straightliner verification for SS questions
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@@ -74,11 +74,6 @@ def _(Path, RESULTS_FILE, data_all, mo):
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return
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@app.cell
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def _():
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return
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@app.cell
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def _(
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S,
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@@ -88,7 +83,8 @@ def _(
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duration_validation,
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mo,
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):
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sl_content, sl_df = check_straight_liners(S.get_ss_green_blue(data_all)[0], max_score=5)
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_ss_all = S.get_ss_green_blue(data_all)[0].join(S.get_ss_orange_red(data_all)[0], on='_recordId')
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sl_content = check_straight_liners(_ss_all, max_score=5)
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mo.md(f"""
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## Data Validation
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@@ -106,18 +102,6 @@ def _(
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return
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@app.cell
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def _(mo):
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mo.md(r"""
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---
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# Data Filter
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Use to select a subset of the data for the following analysis
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""")
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return
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@app.cell(hide_code=True)
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def _(S, mo):
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filter_form = mo.md('''
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147
validation.py
147
validation.py
@@ -1,6 +1,7 @@
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import marimo as mo
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import polars as pl
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import altair as alt
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from theme import ColorPalette
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def check_progress(data):
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"""Check if all responses are complete based on 'progress' column."""
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@@ -115,8 +116,9 @@ def check_straight_liners(data, max_score=3):
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).alias(f"__is_straight__{key}")
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value_expr = safe_val.alias(f"__val__{key}")
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has_data = (list_expr.list.len() > 0).alias(f"__has_data__{key}")
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expressions.extend([is_straight, value_expr])
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expressions.extend([is_straight, value_expr, has_data])
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# collect data with checks
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# We only need _recordId and the check columns
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@@ -157,14 +159,145 @@ def check_straight_liners(data, max_score=3):
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outlier_df = pl.DataFrame(outliers)
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return f"""### Straight-lining Checks:
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# --- Analysis & Visualization ---
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**⚠️ Potential straight-liners detected ⚠️**
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total_respondents = checked_data.height
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Respondents selected the same value (<= {max_score}) for all attributes in the following groups:
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# 1. & 3. Percentage Calculation
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group_stats = []
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value_dist_data = []
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{mo.ui.table(outlier_df)}
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""", outlier_df
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# Calculate Straight-Liners for ALL groups found in Data
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# Condition: Respondent straight-lined ALL questions that they actually answered (ignoring empty/skipped questions)
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# Logic: For every group G: if G has data (len > 0), then G must be straight.
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# Also, the respondent must have answered at least one question group.
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conditions = []
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has_any_data_exprs = []
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for key in multi_attribute_groups.keys():
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flag_col = f"__is_straight__{key}"
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data_col = f"__has_data__{key}"
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# If has_data is True, is_straight MUST be True for it to count as valid straight-lining behavior for that user.
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# Equivalent: (not has_data) OR is_straight
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cond = (~pl.col(data_col)) | pl.col(flag_col)
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conditions.append(cond)
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has_any_data_exprs.append(pl.col(data_col))
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all_straight_count = checked_data.filter(
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pl.all_horizontal(conditions) & pl.any_horizontal(has_any_data_exprs)
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).height
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all_straight_pct = (all_straight_count / total_respondents) * 100
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for key in multi_attribute_groups.keys():
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flag_col = f"__is_straight__{key}"
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val_col = f"__val__{key}"
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# Filter for straight-liners in this specific group
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sl_sub = checked_data.filter(pl.col(flag_col))
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count = sl_sub.height
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pct = (count / total_respondents) * 100
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group_stats.append({
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"Question Group": key,
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"Straight-Liner %": pct,
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"Count": count
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})
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# Get Value Distribution for this group's straight-liners
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if count > 0:
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# Group by the Value they straight-lined
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dist = sl_sub.group_by(val_col).agg(pl.len().alias("count"))
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for row in dist.iter_rows(named=True):
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value_dist_data.append({
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"Question Group": key,
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"Value": row[val_col],
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"Count": row["count"]
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})
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stats_df = pl.DataFrame(group_stats)
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dist_df = pl.DataFrame(value_dist_data)
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# Plot 1: % of Responses with Straight-Liners per Question
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# Vertical bars with Count label on top
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base_pct = alt.Chart(stats_df).encode(
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x=alt.X("Question Group", sort=alt.EncodingSortField(field="Straight-Liner %", order="descending"))
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)
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bars_pct = base_pct.mark_bar(color=ColorPalette.PRIMARY).encode(
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y=alt.Y("Straight-Liner %:Q", axis=alt.Axis(format=".1f", title="Share of all responses [%]")),
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tooltip=["Question Group", alt.Tooltip("Straight-Liner %:Q", format=".1f"), "Count"]
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)
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text_pct = base_pct.mark_text(dy=-10).encode(
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y=alt.Y("Straight-Liner %:Q"),
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text=alt.Text("Count")
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)
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chart_pct = (bars_pct + text_pct).properties(
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title="Share of Responses with Straight-Liners per Question",
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width=800,
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height=300
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)
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# Plot 2: Value Distribution (Horizontal Stacked Normalized Bar)
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# Question Groups sorted by Total Count
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# Values stacked 1 (left) -> 5 (right)
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# Legend on top
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# Total count at bar end
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# Sort order for Y axis (Question Group) based on total Count (descending)
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# Explicitly calculate sort order from stats_df to ensure consistency across layers
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# High counts at the top
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sorted_groups = stats_df.sort("Count", descending=True)["Question Group"].to_list()
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# Base chart for Bars
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# Use JPMC-aligned colors (blues) instead of default categorical rainbow
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# Remove legend title as per plots.py style
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bars_dist = alt.Chart(dist_df).mark_bar().encode(
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y=alt.Y("Question Group", sort=sorted_groups),
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x=alt.X("Count", stack="normalize", axis=alt.Axis(format="%"), title="Share of SL Responses"),
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color=alt.Color("Value:O",
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title=None, # explicit removal of title like in plots.py
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scale=alt.Scale(scheme="blues"), # Professional blue scale
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legend=alt.Legend(orient="top", direction="horizontal")
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),
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order=alt.Order("Value", sort="ascending"), # Ensures 1 is Left, 5 is Right
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tooltip=["Question Group", "Value", "Count"]
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)
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# Text layer for Total Count (using stats_df which already has totals)
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# using same sort for Y
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text_dist = alt.Chart(stats_df).mark_text(align='left', dx=5).encode(
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y=alt.Y("Question Group", sort=sorted_groups),
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x=alt.datum(1.0), # Position at 100%
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text=alt.Text("Count")
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)
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chart_dist = (bars_dist + text_dist).properties(
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title="Distribution of Straight-Lined Values",
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width=800,
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height=500
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)
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analysis_md = f"""
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### Straight-Lining Analysis
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*"Straight-lining" is defined here as selecting the same response value for all attributes within a multi-attribute question group.*
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* **Total Respondents**: {total_respondents}
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* **Respondents straight-lining ALL questions presented to them**: {all_straight_pct:.2f}% ({all_straight_count} respondents)
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"""
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return mo.vstack([
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mo.md(f"### Straight-lining Checks:\n\n**⚠️ Potential straight-liners detected ⚠️**\n\n"),
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mo.ui.table(outlier_df),
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mo.md(analysis_md),
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mo.md("#### Speaking Style Question Groups"),
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alt.vconcat(chart_pct, chart_dist).resolve_legend(color="independent")
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])
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