The CATEGORICAL and NOMINAL options are for dependent variables only. They should not be used for covariates. Covariates can be binary or continuous as in regular regression and are treated as continuous in both cases.
Jiyoung posted on Tuesday, June 23, 2009 - 8:30 am
Thank you for your answer. I have a few more questions.
All of my independent and dependent variables measured on 7-point Likert type of scales. Exceptions include a few nominal control variables.
I wonder what would be the best. These are the options I am thinking of.
1) I can simply treat all of my independent and dependent variables as continuous variables. In social science, we actually consider the variables that are measured on Likert type of scales interval variables.
If I choose this option, I should not use any CATEGORICAL and NOMINAL options for both independent and dependent varaibles. If I use this option, I also have to use ML estimator instead of WLSM estimator.
2) The second option is to use the CATEGORICAL command for the dependent variable to define them as ordinal varaibles, but not for the independent variables. As a result, I simply treat the independent varaibels that are measured on 7-point Likert scale continuous without defining them as ordinal variables. However, I define the dependent variables as ordinal variables using the CATEGORICAL option. If I use this option, I have to use WLSM estimator.
I think that the first option is better. Would you please let me know which option is better between the two. Am I supposed to take a totally different approach?
Muthén, B. & Kaplan D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
Muthén, B. & Kaplan D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30.
Jiyoung posted on Thursday, June 25, 2009 - 7:29 am
Hi- I've received some mixed advice about using nominal (3 categories) indicators for a continuous latent construct. My question is: is it permissible or valid to use both nominal and continuous variables as indicators for a continuous latent construct which will be used as a predictor for the intercept and slope in a Growth Curve model setting.
To use a nominal variable as a control variable, you need to create a set of dummy variables. In your case, it would be 11 dummy variables. The NOMINAL option is for dependent variables only.
Jan Zirk posted on Thursday, August 22, 2013 - 4:02 pm
Do you plan or is there on the 'wish list' the matter of facilitation of analyses with nominal independent variables? (so that creating the dummy variables would not be necessary: it may get extremely complex when there are a few nominal predictors with more than 2 levels; most packages for standard analyses e.g. ANOVA automatically create sets of dummies for nominal predictors...).
Jan Zirk posted on Friday, August 23, 2013 - 11:00 am
This would be a big advantage. Thank you for the prompt response.
Jan Zirk posted on Friday, August 23, 2013 - 11:04 am
P.S. Especially if the facility would provide both the general estimate of the main effect and the interaction term like in factorial anovas with nominal IVs with more than 2 categories, and the more detailed insight into the effects of dummy predictors.
Jan Zirk posted on Wednesday, September 18, 2013 - 6:09 am
I am testing an objective Bayesian regression model with an interaction term of a continuous predictor and 2 dummy variables (as there are 3 groups in the study). The model is defined in this way:
i on S; i on dum1; i on dum2; i on dum1xS; i on dum2xS;
How can I obtain estimates and significance for the joint effect of Group (i.e. of dum1 and dum2) and the joint effect of interaction terms (i.e. dum1xS and dum2xS)?