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 item responses of one scale from Excel or SPSS, and this calculator gives you Cronbach’s alpha for that scale. Per item also you get two numbers, alpha if item deleted and the corrected item–total correlation. Some questions in your scale are negatively worded? Type their column numbers and the tool will reverse-code them first. 5-, 7- and 10-point scales are supported.
Mostly scholars use it at the pilot stage, before main survey goes out. Some use it to cross-check an SPSS output. What you see here is the same thing SPSS gives under Analyze, Scale, Reliability Analysis, so line by line comparison is easy. Nothing is uploaded, the maths happens in your browser itself and data stays on your device.
Method
How this tool works
Alpha is basically comparing two spreads. First is how much each single item varies. Second is how much the total score varies. If the items measure the same thing, a respondent who marks high on one item mostly marks high on the others too. So the totals spread out far more than any one item. Written down, it is α = k ÷ (k − 1) × (1 − Σσᵢ² ÷ σₜ²). In this, k is how many items you have, σᵢ² is the variance of each item and σₜ² is the variance of the total score. We take sample variances (divide by n − 1), same as SPSS does.
About pasting. When even one cell in the first row is not a number, that row is treated as a header. Separating columns with tabs or semicolons? Then a comma is okay as the decimal point. If some respondent has left an item blank or typed a word there, that whole respondent goes out of every calculation. This is called listwise deletion, and the result also show how many rows got dropped this way. For reverse-coded items the new value is (scale points + 1 − value). So on a 5-point scale a 1 becomes 5, and a 4 becomes 2. You need minimum two items and three complete respondents.
Here is the arithmetic on tiny data. Five respondents answer three items, and the answers are (1, 2, 2), (2, 2, 3), (3, 3, 3), (4, 4, 5) and (5, 5, 4). Item variances come to 2.5, 1.7 and 1.3. Add them and you get 5.5. Totals for the five people are 5, 7, 9, 13 and 14, and their variance is 14.8. Put it in the formula and α = 3 ÷ 2 × (1 − 5.5 ÷ 14.8) = 0.943.
Per item there are two diagnostics. The corrected item–total correlation correlates that item with the sum of the other items only. Why leave the item out of the total? Otherwise the item pushes up its own score. For item 1 in our example, the correlation with items 2 and 3 added together is 0.962. Alpha if item deleted is just alpha calculated again after removing that item. Take out item 1 and you have two items left, with variances adding to 3.0 and total score variance 5.3. Alpha now falls to 2 × (1 − 3.0 ÷ 5.3) = 0.868. A fall that big tells you item 1 was really contributing to the scale.
You will get a warning when some item has zero variance, that is, every respondent gave it the same answer. Such an item adds nothing to alpha. Many times it is one statement which everybody agreed with. For the other checks examiners ask about, our guide on reliability and validity has it in one place.
Limits
What this tool can’t do
FAQ
Frequently asked questions
What alpha is acceptable?
Most people take 0.70 as the lower limit and cite Nunnally (1978) for it. It is a convention which researchers follow, nobody has made it a fixed rule. What counts as enough depends on why you are measuring. A pilot can live with a lower value than a scale used for decisions about individual people. With more items alpha goes up on its own, so keep the item count in mind. Short scales with two or three items many times come around 0.60 and still do their job reasonably. On the high side, above about 0.95, suspect that some items are near-copies, the same question with a few words changed.
Why is my alpha negative?
You get negative alpha when, on average, the items are moving in opposite directions. Mostly the culprit is some negatively worded item was not reverse-coded. In the result, find the item whose corrected item–total correlation is negative. Enter its column number in the reverse-coded box and calculate once more. Still negative? Then check that columns you pasted all belong to one scale.
Should I delete items to raise alpha?
Only if you can defend the reason before your guide. Say an item has a low corrected item–total correlation (below about 0.30 is a common flag) and alpha clearly goes up without it, then go back to the wording of that item. Was it confusing, badly translated or off the construct? If the wording explains it, delete the item and write in your report that you did so. Removing items just to cross 0.70 is different. The scale then measures something narrower than you planned, and with an adapted standard scale your results also become hard to compare with earlier studies. Good questionnaire design and piloting avoids most of this trouble itself.
Alpha or composite reliability?
Running SEM in AMOS or SmartPLS, better report both. Alpha assumes every item is equally related to the construct. Composite reliability takes the actual loading of each item, so inside a measurement model it is usually the better estimate. Without SEM, alpha is what most Indian examiners will expect, and adding McDonald’s omega is sensible. The comparison of these measures is there in our reliability guide.
Is my data stored?
No. JavaScript inside your browser does the calculation and the data stays on your device only. We don’t get a copy on our server and nothing is saved. Once you close the tab or reload the page, whatever you pasted is gone.
Why doesn’t my SPSS output match?
Check three things. SPSS also drops incomplete cases listwise, so see that you have pasted exactly the same rows as SPSS used. The reverse-coded items should be the same in both places, and no item should be flipped two times. And in SPSS the model should be Alpha, which is the default anyway. If after all this the numbers differ, our SPSS and R analysis support team can sit with the file along 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.
Statistics Training and Analysis Review (SPSS, R, AMOS, SmartPLS)
Learn the right tests on your own data, run them yourself, and get your output and interpretation checked.
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