Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents

[2608.14606] Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computers and Society arXiv:2608.14606 (cs) [Submitted on 6 Jul 2026] Title: Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents Authors: Mantas Lukauskas , Viktorija Šarkauskaitė View a PDF of the paper titled Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents, by Mantas Lukauskas and 1 other authors View PDF HTML (experimental) Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations ask whether answers look plausible at the individual level. We argue the right question is psychometric: do LLMs preserve the joint distribution, latent structure, reliability, mediation pathways, and demographic effects of real human survey data? We introduce a Lithuanian organisational-psychology dataset (n=263 employees; Dunham Attitudes Toward Change, UWES-17, Koopmans IWPQ; 68 items, 12 subscales) and condition a 37-model lineup spanning OpenAI, Anthropic, Google, and twelve open-weight families on real respondent profiles under a five-level persona-disclosure ladder, presentation and reasoning-effort ablations, counterfactual demographic swaps (gender, role, education), a cross-language check, and a verbatim-recall memorization probe. The resulting Psychometric Similarity Score (PSS) is anchored against five non-LLM statistical baselines and a held-out human-vs-human ceiling, with respondent-bootstrap confidence intervals and an item-permutation null for Tucker's phi. LLMs reproduce the qualitative direction of human psychometric relationships, but a Gaussian-copula baseline beats every LLM on the sample-driven PSS components; the LLM "crowd" is more similar to itself (mean inter-LLM PSS 0.73) than to humans; and memorization does not drive the leaderboard (recall-PSS rank correlation 0.00). Counterfactual swaps reveal education-driven effects (mean |d|=0.56) that dwarf gender (0.12) and role (0.18); Tucker's phi on UWES falls inside the permutation null for 8 of 37 models. Downstream, every LLM shows a strong acquiescence shift (+0.84 SD), synthetic-trained regressors lose predictive validity on held-out humans (mean R^2 -0.18 vs 0.28), and models fabricate indirect effects on 3 of 10 placebo mediation paths. LLM samples are not a drop-in replacement for human survey data. Comments: 50 pages, 9 figures. Under review. Code and data will be released upon publication Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Applications (stat.AP) Cite as: arXiv:2608.14606 [cs.CY] (or arXiv:2608.14606v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.14606 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mantas Lukauskas [ view email ] [v1] Mon, 6 Jul 2026 17:11:16 UTC (216 KB) Full-text links: Access Paper: View a PDF of the paper titled Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents, by Mantas Lukauskas and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.CL stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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