I recently received this question:
I have scale which I want to run Chronbach’s alpha on. One response category for all items is ‘not applicable’. I want to run Chronbach’s alpha requiring that at least 50% of the items must be answered for the scale to be defined. Where this is the case then I want all missing values on that scale replaced by the average of the non-missing items on that scale. Is this reasonable? How would I do this in SPSS?
My Answer:
In RELIABILITY, the SPSS command for running a Cronbach’s alpha, the only options for Missing Data are to include or exclude User-Defined missing data. And by exclude, they mean listwise deletion.
So the only way to include cases with more than 50% observed data would be to impute them in a separate step before you run the reliability analysis.
And while you could impute the mean, I highly recommend you do not. While mean imputation maintains the mean of each separate variable, it does not maintain the relationships among variables.
In fact, with a lot of imputed values all right at the mean, the correlations with other variables become much lower. And since scale reliability entirely depends on correlations among the values in your scale, you will severely underestimate your scale reliability if you have more than a few cases with missing data.
Since you’re doing a Cronbach’s alpha, you could do a single imputation that is based on other variables–a regression or an EM imputaton. This kind of imputation will preserve the relationship among the variables on your scale without inflating them.
The general downside of single imputation is that SPSS will think that the imputed values were true, observed values. It will therefore underestimate standard errors.
But Cronbach’s alpha doesn’t have a standard error and is not involved in a hypothesis test. So for this purpose, the downside isn’t a big deal.
If you were doing a hypothesis test or doing any statistical analysis based on p-values, the best option, is to conduct a Multiple Imputation on the missing values. It’s often the only good one if you have more than about 10% of data missing (that’s 10% of all values, not of cases)
Both the single and multiple imputation techniques are available in SPSS Missing Values Analysis module. Multiple imputation was added in version 17, but single imputation is available in earlier versions.
{ 5 comments… read them below or add one }
I would like to use EM for my missing values in SPSS. I have data for over 1000 participants, each completing several questioannaires. Do I enter all my data into the EM at once or do i do it seperately for each questionnaire?
Also, I would only like values imputed for cases with at least 50% items responded to. How do I do this for EM in SPSS?
Thnk you
Hi Hannah,
If the questionnaires are long (with many items), you may have trouble putting them all in at once (read: SPSS crashes). If the questionnaires are independent of each other, you can do them separately.
But remember EM means and correlations are unbiased, but standard errors are too small. If you need accurate standard errors, you’ll want a multiple imputation.
And to impute cases only if at least 50% present, you’ll have to do a work around. There isn’t an option in SPSS that I know of to do it directly.
SPSS does remove those who do not answer all questions on the survey and mean substitution will effect your variance. You can use relicheck.com. This is an online survey site that has a cronbach analysis as part of the results. The analysis on the site accounts for missing data.
i want to know can i use chronbach alhpa tool for skipping question not liker t scale? if no what type of tool can i use to measure reliability?
Thanx allot
Hi Keto,
I’m not sure I understand your question. Are you trying to see which questions don’t load reliably with the others? And if your data aren’t measured on likert scales, how are they measured?
Karen