http://spssx-discussion.165.s1.nabble.com/Multiple-Imputation-tp4994372p5723114.html
Just a couple of comments/ questions.
It seems that you mention a need to z-transform *items* at
some point. Are you sure? Item-Response Theory (IRT) can use inverse
normal instead of its usual logistic function, but I have never seen
it, and I didn't think that used the z-scores. Is that really so, or were
you working from some other assumption?
In an earlier post, you worried about categorical variables, but
you don't mention them here. Dichotomies are "equal interval" by
convention (only one interval -- certainly not unequal intervals).
Likert are treated as continuous and equal-interval unless you move
to an IRT model.
I was almost always comfortable of using the average of the items
that were present in order to account for Missing. The exceptions
would be due to the meaning of some particular item. That could be
indicated either by its extreme mean or its literal meaning. I mean,
I could read some items and say, "Oh, (given the other answers?) that
would be left blank because blah-blah-blah. -- and therefore, score it XXX."
--
Rich Ulrich
Date: Sat, 16 Nov 2013 10:04:58 -0800
From:
[hidden email]Subject: Re: Multiple Imputation
To:
[hidden email]...
For the DVs, I constructed behavioral items and the response scale
for almost all of the items is picking between a prejudiced or not
prejudiced alternative [0;1]. Other DV items have a Likert
response scale and some an interval. I understand that I have to
z-tranform the variables before building scales, but since
z-transformation uses the mean of a variable i don't know if i can
do this before imputation.
I have 6.1% missing data total on my DVs and I will drop variables
with more than 10% missings.
...