Ch15: Add item–total tables and alpha variants to reliability analysis

Summary

No response

Details

Summary

Extend the Chapter 15 reliability analysis (scripts/ch15_reliability_analysis.py)
with richer psychometrics output:

1. An item–total correlation table for the survey items.
2. One or two alpha variants (e.g., standardized alpha, alpha if item dropped).

Goals

• Use pingouin or pandas to compute item–total correlations for each item.
• Save an item–total table to outputs/ch15/ch15_item_total.csv, including:
  • item name (e.g., q1, q2, …),
  • item–total correlation,
  • (optionally) “alpha if item dropped”.
• Report at least one alpha variant in the JSON summary
  (ch15_reliability_summary.json), e.g.:
  • alpha_standardized
  • alpha_if_item_dropped (as a dict keyed by item)
• Keep existing outputs (Cronbach’s alpha, ICC, Bland–Altman plot) unchanged.

Hints

• pingouin has helpers like cronbach_alpha; its documentation also
  discusses item-level diagnostics.
• An item–total correlation is typically the correlation of each item with
  the sum of the remaining items.
• Follow the existing JSON structure so downstream code and docs remain simple.

Difficulty

Low/medium: good first issue for someone comfortable with pandas and basic stats.

Files to Touch

No response

Contributor Checklist

• I have read CONTRIBUTING.md.
• I can run make lint locally.
• I can run make test locally.
• I have checked for existing issues/PRs that might overlap.

Start with scripts/ch15_reliability_analysis.py and inspect how the current JSON summary and Chapter 15 outputs are produced. Run the existing analysis to establish the current Cronbach’s alpha, ICC, and Bland–Altman outputs, then add the requested item–total CSV and at least one alpha variant while preserving those outputs. Done means the new CSV and ch15_reliability_summary.json contain the requested diagnostics.