{
  "abstract": "A structural equation model can fit its data even when the design had little chance of detecting substantively relevant alternatives. A blind-spot map enumerates single-parameter alternatives to a fitted model, applies each at a standardized magnitude that holds implied variances fixed, and reports the probability that a stated decision rule would flag the model if the alternative were true, beside the probability that the same rule flags a correctly specified model. Analytic detection from the noncentral chi-square is paired with a simulated path that measures each rule’s finite-sample size, and equivalence that follows from a saturated latent structure is certified by proof instead of by a numerical threshold. In ten published designs with N from 20 to 2,566, 2,035 alternatives are detectable, and all 177 admissible directed-path and latent-covariance alternatives are certified equivalent, so the fits test only the measurement structure. Median detection under the likelihood-ratio test rises from .071 at N = 20 to .996 at N = 2,566, and the largest design still has three blind spots, residual correlations of 0.3 detected at most 9.6 percent of the time against a size of 5.8 percent. The RMSEA > .06 rule flags 457 alternatives below .50 that the likelihood-ratio test detects above .80, and its measured size reaches .434 at N = 20. A declared block of correlated residuals among reverse-keyed items is detected with probability .998 where no single pair exceeds .520. Independent R and Julia packages generate reproducible artifacts.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Marek Deja"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dh0jv854measa.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/367dn_v1",
  "fulltext_url": null,
  "id": "dh0jv854measa",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/367dn_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Computational Modeling"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "1803|Psychometric Methodologies and Testing"
    ]
  ],
  "openalex": "W7223227772",
  "pdf_url": "https://osf.io/367dn/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dh0jv854measa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:40Z",
      "native_id": "367dn",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/367dn_v1/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Blind-spot maps: auditing what a structural equation model’s design could not have detected",
  "title_text": "Blind-spot maps: auditing what a structural equation model’s design could not have detected",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:40Z",
  "url": "https://research.draconitic.com/paper/dh0jv854measa",
  "venue": "PsyArXiv",
  "versions": []
}