I benefited from the comments by Anita did as I was advised. It worked fine for my other variables but the one I used as an example in my message acts weirdly. And your comments Hector, helped me to realize the identification problem for categories (at least in my case)
Parenting style has three categories: Authoritative, Permissive and Authoritarian.
In my dataset first one yields more positive results than others in other forms of analysis (linear regression with dummies etc.)
I applied Crosstabs and Chi-square tests confirmed this relationship.
However when I entered this variable into CATREG as integers (Authoritative = 1, Permissive = 2 and Authoritarian =3) I receive following results as quantifications. This is opposite of what it should be. If variables are recoded in reverse order sign of the quantification changes. I select nominal scale with no discretization for this data.
Parenting Style_PSDQ(a)
Category
Frequency
Quantification
Authoritative
271
-1,289
Permissive
310
,411
Authoritarian
186
1,193
a Optimal Scaling Level: Nominal.
If I recode this variable into string format and enter it into analysis "ranking" is selected as discretization automatically and I receive following results which make sense for the dataset.
str_parent(a)
Category
Frequency
Quantification
AT
186
-1,193
AV
271
1,289
PM
310
-,411
a Optimal Scaling Level: Nominal.
(I didn't bother with full labels in my trial :-)
It is same for dummy variables too Hector.
After my trials upon your advices my question becomes what parameters should I use in my CATREG and what should I do for a nominal variable to get unbiased results? As in parenting style the result changes if I change arbitrary order of numerical identification of the categories. Should I take the cumbersome work and convert all nominal variables used in CATREG to string or is there any other way to have CATREG treat this variables equally regardless of what I use to identify them.
Thanks for the help again
David