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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; is@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? 


With 4 classes you want to say c#1c#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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