added ranking

Data/medium_order.csv

@@ -0,0 +1,19 @@
+Participant,Rank1,Rank2,Rank3
+P1,VR,Chat,Video
+P2,Chat,Video,VR
+P3,VR,Chat,Video
+P4,VR,Video,Chat
+P5,VR,Chat,Video
+P6,Chat,Video,VR
+P7,Chat,Video,VR
+P8,Chat,Video,VR
+P9,Chat,VR,Video
+P10,Chat,VR,Video
+P11,Chat,VR,Video
+P12,Chat,VR,Video
+P13,Video,VR,Chat
+P14,Chat,Video,VR
+P15,VR,Video,Chat
+P16,Chat,Video,VR
+P17,VR,Video,Chat
+P18,Chat,Video,VR

README.md

@@ -395,6 +395,12 @@ Four-panel dashboard comparing reading and tutoring phases across IMI subscales,
 
 VR-specific panel comparing social presence, cybersickness, and Godspeed across mediums. VR achieves the highest social presence (M=3.01 vs Chat M=2.10), moderate cybersickness symptoms, and Godspeed impressions comparable to the other mediums. The elevated social presence in VR without a corresponding increase in Godspeed tutor impression suggests that VR enhances the sense of "being there" without necessarily changing how the tutor is perceived.
 
+### F14 – Medium Preference Rankings (Friedman Test)
+
+![Medium Preference Rankings](Data/plots_questionnaires/Q14_medium_ranking.png)
+
+Within-subject ranking analysis: each of the 18 participants ranked the three tutoring mediums from 1 (most preferred) to 3 (least preferred). **Left panel** shows mean rank per condition with individual data points (jitter) and SEM error bars. **Right panel** shows the percentage of participants assigning each rank position to each condition. Chat received the lowest mean rank (M=1.61), indicating it was most often preferred first (50% of participants ranked it 1st). Video received the highest mean rank (M=2.33), most often placed last. A Friedman test showed no statistically significant difference across mediums (χ²(2) = 4.78, p = .092, Kendall's W = 0.13), likely due to limited power with N=18. Post-hoc Wilcoxon tests (Bonferroni-corrected α = .017) revealed a trend for Chat > Video (p = .027 raw, p = .080 adjusted) that did not survive correction.
+
 ---
 
 ## Summary

analysis_medium_ranking.py

@@ -0,0 +1,197 @@
+"""
+Friedman Test on medium preference rankings.
+
+Data format: each row = one participant; Rank1/Rank2/Rank3 hold the
+condition name (VR, Chat, Video) assigned to that rank position.
+
+We reshape so every participant has a numeric rank (1–3) for every
+condition, then run a Friedman test and save a summary plot.
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import seaborn as sns
+from pathlib import Path
+from scipy import stats
+
+# ── 1. Load & reshape ─────────────────────────────────────────────────────────
+df_raw = pd.read_csv("Data/medium_order.csv")
+
+# Build a long table: (Participant, Condition, Rank)
+records = []
+for _, row in df_raw.iterrows():
+    for rank_pos, col in enumerate(["Rank1", "Rank2", "Rank3"], start=1):
+        records.append({
+            "Participant": row["Participant"],
+            "Condition":   row[col],
+            "Rank":        rank_pos,
+        })
+
+df_long = pd.DataFrame(records)
+
+# Pivot to wide: rows = participants, columns = conditions
+df_wide = df_long.pivot(index="Participant", columns="Condition", values="Rank")
+df_wide = df_wide[["VR", "Chat", "Video"]]   # fix column order
+
+print("=" * 55)
+print("Reshaped ranking matrix (numeric ranks per condition)")
+print("=" * 55)
+print(df_wide.to_string())
+print()
+
+# ── 2. Descriptive statistics ─────────────────────────────────────────────────
+desc = df_wide.agg(["mean", "median", "std"]).T
+desc.index.name = "Condition"
+desc.columns   = ["Mean rank", "Median rank", "SD"]
+
+print("=" * 55)
+print("Descriptive statistics (lower rank = more preferred)")
+print("=" * 55)
+print(desc.round(3).to_string())
+print()
+
+# ── 3. Friedman test ──────────────────────────────────────────────────────────
+# scipy expects one array per group (condition), each of length n_participants
+statistic, p_value = stats.friedmanchisquare(
+    df_wide["VR"],
+    df_wide["Chat"],
+    df_wide["Video"],
+)
+
+n  = len(df_wide)
+k  = 3                              # number of conditions
+df_friedman = k - 1                 # degrees of freedom
+# Kendall's W (effect size)
+W = statistic / (n * (k - 1))
+
+print("=" * 55)
+print("Friedman test")
+print("=" * 55)
+print(f"  N (participants) : {n}")
+print(f"  k (conditions)   : {k}")
+print(f"  chi2({df_friedman})           : {statistic:.4f}")
+print(f"  p-value          : {p_value:.4f}")
+print(f"  Kendall's W      : {W:.4f}")
+print()
+if p_value < 0.05:
+    print("  --> Significant difference in rankings (p < .05).")
+else:
+    print("  --> No significant difference in rankings (p >= .05).")
+print()
+
+# ── 4. Post-hoc: Wilcoxon signed-rank tests (Bonferroni-corrected) ────────────
+from itertools import combinations
+
+pairs      = list(combinations(["VR", "Chat", "Video"], 2))
+n_pairs    = len(pairs)
+alpha_corr = 0.05 / n_pairs        # Bonferroni threshold
+
+print("=" * 55)
+print(f"Post-hoc: Wilcoxon signed-rank tests")
+print(f"(Bonferroni-corrected alpha = {alpha_corr:.4f})")
+print("=" * 55)
+
+posthoc_rows = []
+for a, b in pairs:
+    stat_w, p_w = stats.wilcoxon(df_wide[a], df_wide[b])
+    sig = "*" if p_w < alpha_corr else ""
+    posthoc_rows.append({
+        "Pair":       f"{a} vs {b}",
+        "W statistic": round(stat_w, 4),
+        "p (raw)":    round(p_w, 4),
+            "p (x3)":     round(min(p_w * n_pairs, 1.0), 4),
+        "Sig.":       sig,
+    })
+
+df_posthoc = pd.DataFrame(posthoc_rows).set_index("Pair")
+print(df_posthoc.to_string())
+print(f"\n  * Significant after Bonferroni correction (alpha = {alpha_corr:.4f})")
+
+# ── 5. Plot ───────────────────────────────────────────────────────────────────
+CONDITIONS  = ["VR", "Chat", "Video"]
+MED_COLORS  = {"Chat": "#42A5F5", "Video": "#FFA726", "VR": "#66BB6A"}
+RANK_COLORS = ["#4CAF50", "#FFC107", "#F44336"]   # 1st, 2nd, 3rd
+
+sns.set_theme(style="whitegrid", font_scale=1.05)
+plt.rcParams["figure.dpi"]    = 150
+plt.rcParams["savefig.bbox"]  = "tight"
+
+fig, axes = plt.subplots(1, 2, figsize=(13, 5))
+
+# ── Left panel: mean rank + individual jitter ─────────────────────────────────
+ax = axes[0]
+x_pos = np.arange(len(CONDITIONS))
+
+for i, cond in enumerate(CONDITIONS):
+    vals  = df_wide[cond].values
+    jitter = np.random.default_rng(42).uniform(-0.12, 0.12, size=len(vals))
+    ax.scatter(np.full(len(vals), i) + jitter, vals,
+               color=MED_COLORS[cond], alpha=0.55, s=40, zorder=3)
+
+means = [df_wide[c].mean() for c in CONDITIONS]
+sems  = [df_wide[c].sem()  for c in CONDITIONS]
+
+bars = ax.bar(x_pos, means, yerr=sems, capsize=5,
+              color=[MED_COLORS[c] for c in CONDITIONS],
+              edgecolor="gray", linewidth=0.6, alpha=0.75, width=0.5, zorder=2)
+
+for i, (m, s) in enumerate(zip(means, sems)):
+    ax.text(i, m + s + 0.08, f"M={m:.2f}", ha="center", fontsize=10, fontweight="bold")
+
+ax.set_xticks(x_pos)
+ax.set_xticklabels(CONDITIONS, fontsize=12)
+ax.set_ylim(0.5, 3.8)
+ax.set_yticks([1, 2, 3])
+ax.set_yticklabels(["1st\n(most preferred)", "2nd", "3rd\n(least preferred)"], fontsize=9)
+ax.set_ylabel("Rank (lower = more preferred)", fontsize=11)
+ax.set_title("Mean Rank per Condition\n(individual observations + SEM)", fontsize=11, fontweight="bold")
+
+stat_label = (
+    f"Friedman: chi2({df_friedman}) = {statistic:.2f}, "
+    f"p = {p_value:.3f}, W = {W:.2f}"
+)
+ax.text(0.5, 0.03, stat_label, transform=ax.transAxes,
+        ha="center", va="bottom", fontsize=8.5, color="dimgray",
+        bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="lightgray", alpha=0.8))
+
+# ── Right panel: stacked rank-distribution bars ───────────────────────────────
+ax = axes[1]
+n_participants = len(df_wide)
+rank_counts = {
+    cond: [(df_wide[cond] == r).sum() / n_participants * 100 for r in [1, 2, 3]]
+    for cond in CONDITIONS
+}
+
+bottoms = np.zeros(len(CONDITIONS))
+for rank_idx, (rank_label, color) in enumerate(
+    zip(["1st choice", "2nd choice", "3rd choice"], RANK_COLORS)
+):
+    heights = [rank_counts[c][rank_idx] for c in CONDITIONS]
+    bars = ax.bar(x_pos, heights, bottom=bottoms,
+                  color=color, edgecolor="white", linewidth=0.8,
+                  width=0.55, label=rank_label)
+    for xi, (h, b) in enumerate(zip(heights, bottoms)):
+        if h >= 8:
+            ax.text(xi, b + h / 2, f"{h:.0f}%",
+                    ha="center", va="center", fontsize=10,
+                    fontweight="bold", color="white")
+    bottoms += np.array(heights)
+
+ax.set_xticks(x_pos)
+ax.set_xticklabels(CONDITIONS, fontsize=12)
+ax.set_ylim(0, 105)
+ax.set_ylabel("Percentage of participants (%)", fontsize=11)
+ax.set_title("Rank Distribution per Condition\n(% participants assigning each rank)", fontsize=11, fontweight="bold")
+ax.legend(loc="upper right", fontsize=9,
+          handles=[mpatches.Patch(color=c, label=l)
+                   for c, l in zip(RANK_COLORS, ["1st choice", "2nd choice", "3rd choice"])])
+
+fig.suptitle("Medium Preference Rankings (N=18, within-subjects)", fontsize=13, fontweight="bold")
+fig.tight_layout()
+
+out_path = Path("Data/plots_questionnaires/Q14_medium_ranking.png")
+fig.savefig(out_path)
+plt.close(fig)
+print(f"\nPlot saved: {out_path}")