I'm running a CFA with multiple imputed data (I generated outside Mplus), the estimator is WLSMV because I have non-normal likert scale. All the fit index values in the mplus output are showed as means over multiple datasets and there is no p-value for chi-square. How should I interpret them? Thank you!
I have a related question to this thread. I have a data set N=107 in which about 25 observations are missing data on all outcome variables (4 outcome). I am trying to run a simple path analysis with a few predictors and these four outcomes. Since I have cases in which data is missing on all variables except x-variables, FIML does not estimate that missingness. I have resorted to multiple imputation. Is there anything else I can do, other than multiple imputation?
I noticed that when I use MLR estimator (I have skewed data), the only fit indices reported are AIC, BIC, and SRMR. Should I interpret the SRMR normally? It seems a lot higher than it should be, at least it's a lot higher than in the non-imputed analysis I ran. I have seen you say before that fit indices for multiple imputation do not have strong theory behind them.
Thanks for the answer. I'm a bit confused however, when I don't impute, 25 observations are excluded because of missing on all variables except for x-variables. This reduces my total observations to 82 when using FIML via MLR estimation. When I use MI, I have my full sample size of 107. Does it still give you the same approximate information, despite excluding that many observations?
The MI run works with subjects that have missing on all y's because it treats all variables equally (essentially as y variables). You can get the same FIML behavior as MI if you bring in the x's into the model by mentioning their variances.
Initially my EF variable had a variance of 2311 while both gender and IQ had variances around .5. I re-scaled the variances so they would all be between 0 and 10 using the define command. In my path analysis I regressed all four outcomes onto each of the three predictors. I mentioned the variances: