The present study employed data simulation techniques to investigate the 1-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 a previously published study were used to construct a growth model of reading development. The parameters estimated from this model were then used to construct three simulated data sets 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.