Using cross-classified multilevel models to disentangle school and neighborhood effects: An example focusing on smoking behaviors among adolescents in the United States

Background: Despite much interest in understanding the influence of contexts on health, most research has focused on one context at a time, ignoring the reality that individuals have simultaneous memberships in multiple settings. Method: Using the example of smoking behavior among adolescents in the National Longitudinal Study of Adolescent Health, we applied cross classified multilevel modeling (CCMM) to examine fixed and random effects for schools and neighborhoods. We compared the CCMM results with those obtained from a traditional multilevel model (MEM) focused on either the school and neighborhood separately. Results: In the MEMs, 52% of the variation in smoking was due to differences between neighborhoods (when schools were ignored) and 6.3% of the variation in smoking was due to differences between schools (when neighborhoods were ignored). However in the CCMM examining neighborhood and school variation simultaneously, the neighborhood level variation was reduced to 0.4%. Conclusion: Results suggest that using MEM, instead of CCMM, could lead to overestimating the importance of certain contexts and could ultimately lead to targeting interventions or policies to the wrong settings. (C) 2014 Elsevier Ltd. All rights reserved.