Yes, you understood the RQ and the problems perfectly.Gutnre wrote:Also, I can imagine that there are different means for the different items, since there is a question about suicidal ideation and about concentration.
Usually people actually use a mean or some other total scale score, but you want to look at the items separately.
So actually your paper is not on depression but on depressive symptoms.
Your RQ is whether several variables predict specific depressive symptoms.
If I understand correctly, you have a depression scale which has 9 items about 9 symptoms.
You measured this scale at 5 timepoints.
That was the test to see whether missings miss randomly? I think I ran it a while ago and they were not missing randomly (I guess because later measurement points have more missings). I will rerun in as soon as my license it up (tomorrow).Gutnre wrote:Most important question is: Do you have an idea about why some of the missings are missings?
Run Little's mcar test to see if they are not mcar.
Okay, I didn't understand that 100%, but I'm sure that I will be able to read up on this with the keywords you mention.Gutnre wrote:if they are not not mcar, you can think about the multiple imputation option in spss.
with this procedure you can impute data, but it is done 5 times (i.e., you get 5 different datasets), and if you subsequently run a regression (it sounds like thats what you want to do) you get a pooled result for those 5 datasets.
I just followed a workshop on this, it was very interesting. To be fair, I always thought that imputing data was worse than just deleting cases with missing data - that deleting those cases would be more conservative- but its not.
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