Alpha-Omega Comparison

Coming soon

Reliability (legacy hub)

This test is implemented and is currently going through StatMinds’ production verification: every statistic is independently checked against a trusted reference (scipy / R), locked with regression tests, and the screen is exercised across assumption-met/violated and significant/non-significant scenarios before it opens up.

See what’s live now

Reliability of a scale — how consistently its items measure one thing — reported as both Cronbach's α and McDonald's ω.

Combines the two standard internal-consistency coefficients: alpha (assumes equal item loadings) and omega (from a factor model, more accurate when loadings differ). Both range 0–1; ≥ .70 is acceptable, ≥ .80 good.

Worked example

Is a 12-item burnout scale internally consistent?

220 respondents completed the 12 items; alpha and omega estimate how consistently the items tap one construct.

Result

Both indicated good reliability, α = .88 and ω = .88.

How you'd report it (APA)

The 12-item scale showed good internal consistency, Cronbach's α = .88, McDonald's ω = .88.

Try it yourself: Load this ready-made sample and follow the run above.

When NOT to — use instead

  • Items are right/wrong (dichotomous)
    Use the binary-item reliability index. → KR-20

Ready to run a Alpha-Omega Comparison on your own data?

Guided setup, automatic assumption checks, effect sizes, figures and an APA write-up.

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