Multiple Imputation and EFA PreviousNext
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 Emily Rosenzweig posted on Monday, May 25, 2015 - 6:27 pm
Hi --

I need to conduct an exploratory factor analysis on a dataset that requires multiple imputation for MCAR values. Is it possible to include the multiple imputation command in my exploratory factor analysis? And if not, how should I go about this? If I do the multiple imputation on its own, it will generate multiple datasets. What then? Would I simply run the EFA on all of them? Or is there a way to combine the multiple datasets into one and then EFA on that?

Thanks!
 Bengt O. Muthen posted on Tuesday, May 26, 2015 - 2:36 pm
First you should ask if MI is necessary. EFA using ML handles missing under the MCAR and MAR assumption.
 Emily Rosenzweig posted on Wednesday, May 27, 2015 - 12:08 pm
Hi Bengt --

Thanks for your reply. Perhaps I can describe my dataset and you can give me a sense of what you think the best approach might be? I have a dataset with 400 variables, and each participant completed a random selection of 40 of those. Thus my data is truly MCAR. My hope was that multiple imputation prior to EFA would allow me to take into account both variable-specific and participant-specific sources of variance. But if EFA using ML already accounts for both of those, that is fantastic -- I'm happy to cut out that additional imputation step.

Many thanks for your input!
 Bengt O. Muthen posted on Wednesday, May 27, 2015 - 3:31 pm
Yes, you don't need MI.
 Claire Boeck posted on Saturday, July 25, 2020 - 11:15 am
Hello,

I am trying to do multiple imputation and EFA in version 8.3. I do not get an error message, but Mplus does not generate datasets, just the file that should have the name of the datasets but its blank. The output file says "input terminated normally." Can you please advise?

Output file with input pasted below:

INPUT INSTRUCTIONS

title: multiple imputation efa of
data:
file is STudent Survey Data for MPlus MI EFA.csv;


variable:
names = TERM CONC ANSQ FIXMIS TRY COMP DEVICE SOFT HARD CPROG MATHMIND GRADE DIFFOK CONFIDT INTRST REWARD ENJOY SATIS TCHLRN TCHLIKE OPREAD OPHMWK OPTHM OPEX ;

usevariables = [all of above except for term] ;

categorical = [all of using]
missing are all (99);


data imputation:
impute = CONC (c) [repeated for rest of vars];
ndatasets = 10;
save = missefa*.dat;

analysis:
type = efa 1 6;


output:
sampstat ;



INPUT READING TERMINATED NORMALLY
 Bengt O. Muthen posted on Sunday, July 26, 2020 - 2:45 pm
To diagnose this, we need to see your data and output - send to Support along with your license number.
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