# Comparing Hierarchical Linear Model with non-HLM techniques

I'm a fairly inexperienced statistician fighting a huge deadline and just need some peace of mind that I'm not making a massive error here. I'd be most grateful for any pointers.

I've been working on fitting an lmer() model that explores the spatial relationships between crime and socio-economic factors. The ICC demonstrates a 40% attribution of variances to group factors, which is encouraging. The model's evolution has seen a steady improvement of AIC scores. I am now a little confused about how to demonstrate its effectiveness against other non-HLM techniques. Is this purely by standard error residuals, or am I missing something important?!

Many thanks

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My comment about your other question also goes here. Moreover, it's not clear if you should have 2 questions, or if these are ultimately one question. –  gung Sep 5 '12 at 2:51
Hi @gung, I see your point but I think they're separate :) The other is about the risk of mixed-modelling with multiple correlated variables. This is about how to compare an HLM model with other analytical techniques like OLS regression. I am exploring the relative benefits of the approach, so need an idea of how to compare different models. I'm entirely self-taught and have found it pretty tough getting into the language, so just need some guidance. –  geotheory Sep 5 '12 at 9:38