I have a panel survey of self-assessed levels of household resilience in three nearby villages, with observations collected over the course of 10 separate survey waves (observations are collected on a continual basis, with each full wave requiring 6-8 weeks to finish, before the next one then starts up again).
By linking this with observations from a single weather station that is close to all three villages, I can create a dataset that matches household resilience with the weather on the day of each interview.
What I would like to do is examine the how changes in weather within a season, and within a year, affect self-assessed resilience - preferably in a way that allows me to look at each separately.
My first thought is to use a fixed effects model of the sort:
lm(resilience ~ temperature + precipitation + factor(wave) + factor(household), data=df)
Using wave fixed effects means that I have a one observations per household, and allows me to account for time-invariant confounders.
However, while the 6-week period under each wave is useful, it does not overlap with the seasons observed in the surveyed area (of which three are three per year, each 3 months in duration).
Another thought would therefore be to use:
lm(resilience ~ temperature + precipitation + factor(season) + factor(household), data=df)
The same could also be done to look at within-year associations:
lm(resilience ~ temperature + precipitation + factor(year) + factor(household), data=df)
However, in both cases this would mean multiple observations for each time period. I assume this is problematic for inference?
Another option would be to aggregate the data, and average resilience and weather values on the day of interview. But I don't think this would be conceptually valid either.
Any tips would be warmly appreciated!
