I used AMOS's SEM for a mediation analysis.

General Info-

1) exposure, outcome and one of the mediators are Latent variables.

2) 2 mediators are continuous observed variables

3) 4 covariate as confounders- age, gender, income, and race

Steps

1) Using complete cases (n = 757), performed a regular SEM- mediation analysis in AMOS (without confounders)

2) After eliminating all the variables with Factor loading >0.30 and adjusting for co-variance, got a good model fit.

3) Used the same skeleton, used the incomplete data (n= 1330) to conduct FIML imputation

4) I got a complete dataset (n= 1330) with complete columns for all the variables and newly constructed latent variables.

Issues-

1) After imputation, convert the nominal data to continuous data

For ex.-

In original- Gender- 0- Male ; 1- Female

In imputed- Gender- 0 (Male); 0.44; 0.45; 0.46; 0.47; 1(Female)

2) In the imputed dataset, there are 3 new columns for all the 3 latent variables, which was calculated on its own during the imputation.

For my analysis, which one shall I use

a) the same old skeleton and adding all the unobserved variables and perform the SEM

b) just stick to the newly SEM-FIML created the latent variables from imputed data.

I will really appreciate a response on this issue.