New Approaches to Multilevel Analysis

Excerpt

The paper by Yu-Sheng and Ying-Chih in this issue highlights some of the challenges confronting studies examining neighborhood-level influences on health. Studies of this sort often involve data on individuals nested within neighborhoods. A key issue that arises in analyses of these types of data structure is the potential for nonindependence of observations (i.e., the possibility of within-neighborhood correlations between individual-level outcomes). Ignoring this can result in invalid standard errors, incorrect (typically anticonservative) inferences, and inefficient estimates.1 Multilevel models are particularly suited to analysis of these types of data structures and are being increasingly used in studies of neighborhood-level effects.2,3 …