Fix index under WLSMV with multiple i... PreviousNext
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 Sywang posted on Friday, July 13, 2012 - 10:46 pm

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!
 Linda K. Muthen posted on Friday, July 13, 2012 - 11:09 pm
The values given are averages over the imputations. The only fit statistic that has been developed for multiple imputation is chi-square for maximum likelihood estimation for continuous variables.
 Sywang posted on Friday, July 13, 2012 - 11:14 pm
Thank you for your reply.
Do you have any suggestion? How do I know my model fit?
 Linda K. Muthen posted on Saturday, July 14, 2012 - 4:38 pm
Unless you are using maximum likelihood with continuous outcomes, there are no fit statistics developed for multiple imputation.
 Eric Thibodeau posted on Friday, September 05, 2014 - 3:01 pm

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.


 Bengt O. Muthen posted on Friday, September 05, 2014 - 8:17 pm
I would use FIML. You don't gain anything by first doing multiple imputation of the missing values on those 4 outcomes. FIML and MI use exactly the same information from the data.
 Eric Thibodeau posted on Friday, September 05, 2014 - 8:24 pm
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?
 Bengt O. Muthen posted on Friday, September 05, 2014 - 11:49 pm
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.
 Eric Thibodeau posted on Monday, September 08, 2014 - 5:18 pm
Thanks. I'm still running into problems. Let me tell you exactly what my data is, and what I have done thus far.

I have 107 observations. Four continuos outcome variables:

1. peer competence
2. academic outcome
3. conduct problems
4. hyperactivity

I have three predictors:

1. Gender
2. IQ
3. Executive functioning (EF)

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:


Now my output is telling me that the chi-square is negative, any reason why? If I don't specify the exact variances:


I get the warning message that of NON-POSITIVE DEFINITE, and it mentions there being a problem with my gender variable. I'm pretty sure this is not identified anyways.

 Linda K. Muthen posted on Monday, September 08, 2014 - 5:20 pm
Please send the output and your license number to
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