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Cronbach's alpha

The internal consistency reliability for Likert-type response sets

Cronbach's alpha is used for calculating reliability coefficients for survey instruments that use Likert-type response sets. Cronbach's alpha coefficient ranges from 0 to 1.0 with higher values denoting increased reliability. The criterion for an acceptable Cronbach's alpha coefficient is debated in the literature, but to be conservative, any alpha coefficient below .75 is a cause for concern. If at all possible, use the standardized alpha coefficient, which is an even more conservative method for calculating reliability.

The steps for conducting Cronbach's alpha in SPSS

1. The data is entered in a within-subjects fashion.

2. Click Analyze.

3. Drag the cursor over the Scale drop-down menu.

4. Click on Reliability Analysis.

5. Click on the first survey item to highlight it.

6. Click on the arrow to move the item into the Items: box.

7. Repeat Steps 5 and 6 until all of the survey items are in the Items: box.

8. Click on the Statistics button.

9. In the Descriptives table, click on the Item, Scale, and Scale if item deleted boxes to select them.

10. Click Continue.

11. Click OK.  

The steps for interpreting the SPSS output for Cronbach's alpha

1. Look at the Reliability Statistics table, in the Cronbach's Alpha column. This is the alpha coefficient associated with the items. It needs to be more than .75 to be acceptable.

2. Look in the Item-Total Statistics table, in the Corrected Item-Total Correlation, record every item that has a value LESS THAN .30.

3. If the alpha coefficient was LESS THAN .75 and there were items with a Corrected Item-Total Correlation of LESS THAN .30, then delete those items and rerun the Cronbach's alpha analysis.

Continue iterations until the alpha coefficient meets the criterion of .75 or all items are correlated at above .30.

If the coefficient does not approach .75, then review the items for systematic error.  

Click on the Principal Components Analysis button to continue.
Principal Components Analysis
Back to Internal Consistency Reliability
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