Cronbach's Alpha Calculator

Compute Cronbach's alpha (raw and standardized) for scale reliability from item-level Likert data. See alpha if each item is deleted and the mean inter-item correlation.

What Is Cronbach's Alpha?

Cronbach's alpha is the most widely used measure of internal consistency reliability. It estimates how strongly the items of a scale correlate with one another as a group, based on the average inter-item covariance relative to the total score variance.

Alpha ranges from 0 to 1. Values of 0.70 or above are commonly considered acceptable for research scales, while higher values (0.80–0.90) are preferred for individual decision-making. Very high values (above 0.95) may indicate redundant items.

The raw alpha uses item variances and total-score variance directly. The standardized alpha uses the mean inter-item correlation and assumes all items have equal variance. When item variances differ greatly, the two can diverge noticeably.

Key Formulas

Raw alpha compares the sum of item variances to the total-score variance; standardized alpha is a function of the mean inter-item correlation r̄:

α = (k / (k−1)) × (1 − Σσᵢ² / σ²ₓ)
α_std = (k × r̄) / (1 + (k−1) × r̄)

How to Use This Calculator

Follow these steps:

  1. Arrange your data as a matrix: one row per respondent, with item scores separated by spaces, commas or tabs.
  2. Paste the matrix into the text area and click “Compute Alpha”.
  3. Read raw and standardized alpha, the mean inter-item correlation, and the alpha-if-item-deleted table to check whether any item hurts reliability.

Worked Example

A 4-item scale rated by 6 respondents (rows are people, columns are items):

4 3 5 2 3 4 4 3 5 5 5 4 2 3 4 3 4 4 5 5 3 4 4 4 Raw α ≈ 0.74, standardized α ≈ 0.74 Mean inter-item correlation r̄ ≈ 0.42 No single item deletion raises α above 0.80.

Frequently Asked Questions

Have another question? Let us know.

What is the difference between raw and standardized alpha?

Raw alpha uses the actual item variances, so it does not assume equal variances. Standardized alpha replaces item variances with the average inter-item correlation, effectively assuming equal variances. They agree when item variances are similar and diverge when they are not.

How do I interpret alpha-if-item-deleted values?

For each item, this value is the alpha computed from the remaining items. If deleting an item raises alpha noticeably, that item may be poorly correlated with the rest of the scale. Use it as a diagnostic, not as a reason to remove items solely for a higher number.

What is a good alpha value?

For research purposes 0.70 is a common minimum, with 0.80 preferred. Values above 0.95 can suggest item redundancy. Alpha depends on scale length: all else equal, longer scales produce higher alpha.

Why do some rows or columns contain missing values?

This calculator performs listwise analysis and requires a complete matrix: every row must contain the same number of items and all values must be numeric. In practice, handle missing responses (e.g., with imputation or partial scales) before computing alpha.

Related Reading

Buy me a coffee