Longitudinal diagnostic classification models (DCMs) with hierarchical attributes can characterize learning trajectories in terms of the transition between attribute profiles for formative assessment. A longitudinal DCM for hierarchical attributes was proposed by imposing model constraints on the transition DCM. To facilitate the applications of longitudinal DCMs, this paper explored the critical topic of the Q-matrix design with a simulation study. The results suggest that including the transpose of the R-matrix in the Q-matrix improved the classification accuracy; ten-item tests measuring three (...) linear attributes across three time points provided satisfactory classification accuracy for low-stakes assessment. Educational implications and future directions were discussed. (shrink)