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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. 

Soohyun Shin posted on Tuesday, September 15, 2015  2:20 pm



Dear Dr. Muthen I ran LCGA with a predictor of parental overprotection(PBI) on class membership. I am confused about the interpretation. According to ALTERNATIVE PARAMETERIZATIONS ....REGRESSION results, higher PBI only predicted higher chance of being in class 2 than class 1. Now the question is.. 1) In LOGISTIC REGRESSION ODDS RATIO RESULTS, how do I know if these odds ratios are significant? 2) These odd ratios seem to show that higher PBI increased the chance of being in class 1 than class 3, and being in the class 3 than class 2. They are inconsistent with the other result that showed higher PBI only increased the chance of being in class 2 than class 1. I think I'm missing something here. How do I interpret them? Thank you so much in advance. LOGISTIC REGRESSION ODDS RATIO RESULTS Categorical Latent Variables C#1 ON PBIF_O 1.016 C#2 ON PBIF_O 0.956 ALTERNATIVE PARAMETERIZATIONS FOR THE CATEGORICAL LATENT VARIABLE REGRESSION Parameterization using Reference Class 1 C#2 ON PBIF_O 0.062 0.026 2.387 0.017 C#3 ON PBIF_O 0.016 0.046 0.350 0.727 


The output you show: Parameterization using Reference Class 1 C#2 ON PBIF_O 0.062 0.026 2.387 0.017 says that relative to class 1, the class 2 probability is lowered by high PBIF_0 value. Isn't that consistent with the odds ratio being lower for C#2 than C#1 below? C#1 ON PBIF_O 1.016 C#2 ON PBIF_O 0.956 

Soohyun Shin posted on Wednesday, September 16, 2015  2:02 pm



Thank you for your response. Should I report OR = 1.016 (C#1 ON PBIF_O) to show that relative to class 1, the class 2 probability is lowered by high PBIF_0 value? Also how do I know if they are significant (p value)? Thank you again. 


I would interpret the logit values that are printed and have SEs. If you prefer ORs you can get their SEs and significance as described in the FAQ on our website: Odds ratio confidence interval from logOR estimate and SE 

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