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Decomposing spatial effects of state-level health outcomes: a methodological demonstration and re-analysis
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Resumo(s)
While spatial autoregressive (SAR) models are increasingly used in population-level psychological studies, researchers often overlook the crucial step of parsing effects into direct, indirect and total impacts, a standard practice in spatial econometrics. In this paper, we demonstrate the necessity of this practice by re-analyzing Gruda et al.'s (2024) U.S. Dark-Triad and health dataset with heteroskedasticity-robust SAR models and full impact decomposition, revealing significant changes. The previously observed direct protective effect of state-level narcissism on hypertension mortality disappeared when accounting for interstate spillovers. Conversely, the association with lower cancer prevalence and depression strengthened. Several health-behaviour findings reversed direction, indicating naïve regressions conflated within- and between-state effects. Machiavellianism and psychopathy coefficients also shifted. These results demonstrate that spatial spillovers can dilute, negate or reverse local effects, cautioning against policy inferences based solely on direct estimates.
Descrição
Palavras-chave
Methods Narcissism Public health Spatial regressions
Contexto Educativo
Citação
Gruda, D., Hanges, P., & McCleskey, J. A. (2026). Decomposing spatial effects of state-level health outcomes: a methodological demonstration and re-analysis. International Journal of Psychology, 61(1), Article e70152. https://doi.org/10.1002/ijop.70152
Editora
Licença CC
Sem licença CC