About the Cronbach's Alpha Calculator
This Cronbach's alpha calculator measures the internal consistency of a set of survey or test items — how well they hang together as a measure of a single underlying construct such as job satisfaction, anxiety or customer loyalty. Paste the responses with one row per respondent and one column per item, and it returns Cronbach's alpha (α), the standardized alpha, the mean inter-item correlation and a George & Mallery rating from unacceptable to excellent.
Researchers, students writing theses, HR and market-research teams use alpha to report scale reliability and to decide which items to keep. The item table shows each item’s mean, standard deviation, corrected item-total correlation and the alpha you would get if that item were deleted, so weak or reverse-worded items stand out immediately.
Alpha uses sample variances (n − 1), the same convention as SPSS, R’s psych package and Excel-based templates. Reverse-scored items must be recoded before pasting (for a 1–5 scale, new = 6 − old). Rows with a different number of values from the first row are skipped, so remove or impute missing responses first.
With the default inputs, the cronbach's alpha (α) is 0.9263. Change any value above to recalculate instantly.
How to use the cronbach's alpha calculator
- 1Recode any reverse-worded items so that a high score means the same thing on every item.
- 2Paste the responses: one row per respondent, one column per item (copy straight from Excel or Google Sheets).
- 3Read Cronbach’s alpha and its rating; 0.70 or higher is commonly considered acceptable.
- 4Check the item table for low corrected item-total correlations (below about 0.30).
- 5Look at “alpha if deleted” to see whether removing an item would improve reliability.
Formula and method
Cronbach's alpha compares the variance of the individual items with the variance of the total score. If items measure the same thing, they covary, so the variance of their sum is much larger than the sum of their separate variances and alpha approaches 1. If items are unrelated, the two variances are similar and alpha is near 0. All variances here are sample variances with n − 1 in the denominator.
Standardized alpha uses the average Pearson correlation r̄ between all pairs of items instead of covariances; it equals the raw alpha when all items have the same variance and is the right figure if items are standardized before summing. The corrected item-total correlation correlates each item with the sum of the other items, and "alpha if deleted" recomputes alpha without that item.
- k
- Number of items in the scale
- sᵢ²
- Sample variance of item i across respondents
- sₜ²
- Sample variance of respondents’ total scores
- r̄
- Mean inter-item correlation
Worked examples
4-item satisfaction scale, 8 respondents
The four item variances add up to about 5.05, while the variance of respondents’ total scores is about 16.55, because people who score high on one item tend to score high on the others. Alpha = 4/3 × (1 − 5.054 ÷ 16.554) = 0.926, excellent internal consistency.
Scale with a reverse-worded item left uncoded
Items 1–3 move together but item 4 moves in the opposite direction, so its covariances with the others are negative and pull alpha down to about 0.05: the item variances (9.17) nearly equal the total-score variance (9.5). The item table flags item 4 with a strongly negative item-total correlation; recoding it as 6 − score would fix the scale.
Three-item quiz scored 0–10
Item variances total 7.77 against a total-score variance of 18.97, so alpha = 3/2 × (1 − 7.767 ÷ 18.967) = 0.886, good reliability for a short quiz.
Frequently asked questions
What is a good Cronbach's alpha?+
A common rule of thumb (George & Mallery) is: 0.9 or above excellent, 0.8–0.9 good, 0.7–0.8 acceptable, 0.6–0.7 questionable, 0.5–0.6 poor and below 0.5 unacceptable. Early-stage research sometimes accepts 0.6, while high-stakes tests aim for 0.9.
Can Cronbach’s alpha be too high?+
Yes. Values above about 0.95 often mean several items are near-duplicates, which lengthens the survey without adding information. Consider removing redundant items.
Why is my alpha negative?+
A negative alpha means the average covariance between items is negative. The usual cause is a reverse-worded item that was not recoded; recode it (for a 1–5 scale, 6 − score) and recalculate.
Does a high alpha mean the scale is unidimensional?+
No. Alpha measures how strongly items covary overall, and it rises with the number of items. A long scale can have a high alpha even if it measures two related constructs. Use factor analysis to check dimensionality.
What is the difference between raw and standardized alpha?+
Raw alpha uses item variances and covariances, so items with larger spreads carry more weight. Standardized alpha uses correlations, treating every item equally. Report raw alpha if you sum raw scores, and standardized alpha if you standardize items first.
How many respondents do I need?+
Alpha can be computed from a handful of respondents, but estimates are unstable below about 30. Most methodologists recommend 100 or more for a reliable estimate, especially for scales with few items.