Aperçu

Résumé (français)

Veuillez noter que les résumés n'apparaissent que dans la langue de la publication et peuvent ne pas avoir de traduction.

Résumé (anglais)

In the multilevel modeling literature, contextual effect is defined as or identified by the effect of the target group-level variable while controlling for the corresponding individual-level variable. This paper extends the notion of “contextual effects” (or “neighborhood” or “school” effects) to an interaction setting, such that the effect of one explanatory variable Xij on the outcome Yij is modeled as a function of a group-level ‘moderating’ or predisposing variable Zj* as well as its counterpart at the individual level Zij. Researchers frequently use regression models that only contain a cross-level interaction between Xij and Zj* to test contextual hypotheses in an interaction setting, but this modeling strategy is unable to discriminate the immediate rival hypothesis that attributes a causal role to the corresponding individual-level variable. This paper points out the prevalence of this type of fallacy through a review of past research on contextual determinants of psychiatric resilience. It is argued that the simple step of adding an appropriate individual-level interaction XijZij could help more robustly test substantive hypotheses about how neighborhood context alters the effect of proximal stressors on health outcomes.