There is growing evidence that psychological characteristics are spatially clustered across geographic regions and that regionally aggregated psychological characteristics are related to important outcomes. However, much of the evidence comes from research that has relied on methods that are theoretically ill-suited for working with spatial data. Consequently, the validity and generalizability of that work is unclear. The present work addresses two main challenges of working with spatial data (i.e., Modifiable Areal Unit Problem and spatial dependencies) and evaluates data-analytic techniques designed to tackle those challenges. To illustrate these issues, we investigate the robustness of regional Big Five personality differences and their correlates within the U.S. (Study 1: N = 3,387,303) and Germany (Study 2: N = 110,029). First, we visualize regional personality differences using a spatial smoothing approach. Second, we account for the Modifiable Areal Unit Problem by examining the correlates of regional personality scores across multiple levels of spatial aggregation. Third, we account for spatial dependencies by examining the correlates of regional personality scores using spatial econometric models. Our results suggest that regional differences in psychological characteristics are robust and can reliably be studied across countries and spatial levels. At the same time, the results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.
