# Good internal factor structure but poor Cronbach's $\alpha$?

I am running a CFA and getting good fit indices (CFI = .99, RMSEA = .01) for a uni-dimensional scale. However, when I test for internal consistency, I get poor Cronbach's $\alpha$s ($\alpha = .6$). I have tried everything from removing outliers, to dropping items and still end up with the same problem.

I am wondering if there is anything in SEM that shows that the measure is reliable?

I know that there is some debate about whether Cronbach's $\alpha$ (or internal consistency) even measures reliability but since my field requires Cronbach's $\alpha$ to be reported as a measure of psychometric goodness, I need to find a way to show internal consistency as being adequate for this measure.

• Are some of the items reverse coded? When you look at alpha, do some items have negative correlation? Commented Nov 20, 2013 at 19:13
• No, none of the items are reverse coded and nor do these items have a negative correlation with each other. Commented Nov 20, 2013 at 19:16
• How many items do you have? Sometimes having few items (<.5) can lead to very low internal consistency. What is your average inter-item correlation? Commented Nov 20, 2013 at 19:55
• There are 8 items. The inter-item correlations are .15 to .30. I figured that low correlations are driving the low alpha but I am surprised to see the loadings in the CFA ranging from .45 to .69 and the good fit indices. Commented Nov 20, 2013 at 21:04

You can calculate the reliability of your items from the CFA.

From your standardized solution, calculate: (L1+...Lk)*2/[(L1+...Lk)*2+(Var(E1)+...+Var(Ek))]

This will give the composite reliability, which should be close to alpha.

It's harder to have good fit if you have high alpha, and it's harder to have high alpha if you have good fit. The extreme example of this is if all of the items are uncorrelated - chi-square will be zero, and RMSEA will be zero, indicating great fit. But alpha will also be zero, indicating appalling reliability. The usual flag for this is low CFI (because the null model chi-square is so low), but you don't have that. I wrote about that in this paper: http://www.sciencedirect.com/science/article/pii/S0191886906003874 (which I think is not behind a paywall).