I'm looking to do some research with the GSS (the General Social Survey; a survey that asks over a 1000 people every year various questions and collects their demographic information as well). I'd like to look at one of their questions and see how predictors to that question change over time. The response variable that I will be looking at is a 4 level ordinal variable (strongly against to strongly in favor). I will be using a cumulative logit model with demographic variables to predict the response. For example, does income predict where the respondent answers on the likert scale.
While this could be interesting, I think it would be more interesting to see how these demographic predictors change over time. For example, does income predict income in 1972 but not in 2000? (Just to clarify, it's a different sample of people every year and the weights change per year if I recall correctly).
My issue is how I would approach this.
Would I just run a separate regression for every year?
Alternatively, would I include the year as an interaction term like so?
respondent opinion = income * year + age * year + ...
(with the possibility for other interaction terms)
Would it make sense to just collapse the variable into mostly agree and mostly agree? I would sacrifice granularity, but interpretation could be easier?
I'm not familiar with any time dependent data analysis and was told by my professors to stay away from it until I've taken a class on it. If any of these options are wrong, could you explain why (basic theoretical foundation) so I know what I'm doing wrong.