What is a good Cronbach's alpha value?

Alpha ≥ 0.9 is excellent, 0.8–0.9 is good, 0.7–0.8 is acceptable, 0.6–0.7 is questionable, and below 0.6 is generally considered poor for research purposes. Acceptable thresholds vary by field and purpose.

How should I format my data?

Each row represents one respondent and each column represents one item. Values should be separated by tabs (copy directly from Excel or Google Sheets). Missing values are not supported — ensure the matrix is complete.

What does 'alpha if item deleted' mean?

It shows the alpha you would get if that particular item were removed from the scale. An item is a candidate for deletion if removing it noticeably increases alpha, suggesting it does not measure the same construct as the others.

What is the minimum sample size for a reliable estimate?

A common rule of thumb is at least 10 respondents per item, with a minimum of 30 respondents total. Fewer respondents make alpha estimates unstable.

Does higher alpha always mean a better scale?

Not necessarily. Very high alpha (> 0.95) can indicate item redundancy — items that say the same thing in slightly different words. Aim for the acceptable range that fits your construct's breadth.

Cronbach's Alpha Calculator

Name: Cronbach's Alpha Calculator
Availability: InStock
Author: Nham Vu

Paste a tab-separated score matrix and instantly get Cronbach's alpha, item-total correlations, and alpha-if-item-deleted for each item.

Item Score Matrix

Rows = respondents, columns = items. Paste from Excel or Google Sheets (tab-separated).

Item labels (optional):

Interpretation Guide

≥ 0.90 Excellent

0.80–0.89 Good

0.70–0.79 Acceptable

0.60–0.69 Questionable

< 0.60 Poor

Paste your data and click Calculate

Cronbach's Alpha (α)

Respondents

Items

Variance

0.00.51.0

Item Analysis

Item	Mean	SD	Item-Total r	α if deleted

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Summary

Paste a tab-separated score matrix and instantly get Cronbach's alpha, item-total correlations, and alpha-if-item-deleted for each item.

How it works

Paste your item score matrix as tab-separated values (rows = respondents, columns = items).
Each row is one respondent; each column is one survey item or test question.
Click Calculate to compute the overall alpha coefficient.
Review the item analysis table for per-item statistics.
Use the "Alpha if item deleted" column to identify items that hurt reliability.

Use cases

Evaluate the internal consistency of a Likert-scale survey instrument.
Check whether all items in a psychological test measure the same construct.
Identify poorly performing items before finalizing a questionnaire.
Report scale reliability in academic or clinical research papers.
Pre-screen new items added to an established measurement scale.

Frequently Asked Questions

Last updated: 2026-06-11 · Reviewed by Nham Vu