I am using a logistic regression to model a 0/1 event with community characteristics as being my explanatory variables but my data structure is different making the interpretation hard. My unit of analysis is zip-code and for each zip-code, there are different categories of variables in different columns. For example, age has 4 categories; under 19, 25 to 34, 35 to 54, 54 to 64 and 65 above and it gives how many percent of people are under that age category for that zip-code. All the other variables are also similar such as age, race, education, marital status, income, etc. Conventionally, we interpret odds ratio as a 1 unit increase in x, we expect to see z% increase or decrease in the odds of the event happening.
Here is what my data looks like which gives % of population in that category for each zip-code but my each category itself is a variable even though bunch of them belong to same group say age or race. Usually, age or race is one variable with different coding (1,2,3) assigned to different categories such as 1-white, 2-hispanic etc but those datasets are at the individual level. Since this is at the zip-code level, it's not possible to do that which is why it is structured like this.
So when I do regression I am doing using the code below and I am leaving one category out due to multicollinearity issue.
logistic y Age20to44 Age45to54 Age55to64.....
I was told to use one category as reference and when I interpret the results, interpret it by comparing to the reference category but my argument is that how would STATA or any statistical software would know that I am using that category as reference since that reference category is itself a separate variable and I leaving that out of that model. Therefore, I have 2 questions:
- Since my explanatory variables are all in different columns even though they belong to same group such as age, race, and income, I cannot use reference category and please correct me if I am wrong, reference categories are only used when there is categorical variable which is contained all in one column?
- How do I interpret my results? For ex. odds ratio for age20to44 is 1.02. Should I say that this age category increases the odds of the event by 2% or should I say that a unit increase in age with in this age category increases the odds of the event by 2%.