Date:         Fri, 13 May 2011 05:29:47 -0700
Reply-To:     Pia <ammann@sad.ch>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Pia <ammann@sad.ch>
Subject:      Re: Multiple Imputation
In-Reply-To:  <4DCD154B.1060002@DrKendall.org>
Content-Type: text/plain; charset=us-ascii

It is a longitudinal (5 time points) quasi-experimental research project
about testing and evaluating an intervention.  The questionnaire was quite
large containing many different standardized psychological scales (e.g.
Depression, Grief, Social Support) but also non-standardized questions.
Question: do the interventions show any effect on these measures but also
more in-depth modeling about risk and protective factors for our specific
sample. We used different missing codes (e.g. not applicable, interviewer
omission). The missing values we want to impute were mainly omitted by the
interviewer for different reasons. All of them are scale items at different
time points (not all of them show more than 5% missings at all time points).
The analysis we are going to do are: MANCOVA, repeated measures, SEM. For
the 8 different variables there are, of course, different correlating
variables/predictors in the dataset. I don't think the missingness is
correlated across the 8 variables (different reasons for missingness
depending on the variable).
Hope that helps!

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