I have the variable age which I want to categorize, but I'm not fully sure which version of the categorization I should use. I choose these 2 versions based on past research and what was used in them.
Age is continuous on the interval [15 - 90]
I categorize this as:
Age_cat_1:
Less than 20 - young
20- 50 = old
> 50 = really old
Age_cat_2:
Less than 40 - young
> 40 = old
Then my main analysis is whether age has an association with math scores (continuous variable), after factoring for differences in sex, high school and English scores
Under model 1:
ologit Age_cat_1 Mathscores i.sex i.high_school English
Under model 2:
ologit Age_cat_2 Mathscores i.sex i.high_school English
Let's say both models are statistically significant - i.e. math scores are clearly statistically significant predictors of age.
Now how do I determine which model to use for the age categorization?
Age is continuous on the interval [15 - 90]
I categorize this as:
Age_cat_1:
Less than 20 - young
20- 50 = old
> 50 = really old
Age_cat_2:
Less than 40 - young
> 40 = old
Then my main analysis is whether age has an association with math scores (continuous variable), after factoring for differences in sex, high school and English scores
Under model 1:
ologit Age_cat_1 Mathscores i.sex i.high_school English
Under model 2:
ologit Age_cat_2 Mathscores i.sex i.high_school English
Let's say both models are statistically significant - i.e. math scores are clearly statistically significant predictors of age.
Now how do I determine which model to use for the age categorization?
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