MAR and Type=RANDOM with AUXILIARY PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 John Flournoy posted on Friday, February 26, 2016 - 11:15 am
Hi all,

I have tried to track down other discussions of this topic and perhaps overlooked something, but couldn't find anything that completely answers my questions.

I am using TYPE=RANDOM in order to use TSCORES for analysis of a cohort-sequential design. I would like to also use the AUXILIARY command with the '(m)' option for missing data correlates, but it appears that I cannot: "The 'm' specifier in the AUXILIARY option is not available for TYPE=RANDOM."

Is there any way to overcome this limitation, e.g., with a newer version of Mplus? Or is there some fundamental analytic limitation here?

Thank you for any advice.

Very best,
 Bengt O. Muthen posted on Friday, February 26, 2016 - 3:05 pm
Try to capture the different cohorts using multiple-group analysis instead. See UGT ex 6.18.
 Paulina Perez Mejias posted on Monday, May 14, 2018 - 9:50 am
I was looking at an example of a linear growth model with missing data on a continuous outcome using a missing data correlate to improve the plausibility of MAR in this link:

I was wondering what are the hypothesis tested in relation to the auxiliary variables. In other words, how do I interpret the output regarding the auxiliary part of the model?


Number of Free Parameters 9

Loglikelihood Including the Auxiliary Part

H0 Value -1009.197
H1 Value -1007.232

Information Criteria Including the Auxiliary Part

Number of Free Parameters 15
Akaike (AIC) 2048.394
Bayesian (BIC) 2097.869
Sample-Size Adjusted BIC 2050.347
(n* = (n + 2) / 24)

Chi-Square Test of Model Fit

Value 3.930
Degrees of Freedom 5
P-Value 0.5595

 Bengt O. Muthen posted on Monday, May 14, 2018 - 4:31 pm
The auxiliary part is saturated so does not impact the test of model fit. See the tech report

Auxiliary Variables Predicting Missing Data

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