add analysis python code & reorganize folder

analysis-code/ahp_process.py

@@ -0,0 +1,326 @@
+import pandas as pd
+import numpy as np
+
+# ========== Settings ==========
+
+INPUT_CSV  = "raw.csv"
+OUTPUT_CSV = "res.csv"
+
+# 제외 기준
+CR_THRESHOLD        = 0.10
+INTUITION_SD_MIN    = 0.5
+PAIRWISE_SAME_ALL   = True
+
+# 불일치 판정 기준
+MISMATCH_THRESHOLD  = 2
+
+# 모델 점수 → 리커트 변환 경계값; 점수(0~100) → 1~5점
+SCORE_BREAKS = [28, 42, 58, 72]
+
+# 사례 카드 15개
+CASES = [
+    {"id": 1,  "안정성": 85, "수익_가능성": 82, "자금_효율성": 80, "사업_계획_완성도": 88, "사회적_가치": 84, "type": "baseline"},
+    {"id": 2,  "안정성": 18, "수익_가능성": 22, "자금_효율성": 15, "사업_계획_완성도": 20, "사회적_가치": 25, "type": "baseline"},
+    {"id": 3,  "안정성": 68, "수익_가능성": 72, "자금_효율성": 65, "사업_계획_완성도": 70, "사회적_가치": 66, "type": "baseline"},
+    {"id": 4,  "안정성": 35, "수익_가능성": 38, "자금_효율성": 42, "사업_계획_완성도": 30, "사회적_가치": 35, "type": "baseline"},
+    {"id": 5,  "안정성":  5, "수익_가능성": 68, "자금_효율성": 72, "사업_계획_완성도": 70, "사회적_가치": 65, "type": "spike"},
+    {"id": 6,  "안정성": 70, "수익_가능성": 97, "자금_효율성": 68, "사업_계획_완성도": 65, "사회적_가치": 72, "type": "spike"},
+    {"id": 7,  "안정성": 65, "수익_가능성": 70, "자금_효율성":  4, "사업_계획_완성도": 68, "사회적_가치": 72, "type": "spike"},
+    {"id": 8,  "안정성": 68, "수익_가능성": 65, "자금_효율성": 70, "사업_계획_완성도": 96, "사회적_가치": 66, "type": "spike"},
+    {"id": 9,  "안정성": 72, "수익_가능성": 68, "자금_효율성": 65, "사업_계획_완성도": 70, "사회적_가치":  6, "type": "spike"},
+    {"id": 10, "안정성": 95, "수익_가능성": 95, "자금_효율성": 15, "사업_계획_완성도": 20, "사회적_가치": 25, "type": "conflict"},
+    {"id": 11, "안정성": 20, "수익_가능성": 18, "자금_효율성": 22, "사업_계획_완성도": 25, "사회적_가치": 95, "type": "conflict"},
+    {"id": 12, "안정성": 88, "수익_가능성": 85, "자금_효율성": 90, "사업_계획_완성도": 92, "사회적_가치": 12, "type": "conflict"},
+    {"id": 13, "안정성": 15, "수익_가능성": 92, "자금_효율성": 18, "사업_계획_완성도": 20, "사회적_가치": 88, "type": "conflict"},
+    {"id": 14, "안정성": 90, "수익_가능성": 22, "자금_효율성": 88, "사업_계획_완성도": 85, "사회적_가치": 82, "type": "conflict"},
+    {"id": 15, "안정성": 48, "수익_가능성": 52, "자금_효율성": 50, "사업_계획_완성도": 47, "사회적_가치": 53, "type": "conflict"},
+]
+
+VARS      = ["안정성", "수익_가능성", "자금_효율성", "사업_계획_완성도", "사회적_가치"]
+PAIR_IDX  = [(0,1),(0,2),(0,3),(0,4),(1,2),(1,3),(1,4),(2,3),(2,4),(3,4)]
+RI        = {1:0.00, 2:0.00, 3:0.58, 4:0.90, 5:1.12, 6:1.24, 7:1.32, 8:1.41}
+
+# ══════════ AHP 계산 함수 ══════════
+
+def build_matrix(pair_values: list) -> np.ndarray:
+    """쌍대비교 10개 값 → 5×5 비율 행렬"""
+    A = np.ones((5, 5))
+    for k, (i, j) in enumerate(PAIR_IDX):
+        v = pair_values[k]
+        if v < 0: # Left more important
+            a_ij = float(abs(v) + 1)
+        elif v > 0: # Right more important
+            a_ij = 1.0 / (float(v) + 1)
+        else: # Same
+            a_ij = 1.0
+        A[i][j] = a_ij
+        A[j][i] = 1.0 / a_ij
+    return A
+
+
+# 근사
+def power_method(A: np.ndarray, max_iter: int = 1000, tol: float = 1e-9) -> np.ndarray:
+    n = len(A)
+    v = np.ones(n) / n
+    for _ in range(max_iter):
+        Av    = A @ v
+        s     = Av.sum()
+        if s == 0:
+            break
+        v_new = Av / s
+        if np.max(np.abs(v_new - v)) < tol:
+            v = v_new
+            break
+        v = v_new
+    return v
+
+
+# CR, CI, λmax 계산
+def calc_cr(A: np.ndarray, w: np.ndarray) -> tuple:
+    n       = len(A)
+    Aw      = A @ w
+    ratios  = np.where(w > 1e-10, Aw / w, 0.0)
+    lam_max = float(ratios.mean())
+    CI      = (lam_max - n) / (n - 1)
+    ri      = RI.get(n, 1.12)
+    CR      = CI / ri if ri > 0 else 0.0
+    return float(CR), float(CI), float(lam_max)
+
+
+# Calculate AHP of single person
+def ahp_single(pair_values: list) -> dict:
+    try:
+        A  = build_matrix(pair_values)
+        w  = power_method(A)
+        CR, CI, lam = calc_cr(A, w)
+        return {"weights": w.tolist(), "CR": CR, "CI": CI,
+                "lambda_max": lam, "error": None}
+    except Exception as e:
+        return {"weights": [np.nan]*5, "CR": np.nan,
+                "CI": np.nan, "lambda_max": np.nan, "error": str(e)}
+
+
+# ========== Score / Difference calculate ==========
+
+# Score = sum of 변인값 * 가중치
+def model_score(case: dict, weights: list) -> float:
+    vals = np.array([case[v] for v in VARS])
+    return float(np.dot(vals, weights))
+
+
+# 0~100 -> 1~5 Score
+def score_to_liker(score: float) -> int:
+    if score >= SCORE_BREAKS[3]:   return 5
+    elif score >= SCORE_BREAKS[2]: return 4
+    elif score >= SCORE_BREAKS[1]: return 3
+    elif score >= SCORE_BREAKS[0]: return 2
+    else:                          return 1
+
+
+# ========== Preprocessing ==========
+
+def check_exclusion(row: pd.Series,
+                    pair_cols: list,
+                    intuit_cols: list,
+                    cr: float) -> str:
+    
+    # CR
+    if not np.isnan(cr) and cr > CR_THRESHOLD:
+        return "제거_CR초과"
+
+    # Standard deviation
+    intuit_vals = row[intuit_cols].values.astype(float)
+    if not np.any(np.isnan(intuit_vals)):
+        if np.std(intuit_vals) < INTUITION_SD_MIN:
+            return "제거_직관무성의"
+
+    # All same
+    if PAIRWISE_SAME_ALL:
+        pair_vals = row[pair_cols].values.astype(float)
+        if not np.any(np.isnan(pair_vals)):
+            if np.std(pair_vals) == 0:
+                return "제거_쌍대무성의"
+
+    return "유효"
+
+
+# ========== Main Code ==========
+
+def main():
+    # ── 1. 데이터 로드 ────────────────────────────────────────
+    print(f"파일 읽는 중: {INPUT_CSV}")
+    df = pd.read_csv(INPUT_CSV, encoding="utf-8-sig")
+    print(f"  총 {len(df)}명 로드")
+
+    # 열 자동 탐지
+    pair_cols = sorted([c for c in df.columns if c.startswith("AHP_")])
+
+    # 직관판단 열: 다양한 형식 자동 탐지
+    # 형식 A: "직관판단_Q01" / 형식 B: "직관_사례01" / 형식 C: "직관_Q01"
+    intuit_cols = sorted([c for c in df.columns
+                          if c.startswith("직관판단_Q")
+                          or c.startswith("직관_사례")
+                          or c.startswith("직관_Q")])
+
+    if len(pair_cols) != 10:
+        raise ValueError(f"쌍대비교 열이 10개여야 함 (현재 {len(pair_cols)}개). "
+                         f"열 이름이 'AHP_01_...' 형식인지 확인.")
+    if len(intuit_cols) != 15:
+        raise ValueError(f"직관판단 열이 15개여야 함 (현재 {len(intuit_cols)}개). "
+                         f"열 이름이 '직관판단_Q01' / '직관_사례01' / '직관_Q01' 중 하나여야 함.")
+
+    print(f"  쌍대비교 열: {pair_cols[0]} ~ {pair_cols[-1]}")
+    print(f"  직관판단 열: {intuit_cols[0]} ~ {intuit_cols[-1]}")
+
+    # ── 2. AHP 계산 (전원) ────────────────────────────────────
+    print("\nAHP 계산 중...")
+    ahp_results = []
+    for _, row in df.iterrows():
+        pv  = [int(row[c]) if pd.notna(row[c]) else 0 for c in pair_cols]
+        res = ahp_single(pv)
+        ahp_results.append(res)
+
+    # ── 3. 전처리 판정 ────────────────────────────────────────
+    exclusion_flags = []
+    for idx, (_, row) in enumerate(df.iterrows()):
+        cr  = ahp_results[idx]["CR"]
+        flag = check_exclusion(row, pair_cols, intuit_cols, cr)
+        exclusion_flags.append(flag)
+
+    df["전처리결과"] = exclusion_flags
+
+    print("\n[ 전처리 결과 ]")
+    for k, v in df["전처리결과"].value_counts().items():
+        print(f"  {k}: {v}명")
+
+    # ── 4. 유효 응답자만 추출 ─────────────────────────────────
+    valid_mask = df["전처리결과"] == "유효"
+    df_valid   = df[valid_mask].copy()
+    print(f"\n  → 최종 분석 대상: {len(df_valid)}명")
+
+    # ── 5. 결과 컬럼 초기화 ──────────────────────────────────
+    result_rows = []
+
+    for idx in df_valid.index:
+        row     = df.loc[idx]
+        ahp_res = ahp_results[list(df.index).index(idx)]
+        weights = ahp_res["weights"]
+
+        result = {"피실험자ID": row.get("피실험자ID", idx + 1)}
+
+        # 가중치
+        for i, v in enumerate(VARS):
+            result[f"가중치_{v}"] = round(weights[i], 4)
+
+        result["CR"]         = round(ahp_res["CR"], 4)
+        result["CI"]         = round(ahp_res["CI"], 4)
+        result["lambda_max"] = round(ahp_res["lambda_max"], 4)
+
+        # ── 6. 사례별 모델 점수 / 판단 / 불일치 ──────────────
+        mis_total    = 0
+        mis_baseline = 0
+        mis_spike    = 0
+        mis_conflict = 0
+        n_baseline   = 0
+        n_spike      = 0
+        n_conflict   = 0
+
+        for k_case, case in enumerate(CASES):
+            cid = case["id"]
+
+            # 직관 판단 — intuit_cols 정렬 순서로 접근
+            intuit_col = intuit_cols[k_case]
+            i_liker    = int(row[intuit_col]) if pd.notna(row[intuit_col]) else np.nan
+
+            # 모델 점수 & 판단
+            m_score  = model_score(case, weights)
+            m_liker  = score_to_liker(m_score)
+
+            # 불일치 판정
+            if not np.isnan(i_liker):
+                diff     = abs(int(i_liker) - m_liker)
+                mismatch = int(diff >= MISMATCH_THRESHOLD)
+            else:
+                mismatch = np.nan
+
+            result[f"직관_Q{cid:02d}"]       = i_liker
+            result[f"모델점수_Q{cid:02d}"]   = round(m_score, 1)
+            result[f"모델판단_Q{cid:02d}"]   = m_liker
+            result[f"판단차이_Q{cid:02d}"]   = (int(i_liker) - m_liker) if not np.isnan(i_liker) else np.nan
+            result[f"불일치_Q{cid:02d}"]     = mismatch
+
+            if not np.isnan(mismatch):
+                mis_total += mismatch
+                ctype      = case["type"]
+                if ctype == "baseline":
+                    mis_baseline += mismatch; n_baseline += 1
+                elif ctype == "spike":
+                    mis_spike    += mismatch; n_spike    += 1
+                else:
+                    mis_conflict += mismatch; n_conflict += 1
+
+        # ── 7. 불일치율 ───────────────────────────────────────
+        result["불일치_합계"]          = mis_total
+        result["불일치율_전체(%)"]     = round(mis_total / 15 * 100, 1)
+        result["불일치율_베이스라인(%)"] = round(mis_baseline / n_baseline * 100, 1) if n_baseline else np.nan
+        result["불일치율_단일극값(%)"]  = round(mis_spike    / n_spike    * 100, 1) if n_spike    else np.nan
+        result["불일치율_변인충돌(%)"]  = round(mis_conflict / n_conflict * 100, 1) if n_conflict else np.nan
+
+        # 사후 설문 (있을 경우 포함)
+        for q in ["사후Q1_모델과직관차이", "사후Q2_모델기준이해", "사후Q3_모델합리성"]:
+            if q in row.index:
+                result[q] = row[q]
+
+        result_rows.append(result)
+
+    df_out = pd.DataFrame(result_rows)
+
+    # ── 8. 저장 ──────────────────────────────────────────────
+    df_out.to_csv(OUTPUT_CSV, index=False, encoding="utf-8-sig")
+
+    # ── 9. 요약 출력 ─────────────────────────────────────────
+    print("\n" + "=" * 60)
+    print("계산 완료 요약")
+    print("=" * 60)
+
+    print(f"\n[ 표본 ]")
+    print(f"  전체 입력: {len(df)}명")
+    for k, v in df["전처리결과"].value_counts().items():
+        print(f"  {k}: {v}명")
+    print(f"  최종 분석: {len(df_out)}명")
+
+    print(f"\n[ AHP 가중치 평균 ]")
+    for v in VARS:
+        m = df_out[f"가중치_{v}"].mean()
+        print(f"  {v}: {m:.4f} ({m*100:.1f}%)")
+
+    print(f"\n[ CR 분포 ]")
+    cr_vals = df_out["CR"].dropna()
+    print(f"  평균: {cr_vals.mean():.4f}  최소: {cr_vals.min():.4f}  최대: {cr_vals.max():.4f}")
+
+    print(f"\n[ 불일치율 평균 ]")
+    for col in ["불일치율_전체(%)", "불일치율_베이스라인(%)",
+                "불일치율_단일극값(%)", "불일치율_변인충돌(%)"]:
+        m = df_out[col].mean()
+        s = df_out[col].std()
+        print(f"  {col}: M={m:.1f}%  SD={s:.1f}%")
+
+    print(f"\n[ 사례별 불일치율 ]")
+    type_label = {"baseline": "베이스라인", "spike": "단일극값", "conflict": "변인충돌"}
+    for case in CASES:
+        cid  = case["id"]
+        col  = f"불일치_Q{cid:02d}"
+        mis  = df_out[col].mean() * 100
+        i_m  = df_out[f"직관_Q{cid:02d}"].mean()
+        m_m  = df_out[f"모델판단_Q{cid:02d}"].mean()
+        tl   = type_label[case["type"]]
+        print(f"  사례{cid:02d}({tl}): 불일치={mis:4.0f}%  직관={i_m:.2f}  모델={m_m:.2f}")
+
+    print(f"\n저장 완료: {OUTPUT_CSV}")
+    print(f"  {len(df_out)}명 × {len(df_out.columns)}열")
+
+    return df_out
+
+
+if __name__ == "__main__":
+    df_result = main()

analysis-code/raw.csv

@@ -0,0 +1,113 @@
+피실험자ID,AHP_01_안정성_vs_수익_가능성,AHP_02_안정성_vs_자금_효율성,AHP_03_안정성_vs_사업_계획_완성도,AHP_04_안정성_vs_사회적_가치,AHP_05_수익_가능성_vs_자금_효율성,AHP_06_수익_가능성_vs_사업_계획_완성도,AHP_07_수익_가능성_vs_사회적_가치,AHP_08_자금_효율성_vs_사업_계획_완성도,AHP_09_자금_효율성_vs_사회적_가치,AHP_10_사업_계획_완성도_vs_사회적_가치,직관판단_Q01,직관판단_Q02,직관판단_Q03,직관판단_Q04,직관판단_Q05,직관판단_Q06,직관판단_Q07,직관판단_Q08,직관판단_Q09,직관판단_Q10,직관판단_Q11,직관판단_Q12,직관판단_Q13,직관판단_Q14,직관판단_Q15,사후Q1_모델과직관차이,사후Q2_모델기준이해,사후Q3_모델합리성,응답유형
+1,0,-2,0,-4,-2,1,-4,1,-4,-4,5,1,4,1,3,5,3,4,2,5,3,5,3,4,3,1,4,5,정상
+2,4,0,1,-2,-4,-4,-4,1,-3,-1,5,3,3,3,3,5,3,5,3,5,2,5,5,4,2,2,4,4,정상
+3,2,0,-2,-4,-1,-3,-4,0,-1,-3,5,1,4,2,1,5,4,5,3,5,4,3,3,5,3,2,3,3,정상
+4,4,0,2,0,-4,-4,-4,0,1,-3,5,1,5,2,4,5,4,5,3,5,3,5,5,4,4,1,3,2,정상
+5,3,-4,4,-2,-4,-2,-4,4,3,-4,5,1,4,2,3,5,4,5,3,5,3,5,4,5,2,1,2,3,정상
+6,2,0,3,1,-4,0,-4,4,1,-2,5,1,4,2,3,5,3,4,3,5,3,5,5,5,5,2,4,2,정상
+7,-2,-4,2,-2,-4,0,-1,4,4,-4,5,1,5,2,1,5,3,5,2,4,3,5,4,5,2,1,5,2,정상
+8,4,-2,3,-4,-4,-1,-4,4,0,-4,5,1,5,2,3,5,4,4,3,5,3,5,4,5,3,2,3,4,정상
+9,4,-4,0,-3,-4,-4,-4,4,4,-4,5,2,4,2,2,5,4,5,4,5,3,5,5,4,3,1,4,2,정상
+10,4,1,4,1,-3,-1,-1,0,3,-2,5,2,4,3,2,5,4,5,1,5,5,5,5,5,3,2,4,3,정상
+11,4,-4,4,-4,-4,-3,-4,4,0,-4,5,1,5,1,2,5,3,5,4,5,3,5,4,5,3,3,4,3,정상
+12,-3,-4,0,-4,-4,0,-1,4,4,-4,5,2,4,2,3,5,5,5,3,5,4,4,3,5,3,2,5,4,정상
+13,3,-4,4,1,-4,-1,0,4,4,2,5,2,5,3,1,5,4,5,1,5,4,4,4,5,4,1,5,3,정상
+14,0,-4,2,-4,-3,1,-4,4,0,-4,5,1,5,2,2,5,3,5,2,5,3,5,4,5,4,2,5,3,정상
+15,4,0,1,-3,-4,0,-4,4,0,-4,5,1,4,2,3,5,3,5,3,5,4,5,5,5,3,1,5,3,정상
+16,4,-1,0,2,-3,-2,-3,0,1,2,5,1,4,3,3,5,3,5,3,5,4,3,5,5,4,1,5,4,정상
+17,4,4,4,3,-4,-1,-4,4,1,-4,5,2,5,2,3,5,3,5,3,5,4,5,5,3,2,2,4,3,정상
+18,0,-3,2,-2,4,3,-4,4,-4,-4,5,2,4,2,3,5,2,5,3,4,3,5,3,5,3,1,2,2,정상
+19,4,0,1,-4,-4,-1,-4,0,-4,-4,5,2,5,1,3,5,3,5,3,5,4,5,3,3,2,2,4,4,정상
+20,4,-1,0,-4,1,-4,0,0,-4,1,5,2,4,2,4,5,2,4,2,5,4,5,5,5,4,2,1,2,정상
+21,1,-4,0,-4,-4,-2,-4,3,2,-3,5,1,3,1,1,5,3,5,3,4,4,5,4,5,3,2,5,2,정상
+22,4,0,4,3,-4,1,-2,4,2,-2,5,1,5,1,3,5,3,5,2,4,4,4,2,5,2,2,4,3,정상
+23,1,-4,-1,-4,-4,2,-4,4,2,-4,5,1,4,2,2,5,3,5,2,5,2,4,4,5,2,2,3,3,정상
+24,0,-4,-4,0,-4,-4,-1,4,4,3,5,1,4,2,3,5,3,5,3,5,4,5,4,5,3,1,3,4,정상
+25,3,-3,2,-4,-2,0,-3,4,0,-4,5,2,5,2,2,5,3,5,2,5,2,5,4,5,3,2,3,3,정상
+26,0,0,-4,-4,-1,1,-4,0,-4,-2,5,1,5,2,2,5,2,5,2,5,3,4,5,4,4,2,3,2,정상
+27,0,-4,1,-2,-1,0,-4,2,-1,-1,5,1,4,2,3,5,2,5,2,5,2,5,4,5,2,2,3,4,정상
+28,-4,-4,0,-2,-4,3,1,4,4,-2,4,1,4,2,2,5,2,5,1,5,2,5,2,5,2,2,5,1,정상
+29,-2,-4,0,0,0,0,4,3,3,0,5,2,4,2,4,5,4,5,2,5,4,5,3,5,2,1,3,3,정상
+30,2,-1,4,-1,-4,-2,-4,3,-4,-4,5,1,4,2,3,5,3,5,3,3,3,5,5,5,3,1,4,3,정상
+31,1,-2,1,3,0,1,0,1,2,1,5,1,4,2,2,5,3,5,4,4,4,4,5,5,2,2,4,4,정상
+32,0,-4,2,4,-4,0,4,4,4,3,5,1,5,2,3,5,4,5,2,3,5,4,5,5,4,1,3,4,정상
+33,4,4,4,1,-3,-4,-4,0,-4,-1,4,2,5,2,3,4,2,5,3,5,4,3,5,4,4,2,5,3,정상
+34,0,0,4,4,2,4,0,2,0,-4,5,2,4,2,4,5,3,5,3,4,5,5,4,5,2,2,4,2,정상
+35,4,1,0,-2,-4,-4,-4,-2,-2,-4,5,1,4,3,2,5,3,5,2,5,2,5,5,4,3,1,5,3,정상
+36,1,-4,-1,-4,-4,0,-4,4,4,0,5,1,4,2,1,5,4,5,2,5,3,2,5,5,1,1,2,3,정상
+37,2,1,1,-4,-1,0,-4,0,-1,-2,5,1,3,3,2,5,4,5,2,4,2,5,4,4,3,2,5,3,정상
+38,4,-2,1,1,-4,-3,-1,0,0,4,5,1,4,2,3,5,4,5,2,5,5,4,5,3,3,1,3,3,정상
+39,-4,-4,0,-1,-4,4,1,4,2,-4,5,1,4,2,1,5,5,5,3,4,4,4,2,5,2,3,4,3,정상
+40,2,-4,0,-2,-4,-4,-4,2,2,0,5,1,3,2,2,5,4,5,3,5,3,5,5,3,3,1,5,3,정상
+41,4,-4,0,1,-4,1,-4,4,2,0,5,1,4,2,3,5,4,5,1,5,4,5,5,5,4,2,2,4,정상
+42,4,-4,-1,-1,-4,-1,-3,1,1,0,5,2,5,2,1,5,2,5,3,5,3,4,4,5,4,2,5,3,정상
+43,4,-4,1,-4,-4,-1,-4,4,-4,-4,5,2,5,2,4,5,4,5,1,5,3,4,3,5,2,2,4,3,정상
+44,4,-2,1,-1,-4,-1,-4,4,1,-1,5,1,4,2,3,5,3,5,4,5,2,4,2,5,3,1,5,3,정상
+45,1,-2,2,4,-4,1,4,4,4,-2,5,1,5,3,2,5,5,5,1,3,4,5,5,5,3,2,5,2,정상
+46,1,-1,-4,-3,-2,-1,-1,0,-1,0,5,2,5,3,2,5,2,4,2,4,4,3,5,4,2,2,3,4,정상
+47,4,0,0,1,-4,-4,-3,-4,0,2,5,1,4,2,4,5,3,5,1,5,5,5,5,5,3,1,5,3,정상
+48,1,-4,-1,1,-4,0,-2,3,4,0,5,1,5,1,3,5,3,4,4,5,2,5,4,4,3,1,4,4,정상
+49,3,-2,-2,4,-4,0,3,4,4,4,5,2,5,2,3,5,4,5,1,4,3,5,5,5,5,1,2,4,정상
+50,0,-4,1,0,-4,-4,-4,2,4,1,5,1,4,2,3,5,4,5,4,5,3,3,5,5,3,1,3,3,정상
+51,3,-3,-2,4,-4,-3,0,4,4,3,5,1,4,1,3,5,3,5,2,4,3,5,5,5,4,1,3,4,정상
+52,-4,-4,0,-4,-4,2,-4,4,0,-4,5,1,4,2,2,5,3,5,4,5,2,5,3,5,2,1,3,4,정상
+53,2,-4,-4,-1,-4,-1,-3,4,4,3,5,2,4,2,2,5,4,5,2,5,4,5,5,4,4,1,2,4,정상
+54,1,-4,4,0,-4,-3,3,4,4,0,5,1,4,2,3,5,2,5,1,5,4,5,5,5,3,3,3,3,정상
+55,3,-3,-1,-4,-4,-4,-4,4,-4,-4,5,1,4,2,2,5,3,5,3,5,3,5,5,4,1,3,5,3,정상
+56,2,0,-1,-3,-2,-2,-3,1,0,0,5,2,4,1,2,5,1,5,1,5,4,5,5,5,3,3,5,2,정상
+57,0,-4,-4,-1,-4,-1,1,4,4,3,4,1,4,1,2,5,4,5,2,5,4,4,4,5,3,2,5,1,정상
+58,4,-2,1,1,-4,-4,-4,1,4,2,5,1,5,2,4,5,4,5,3,5,4,5,5,5,3,2,5,4,정상
+59,4,1,-1,-3,-4,-4,-4,-1,-2,-1,5,1,5,1,3,5,2,4,3,5,3,5,4,3,3,1,2,3,정상
+60,3,-4,2,-4,-4,-2,-4,4,-1,-4,5,2,4,1,3,5,4,5,3,5,3,4,5,5,3,2,4,3,정상
+61,-4,-4,-4,-4,-4,-2,-4,4,0,-4,5,1,5,2,1,5,3,5,3,5,2,4,3,5,3,1,3,4,정상
+62,4,-2,4,3,-4,0,-4,4,4,-4,5,1,5,1,2,5,3,5,3,5,2,5,4,5,3,1,4,3,정상
+63,4,-2,0,2,-4,-1,-3,4,4,0,5,1,5,2,2,5,3,5,1,5,4,5,5,4,3,1,4,3,정상
+64,0,-4,-2,-3,-4,-2,-2,4,4,0,5,1,4,2,1,5,3,5,2,5,4,5,3,5,2,1,4,2,정상
+65,1,-4,1,-4,-4,-2,-4,4,-1,-4,5,2,5,1,2,5,3,5,3,4,2,5,4,5,3,1,4,3,정상
+66,2,-3,2,-2,-4,-1,-4,1,0,-4,5,1,4,2,2,5,4,5,3,5,2,5,5,5,2,1,3,4,정상
+67,3,-2,0,-3,-3,-4,-4,-1,0,0,5,1,4,2,3,5,3,5,3,5,4,5,5,5,2,1,4,2,정상
+68,0,-4,-4,-4,-3,-1,-4,1,-1,-1,5,1,4,2,2,5,2,5,3,5,3,5,5,4,2,2,5,4,정상
+69,4,-4,-1,-4,-4,-4,-4,3,3,-4,5,2,5,1,2,5,3,5,4,5,2,5,5,4,2,1,2,2,정상
+70,4,-4,-1,3,-4,-4,-4,4,4,-1,5,1,4,2,3,5,3,4,2,5,2,3,4,5,3,2,4,2,정상
+71,0,-3,0,0,0,2,1,4,4,2,5,2,4,3,2,5,4,5,2,5,5,5,5,5,3,2,3,2,정상
+72,0,-4,1,-2,-2,0,-3,4,0,-2,5,2,5,2,2,5,3,5,2,5,2,5,3,4,3,1,5,3,정상
+73,4,-1,4,0,-4,-3,-4,3,2,0,5,1,4,3,3,5,4,5,3,5,3,5,5,5,2,1,3,4,정상
+74,3,-3,-1,-2,-4,-4,-4,1,-2,-2,5,1,4,2,3,5,4,3,3,5,1,5,5,5,2,2,4,5,정상
+75,4,-3,4,4,-4,-2,-4,4,4,1,5,1,3,2,3,5,4,5,3,5,2,2,5,3,2,2,4,2,정상
+76,2,-2,1,0,2,1,1,3,0,-1,5,1,4,2,3,5,3,5,3,4,3,5,4,5,2,1,3,4,정상
+77,0,-1,0,3,-4,3,-3,4,4,2,5,1,3,2,2,5,4,5,2,5,3,2,5,4,2,2,1,3,정상
+78,0,-4,-2,-4,-4,-2,-2,4,1,-1,5,1,4,2,1,5,3,5,2,5,2,5,2,3,2,2,3,3,정상
+79,4,-4,1,0,-4,-4,-2,4,4,-1,5,2,5,2,2,5,4,5,3,4,4,5,5,5,2,2,4,2,정상
+80,4,4,4,3,-4,-4,-4,4,2,-4,5,1,5,2,3,5,5,5,2,5,2,5,5,3,3,1,1,2,정상
+81,2,-4,-2,-4,-4,-4,-4,4,4,-2,5,1,4,2,3,5,4,5,1,5,2,3,5,3,4,1,4,4,정상
+82,0,3,1,4,-4,0,-3,4,0,0,5,1,4,3,1,5,5,5,4,5,3,3,5,5,4,2,2,2,정상
+83,0,-4,0,-4,-4,2,-3,4,4,-3,5,2,5,2,1,5,3,5,3,5,4,5,3,5,4,2,4,3,정상
+84,3,0,4,-3,-4,-2,-4,1,1,-2,5,1,5,2,3,5,4,4,2,5,3,4,4,5,3,2,5,2,정상
+85,-1,-4,0,-4,-4,0,-4,4,1,-4,5,1,5,2,3,5,4,5,3,5,2,5,5,5,4,2,4,3,정상
+86,-1,-1,0,0,4,-2,4,0,4,2,5,1,4,2,4,5,4,5,3,5,3,3,4,5,3,1,3,3,정상
+87,4,2,4,4,-4,-4,1,4,4,-3,5,1,5,2,4,5,5,5,2,5,3,3,4,4,3,1,1,2,정상
+88,0,-4,1,-3,-1,-1,-4,0,0,0,5,1,4,1,3,5,3,5,3,5,4,5,4,5,4,2,4,3,정상
+89,0,0,0,4,-1,-1,2,-1,1,0,5,1,4,2,2,5,3,5,2,4,5,2,5,5,4,1,4,4,정상
+90,4,1,4,4,-4,-1,-4,4,4,-4,5,1,5,2,4,5,4,5,2,5,4,5,5,5,3,2,5,3,정상
+91,0,-4,0,-4,-4,-4,-4,4,4,0,5,1,4,2,3,5,3,5,3,5,4,4,5,5,3,1,4,4,정상
+92,-2,-1,0,-1,-2,0,-4,4,-3,-4,5,2,4,2,3,5,4,5,4,4,3,5,4,5,3,2,4,4,정상
+93,4,4,4,4,-2,-3,0,1,0,0,5,1,4,2,3,5,3,4,3,4,5,5,5,5,4,1,5,5,정상
+94,0,-4,-4,0,-4,-3,2,4,4,1,4,1,4,2,3,5,2,5,2,5,4,3,5,5,4,2,2,4,정상
+95,4,-3,-1,-3,-4,-1,0,4,0,-4,5,1,5,3,2,5,3,5,2,5,3,5,5,5,3,1,2,2,정상
+96,0,-4,-2,-1,-4,-2,-1,4,4,0,4,1,3,2,3,5,4,5,2,5,3,5,5,5,3,1,4,3,정상
+97,4,-4,2,4,-4,-1,2,4,4,4,5,2,4,2,2,5,4,4,2,5,4,5,5,5,4,2,3,4,정상
+98,2,-2,-2,-4,-2,-3,-4,-1,-2,0,5,1,4,1,1,5,1,5,2,4,3,5,5,5,3,2,4,3,정상
+99,1,2,4,-3,1,2,-4,1,-4,-4,4,2,4,3,3,5,4,5,3,4,3,5,4,5,2,2,5,5,정상
+100,3,-1,3,-4,-4,-2,-3,3,-4,-4,5,1,5,2,3,5,2,5,4,5,2,5,3,5,3,1,3,3,정상
+101,-4,-3,-1,-4,0,3,1,0,2,-1,5,1,4,1,3,5,2,5,3,5,2,5,4,5,3,1,4,2,정상
+102,-4,-4,-1,-4,-4,0,-2,4,4,-3,5,2,4,1,2,4,4,5,2,5,2,5,3,5,3,2,4,3,정상
+103,2,0,4,2,0,4,0,4,4,-1,5,2,4,2,3,5,3,5,3,4,5,5,4,5,2,2,4,3,정상
+104,4,2,4,-4,-3,-4,-4,1,-4,-4,5,1,5,2,3,5,1,5,4,4,3,5,3,5,3,1,3,3,정상
+105,1,0,0,-4,-4,-4,-4,-2,-4,-4,5,1,4,2,3,5,3,5,3,5,3,5,4,5,3,2,4,2,정상
+106,0,0,0,0,0,0,0,0,0,0,2,5,4,3,4,3,1,3,3,1,4,2,1,2,5,4,4,3,이상_쌍대동일(0)
+107,4,4,4,4,4,4,4,4,4,4,2,1,5,3,5,5,2,2,2,5,4,1,1,1,5,3,3,1,이상_쌍대동일(4)
+108,-4,-4,-4,-4,-4,-4,-4,-4,-4,-4,3,4,4,5,2,5,4,3,4,5,2,2,1,4,4,5,1,1,이상_쌍대동일(-4)
+109,-2,-4,-1,-4,-4,2,-4,4,0,-4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,2,3,1,이상_직관전부5점
+110,-1,-4,0,-2,-4,-2,-3,4,3,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,3,1,이상_직관전부1점
+111,-2,4,4,3,-2,4,-1,-4,3,-3,3,5,3,5,5,4,4,4,1,3,1,2,5,3,4,1,5,2,이상_완전무작위
+112,0,-4,-4,4,-2,-3,4,3,-2,-2,3,1,1,5,5,1,2,2,2,2,3,5,3,4,5,1,3,3,이상_완전무작위

analysis-code/res.csv

@@ -0,0 +1,57 @@
+피실험자ID,가중치_안정성,가중치_수익_가능성,가중치_자금_효율성,가중치_사업_계획_완성도,가중치_사회적_가치,CR,CI,lambda_max,직관_Q01,모델점수_Q01,모델판단_Q01,판단차이_Q01,불일치_Q01,직관_Q02,모델점수_Q02,모델판단_Q02,판단차이_Q02,불일치_Q02,직관_Q03,모델점수_Q03,모델판단_Q03,판단차이_Q03,불일치_Q03,직관_Q04,모델점수_Q04,모델판단_Q04,판단차이_Q04,불일치_Q04,직관_Q05,모델점수_Q05,모델판단_Q05,판단차이_Q05,불일치_Q05,직관_Q06,모델점수_Q06,모델판단_Q06,판단차이_Q06,불일치_Q06,직관_Q07,모델점수_Q07,모델판단_Q07,판단차이_Q07,불일치_Q07,직관_Q08,모델점수_Q08,모델판단_Q08,판단차이_Q08,불일치_Q08,직관_Q09,모델점수_Q09,모델판단_Q09,판단차이_Q09,불일치_Q09,직관_Q10,모델점수_Q10,모델판단_Q10,판단차이_Q10,불일치_Q10,직관_Q11,모델점수_Q11,모델판단_Q11,판단차이_Q11,불일치_Q11,직관_Q12,모델점수_Q12,모델판단_Q12,판단차이_Q12,불일치_Q12,직관_Q13,모델점수_Q13,모델판단_Q13,판단차이_Q13,불일치_Q13,직관_Q14,모델점수_Q14,모델판단_Q14,판단차이_Q14,불일치_Q14,직관_Q15,모델점수_Q15,모델판단_Q15,판단차이_Q15,불일치_Q15,불일치_합계,불일치율_전체(%),불일치율_베이스라인(%),불일치율_단일극값(%),불일치율_변인충돌(%),사후Q1_모델과직관차이,사후Q2_모델기준이해,사후Q3_모델합리성
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