Eric Teman posted on Friday, June 17, 2011 - 8:46 pm
Thanks Tihomir. I read the pdf and have a question. On page 13, it reads, "Note here that the ML estimation method can also be used for the estimation of this data directly. However the ML estimation method would heavily rely on the fact that the data is generated from a one factor analysis model. In general the true model could have many more factors and residual correlations. So in that respect the ML method would not be an appropriate substitute for the imputation methods, because the ML estimation method will be computationally very intensive and does not support residual correlations."
I am unclear as to why ML is inappropriate. Is it only inappropriate with the H0 model or with all the models?
The argument is made that H1 imputation is safer than H0 imputation because it does not rely on the H0 model being correct. Also, Table 6 shows that the "H1-Cov Imputed WLSMV" column gives almost as good results as the two H0 columns. ML would have to use an H0 model because with categorical outcomes it cannot do an H1 model due to too many dimensions of integration.
If the H0 model is correct and can be estimated by ML, ML H0 imputations will be excellent.
Eric Teman posted on Saturday, June 18, 2011 - 4:39 pm
Oh, OK. So this has nothing to do with using full information maximum likelihood in place of MI?