Missing data in Structural Equation Modeling

Hi everyone,

I am new to the topic of SEM, so my question may seem a bit naive.

I have about 60 observed variables that will be grouped in some latent variables to explain one outcome measure.
One of the variables contains missing data.
If I impute these data, the new data will be based on the already existing data (as I could understand some (if not all the 59 remaining) variables will go into the regression analysis to replace the missing data, right? But how come that the SEM is then not biased by the fact that it's being looking for a correlation between variables, one of which is partly a result of a correlation between the same variables?


Doesn't actually exist
Good. Because regression-based imputation is bad for the reason you point out and many others. Mean-imputation, regression-based imputation, mode-imputation, listwise and pairwise deletion (unless the missing mechanism is MCAR) are all bad for all kinds of reasons.

Good ones are FIML, robust-FIML (if data are not multivariate normal) or multiple imputation.