Posted by
Art Kendall on
URL: http://spssx-discussion.165.s1.nabble.com/Multiple-Imputation-tp4994372p5723121.html
comments interspersed
below.
Art Kendall
Social Research Consultants
On 11/17/2013 5:45 AM, therp [via SPSSX Discussion] wrote:
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?
You could just use mean.7. That uses an assumption that the missing
data is equal to the mean of the valid items in the case. You could
also check how many cases you would lose if you use mean.8 or
mean.9.
Aside: One lesson you should learn from this thesis is the
importance of good data gathering (test administration). That
greatly minimizes the amount of missing data.
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.
Just use that as the justification. I do not know of an article
that suggests mean substitution for items in a scale.
Perhaps some else has a cite for this ages old practice.
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?
Yes and no. The can be considered categorical but they are also
considered interval level. The single interval is perfectly equal
to itself. Do FREQUENCIES on some of them. Look at the percentages
and the means.
Would you consider a spelling test that used 1 for right and 0 for
wrong and summed the item scores an invalid test? Why would that be
different?
Another lesson to take away from this thesis exercise. Use as fine
grained a response scale as is practical under the circumstances. An
extent scale that had more possible values on the response scale
would restrict the variance less. It seems that the construct
"prejudice" is a continues variable. Why else would you use a
summative scale? A dichotomy is the coarsest possible
operationalization of a continuous construct.
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?
Are you putting those items into the same scale? Are you getting
meaningful scoring keys form the factor analysis that includes items
with very different response scales? If so, yes, you would z
transform the items before summing them. If there is not a mix of
response scales, then there is no need to transform them.
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?
If you want to also try multiple imputation, only use contributors
from items that are on the same scoring key.
None of your data seems to be categorical. Before you create the
imputed values use the mean.n function to get scores. Then use the
mean function without the .n. Scatter plot the scores with the
missing assumed to be at the mean of the other items vs those from
the multiple imputation. How do they look?
Subtract the scores using the mean.n from the score using imputed
items. What is the mean min and max difference?
Use both sets of score in your actual analysis model? How do the
substantive conclusions compare?
If you have CFA available that is fine. Do that with listwise
deletion, imputed values, and mean substitution from items in the
same scale.
Most people do not have CFA available. So just do EFA with both
options on each set of items.
Do parallel analysis with both sets of data. Plot all 6 sets of
eigenvalues. How do the they look?
How do the scoring keys compare across the three sets?
How do the scoring keys you find compare to the scoring keys used by
the original research when there is some earlier research?
You
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!!
Art Kendall
Social Research Consultants