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Cronbach’s alpha calculator

Internal-consistency reliability for multi-item scales — α plus corrected item–total correlations and alpha-if-item-deleted diagnostics.

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Your data

Paste your response grid: one row per respondent, one column per scale item (separated by spaces, commas or tabs). Example: a 4-item scale answered by 10 people = 10 lines of 4 numbers.

 
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When to use this

Cronbach’s alpha is the standard report of internal consistency for a multi-item scale: when several questionnaire items are meant to measure the same construct (job satisfaction, anxiety, brand trust), alpha estimates how consistently respondents answer across those items. Compute it for every scale (and subscale) you use, before averaging items into a score. The item diagnostics matter as much as alpha itself: a low corrected item–total correlation flags an item that does not belong, and "alpha if item deleted" shows whether removing it would help.

Key assumptions

  • All items measure a single underlying construct (unidimensionality) — alpha is not a test of this; check with factor analysis.
  • Items are scored in the same direction — reverse-code negatively worded items first.
  • Responses are at least roughly interval (summed Likert items are conventionally accepted).
  • Complete responses; this calculator drops respondents with missing item answers listwise.

Common mistakes

  • Forgetting to reverse-code items — one reversed item can crater alpha (even make it negative).
  • Treating high alpha as proof of unidimensionality; a two-factor scale can still show α > .8.
  • Chasing a higher alpha by adding redundant items — alpha mechanically rises with scale length.
  • Computing one alpha for a whole questionnaire instead of one per subscale.

Frequently asked questions

What is an acceptable Cronbach’s alpha?
Common guidance: ≥ .90 excellent, ≥ .80 good, ≥ .70 acceptable, .60–.70 questionable, below .60 poor. For high-stakes individual decisions, aim higher (≥ .90). Very high values (≥ .95) can indicate redundant items rather than a better scale.
How should I format my data?
One row per respondent, one column per item — exactly as it would appear in a spreadsheet. Separate values with spaces, commas or tabs. Reverse-code negatively worded items before pasting (e.g. on a 1–5 scale, recode 1→5, 2→4, …).
What does “alpha if item deleted” tell me?
It recomputes alpha for the scale without that item. If deleting an item would clearly raise alpha and the item also has a low corrected item–total correlation (below ~.30), the item probably measures something different — a candidate for revision or removal.

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