Free tool · Data & statistics
Cronbach’s alpha calculator
Alpha, alpha if item deleted and item–total correlations, from data pasted straight from Excel.
Paste the responses to one scale from Excel or SPSS and this calculator gives you Cronbach’s alpha, along with alpha if each item is deleted and the corrected item–total correlation for every item. It can reverse-code negatively worded items for you, and it handles 5-, 7- and 10-point scales.
Use it on pilot data before the main survey, or to double-check an SPSS output. The numbers are the ones SPSS reports under Analyze, Scale, Reliability Analysis, so you can compare them line by line. The calculation happens in your browser, and your data stays on your device.
Method
How this tool works
Cronbach’s alpha compares how much each item varies on its own with how much the total score varies. If the items measure the same thing, people who score high on one tend to score high on the others, and the total spreads out much more than the items do separately. The formula is α = k ÷ (k − 1) × (1 − Σσᵢ² ÷ σₜ²), where k is the number of items, σᵢ² is the variance of each item and σₜ² is the variance of the total score. The tool uses sample variances (dividing by n − 1), as SPSS does.
Before calculating, it reads the first row as a header if any cell in it isn’t a number. Commas are accepted as decimal points when the columns are separated by tabs or semicolons. Any respondent with a blank or a word in one of the items is dropped from every calculation (listwise deletion), and the result tells you how many rows were dropped. Items you list as reverse-coded are recoded as (scale points + 1 − value), so on a 5-point scale a 1 becomes 5 and a 4 becomes 2. You need at least two items and three complete respondents.
A small worked example shows the arithmetic. Five respondents answer three items: (1, 2, 2), (2, 2, 3), (3, 3, 3), (4, 4, 5) and (5, 5, 4). The item variances are 2.5, 1.7 and 1.3, which add up to 5.5. The totals are 5, 7, 9, 13 and 14, with a variance of 14.8. So α = 3 ÷ 2 × (1 − 5.5 ÷ 14.8) = 0.943.
For each item the tool then shows two diagnostics. The corrected item–total correlation correlates the item with the sum of the other items, not with a total that includes it, so the item can’t inflate its own score. In the example, item 1 correlates 0.962 with items 2 and 3 combined. Alpha if item deleted recalculates alpha without that item: dropping item 1 leaves two items with variances adding to 3.0 and a total variance of 5.3, so alpha falls to 2 × (1 − 3.0 ÷ 5.3) = 0.868. A fall like that tells you the item is pulling its weight.
The tool also warns you about any item with zero variance, where every respondent gave the same answer. Such an item adds nothing to alpha and usually means a question every respondent agreed with. Our guide to reliability and validity explains how alpha fits with the other checks an examiner will expect.
Limits
What this tool can’t do
FAQ
Frequently asked questions
What alpha is acceptable?
0.70 is the usual floor, and it is commonly cited to Nunnally (1978). Treat it as a convention rather than a rule. What counts as enough depends on the purpose (an exploratory pilot can accept less than a scale used to make decisions about individuals) and on the number of items, because alpha rises as a scale gets longer. Short scales of two or three items often sit around 0.60 while still working reasonably well. At the other end, values above about 0.95 can mean some items are near-copies of each other, asking the same thing in slightly different words.
Why is my alpha negative?
A negative alpha means the items, on average, move in opposite directions. The usual cause is a negatively worded item that hasn’t been reverse-coded. Look for an item with a negative corrected item–total correlation, enter its column number in the reverse-coded box and calculate again. If alpha stays negative, check that the columns you pasted really belong to one scale.
Should I delete items to raise alpha?
Only with a reason you can defend. If an item has a low corrected item–total correlation (below about 0.30 is a common flag) and alpha rises clearly without it, read the item again: was it confusing, badly translated or off the construct? Delete it if the wording explains the problem, and report that you did. Deleting items just to cross 0.70 narrows what the scale measures, and with an adapted standard scale it can make your results hard to compare with earlier studies. Good questionnaire design and piloting prevents most of these problems.
Alpha or composite reliability?
If you are running SEM in AMOS or SmartPLS, report both. Alpha treats every item as equally related to the construct, while composite reliability uses each item’s actual loading, so it is usually the better estimate in a measurement model. If you aren’t doing SEM, alpha is what most Indian examiners expect, and McDonald’s omega is a sensible addition. The reliability guide compares them.
Is my data stored?
No. The calculation runs in JavaScript inside your browser. What you paste isn’t sent to our server, isn’t saved, and disappears when you close or reload the page.
Why doesn’t my SPSS output match?
Check three things. SPSS also drops incomplete cases listwise, so make sure you pasted the same rows. Make sure the same items were reverse-coded in both places, and that none was reversed twice. And check that SPSS was set to the Alpha model, which is its default. If it still differs, our SPSS and R analysis support can look at the file with you.
Keep reading
Related guides and services
Reliability and validity in research
Cronbach’s alpha, CR, AVE and discriminant validity, and what examiners expect reported.
Designing a research questionnaire
Adapting validated scales, item wording, translation, piloting and common method bias.
Statistical Analysis (SPSS, R, AMOS, SmartPLS)
The right tests, assumptions checked, results interpreted — and syntax you can rerun and defend.
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