I have data on family care for elderly people. Data stem from 6 EU counries. People were asked at baseline and followed-up one year later. Now I'd like to find predictors that explain why people stopped caring for their elder relatives (or continued caring), or in short: to find barriers and facilitators that keep care-at-home settings stable (in 6 country comparison).
My idea was to use mixed effects models with country as random intercept. But how would I include the time comparison (i.e. all people were caring at baseline, but a certain percentage stopped caring one year later).
Would this be another random intercept? Or a random slope? Or would I include this predictor as interaction?
I'm planning to analyse my data with R and the lme4 package.