I'm wondering whether a fixed-effects (logit) model fits the purpose of my study, and would like to get some feedback or ideas which other models could potentially be useful:
I have an unbalanced panel (daily observations, 3 months time period) with Twitter user data. Among the independent variables are
- number of followers
- is the user verified (binary)
- number of tweets on that day
- time between tweet
the dependent variable is binary (0,1).
As far as I understand, the fixed effects model does not consider the variance between the individuals. However, isn't the variance between the user what really matters here? Given that I suspect that there is much less variation for the independent variables within each user (e.g. the number of followers does not increase so much during the study period. Also whether or not a user is a verified user does not change).
Still, I understand the advantages of the fixed-effects model, given that there are likely individual time-constant differences between the users (e.g. intelligence, talent,...) which the FE model would control for. Is there a model that accounts for individual fixed effects but still considers between variance?
Also, in case I wanted to do a follow-up cluster analysis with the variables that turned out to be statistically significant in the regression, wouldn't I need to consider the between variance in the regression in the first step, because any cluster algorithm essentially tries to separate (between) users.
Thanks a lot for your help and ideas!