I have a question regarding using the i. prefix for categorical variables.
Here is my model: ologit china_virus CN_Index i.factor_reltrad church_attendance prayer bible_authority if complete_case [pweight=WEIGHT]; The DV measures levels of agreement with Trump's use of the term "China Virus."
Here I used the i. prefix for reltrad because its categories are different religious groups (e.g., mainline, evangelical, catholic etc.) and I want to compare each category to the baseline.
However, church attendance ( coded from 1 to 8 where 1 =never attends and 8=attends weekly) , prayer (coded 1 to 6 with 1= never and 6=several times a day) , and bible authority (coded 1 to 4 where 4=The Bible means exactly what it says and 1=The Bible is an ancient book of history and legends) are also ordinal variables, but I am not interested in the difference within the categories, but whether the variable overall is a significant predictor of the DV (and the direction of impact). In that case, should I still use the i prefix for these categorical variables?
Here is my model: ologit china_virus CN_Index i.factor_reltrad church_attendance prayer bible_authority if complete_case [pweight=WEIGHT]; The DV measures levels of agreement with Trump's use of the term "China Virus."
Here I used the i. prefix for reltrad because its categories are different religious groups (e.g., mainline, evangelical, catholic etc.) and I want to compare each category to the baseline.
However, church attendance ( coded from 1 to 8 where 1 =never attends and 8=attends weekly) , prayer (coded 1 to 6 with 1= never and 6=several times a day) , and bible authority (coded 1 to 4 where 4=The Bible means exactly what it says and 1=The Bible is an ancient book of history and legends) are also ordinal variables, but I am not interested in the difference within the categories, but whether the variable overall is a significant predictor of the DV (and the direction of impact). In that case, should I still use the i prefix for these categorical variables?
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