{
  "artifact": "advisor_guide",
  "direction": "ltr",
  "full_versions": [
    {
      "full_markdown": "An association analysis was conducted to evaluate this hypothesis.\n\nThe findings are consistent with the hypothesis, indicating a statistically significant positive correlation between Academic self efficacy and Learning persistence, such that students reporting greater self efficacy tended also to report greater persistence in their learning. As this is a cross-sectional, correlational design, this association should not be interpreted as evidence that self efficacy causes persistence.\n\nN = 180, r(178) = .43, p < .001, p_adj < .001, 95% CI [.31, .55] (medium effect)\n\nThe result was: supported.",
      "omitted_elements": [
        "effect_ci"
      ],
      "section_id": "inferential.pearson_correlation|H1",
      "title": "Hypothesis 1"
    }
  ],
  "language": "en",
  "order_id": "00000000-0000-0000-0000-000000000101",
  "other_tails": [
    {
      "before_correction": false,
      "chapter_tail": "two",
      "observed_direction_only": true,
      "opposite": false,
      "other_significant": true,
      "other_tail": "one",
      "p_chapter": "p < .001",
      "p_other": "p < .001",
      "section_id": "inferential.pearson_correlation|H1",
      "statistic": "r(178) = .43",
      "test_id": "pearson_correlation",
      "title": "Hypothesis 1"
    }
  ],
  "provenance": [
    ".001",
    ".31",
    ".43",
    ".55",
    "1",
    "178",
    "180",
    "95"
  ],
  "questions": [
    {
      "answer": "We chose the Pearson correlation because H1 simply asks how two continuous composite scores—academic self efficacy and learning persistence—relate to one another, without any grouping or causal claim. Since both scales are means of multiple items, treated as continuous, and we checked the scatterplot for a linear pattern, reasonable spread, and no overly influential points, Pearson correlation is the natural and defensible way to summarize the strength and direction of their association along with its confidence interval. This matches the cross-sectional, single-time-point design, where we are only describing an association, not testing group differences or making causal statements.",
      "fact_sentence": "The Pearson correlation analysis was selected for H1 based on the study's design and measurement structure.",
      "facts": {
        "hypothesis_id": "H1",
        "test_id": "pearson_correlation"
      },
      "id": "advisor.q.1",
      "kind": "explain_choice",
      "question": "Why was the Pearson correlation analysis chosen for H1?"
    },
    {
      "answer": "We used the Pearson correlation here because both scale scores are interval-level, continuous measures, and the choice of test is made at the design/selection stage before any assumption checks are run. Our reading is that the Spearman correlation exists only as the pre-specified fallback that would be triggered if normality or related assumptions failed once the data were actually examined, so applying it at selection would be premature. Since the scatterplot, linearity, and influential-observation checks are part of the Pearson workflow itself, we're confident the Pearson correlation is the structurally appropriate and defensible choice for this analysis.",
      "fact_sentence": "Pearson correlation was used instead of Spearman correlation because Spearman correlation was kept as a backup. It is used only if your data do not meet the conditions Pearson correlation needs, which we check before the test is run..",
      "facts": {
        "alternative_test_id": "spearman_correlation",
        "applied_test_id": "pearson_correlation",
        "hypothesis_id": "H1",
        "reason": "The data are interval/ratio-scaled, so the parametric default pearson_correlation is selected. The non-parametric equivalent spearman_correlation is the Story 1.7 assumption-ladder fallback, applied on normality/homogeneity FAILURE at execution time -- it is NOT chosen at selection (selection sees no assumption outcomes; FR22).",
        "reason_code": "parametric_default_preferred"
      },
      "id": "advisor.q.2",
      "kind": "why_not_alternative",
      "question": "Why this test and not Spearman correlation?"
    }
  ],
  "schema_version": 1
}