Dear Drs. Muthen, I have never used Mplus. Before I buy and learn how to use it I want to make sure that it can run the SEM analyses I need. I have a sample of 143 sites sampled at different environmental conditions and localities and in each site I have measured the abundance of three species. I wish to explore the direct and indirect effects on the relative abundance of the three species by testing the relations between these three species and ~14 different variables, and within the 14 different variables. Some of these variables are dichotomic, some ordinal and some continuous. Here are the questions: 1. If I consider the presence/absence of the three species some of my endogenous variables become dichotomic; is there a way to create a model including both endogenous and exogenous dicotomic variables together with continuous ones? 2. If I consider the relative abundance of the species, most of the sites do not have the species and only few have many, so the distribution of the endogenous variables is negative binomial. Is there a way to account for the discrepancy from normality in this case? 3. My main aim is to find the best model out of few candidates; can I calculate for the models that are fit to my previous questions AIC values? Thanks in advance, Hadas
Dear Dr. Muthen Thanks for your super-fast and helpful answer. I have read through the article, however I didn’t manage to fully understand how one can use nominal independent variable in the model. In particular, I didn’t understand how latent variables are used. I managed to run the model when I defined my independent variable as categorical but not as nominal. For example if x->y and x is nominal with three categories and y is binary categorical. How do I formulate it into the Mplus script? Thanks in advance, Hadas
Do you mean that after I create the two dummy variables I define these two new variables as categorical and run the analyses as I would with categorical variables? So, if I would have just 2 categories (for example female-0 and male-1) than I just treat the variable as categorical? Thanks, Hadas
The CATEGORICAL option is for dependent variables only. The dummy variables are independent variables. You simply use them on the right-hand side of an ON statement. In regression, independent variables can be binary or continuous. In both cases, they are treated as continuous.
When I use the dummy variables as independents I get two odd ratios for the relation of each new variable on the dependent variable. Should I average them?
Also, what should I do if the mediator is nominal. For example if x->m->y and x is categorical, m is nominal with three categories and y is binary categorical. It was hard for me to follow the relevant example in the article. Should it be the same?
Tracy Witte posted on Wednesday, March 20, 2013 - 6:12 am
I have noticed that with version 7, Mplus lets me specify binary predictor variables as categorical. In previous versions, I used to get an error message when I tried to do this, saying that the categorical descriptor only worked for dependent variables. Is this a new feature of version 7? If so, what is different about the modeling that allows this to be done?