Cronbach’s alpha
Descriptive statistics · Method and input reference
Rows are subjects, columns are numeric variables/items/raters. Partial correlation controls for columns 3 onward. Reliability reports nominal agreement for two raters or Fleiss kappa for multiple raters. Synthetic example: 180 respondents answering six 1–5 symptom items. Shared severity creates correlated responses with ties.
Open calculator · Compare methods in the decision matrix
When to consider it
- Summarize internal consistency of numeric questionnaire items.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Use the outcome and predictor coding required by the calculator; review missing data and model diagnostics.
Required inputs
- Data matrix (one observation per line; comma-separated columns) — Numeric observations; preserve the row and column structure described above.
Review missing values and data coding before running. Sample inputs are synthetic teaching examples.
Implementation
Lifescio formulas and summaries over NumPy arrays. Sample variances use ddof=1; custom formulas are described in the method help.
- numpy 2.2.6: numpy.var
These are pinned library versions; saved results record the versions actually used at runtime.