Dear adschai, I'm not sure that I entirely follow what you want to do, but if the response really is qualitative I don't see the sense in transforming it into a single quantitative variable. Nor does the strategy of generating 24 dichotomous, separately modelled responses make sense, since these are correlated. In some circumstances, however, one can resolve a polytomous response into a set of *nested* dichotomies, which are then independent of one another. Finally, I wouldn't as a general matter recommend fitting any statistical model to a 24-category response.
I suspect that you'd do well to find someone with whom you can about your research problem. Regards, John -------------------------------- John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of > [EMAIL PROTECTED] > Sent: Saturday, April 21, 2007 4:32 PM > To: [email protected] > Subject: [R] Fitting multinomial response in structural equation > > Hi - I am confronting a situation where I have a set of > structural equation and one or two of my responses are > multinomial. I understand that sem would not deal with the > unordered response. So I am thinking of the following two ways: > > 1. Expanding my response to a new set of binary variables > corresponding to each label of my multinomial response. Then > use each of these as a separate response in my model. > However, since I have about 24 labels in this single > variable, it will be very expensive to do this way. > 2. I am thinking of transforming this variable into a > continous-valued variable. I am thinking of using the > observed count to transform this variable using the probit > function. Then my new variable is just a step-wise function. > The trouble that I am struggling with is that this response > variable will also serve as a predictor in another equation > in my structural model. The interpretation of this equation > is not so straightforward for me. The coefficient of this > variable is no longer reading 'a unit change in this variable > holding everything else fixed corresponds to the x unit > change of the response'. All I can read from this method is > that when I change from one label to another, it means p > amount change in my step-wise-function predictor variable and > it corresponds to x unit change of the response holding > everything fixed. > > The main purpose here for myself to post my question here is > to obtain your insight especially with respect to using sem > with the two approaches above. I would like to ensure that my > approaches make sense within the context of sem. Any > comments/opinions would be really appreciated. Thank you so > much in advance. > > - adschai > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
