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lamjas posted on Thursday, January 10, 2013 - 8:10 am
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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!! |
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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. |
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lamjas posted on Thursday, January 10, 2013 - 5:13 pm
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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 = 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; 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 x1-x5 m1-m5 pv1-pv5 w clus; Weight is w; Cluster is clus; Analysis: Type = complex; Estimator = MLR; Model: 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. |
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No, you don't do the first step. That has already been done by TIMSS when they gave you the 5 plausible values. |
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lamjas posted on Thursday, January 10, 2013 - 6:35 pm
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Then, what if I have missing data of x1-x5 and m1-x5? Should I skip step 1? |
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One simple approach is to let "FIML" handle that without imputation. |
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Huang Wu posted on Saturday, June 09, 2018 - 3:32 pm
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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? Thanks |
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1) Use ML or MLR 2) Create 10 data sets and use Data: Type=Imputation. |
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