This video demonstrates how to conduct and interpret a multinomial logistic regression in SPSS. The example shown has one dichotomous independent variable and one continuous variable. The outcome variable contains three levels.
Is the interpretation for program odds correct? From my understanding, the way to interpret would mean that as there's a unit increase (or change from 0 to 1 in this situation, otherwise from 0 = experimental program, to 1 = treatment as usual) there's a greater chance (13 times) of having some improvement over no improvement. So being in the treatment as usual grants 13 times more odds that they'll have some improvement over no improvement? Am I missing something here?
I have a question. Is there a precedence of taking a random sample of one category of the dependent variable so as to have similar proportions to the second for a three category dependent variable. My category proportions are 0.77, 0.20, and 0.027. Is there any other way to model the three categories dependent variable for these proportions.
Thanks, Dr. Todd, It would be great if you could assist me with finding the adjusted odd ratio when adjusting two factors such as age and sex, but the odd ratio was one number. I found this in some papers but I can't figure out how to do it.
Hi Dr. Grande, then how to check the assumptions in multinomial logistic regression? It seems that some other models you presented all include assumptions testing, but not this one.
Great video, Todd! However, like Miguel, I did not quite get how to interpret the odds ratio of program. How is the odds ratio saying something about the probability of the two different programs and, at the same time, something about the probability of group membership compared to the reference group?
Hi Todd, How big should be the sample(Nobs) for carrying out Multinomial logit (MNL) regression? I have a sample of 35 nobs and my observations are the countries, so no possibility to increase... Would you recommend me the use of MNL?
Hi! i have ordinal DV an i ran Ordinal Logistic regression but it didnt met proportional odds assumption then i ran Multimoninal Logistic regression but the model fitting information is not significant. what should i do?