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 Kelly Landman posted on Friday, November 22, 2013 - 6:25 pm
I'm running a 4 class model on reading scores across 4 time points with gender(a dummy coded variable) as a categorical variable. I'm trying to determine the odds of a student being male or female in each class. It looks like the output only gives me the odds for the first class:

LOGISTIC REGRESSION ODDS RATIO RESULTS

Categorical Latent Variables

C#1 ON
G 0.794


my original model specifies:
model:
%OVERALL%
i s | R3@0 R4@1 R5@2 R6@3;
i-s@0;
i s on G;
c#1 on G

Do I need to add c#2 on G to get the odds ratios for the second class?
 Bengt O. Muthen posted on Saturday, November 23, 2013 - 6:29 am
With 4 classes you want to say

c#1-c#3 on G;

or simply

c on G;

That will give you 3 odds ratios in the output, one for each regression slope.

Some teaching can be helpful in shedding more light.

Look at our Topic 2 handout on page 29 (slide 58) and you find equation (94) which gives the log odds equal to a sum of beta_{0c} and beta_{1c}*x, where x is your G and your c=1, 2, 3, 4.

For G=0, the log odds is the intercept beta_{0c}, so the odds is exp(beta_{0c}), where exp is exponentiation. For G=1, the log odds is beta_{0c}+beta_{1c}*1, so you have to exponentiate that. And you do this for each of the first 3 classes (the last class is the reference class for the odds).

The Mplus output gives you the exponentiation only of the slope beta_{1c} which therefore gives you the odds ratio, directly comparing males and females on their odds.
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