I'd like to do a structural equation modeling for an ordinal dependent variable. Moreover, I have ordinal and categorical independent variables in the model.
The ordinal dependent variable is the frequency of visiting parks. The ordinal independent variables are demographic variables, including age, education level.
The categorical independent variables are some demographic variables. Such as gender(female, male), marriage(single, married, widowed), occupation(student, employee, retire, and unemployed).
I've learned that Mplus could deal with categorical dependent variables? But how about the categorical independent variables?
What software and method could address such issue? Thanks!
Best Answer
As Patrick Malone indicates, virtually every SEM software option is going to provide you with the capacity to analyze categorical predictors, assuming you have coded them appropriately (e.g., dummy-, effect-, or contrast-coded).
Mplus is definitely one of the more feature-rich SEM software options, but there are open-access alternatives that will do what you need. The lavaan() package (Rosseel, 2012) for R, for example, can definitely accommodate both the categorical predictors and the ordinal outcome that you have.
However, depending on how many levels there are in your outcome of frequency of visiting parks, it may not be necessary to use an ordinal estimator. Rhemtulla et al. (2012) have a nice simulation paper demonstrating that with 6-7 (and sometimes as few as 5) ordinal response categories, robust maximum likelihood estimators appear to perform just fine.
References
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354-373.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36.