Are Regional Differences in Psychological Characteristics and Their Correlates Robust? Applying Spatial-Analysis Techniques to Examine Regional Variation in Personality

Abstract

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.

Openness Relates to COVID-19 Vaccination Rates across 48 United States but Politics Trump Personality

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“…In a first analytic step, we mapped out the spatial distributions for both regional personality and regional prototypical spending (see Figure 2) using Actor-Based Clustering (Brenner, 2017; Buecker, Ebert, Götz, Entringer, & Luhmann, 2020; Ebert, Gebauer, et al, 2019). Actor-Based Clustering is a statistical approach that utilizes the finest geographical information available to reveal areas where high or low values of a variable cluster geographically (see for further details).…”

“…Therefore, we estimated the spatial autocorrelation among error terms and found significant positive spatial autocorrelation of small to medium sized magnitude for four of the five traits (Table 1, column 2). For these four traits (all except for conscientiousness), we fitted additional models that included spatially lagged scores of the dependent variable as an additional predictor (Ebert, Gebauer, et al, 2019). Including these spatial lags successfully captured any existing spatial dependencies among error terms (Table 1, column 4).…”

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