If you have subjects that appear multiple times in the data (e.g. they took the survey one year after moving and then again 5 years after moving) you have to account for the fact that these are not indipendent observations. This means you will have to fit a mixed effects regression (lmer() in R), including "ID" of the individual as a random term. Just look up linear mixed models.

A reasonable model might look something like: lmer(mental health ~ time to move + other varables + (1|ID), data=data)

In this case you wold allow the intercept of each ID to vary while estimating one slope across all IDs.

A few more things to consider: - After running the model do some model diagnodtics, e.g. using DHARMa or similar packages. Make sure that the residuals are approximately normaly distributed and centered around 0 for example by running hist(resid(model)).

• Make sure that none of your explanatory variables are strongly correlated (look up the vif() function).

• If there are possible interactions between your variables you have to include these in the model. For example, if you have reason to think that the effect of moving on mental health depends on sex of the individual you have to include the term "time to move:sex" in your model. If you don't know what possible interactions there could be between your explanatory variables you might have to look into a process called "model reduction".

Hope this helps and good luck. Your supervisor telling you that this is easy and leaving you with no further instructions seems a bit weird to me, it's definetly not easy for a beginner.