{
"artifact": "decisions_report",
"direction": "rtl",
"language": "he",
"order_id": "00000000-0000-0000-0000-000000000102",
"schema_version": 1,
"sections": [
{
"id": "consultation_questions",
"items": [],
"markdown": "\n\n## השאלות וההחלטות בייעוץ\n\nלא תועד דבר.",
"title": "השאלות וההחלטות בייעוץ"
},
{
"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## מה נבחר\n\n- עבור השערה H1, הניתוח שנבחר היה pearson_correlation.\n- עבור השערה H2, הניתוח שנבחר היה multiple_regression.\n- עבור השערה H3, הניתוח שנבחר היה independent_t_test.\n- עבור השערה A1, הניתוח שנבחר היה multiple_regression.",
"title": "מה נבחר"
},
{
"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## מדוע נבחרו החלטות אלו\n\n- בחירת pearson_correlation עבור השערה H1 נבעה מכלל ההחלטה assoc.pearson.\n- בחירת multiple_regression עבור השערה H2 נבעה מכלל ההחלטה predict.multiple.\n- בחירת independent_t_test עבור השערה H3 נבעה מכלל ההחלטה diff.between.1iv.t.\n- בחירת multiple_regression עבור השערה A1 נבעה מכלל ההחלטה predict.multiple.",
"title": "מדוע נבחרו החלטות אלו"
},
{
"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## חלופות שנשקלו\n\n- השערה H1: את מתאם ספירמן שמרנו כגיבוי. משתמשים בו רק אם הנתונים שלכם לא עומדים בתנאים שמתאם פירסון צריך, ואת התנאים האלה אנחנו בודקים לפני שהמבחן מורץ.\n- השערה H2: רגרסיה ליניארית פשוטה מתאים למחקרים שבנויים קצת אחרת (מספר אחר של קבוצות או של מדידות, או סוג אחר של תשובות). רגרסיה ליניארית מרובה מתאים לאופן שבו המחקר שלכם בנוי.\n- השערה H3: את מבחן מאן-ויטני שמרנו כגיבוי. משתמשים בו רק אם הנתונים שלכם לא עומדים בתנאים שמבחן t למדגמים בלתי תלויים צריך, ואת התנאים האלה אנחנו בודקים לפני שהמבחן מורץ.\n- השערה A1: רגרסיה ליניארית פשוטה מתאים למחקרים שבנויים קצת אחרת (מספר אחר של קבוצות או של מדידות, או סוג אחר של תשובות). רגרסיה ליניארית מרובה מתאים לאופן שבו המחקר שלכם בנוי.",
"title": "חלופות שנשקלו"
},
{
"id": "multiplicity",
"items": [],
"markdown": "\n\n## תיקון להשוואות מרובות\n\nלא תועד דבר.",
"title": "תיקון להשוואות מרובות"
},
{
"id": "robustness",
"items": [],
"markdown": "\n\n## מה נבדק לחוסן\n\nלא תועד דבר.",
"title": "מה נבדק לחוסן"
},
{
"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## הכנת הנתונים\n\n- missing.variable_analyzed על d1\n- missing.variable_analyzed על d2\n- missing.variable_analyzed על q1_1\n- missing.variable_analyzed על q1_2\n- missing.variable_analyzed על q1_3\n- missing.variable_analyzed על q1_4\n- missing.variable_analyzed על q1_5\n- missing.variable_analyzed על q2_1\n- missing.variable_analyzed על q2_2\n- missing.variable_analyzed על q2_3\n- missing.variable_analyzed על q2_4\n- missing.variable_analyzed על q2_5\n- missing.variable_analyzed על q3_1\n- missing.variable_analyzed על q3_2\n- missing.variable_analyzed על q3_3\n- missing.variable_analyzed על q3_4\n- missing.variable_analyzed על q3_5\n- reliability.scale_analyzed על לא תועד\n- reliability.scale_reliable על לא תועד\n- reliability.scale_analyzed על לא תועד\n- reliability.scale_reliable על לא תועד\n- reliability.scale_analyzed על לא תועד\n- reliability.scale_reliable על לא תועד",
"title": "הכנת הנתונים"
},
{
"id": "what_changed",
"items": [],
"markdown": "\n\n## מה השתנה בדרך\n\nלא תועד דבר.",
"title": "מה השתנה בדרך"
},
{
"id": "what_was_not_done",
"items": [],
"markdown": "\n\n## מה לא נעשה\n\nלא תועד דבר.",
"title": "מה לא נעשה"
},
{
"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## גילויים\n\n- מדיניות השימוש בבינה מלאכותית של המוסד לא נבדקה. Statikali אינה בודקת אם המוסד האקדמי שלכם, כתב העת או המנחה מתירים שימוש בכלי ניתוח מבוססי בינה מלאכותית. האחריות לבדיקת מדיניות זו ולעמידה בה נותרת עליכם.\n- מודל שפה מבוסס בינה מלאכותית סייע אך ורק בכתיבת הפרוזה המסבירה בחבילה זו. כל חישוב סטטיסטי, כל מספר וכל החלטת בחירת מבחן הופקו על ידי מנוע ניתוח דטרמיניסטי — לעולם לא על ידי הבינה המלאכותית.",
"title": "גילויים"
}
]
}