Independent observed variables ("x" variables in Mplus terminology) should not be declared nominal, but should instead be broken up into a set of dummy variables appearing on the RHS of ON. I assume that your independent nominal variable is not in turn predicted by another variable, in which case something different needs to be done.
freek bucx posted on Monday, March 19, 2007 - 4:18 am
Thanks for your answer. The latter is however the case: my independent (nominal) variable (with four categories) is also predicted itself by other variables. How can I fix this in Mplus?
The only way to do this is to create a categorical latent variable that is equal to the nominal observed variable and use the categorical latent variable in the regression. For example, if you have a model:
x -> u -> y
where u is a nominal variable, after you create a categorical latent variable c which is equal to u, you will have
x -> c -> y
where c is a categorical latent variable with four classes. The relationship between x and c is the multinomial regression of c on x and the relationship between c and y is found in the class-varying means of y.
Hi, I wanna to create a latent variable (L) which is measured by C (4 level categorical variable) and N (nominal variable as indicated by 0 & 1) to reflect the demo characteristics, where IV is independent variable, M is mediator and DV is dependent variable.
L by C N; IV on L; D on M IV; M on IV L;
nominal = N#1 N#2;
however, error message prompt for unknown nominal variable, how can I fix this problem?
And, If an indicator belongs to two latent factors, how to interpret this structure? How about if two indicators of two latent factors are correlated?
However, if it is binary, just put it on the CATEGORICAL list with the other categorical variables.
Factor indicators can used for more than one factor and factors can be correlated.
Jinseok Kim posted on Sunday, December 08, 2013 - 7:47 am
Hi, I am trying to estimate a model in which a nominal variable (u) is predicted by other variables and the same nominal variable (u) is acting as an independent variable of another variable.
Earlier in this thread, you mentioned that, in such a case, I will have to create a categorical latent variable, or a latent class (c) that is equivalent to the nominal variable (u). Would you please give us an example of mplus syntax that may do the job?
I'm struggling with a problem concerning dummy variables in a SEM derived from a nominal variable.
The way I was used to include nominal variables as predictors in a Regression/SEM Model was splitting the nominal variable into k-1 dummys with the remaining categeory as reference.
I recently learned from a book, that including dichotomous variables into a regression model is highly problematic.
According to this book this is because the standard deviation is a direct function of the variable's skewness, which results in unreliable standardized coefficients and level of significance. Thus the interpretation of effects would be hard.
I don't see that there is a problem here. You don't want to standardize with respect to the variance of a dummy variable because you are not interested in a standard deviation change in the variable but in the (raw) change from 0 to 1.
Under this circumstances, would it be possible to compare other path coefficients (e.g. between two metric variables) with the path coefficient of the dichotomous variable on a metric variable (like: effect a is stronger than effect b)?
And is the level of signifiance of the coefficients trustworthy? Will it be influenced/interpretable?
I would present the logit estimates that are printed and also odds ratios. Note that odds ratios for continuous x's refer to a one unit change and you may instead use a one SD change. This would be obtained if you standardize the x's. See also Chapter 5 in our new book including graphical displays.
If you don't want to work in the original metric of your x variables the simplest approach is to standardize them first. Then report significance of the slope estimates (raw form) and also give the odds ratios we print. See also Chapter 5 of our new book.