http://spssx-discussion.165.s1.nabble.com/Multiple-Imputation-tp4994372p5723116.html
Dichotomies are perhaps
the only instance of all intervals being perfectly interval
because there is only 1 interval and it is necessarily equal to
itself.
Likert items are usually not severely discrepant from interval
level. What is the response scale on you other interval level
items?
If it is an established questionnaire, I would not want to impute
values from one construct from another. This is especially so if
one is an IV and one is a DV or if they were derived as factors
and divergent validity is important.
If it is an established scale, I would only bother with a factor
analysis and reliability run to check that I was using the scoring
key correctly.
If you use the mean.n function to get the score that should work
satisfactorily.
If you are using correlation based analysis the correlation
process itself standardizes (Zs) the scores.
For those variables that you are considering scoring by summing,
you could check the factor structure to see if it is consistent
with what you think should go together by factoring with listwise
deletion and with mean substitution to see if there are meaningful
differences in the scoring keys you would derive.
If there are not, I would just go ahead with the mean.n function
to form the summative score.
I would not worry about CATPCA unless you had some nominal level
variables in the mix, and that would be unusual in scale
construction.
Keep in mind that the rationale behind using summative scores
(sums or means of a set of items) is that each item is an
imperfect measure of a construct. Repeating measurement of the
construct with several different items, yields a summative
measure that is a more credible measurement of the underlying
construct.
In factor analytic terms, we are interested in the common variance
of the set of items as an operationalization of the underlying
construct.
Art Kendall
Social Research Consultants
On 11/16/2013 1:04 PM, therp [via SPSSX Discussion] wrote:
Hello Art, thank you for your answer!
I have missing values on both my IVs and DVs. IVs and DVs were
measured in seperate sessions because I'm working on a
prediction model.
The response scale of my IV items is a Likert scale -here I'm
using an established questionnaire for two constructs. I have
3.3% missing values on the first and 0.4% on the second
construct.
Little's test showed that MCAR is present only for one of the
constructs and seperate variance t tests could not be computed
on the other construct because I only had single missing values
on variables in that construct. Not sure what I will do with
this result yet... Some missings are due to a software error
(first construct) and some are random that I can't explain
(second construct).
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.
Yes, for the DVs I know that some of my variables allowed
nonresponse too easily (that's the 3 i dropped) and some
missings are due to participants abandoning the experiment at
the very end, and some due to technical mistakes.
Yes, I considered CATPCA, but since i have a dataset with mixed
scales i wasn't sure..but I will look into it again, thank you!
On top of that, a CFA might be even more right vor my procedure
since I have a theory behind the item structure...either way, if
I built scales for my DVs, my missing values would lead to up to
1/3 missing values on my new scales.
Do you think that a better procedure in my case would be running
a CFA with the missing values, build scales, and then impute
missings on scale level?
Thank you very very much for any further advice!
regards,
therp
Am 16.11.2013 18:06, schrieb Art Kendall [via SPSSX Discussion]:
You say you are
building scales. What is the response scale on the
items you are considering? Usually scales are built of
strictly interval level items (e.g., Z's or temperature) or of
items that are not severely discrepant from interval level
such as extent response scales or Likert response scales.
How extensive is your missing data? Do you know why data is
missing for some items?
Were you thinking of CATPCA rather than FACTOR for EFA?
Art Kendall
Social Research Consultants
On 11/16/2013 10:28 AM, therp [via SPSSX Discussion] wrote:
Hello!
Thank you so much for this interesting discussion.
Unforunately as far as I understand the suggestions on using
EM for EFA don't solve the problems for categorical variables
with missing values, since EM cannot be applied to categorical
data in SPSS (and in general??). On top of that, SPSS won't
compute Little's MCAR test for categoriacal variables
Does someone have any suggestions for dealing with missing
values on categorical variables? My proximal goal is to build
scales using EFA and reliability analyses. I know MI is the
state of the art, but as it has already been noted, spss
doesn't produce pooled results for EFA yet.
I appreciate any help/advice/comment!!!!
Art
Kendall
Social Research Consultants