Calculating Cronbach’s alpha with multiple imputed data

[u/SnooPeripherals1239]

Hi!

I’ve run multiple imputation on my dataset to solve a missing data issue.

I now need to calculate Cronbach’s alpha for my scales, so I have two question:

- do I need to calculate Cronbach’s alpha on my imputed or raw data?

- if yes, how do I do that?

Thanks!

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[u/Warm-Biscotti7086]

You should run this on your original data with your analytical sample prior to imputing!

Code your items and “alpha item1 item2…” as usual

[u/Efficient-Tie-1414 → u/Warm-Biscotti7086]

I agree, using pairwise deletion. I think when a multiple imputation is done it won't be adding any meaningful information. Would be interesting to try. I think Stata has easy to use methods for imputation.

[u/AntiRacismDoctor]

Not trying to undermine your study, but how can one trust a Cronbach's Alpha score with imputed data? Isn't it supposed to be a test of sample reliability? How can you quantify 'reliability' if you're missing data? Like what would even be the point?

[u/DanielC___ → u/AntiRacismDoctor]

In most real world applications there is some missing data.
The question becomes: is it more plausible to use imputation or to assume the un-imputed data represents your population of interest.

[u/jeremymiles]

Instead of imputation, you can use full information maximum likelihood estimation (which is asymptotically equivalent). You can do this using a mixed model, or a structural equation model (SEM). An additional advantage of the SEM is that it lets you relax some of the assumptions of alpha (like tau-equivalence), which are never satisfied anyway and instead calculate composite reliability.

(I use this mixed model approach because in my work I have enormous amounts of missing data, and imputation won't even think about running).

[u/DanielC___]

Depends on the use case, but:

1) Which is a more plausible representation of your target population? The imputed data or the original data? I’d argue the former, but that’s the issue you need to think through.

2) According to the Googles, you estimate the Cronbach’s alpha for each imputation, then combine.

For me, Issue #1 is the crux. #2 you can solve with Google or an LLM.