Paul Cross writes:

In reading your book and papers on multilevel modeling I’ve noticed that you do not do much explicit modeling of spatial or temporal effects. I’m wondering if this is philosophically driven, perhaps because you prefer to get at underlying cause of the spatial correlations rather than just describing the spatial (or temporal) patterns. On a more technical level though, are there issues associated with including hierarchical effects in spatial models (e.g. Besag-York and Mollie convolution models)? Do spatial and non-spatial predictors compete with one another to predict the outcome resulting in biased estimates of both? Is this a simple issue of confounding, and if so how would one know that a non-spatial explanatory variable was confounding the spatial effects? Would it require a separate analysis of the spatial properties of all explanatory variables. As a disease ecologist, separating importance of covariates from the contagious process across time and space is a central problem.

My response: I think I’d be a better person if I fit spatial and time correlations. And similar models for other continuous predictors: for example, when modeling voting given income, maybe some sort of spline model instead of a sloppy combination of linear and categorical factors. I think the big reason I don’t do it is that it takes a lot of work, and I’d rather put the effort into modeling interactions. Often I put in spatial patterns in a crude way, for example including regions as predictors when modeling U.S. states. But I do think that spatial modeling can be a good idea–don’t take my laziness as an anti-endorsement.

P.S. I prefer the term “patterns” rather than “effects” in this context.