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 relied on methods that are theoretically ill-suited for working with spatial data. The validity and generalizability of this work are thus unclear. Here we address two main challenges of working with spatial data (i.e., modifiable areal unit problem and spatial dependencies) and evaluate data-analysis 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 United States (Study 1; N = 3,387,303) and Germany (Study 2; N = 110,029). First, we display 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 spatial levels. Third, we account for spatial dependencies using spatial regression models. Our results suggest that regional psychological differences are robust and can reliably be studied across countries and spatial levels. The results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.

越来越多的证据表明，心理特征在地理区域之间是空间聚集的，并且区域聚集的心理特征与重要结果有关。然而，大部分证据来自依赖于理论上不适合处理空间数据的方法的研究。因此，这项工作的有效性和普遍性尚不清楚。在这里，我们解决了处理空间数据的两个主要挑战（即，可修改的区域单元问题和空间依赖性），并评估了旨在应对这些挑战的数据分析技术。为了说明这些问题，我们调查了美国（研究 1；N = 3,387,303）和德国（研究 2；N = 110,029）。首先，我们使用空间平滑方法显示区域个性差异。其次，我们通过检查跨多个空间级别的区域人格得分的相关性来解释可修改的区域单元问题。第三，我们使用空间回归模型来解释空间依赖关系。我们的研究结果表明，区域心理差异是强大的，并且可以跨国家和空间水平进行可靠的研究。结果还表明，忽视空间数据的方法学挑战可能会对关注区域心理差异的研究产生严重后果。