In community-based alcoholism and drug abuse treatment programs, the vast majority of interventions are delivered in a group therapy context. In turn, treatment providers and funding agencies have called for more research on interventions delivered in groups in an effort to make the emerging empirical literature on the treatment of substance abuse more ecologically valid. Unfortunately, the complexity of data structures derived from therapy groups (because of member interdependence and changing membership over time) and the present lack of statistically valid and generally accepted approaches to analyzing these data have had a significant stifling effect on group therapy research. This article (a) describes the analytic challenges inherent in data generated from therapy groups, (b) outlines common (but flawed) analytic and design approaches investigators often use to address these issues (e.g., ignoring group-level nesting, treating data from therapy groups with changing membership as fully hierarchical), and (c) provides recommendations for handling data from therapy groups using presently available methods. In addition, promising data-analytic frameworks that may eventually serve as foundations for the development of more appropriate analytic methods for data from group therapy research (i.e., nonhierarchical data modeling, pattern-mixture approaches) are also briefly described. Although there are other substantial obstacles that impede rigorous research on therapy groups (e.g., evaluation and measurement of group process, limited control over treatment delivery ingredients), addressing data-analytic problems is critical for improving the accuracy of statistical inferences made from research on ecologically valid group-based substance abuse interventions.