I have >1 million postcodes clustered in ~7000 middle super output areas for England and Wales which I'm analysing using MLwiN. I have used a multilevel model with random effects and have clustered the postcode observations at the middle super output area.
My concern is that when analysing the middle super output areas using a Queens 1st order contiguity weights matrix (in GeoDa), Moran's I came to 0.00324916 (p=0.013) (see graph below). While there is some spatial autocorrelation, this seems to be a very, very small amount - is it small enough for me to ignore?
If I couldn't ignore this then I would have to cluster at the regional level leaving me with only 32 clusters. As I'm using MCMC estimation this would dramatically increase the computational intensity of my model runs (leading to days per run instead of hours), hence I would quite like to avoid this if possible.