Test the parallel regression assumption PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Kaigang Li posted on Thursday, July 03, 2008 - 7:16 am
Hello Prof. Muthen,

If I test a linear growth model for a categorical outcome with time-invariant and time-varying covariates, is there any way to test the parallel regression assumption/proportional odds assumption in Mplus?


 Linda K. Muthen posted on Thursday, July 03, 2008 - 4:22 pm
Do you mean the assumption behind the logistic regression model for ordinal outcomes?
 Kaigang Li posted on Friday, July 04, 2008 - 8:44 am
Yes. sorry to confuse you.
 Bengt O. Muthen posted on Friday, July 04, 2008 - 11:39 am
No, there is not such a test in Mplus. You can read about this in the Sage (white series) book by Long (1997), Regression models for categorical and limited dependent variables, pp. 140-145. His discussion includes the simple checking of a similar slope for a set of binary regressions ("An informal test", p. 141).
 Kaigang Li posted on Friday, July 04, 2008 - 1:45 pm
Thanks, I will read that book. Following that question, I have another one. It seems that the nominal (unordered) variable cannot be the outcome variables in the growth model when I read the CHAPTER 6 of Mplus manual, is that right?

So if the proportional odds assumption for ordinal outcomes is violated according to the Long's informal test, what can I do in terms of the growth model?

Thanks and have a good holiday!
 Bengt O. Muthen posted on Sunday, July 06, 2008 - 9:38 am
I have not seen growth modeling done with a nominal outcome - it doesn't seem like a natural fit. Perhaps you want to switch to latent transition analysis if you are concerned with the proportional odds specification.
 Kaigang Li posted on Sunday, July 06, 2008 - 1:48 pm
Thanks Professor Muthen,

I have another question which is related to the example at the website http://www.ats.ucla.edu/stat/Mplus/output/lgcm2.htm.

For testing the models as follows:

Level-1 Model
Y = B0 + B1*(A) + B2*(TIME) + R
Level-2 Model
B0 = G00 + G01*(X1) + G02*(X2) + U0
B1 = G10
B2 = G20 + G21*(X1) + G B2 = G20 + G21*(X1) + G22*(X2) + U2

The following Mplus syntax can be used.

i s | y11@0 y12@1 y13@2 y14@3;
i s ON x1 x2;
y11 ON a31 (1);
y12 ON a32 (1);
y13 ON a33 (1);
y14 ON a34 (1);
y11 y12 y13 y14 (2);

My question is that "if I want to look at the random effect of B1 and include X1 and X2 in the model,

i.e. B1 = G10 + G11*(X1) + G12*(X2) + U1

how can I write the Mplus syntax?

Thanks for help,

 Linda K. Muthen posted on Monday, July 07, 2008 - 9:41 am
See Example 6.12.
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