Data from psychotherapy groups are nested by nature. Ignoring this nesting when performing statistical tests can result in inflated Type I error rates and spurious significant results. This presents a serious problem for interpreting research that does not account for members nested within a specific group. Factor analytic methods are not immune to the negative effects of nesting, and when it is ignored, a poorly fitting factor structure can be hidden if one does not examine model fit at both the between or within-group level. Multilevel confirmatory factor analysis allows the researcher to account for nesting by separately examining both the between- and within-group structures of measures used in group research. This article presents an overview of methods for evaluating the level of group dependency using the intraclass correlation coefficient (ICC) and a comparison of 2 methods for calculating ICCs. It then provides an overview and an example of multilevel factor analysis as a method for testing the model fit at the between and within levels separately by using partially saturated models. The authors end by reviewing common problems and offering guidelines for interpreting differences in within- and between-level fit in group research.