Multilevel models to identify contextual effects on individual group member outcomes: a family example

Abstract

This manuscript illustrates methods for utilizing measurements of individuals to identify group contextual effects on individual outcomes. Contextual effects can be identified by 1 of 3 methods: (1) divergence of the simple within- and between-group regression coefficients, (2) the presence of a cross-level interaction of the within- and between-group predictor variable, or (3) the effect of discrepancies within the group. These methods can be used to incorporate group context into an individual model and can be utilized for any individual process variable that might be affected by a group context. Example data include measures of hassles and coping adequacy of inner city, poor, African American new mothers, and their family members.