Monte Carlo simulations are the primary methodology for evaluating Item Response Theory (IRT) methods, yet marginal reliability - the fundamental metric of data informativeness - is rarely treated as an explicit design factor. Unlike in multilevel modeling where the intraclass correlation (ICC) is routinely manipulated, IRT studies typically treat reliability as an incidental outcome, creating a "reliability omission" that obscures the signal-to-noise ratio of generated data. To address this gap, we introduce a principled framework for reliability-targeted simulation, transforming reliability from an implicit by-product into a precise input parameter. We formalize the inverse design problem, solving for a global discrimination scaling factor that uniquely achieves a pre-specified target reliability. Two complementary algorithms are proposed: Empirical Quadrature Calibration (EQC) for rapid, deterministic precision, and Stochastic Approximation Calibration (SAC) for rigorous stochastic estimation. A comprehensive validation study across 960 conditions demonstrates that EQC achieves essentially exact calibration, while SAC remains unbiased across non-normal latent distributions and empirical item pools. Furthermore, we clarify the theoretical distinction between average-information and error-variance-based reliability metrics, showing they require different calibration scales due to Jensen's inequality. An accompanying open-source R package, IRTsimrel, enables researchers to standardize reliability as a controlled experimental input.
Joon-Ho Lee (Wed,) studied this question.