Re: Multiple Imputation

Posted by therp on
URL: http://spssx-discussion.165.s1.nabble.com/Multiple-Imputation-tp4994372p5723119.html

Thank you again for your comments and advice!

To make sure i understood you correctly:

IV - questionnaire
Since I'm using an established questionnaire as my IV, I don't have to impute missing values and just sum the items by mean.n. As far as I know this procedure requires that at least 2/3, or better 3/4, of the items i use for summing are complete, which is not the case in my questionnaire, i.e. one scale has 11 items and i have missings on 4 of them (->only 63.3% are complete). Can I still use the mean.n fuction or do I have to drop this scale?
Can you advice me on literature for that procedure (since the analysis is for my thesis and I have to justify my procedure)? Also, are you implying that I don't have to check MCAR or MAR for that questionnaire?
Indeed, without imputation, I could replicate the factor structure of that questionnaire.

DVs - behavioral measures
I was a little confused by Rich's comment that I don't mention categorical items. Most of my DV items have a response format, i.e. "not prejudiced behavior" vs. " prejudiced behavior". Doesn't that make them categorical? By z-transform I meant Fisher's z-transformation (my supervisor suggested that) because I will have to build scales, and 39 are categorical, one is answered on a 7-point liker scale, one is the amount of leaflets participants take with them (interval). I understand that I don't have to use Z-transformation for correlational analyses and factor analysis, right?
So your advice, Art, is that I check the factor structure with CFA with listwise deletion and mean imputation and compare them. But before using the summative score or listwise deletion, don't I have to check if the data is MCAR or MAR? I understand from the literature that every method of imputation or deletion of cases assumes that data is MCAR/MAR.

Thank you so much for your help!!