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 (MLM) focused on either the school and neighborhood separately.

RESULTS: In the MLMs, 5.2% 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 MLM, 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.