{
  "abstract": "The network perspective conceptualizes psychological phenomena as systems of interacting components, placing dynamics at the center of explanation. This has justified criticism of studies that use cross-sectional networks to claim within-person dynamics, uniquely identified causal mechanisms, or effective intervention targets. In the current methodological climate, however, an implicit design hierarchy has emerged in which cross-sectional design is itself treated as the objection. In an audit of 157 papers across three journals, 65% of 127 eligible papers named cross-sectional design as a limitation and 68% recommended longitudinal research as a remedy. Such a hierarchy mischaracterizes designs operating at different temporal scales: they estimate different structures rather than progressively better answers to one question. Cross-sectional networks can distinguish conditional from marginal associations, characterize the conditional-association structure, support measurement development, identify patterns for replication, and generate constrained hypotheses, particularly when measures represent characteristic tendencies or experiences over a defined period. Longitudinal and intensive longitudinal designs are not automatic upgrades over cross-sectional designs; their evidential value depends on temporal resolution, lag selection, measurement quality, model assumptions, and the distinction between group-average and person-specific inference. Designs should therefore be judged by alignment of question, measurement, model, and claims—not by position in an assumed hierarchy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Adela-Maria Isvoranu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6fytv7h4vjvge.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/gnwu4_v1",
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  "id": "6fytv7h4vjvge",
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  "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",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Quantitative Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "3205|Mental Health Research Topics"
    ]
  ],
  "openalex": "W7214073021",
  "pdf_url": "https://osf.io/gnwu4/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6fytv7h4vjvge.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:55Z",
      "native_id": "gnwu4",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/gnwu4_v1/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "The Case Against the Design Hierarchy in Psychological Network Analysis",
  "title_text": "The Case Against the Design Hierarchy in Psychological Network Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:55Z",
  "url": "https://research.draconitic.com/paper/6fytv7h4vjvge",
  "venue": "PsyArXiv",
  "versions": []
}