Generating Models for Item Preknowledge

Abstract

AbstractDetection methods for item preknowledge are often evaluated in simulation studies where models are used to generate the data. To ensure the reliability of such methods, it is crucial that these models are able to accurately represent situations that are encountered in practice. The purpose of this article is to provide a critical analysis of common models that have been used to simulate preknowledge. Both response accuracy (RA) and response time (RT) models are considered. The justifications and supporting evidence for each model are evaluated using three real data sets, and the impact of generating model on detection power is examined in two simulation studies.