Complex data doing with plausible values PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 lamjas posted on Thursday, January 10, 2013 - 8:10 am
I am using TIMSS database and want to do a SEM (e.g., X --> M --> Y).

Y is achievement. In the dataset, however, achievement is represented by five plausible values (say pv1, pv2, pv3, pv4, pv5). I understand that they should not be treated as indicators in the model. I read some threads and it seems that I need to do "multiple imputation" (In Mplus, using Type = Imputation).

However, I am still very confused the appropriate procedures to deal with a SEM model with plausible values as an outcome variable. Could anyone explain step by step in layman terms? Is UG Ex 11.7 exactly the way I use imply on my model (of course, I need to change from LGM to SEM)?

Thank you!!
 Bengt O. Muthen posted on Thursday, January 10, 2013 - 10:42 am
The second part of UG ex11.8 (pp. 405-407) shows how to use DATA TYPE=IMPUTATION.

More information about plausible values are found in

Asparouhov, T. & Muthén, B. (2010). Plausible values for latent variables using Mplus. Technical Report.

which also refers to a report by von Davier et al (2009) that is more applied.
 lamjas posted on Thursday, January 10, 2013 - 5:13 pm
Hi Bengt,

Thanks for your quick reply.

Let's say I am doing a SEM (X-->M-->Y) and Y is represented by five plausible values.

So, the first step is to do a multiple imputation and construct five datasets (UG Ex 11.5). Syntax as follows:

Data: File is raw.dat;

Names = x1-x5 m1-m5 pv1-pv5 w clus;
Missing = ALL (-99);

Weight is w;
Cluster is clus;

Data imputation:
Impute = x1-x5 m1-m5 pv1-pv5;
Ndataset = 5;
Save = mp*.dat;

Type = Complex;
Estimator = MLR;

Output: Tech8;


Then, the second step is the second part of UG Ex 11.8 and examine the model. Syntax as follows:

Data: File is mplist.dat;
Type = imputation;

Names are x1-x5 m1-m5 pv1-pv5 w clus;

Weight is w;
Cluster is clus;

Type = complex;
Estimator = MLR;

x by x1-x5;
m by m1-m5
y by pv1-pv5;

y on m x;
m on x;

output: tech1 tech4 standardized;


Are these two steps correct to do a SEM with plausible values?

Any comments are welcome.

Thanks again.
 Bengt O. Muthen posted on Thursday, January 10, 2013 - 6:25 pm
No, you don't do the first step. That has already been done by TIMSS when they gave you the 5 plausible values.
 lamjas posted on Thursday, January 10, 2013 - 6:35 pm
Then, what if I have missing data of x1-x5 and m1-x5? Should I skip step 1?
 Bengt O. Muthen posted on Thursday, January 10, 2013 - 8:22 pm
One simple approach is to let "FIML" handle that without imputation.
 Huang Wu posted on Saturday, June 09, 2018 - 3:32 pm
I am using PISA data for analysis. These discussions are very helpful but I am still have some questions.
(1) I could not find "FIML" in the estimator;
(2) PISA give us 10 plausible value for each subject. Should us create 10 dataset first (each one only have one plausible value for different subject?) or just keep them in a dataset and treated them as indicators?
(3) I have read lamjas's post and he/she said we could not treat plausible as indicator but in his/her code, he/she load 5 plausible value in y. If one dataset only contains one plausible value, how can we load them together?
 Bengt O. Muthen posted on Sunday, June 10, 2018 - 6:03 pm
1) Use ML or MLR

2) Create 10 data sets and use Data: Type=Imputation.
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