You can do cluster analysis using the latent class analysis that Mplus provides. For a discussion of this type of cluster analysis, see the Hagenaars-McCutcheon LCA book in the Mplus web site references. This can handle all the kinds of observed outcome types that Mplus allows.
Thank you Prof. ... let me have a look at their book ... in between I'm going through our Mplus User's guide chapter 7 for LCA ...I'm still looking for the command/code so that in the result section we can have our Identifying variable in specified cluster/ class, since that is what I need first to establish my hypotheses(as an exploratory analysis only) ... can u help me please
I have 240 observation ... my reasonable guess there should be 3, at most 4 classes ... Now, my output should show in which class these 240 observations belong to ... we are doing the clustering on Six indicator variables, and they are categorical
Oh Madam!Thank u so much ... this is exactly what I was looking for ... Mplus is really cool :-) ... thank u all once again
Lois Downey posted on Friday, November 17, 2006 - 11:58 am
I ran 15 unclustered multiple regression analyses with an ordered categorical outcome, looking at 15 predictors of interest, with each analysis adjusted for a set of 9 potential confounders. (In most of the analyses, the sample size was over 200.) We then wanted to compare these analyses to a parallel set of analyses with the same observations, but clustered within 10 hospitals. I was expecting to see the standard error for the predictor of interest in each analysis to increase in the clustered analysis. In fact, however, in 11 of the 15 analyses, the standard error dropped. Should I be suspicious of the results?
I have a single continuous variable that represents country gender development index and a nominal variable that has the names of the country associated with the score in the continuous variable. If I am running a mixture model, how do I get the names of each country from the nominal variable in the model results and graph?