{
  "abstract": "Bayesian graphical modeling (BGM) is an emerging approach to psychological network analysis that offers proper uncertainty quantification and allows for incorporating prior information. However, resources that integrate the full analytic workflow within user-friendly software and provide guidance for transparent reporting remain scarce. In this tutorial, we address this gap by offering an integrated analysis and reporting guide for BGM. We first briefly outline the theoretical foundations of the framework. We then introduce the updated Bayesian network analysis module in the open-source software JASP and demonstrate three core analyses using an empirical dataset: edge selection and parameter estimation, clustering analysis, and comparing networks across groups. For each analysis, we provide minimal template paragraphs for the Methods and Results sections. We conclude with recommendations for prior robustness analyses and open research practices.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Nikola Sekulovski"
    },
    {
      "name": "Karoline Huth"
    },
    {
      "name": "Don van den Bergh"
    },
    {
      "name": "Johnny van Doorn"
    },
    {
      "name": "Tessa F. Blanken"
    },
    {
      "name": "Eric-Jan Wagenmakers"
    },
    {
      "name": "Maarten Marsman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/etsyfwtancffn.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/xce7j_v1",
  "fulltext_url": null,
  "id": "etsyfwtancffn",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/xce7j_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",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7164245581",
  "pdf_url": "https://osf.io/xce7j/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/etsyfwtancffn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "xce7j",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/xce7j_v1/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Analysis and Reporting Guidelines for Bayesian Graphical Modeling of Cross-Sectional Data: A Tutorial in JASP",
  "title_text": "Analysis and Reporting Guidelines for Bayesian Graphical Modeling of Cross-Sectional Data: A Tutorial in JASP",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:59Z",
  "url": "https://research.draconitic.com/paper/etsyfwtancffn",
  "venue": "PsyArXiv",
  "versions": []
}