ABSTRACT
As they do for many other applications that rely on a strong mathematical model, computer simulation techniques play key roles in the research and practice of item response theory (IRT), a popular and valuable framework for modeling and analyzing educational and psychological test data. An important advantage gained from using computer-simulated data in IRT is that it establishes true and known parameter values, enabling the direct evaluation of parameter estimates under various measurement conditions. This is often critical for applications including (but not limited to) evaluating estimators, analyzing model fit and differential item functioning (DIF), and computing conditional standard error of measurement. Relatively speaking, in addition, the use of computer-simulated data is more efficient than that of empirical data both in terms of time and cost, which renders it a valuable tool for practitioners to evaluate and validate test programs.
