting that SJT content and required social inference contributed to the disparity rather than cognitive ability alone.
Are GMA tests a fair alternative to interviews for autistic candidates?
Direct answer: GMA tests are not a guaranteed fix, because Whelpley et al., 2026 found mean GMA differences in their sample (June 2022) and cautioned about sample-specific effects.
Whelpley et al., 2026 reported GMA means of 5.96 (neurotypical) and 4.73 (autistic) with Cohen’s d = 0.52 in June 2022, and they noted that population-level GMA differences are not established universally for autism.
What practical next steps should talent teams take right now?
Direct answer: Start combining quantitative adverse-impact checks with AI-enabled qualitative reviews of item wording and candidate open-text responses, as recommended by Whelpley et al., 2026.
Whelpley et al., 2026 urge organizations to prioritize task-based work samples and assessment center techniques when social judgment is not a core job requirement, and to validate that any adapted assessment predicts job performance for autistic employees.
Can AI platforms safely analyze candidate data?
Direct answer: Yes, if you choose platforms with clear encryption, data controls, and non-training policies for third-party models.
Evidano’s platform encrypts data and does not use customer data to train third-party models; teams should verify privacy and compliance before uploading sensitive candidate information.
Conclusion & Next Steps
Whelpley et al., 2026 in PLOS One provide clear quantitative evidence that widely used selection tools can produce subgroup differences unfavorable to autistic applicants and that SJT disparities can persist after standard statistical controls.
AI-enabled qualitative research turns those numeric findings into actionable fixes by identifying which item framings, social cues, or normative assumptions cause misalignment.
If your team wants to pair quantitative adverse-impact checks with rapid qualitative diagnostics and redesign, Try Evidano for free to pilot thematic analysis on your selection items and candidate responses.
Topics
- qualitative analysis of hiring tests
- hiring tests autism
- AI qualitative research autism hiring
- situational judgment test autism
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