{
"artifact": "decisions_report",
"direction": "ltr",
"language": "en",
"order_id": "00000000-0000-0000-0000-000000000102",
"schema_version": 1,
"sections": [
{
"id": "consultation_questions",
"items": [],
"markdown": "\n\n## The consultation's questions and decisions\n\nNone recorded.",
"title": "The consultation's questions and decisions"
},
{
"id": "what_was_chosen",
"items": [
{
"fields": {
"hypothesis_id": "H1",
"role_binding": {
"x": "job_autonomy",
"y": "work_engagement"
},
"test_id": "pearson_correlation"
},
"id": "what_was_chosen.1",
"kind": "chosen_test"
},
{
"fields": {
"hypothesis_id": "H2",
"role_binding": {
"outcome": "work_engagement",
"predictor1": "supervisor_support",
"predictor2": "job_autonomy"
},
"test_id": "multiple_regression"
},
"id": "what_was_chosen.2",
"kind": "chosen_test"
},
{
"fields": {
"hypothesis_id": "H3",
"role_binding": {
"dv": "work_engagement",
"group": "d1"
},
"test_id": "independent_t_test"
},
"id": "what_was_chosen.3",
"kind": "chosen_test"
},
{
"fields": {
"hypothesis_id": "A1",
"role_binding": {
"outcome": "work_engagement",
"predictor1": "supervisor_support",
"predictor2": "job_autonomy",
"predictor3": "d1",
"predictor4": "d2"
},
"test_id": "multiple_regression"
},
"id": "what_was_chosen.4",
"kind": "chosen_test"
}
],
"markdown": "\n\n## What was chosen\n\n- For hypothesis H1, the chosen analysis was pearson_correlation.\n- For hypothesis H2, the chosen analysis was multiple_regression.\n- For hypothesis H3, the chosen analysis was independent_t_test.\n- For hypothesis A1, the chosen analysis was multiple_regression.",
"title": "What was chosen"
},
{
"id": "why",
"items": [
{
"fields": {
"hypothesis_id": "H1",
"rule_path": [
{
"matched": true,
"observed": "association",
"predicate": "relationship==association"
},
{
"matched": true,
"observed": "2",
"predicate": "n_content==2"
},
{
"matched": true,
"observed": "interval,interval",
"predicate": "content.all_continuous"
}
],
"selected_rule_id": "assoc.pearson",
"test_id": "pearson_correlation"
},
"id": "why.1",
"kind": "selection_rationale"
},
{
"fields": {
"hypothesis_id": "H2",
"rule_path": [
{
"matched": true,
"observed": "prediction",
"predicate": "relationship==prediction"
},
{
"matched": true,
"observed": "interval",
"predicate": "dv.level in {interval,ratio}"
},
{
"matched": true,
"observed": "2",
"predicate": "n_iv>=2"
}
],
"selected_rule_id": "predict.multiple",
"test_id": "multiple_regression"
},
"id": "why.2",
"kind": "selection_rationale"
},
{
"fields": {
"hypothesis_id": "H3",
"rule_path": [
{
"matched": true,
"observed": "difference",
"predicate": "relationship in {difference,trend}"
},
{
"matched": true,
"observed": "between",
"predicate": "design.structure==between"
},
{
"matched": true,
"observed": "1",
"predicate": "n_dv==1"
},
{
"matched": true,
"observed": "interval",
"predicate": "dv.level in {interval,ratio}"
},
{
"matched": true,
"observed": "1",
"predicate": "n_iv==1"
},
{
"matched": true,
"observed": "0",
"predicate": "n_covariate==0"
},
{
"matched": true,
"observed": "2",
"predicate": "n_groups==2"
}
],
"selected_rule_id": "diff.between.1iv.t",
"test_id": "independent_t_test"
},
"id": "why.3",
"kind": "selection_rationale"
},
{
"fields": {
"hypothesis_id": "A1",
"rule_path": [
{
"matched": true,
"observed": "prediction",
"predicate": "relationship==prediction"
},
{
"matched": true,
"observed": "interval",
"predicate": "dv.level in {interval,ratio}"
},
{
"matched": true,
"observed": "4",
"predicate": "n_iv>=2"
}
],
"selected_rule_id": "predict.multiple",
"test_id": "multiple_regression"
},
"id": "why.4",
"kind": "selection_rationale"
}
],
"markdown": "\n\n## Why these choices were made\n\n- The choice of pearson_correlation for hypothesis H1 followed decision rule assoc.pearson.\n- The choice of multiple_regression for hypothesis H2 followed decision rule predict.multiple.\n- The choice of independent_t_test for hypothesis H3 followed decision rule diff.between.1iv.t.\n- The choice of multiple_regression for hypothesis A1 followed decision rule predict.multiple.",
"title": "Why these choices were made"
},
{
"id": "alternatives_considered",
"items": [
{
"fields": {
"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": "alternatives_considered.1",
"kind": "rejected_alternative"
},
{
"fields": {
"alternative_test_id": "linear_regression",
"applied_test_id": "multiple_regression",
"hypothesis_id": "H2",
"reason": "linear_regression is a plausible sibling for this design cell but does not match the observed structure (variable roles / arity / measurement levels); multiple_regression does.",
"reason_code": "structural_alternative"
},
"id": "alternatives_considered.2",
"kind": "rejected_alternative"
},
{
"fields": {
"alternative_test_id": "mann_whitney_u",
"applied_test_id": "independent_t_test",
"hypothesis_id": "H3",
"reason": "The data are interval/ratio-scaled, so the parametric default independent_t_test is selected. The non-parametric equivalent mann_whitney_u 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": "alternatives_considered.3",
"kind": "rejected_alternative"
},
{
"fields": {
"alternative_test_id": "linear_regression",
"applied_test_id": "multiple_regression",
"hypothesis_id": "A1",
"reason": "linear_regression is a plausible sibling for this design cell but does not match the observed structure (variable roles / arity / measurement levels); multiple_regression does.",
"reason_code": "structural_alternative"
},
"id": "alternatives_considered.4",
"kind": "rejected_alternative"
}
],
"markdown": "\n\n## Alternatives considered\n\n- hypothesis H1: 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.\n- hypothesis H2: Simple linear regression fits studies that are built a little differently (another number of groups or measurements, or another kind of answer). Multiple linear regression fits the way your study is built.\n- hypothesis H3: Mann-Whitney U test was kept as a backup. It is used only if your data do not meet the conditions Independent-samples t-test needs, which we check before the test is run.\n- hypothesis A1: Simple linear regression fits studies that are built a little differently (another number of groups or measurements, or another kind of answer). Multiple linear regression fits the way your study is built.",
"title": "Alternatives considered"
},
{
"id": "multiplicity",
"items": [],
"markdown": "\n\n## Multiple-comparison correction\n\nNone recorded.",
"title": "Multiple-comparison correction"
},
{
"id": "robustness",
"items": [],
"markdown": "\n\n## What was tested for robustness\n\nNone recorded.",
"title": "What was tested for robustness"
},
{
"id": "data_preparation",
"items": [
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "d1"
},
"id": "data_preparation.1",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "d2"
},
"id": "data_preparation.2",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q1_1"
},
"id": "data_preparation.3",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q1_2"
},
"id": "data_preparation.4",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q1_3"
},
"id": "data_preparation.5",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q1_4"
},
"id": "data_preparation.6",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q1_5"
},
"id": "data_preparation.7",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q2_1"
},
"id": "data_preparation.8",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q2_2"
},
"id": "data_preparation.9",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q2_3"
},
"id": "data_preparation.10",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q2_4"
},
"id": "data_preparation.11",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q2_5"
},
"id": "data_preparation.12",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q3_1"
},
"id": "data_preparation.13",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q3_2"
},
"id": "data_preparation.14",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q3_3"
},
"id": "data_preparation.15",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q3_4"
},
"id": "data_preparation.16",
"kind": "missing_action"
},
{
"fields": {
"count": 0,
"event_type": "missing.variable_analyzed",
"no_op": false,
"variable": "q3_5"
},
"id": "data_preparation.17",
"kind": "missing_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_analyzed",
"no_op": false,
"variable": null
},
"id": "data_preparation.18",
"kind": "reliability_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_reliable",
"no_op": false,
"variable": null
},
"id": "data_preparation.19",
"kind": "reliability_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_analyzed",
"no_op": false,
"variable": null
},
"id": "data_preparation.20",
"kind": "reliability_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_reliable",
"no_op": false,
"variable": null
},
"id": "data_preparation.21",
"kind": "reliability_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_analyzed",
"no_op": false,
"variable": null
},
"id": "data_preparation.22",
"kind": "reliability_action"
},
{
"fields": {
"count": null,
"event_type": "reliability.scale_reliable",
"no_op": false,
"variable": null
},
"id": "data_preparation.23",
"kind": "reliability_action"
}
],
"markdown": "\n\n## Data preparation\n\n- missing.variable_analyzed on d1\n- missing.variable_analyzed on d2\n- missing.variable_analyzed on q1_1\n- missing.variable_analyzed on q1_2\n- missing.variable_analyzed on q1_3\n- missing.variable_analyzed on q1_4\n- missing.variable_analyzed on q1_5\n- missing.variable_analyzed on q2_1\n- missing.variable_analyzed on q2_2\n- missing.variable_analyzed on q2_3\n- missing.variable_analyzed on q2_4\n- missing.variable_analyzed on q2_5\n- missing.variable_analyzed on q3_1\n- missing.variable_analyzed on q3_2\n- missing.variable_analyzed on q3_3\n- missing.variable_analyzed on q3_4\n- missing.variable_analyzed on q3_5\n- reliability.scale_analyzed on not recorded\n- reliability.scale_reliable on not recorded\n- reliability.scale_analyzed on not recorded\n- reliability.scale_reliable on not recorded\n- reliability.scale_analyzed on not recorded\n- reliability.scale_reliable on not recorded",
"title": "Data preparation"
},
{
"id": "what_changed",
"items": [],
"markdown": "\n\n## What changed along the way\n\nNone recorded.",
"title": "What changed along the way"
},
{
"id": "what_was_not_done",
"items": [],
"markdown": "\n\n## What was not done\n\nNone recorded.",
"title": "What was not done"
},
{
"id": "disclosures",
"items": [
{
"fields": {
"key": "ai_policy_not_verified"
},
"id": "disclosures.1",
"kind": "disclosure"
},
{
"fields": {
"key": "software_framing"
},
"id": "disclosures.2",
"kind": "disclosure"
}
],
"markdown": "\n\n## Disclosures\n\n- Institutional AI-use policies were not verified. Statikali does not check whether your academic institution, journal, or supervisor permits the use of AI-assisted analysis tools. Verifying those policies and complying with them remains your responsibility.\n- An AI language model assisted only by writing the explanatory prose in this package. Every statistical computation, every number, and every test-selection decision was produced by a deterministic analysis engine — never by the AI.",
"title": "Disclosures"
}
]
}