Using Item Parameter Predictions for Reducing Calibration Sample Requirements—A Case Study Based on a High‐Stakes Admission Test

Abstract In item difficulty modeling (IDM), item parameters are predicted from the items' linguistic features, aiming to ultimately render item calibration redundant. Current IDM applications, however, commonly do not yield the required prediction accuracy. To immediately exploit even somewhat inaccurate IDM predictions, we blend IDM with established Bayesian estimation techniques. We propose a two‐step approach where (1) IDM predictions are obtained and (2) employed to construct informative prior distributions. We evaluate the approach in a case study on small‐sample calibration of the 3PL in a high‐stakes test. First, concerning implementation, we find computationally efficient penalized maximum likelihood estimation to be comparable to the best‐performing MCMC‐based approach. Second, we investigate sample size reductions achievable with state‐of‐the‐art IDM predictions, finding negligible gains compared to merely considering the historical distribution of parameters. Third, we evaluate the prediction accuracy required for a targeted sample size reduction by gradually increasing simulated IDM prediction accuracies. We find that required accuracies can counterbalance each other, allowing calibration sample size to be reduced when either high‐quality item difficulty predictions or good predictions of item discriminations and pseudo‐guessing parameters are available. We argue that these evaluations provide new, portable IDM benchmarks quantifying performance in terms of achievable sample size reductions.