statistical significance

XX_statistical_significance.script.py

@@ -186,6 +186,20 @@ S.plot_significance_heatmap(_pairwise_df, metadata=_meta, title="Statistical Sig
 Yes, "The Coach" can be considered statistically more significant than the other options. It is clearly superior to the bottom two options and holds a statistically significant lead over the runner-up ("Personal Assistant") in direct comparison.
 """
 
+# %% Mentions significance analysis
+
+char_pairwise_df_mentions, _meta_mentions = S.compute_mentions_significance(
+    char_rank,
+    alpha=0.05,
+    correction="none",
+)
+
+S.plot_significance_heatmap(
+    char_pairwise_df_mentions,
+    metadata=_meta_mentions,
+    title="Statistical Significance: Character Total Mentions (Top 3 Visibility)"
+)
+
 
 # %% voices analysis
 top3_voices = S.get_top_3_voices(data)[0]
@@ -200,4 +214,119 @@ S.plot_significance_heatmap(
     metadata=_metadata,
     title="Statistical Significance: Voice Top Choice Preference"
 )
+# %% Total Mentions Significance (Rank 1+2+3 Combined)
+# This tests "Quantity" (Visibility) instead of "Quality" (Preference)
+
+_pairwise_df_mentions, _meta_mentions = S.compute_mentions_significance(
+    top3_voices,
+    alpha=0.05,
+    correction="none"
+)
+
+S.plot_significance_heatmap(
+    _pairwise_df_mentions,
+    metadata=_meta_mentions,
+    title="Statistical Significance: Voice Total Mentions (Top 3 Visibility)"
+)
+# %% Male Voices Only Analysis
+import reference
+
+def filter_voices_by_gender(df: pl.DataFrame, target_gender: str) -> pl.DataFrame:
+    """Filter ranking columns to keep only those matching target gender."""
+    cols_to_keep = []
+    
+    # Always keep identifier if present
+    if '_recordId' in df.columns:
+        cols_to_keep.append('_recordId')
+        
+    for col in df.columns:
+        # Check if column is a voice column (contains Vxx)
+        # Format is typically "Top_3_Voices_ranking__V14"
+        if '__V' in col:
+            voice_id = col.split('__')[1]
+            if reference.VOICE_GENDER_MAPPING.get(voice_id) == target_gender:
+                cols_to_keep.append(col)
+                
+    return df.select(cols_to_keep)
+
+# Get full ranking data as DataFrame
+df_voices = top3_voices.collect()
+
+# Filter for Male voices
+df_male_voices = filter_voices_by_gender(df_voices, 'Male')
+
+# 1. Male Voices: Top Choice Preference (Rank 1)
+_pairwise_male_pref, _meta_male_pref = S.compute_ranking_significance(
+    df_male_voices,
+    alpha=0.05,
+    correction="none"
+)
+
+S.plot_significance_heatmap(
+    _pairwise_male_pref,
+    metadata=_meta_male_pref,
+    title="Male Voices Only: Top Choice Preference Significance"
+)
+
+# 2. Male Voices: Total Mentions (Visibility)
+_pairwise_male_vis, _meta_male_vis = S.compute_mentions_significance(
+    df_male_voices,
+    alpha=0.05,
+    correction="none"
+)
+
+S.plot_significance_heatmap(
+    _pairwise_male_vis,
+    metadata=_meta_male_vis,
+    title="Male Voices Only: Total Mentions Significance"
+)
+# %% Male Voices (Excluding Bottom 3: V88, V86, V81)
+
+# Start with the male voices dataframe from the previous step
+voices_to_exclude = ['V88', 'V86', 'V81']
+
+def filter_exclude_voices(df: pl.DataFrame, exclude_list: list[str]) -> pl.DataFrame:
+    """Filter ranking columns to exclude specific voices."""
+    cols_to_keep = []
+    
+    # Always keep identifier if present
+    if '_recordId' in df.columns:
+        cols_to_keep.append('_recordId')
+        
+    for col in df.columns:
+        # Check if column is a voice column (contains Vxx)
+        if '__V' in col:
+            voice_id = col.split('__')[1]
+            if voice_id not in exclude_list:
+                cols_to_keep.append(col)
+                
+    return df.select(cols_to_keep)
+
+df_male_top = filter_exclude_voices(df_male_voices, voices_to_exclude)
+
+# 1. Male Top Candidates: Top Choice Preference
+_pairwise_male_top_pref, _meta_male_top_pref = S.compute_ranking_significance(
+    df_male_top,
+    alpha=0.05,
+    correction="none"
+)
+
+S.plot_significance_heatmap(
+    _pairwise_male_top_pref,
+    metadata=_meta_male_top_pref,
+    title="Male Voices (Excl. Bottom 3): Top Choice Preference Significance"
+)
+
+# 2. Male Top Candidates: Total Mentions
+_pairwise_male_top_vis, _meta_male_top_vis = S.compute_mentions_significance(
+    df_male_top,
+    alpha=0.05,
+    correction="none"
+)
+
+S.plot_significance_heatmap(
+    _pairwise_male_top_vis,
+    metadata=_meta_male_top_vis,
+    title="Male Voices (Excl. Bottom 3): Total Mentions Significance"
+)
 # %%

utils.py

@@ -1588,6 +1588,120 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         
         return results_df, metadata
 
+    def compute_mentions_significance(
+        self,
+        data: pl.LazyFrame | pl.DataFrame,
+        alpha: float = 0.05,
+        correction: str = "bonferroni",
+    ) -> tuple[pl.DataFrame, dict]:
+        """Compute statistical significance for Total Mentions (Rank 1+2+3).
+        
+        Tests whether the proportion of respondents who included a voice in their Top 3
+        is significantly different between voices.
+        
+        Args:
+            data: Ranking data (rows=respondents, cols=voices, values=rank).
+            alpha: Significance level.
+            correction: Multiple comparison correction method.
+            
+        Returns:
+            tuple: (pairwise_df, metadata)
+        """
+        from scipy import stats as scipy_stats
+        import numpy as np
+        
+        if isinstance(data, pl.LazyFrame):
+            df = data.collect()
+        else:
+            df = data
+        
+        ranking_cols = [c for c in df.columns if c != '_recordId']
+        if len(ranking_cols) < 2:
+            raise ValueError("Need at least 2 ranking columns")
+        
+        total_respondents = df.height
+        mentions_data = {}
+        
+        # Count mentions (any rank) for each voice
+        for col in ranking_cols:
+            label = self._clean_voice_label(col)
+            count = df.filter(pl.col(col).is_not_null()).height
+            mentions_data[label] = count
+            
+        labels = sorted(list(mentions_data.keys()))
+        results = []
+        n_comparisons = len(labels) * (len(labels) - 1) // 2
+        
+        for i, label1 in enumerate(labels):
+            for label2 in labels[i+1:]:
+                count1 = mentions_data[label1]
+                count2 = mentions_data[label2]
+                
+                pct1 = count1 / total_respondents
+                pct2 = count2 / total_respondents
+                
+                # Z-test for two proportions
+                n1 = total_respondents
+                n2 = total_respondents
+                
+                p_pooled = (count1 + count2) / (n1 + n2)
+                se = np.sqrt(p_pooled * (1 - p_pooled) * (1/n1 + 1/n2))
+                
+                if se > 0:
+                    z_stat = (pct1 - pct2) / se
+                    p_value = 2 * (1 - scipy_stats.norm.cdf(abs(z_stat)))
+                else:
+                    p_value = 1.0
+                
+                results.append({
+                    'group1': label1,
+                    'group2': label2,
+                    'p_value': float(p_value),
+                    'rank1_count1': count1, # Reusing column names for compatibility with heatmap plotting
+                    'rank1_count2': count2,
+                    'rank1_pct1': round(pct1 * 100, 1),
+                    'rank1_pct2': round(pct2 * 100, 1),
+                    'total1': n1,
+                    'total2': n2,
+                    'effect_size': pct1 - pct2 # Difference in proportions
+                })
+        
+        results_df = pl.DataFrame(results)
+        
+        p_values = results_df['p_value'].to_numpy()
+        p_adjusted = np.full_like(p_values, np.nan, dtype=float)
+        
+        if correction == "bonferroni":
+            p_adjusted = np.minimum(p_values * n_comparisons, 1.0)
+        elif correction == "holm":
+            sorted_idx = np.argsort(p_values)
+            sorted_p = p_values[sorted_idx]
+            m = len(sorted_p)
+            adjusted = np.zeros(m)
+            for j in range(m):
+                adjusted[j] = sorted_p[j] * (m - j)
+            for j in range(1, m):
+                adjusted[j] = max(adjusted[j], adjusted[j-1])
+            adjusted = np.minimum(adjusted, 1.0)
+            p_adjusted = adjusted[np.argsort(sorted_idx)]
+        elif correction == "none":
+            p_adjusted = p_values.astype(float) # pyright: ignore
+            
+        results_df = results_df.with_columns([
+            pl.Series('p_adjusted', p_adjusted),
+            pl.Series('significant', p_adjusted < alpha),
+        ]).sort('p_value')
+        
+        metadata = {
+            'test_type': 'proportion_z_test_mentions',
+            'alpha': alpha,
+            'correction': correction,
+            'n_comparisons': n_comparisons,
+        }
+        
+        return results_df, metadata
+
+
 
 def process_speaking_style_data(
     df: Union[pl.LazyFrame, pl.DataFrame],