In this study, the authors test for and estimate the clustering of marijuana use within United States neighborhoods, making use of data from annual nationally representative household sample surveys conducted during the period 19901995. A recently developed statistical method, alternating logistic regression, was used to quantify the clustering of marijuana users in neighborhoods. The resulting estimates of pairwise odds ratios ranged from 1.3 (95% confidence interval 1.221.42) for the lifetime history of marijuana use to 2.0 (95% confidence interval 1.62.6) for recent sharing of marijuana from one person to another. Exploratory analysis showed a slight decrease of clustering effects after adjustment for individuallevel covariates: age, sex, race, education, annual family income, and history of tobacco use. Nevertheless, the main factors that account for clustering remain to be determined. Alternating logistic regression provided useful estimates of marijuana use clustering and can be used to estimate clustering of the other drug-related behavior, including sharing of needle injection equipment and other human immunodeficiency virus risk behaviors. As a form of multilevel analysis, the alternating logistic regression can accommodate shared, community-level characteristics that might influence drug taking (e.g., collective efficacy), as well as individual-level covariates, such as age and sex.