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Growth mixture modeling (GMM) identifies latent classes exhibiting distinct longitudinal patterns on an outcome. Subgroups identified by GMM may be artifactually influenced by measurement timing (e.g., timing of the initial assessment, length of the interval from the first to the last assessment, and total number of assessments) as well as the theoretically posited developmental patterns of the behavior. The current study investigated this possibility using alcohol data from the 1997 National Longitudinal Survey of Youth (n = 2686; 49.44% female; 71.84% White). Three assessment configurations were examined: all 12 waves, first 6 waves, and last 7 waves. Five subgroups were identified using all 12 waves: Normative (71.33%), Low-Increasing (8.45%), Low-Steady (8.97%), High-Slowly Decreasing (7.67%), and Extreme-Sharply Decreasing (3.57%). When comparing participants’ subgroup membership for all 12 waves to the first six waves, 14% of the sample was differentially classified. When comparing all 12 waves to the last seven waves, 62% of the sample was differentially classified. Alterations in the timing of the initial assessment had a substantial impact on latent class estimation, underscoring the importance of selecting the developmental window a priori based on theory and empirical knowledge. The time-bounded nature of mixture modeling solutions (i.e., a selected developmental window within the course of a phenomenon) suggests that the latent subgroups should not be interpreted as representing subgroups that are present in the population. Future directions and strategies for testing alternative interpretations are presented.