pg-reliabilitylisted

# PG Reliability Use for internal consistency and inter-rater/test-retest reliability. ## Load Read: - `../../references/supervision-gates.md` - `../../references/pingouin-api-quickref.md` - `../../references/apa-output-template.md` if writing results. ## Function Choice - Internal consistency of multiple scale items -> `pg.cronbach_alpha`. - Inter-rater, test-retest, or target-by-rater reliability -> `pg.intraclass_corr`. ## Required Inputs - Item columns and reverse-coded item list for alpha. - Whether items are all intended to measure one construct. - Long-format ICC columns: target, rater, rating. - ICC model decision: single vs average, consistency vs agreement, one-way vs two-way. ## Code Patterns Cronbach alpha: ```python items = df[["item1", "item2", "item3", "item4"]] alpha, ci = pg.cronbach_alpha(data=items) print({"alpha": round(alpha, 3), "ci95": ci}) ``` Item-total screening: ```python 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: ```python icc = pg.intraclass_corr(data=df, targets="target", raters="rater", ratings="rating").round(3) pg.print_table(icc) ``` ## Reporting - Alpha: number of items, alpha, CI, and whether any reverse coding was applied. - ICC: exact row/type from Pingouin output, ICC value, CI, F, df, p if available, and model interpretation. ## Guardrails - Alpha does not prove unidimensionality; me