Checking Scale Reliability with Cronbach's Alpha
If your questionnaire uses multi-item scales, your supervisor or reviewers will almost certainly ask about reliability. Cronbach's alpha is the most common way to report it. In this blog, we'll look at what alpha tells you, how to run it in SPSS, how to read the output, and what to do when the value comes out lower than you hoped.
What Cronbach's Alpha Tells You:
- Cronbach's alpha measures internal consistency: how closely the items in a scale relate to each other as a group. Values usually fall between 0 and 1.
- A high alpha does not prove that your items measure a single construct. If you need to show that, use factor analysis alongside alpha.
- Alpha tends to rise as you add items, so a long scale can reach a high value even when the individual items are only weakly related.
Prepare Your Data First:
- Run alpha separately for each scale or subscale, not for the whole questionnaire at once.
- Reverse-code any negatively worded items before you start (Transform > Recode into Different Variables). Forgetting this is one of the most common reasons for a low or even negative alpha.
- Check for data entry errors and missing values. By default, SPSS only uses cases with complete answers on all the items in the analysis.
How to Run It in SPSS:
- Go to Analyze > Scale > Reliability Analysis.
- Move the items of one scale into the Items box and make sure the Model is set to Alpha.
- Click Statistics and tick Item, Scale and Scale if item deleted under "Descriptives for", and Correlations under "Inter-Item". Click Continue, then OK.
How to Read the Output:
- The Reliability Statistics table gives you Cronbach's Alpha and the number of items. This is the value you report.
- A common rule of thumb treats 0.70 as acceptable and 0.80 or above as good. These are conventions, not strict cut-offs, and expectations differ between fields. Very high values (above about 0.95) can suggest that some items are repeating each other.
- In the Item-Total Statistics table, look at the Corrected Item-Total Correlation. Items with low values (often taken as below about 0.30) fit poorly with the rest of the scale.
- The column Cronbach's Alpha if Item Deleted shows what alpha would be if you removed that item.
What to Do When Alpha Is Low:
- Check reverse coding and data entry first. These simple fixes solve many low-alpha problems.
- Look at the item-total correlations and the "alpha if item deleted" values. Remove an item only if there is also a sound theoretical reason, and report that you removed it.
- Be cautious about changing an established, validated scale. Dropping items just to reach 0.70 makes your results harder to compare with other studies.
- Scales with only two or three items often give a low alpha. In that case, reporting the mean inter-item correlation can be more informative.
- If you suspect the items measure more than one idea, run an exploratory factor analysis to check the structure.
- If the value stays low, report it honestly and discuss it as a limitation. Examiners respect transparency far more than a scale that has been quietly trimmed.
How to Report It:
- Report alpha for each scale using your own sample, for example: "The eight-item job satisfaction scale showed good internal consistency (α = .84)."
- Mention any items you removed or recoded, and why.
Cronbach's alpha takes only a few clicks in SPSS, but reading it well takes a little more care. Prepare your data, look beyond the single number to the item statistics, make changes only when you can justify them, and report what you find clearly. That way, your reliability section will stand up to questions from your supervisor and examiners.