Statistical inference and spatial patterns in correlates of IQ

Cross-national comparisons of IQ have become common since the release of a large dataset of international IQ scores. However, these studies have consistently failed to consider the potential lack of independence of these scores based on spatial proximity. To demonstrate the importance of this omission, we present a re-evaluation of several hypotheses put forward to explain variation in mean IQ among nations namely: (i) distance from central Africa, (ii) temperature, (iii) parasites, (iv) nutrition, (v) education, and (vi) GDP. We quantify the strength of spatial autocorrelation (SAC) in the predictors, response variables and the residuals of multiple regression models explaining national mean IQ. We outline a procedure for the control of SAC in such analyses and highlight the differences in the results before and after control for SAC. We find that incorporating additional terms to control for spatial interdependence increases the fit of models with no loss of parsimony. Support is provided for the finding that a national index of parasite burden and national IQ are strongly linked and temperature also features strongly in the models. However, we tentatively recommend a physiological – via impacts on host–parasite interactions – rather than evolutionary explanation for the effect of temperature. We present this study primarily to highlight the danger of ignoring autocorrelation in spatially extended data, and outline an appropriate approach should a spatially explicit analysis be considered necessary.

► Spatial autocorrelation (SAC) is an important issue in geographical analyses. ► SAC is present in analyses of geographical variation in national mean IQ. ► We re-evaluate hypotheses explaining variation in national IQ with control for SAC. ► Models controlling for SAC explain more variance in national IQ. ► Disease and temperature are the best-supported predictors of national IQ.