

Complex data doing with plausible values 

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lamjas posted on Thursday, January 10, 2013  2:10 pm



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!! 


The second part of UG ex11.8 (pp. 405407) 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  11:13 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; Variable: Names = x1x5 m1m5 pv1pv5 w clus; Missing = ALL (99); Weight is w; Cluster is clus; Data imputation: Impute = x1x5 m1m5 pv1pv5; Ndataset = 5; Save = mp*.dat; Analysis: 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; Variable: Names are x1x5 m1m5 pv1pv5 w clus; Weight is w; Cluster is clus; Analysis: Type = complex; Estimator = MLR; Model: x by x1x5; m by m1m5 y by pv1pv5; 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. 


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 Friday, January 11, 2013  12:35 am



Then, what if I have missing data of x1x5 and m1x5? Should I skip step 1? 


One simple approach is to let "FIML" handle that without imputation. 

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