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Kaigang Li posted on Thursday, July 03, 2008  1:16 pm



Hello Prof. Muthen, If I test a linear growth model for a categorical outcome with timeinvariant and timevarying covariates, is there any way to test the parallel regression assumption/proportional odds assumption in Mplus? Thanks, Kaigang 


Do you mean the assumption behind the logistic regression model for ordinal outcomes? 

Kaigang Li posted on Friday, July 04, 2008  2:44 pm



Yes. sorry to confuse you. 


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. 140145. 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  7:45 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! 


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  7:48 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: Level1 Model Y = B0 + B1*(A) + B2*(TIME) + R Level2 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, Kaigang 


See Example 6.12. 

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