{
  "annotation_id": "1da4a821-e50f-47c4-9a18-ea92d24ad909",
  "date_created": "2026-03-02T18:01:07.401000Z",
  "date_modified": "2026-03-02T18:01:07.401000Z",
  "file_hash": "92eae04ece0442714fc54401782e2e1613811a70bca9f1c709ede1378ae4f91a",
  "private": false,
  "record": {
    "abstract": "The aim of this paper is to present the technique (and its linkage with\nphysics) of overcoming problems connected to modeling social structures, which\nare typically hierarchical. Hierarchical Linear Models provide a conceptual and\nstatistical mechanism for drawing conclusions regarding the influence of\nphenomena at different levels of analysis. In the social sciences it is used to\nanalyze many problems such as educational, organizational or market dilemma.\nThis paper introduces the logic of modeling hierarchical linear equations and\nestimation based on MPlus software. I present my own model to illustrate the\nimpact of different factors on school acceptation level.",
    "arxiv_id": "physics/0605129",
    "authors": [
      "Magdalena Jelonek"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an"
    ],
    "title": "Modeling hierarchical structures - Hierarchical Linear Modeling using   MPlus",
    "url": "https://arxiv.org/abs/physics/0605129"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a68cbc73-e195-470e-bd55-24a8a6a7f699",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}