The present study employed data simulation techniques to investigate the one-year stability of alternative classification schemes for identifying children with reading disabilities. Classification schemes investigated include low performance, unexpected low performance, dual-discrepancy, and a rudimentary form of constellation model of reading disabilities that included multiple criteria. Data from Spencer et al. (2014) were used to construct a growth model of reading development. The parameters estimated from this model were then used to construct three simulated datasets wherein the growth parameters were manipulated in one of three ways: A stable-growth pattern, a mastery learning pattern and a fan-spread pattern. Results indicated that overall the constellation model provided the most stable classifications across all conditions of the simulation, and that classification schemes were most stable in the fan-spread condition, and were the least stable under the mastery learning growth pattern. These results also demonstrate the utility of data simulations in reading research.