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Run or generate Pingouin code for Cronbach alpha, item-scale reliability checks, and intraclass correlation for psychological ratings.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing.
Run or generate Pingouin code for multivariate comparisons — Hotelling's T-squared test (multivariate_ttest) — plus the multivariate assumption checks box_m (equal covariance) and multivariate_normality, for designs with several dependent variables.
Route psychology statistics requests to the smallest Pingouin workflow. Use when the user asks what analysis to run, gives a psychology design, asks to analyze data with Pingouin, or needs lower-token guidance before code generation.
Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.
Run or generate Pingouin code for categorical / contingency-table analyses — chi-square test of independence, McNemar's paired test, 2x2 crosstabs, and chi-square power — for psychology data with nominal variables.
Run or generate Pingouin code for rank-based non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Friedman, and Cochran Q — when outcomes are ordinal or parametric assumptions fail.
| name | pg-reliability |
| description | Run or generate Pingouin code for Cronbach alpha, item-scale reliability checks, and intraclass correlation for psychological ratings. |
Use for internal consistency and inter-rater/test-retest reliability.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.cronbach_alpha.pg.intraclass_corr.Cronbach alpha:
items = df[["item1", "item2", "item3", "item4"]]
alpha, ci = pg.cronbach_alpha(data=items)
print({"alpha": round(alpha, 3), "ci95": ci})
Item-total screening:
for col in items.columns:
alpha_drop, ci_drop = pg.cronbach_alpha(data=items.drop(columns=col))
print(col, round(alpha_drop, 3), ci_drop)
ICC:
icc = pg.intraclass_corr(data=df, targets="target",
raters="rater", ratings="rating").round(3)
pg.print_table(icc)