Comparing Data‐Driven Methods for Removing Options in Assessment Items

Abstract Test items with problematic options often require revision to improve their psychometric properties. When an option is identified as ambiguous or nonfunctioning, the traditional approach involves removing the option and conducting another field test to gather new response data—a process that, while effective, is resource‐intensive. This study compares two methods for handling option removal: the Retesting method (administering modified items to new examinees) versus the Recalculating method (computationally removing options from existing response data). Through a controlled experiment with multiple‐response and matrix‐format items, we examined whether these methods produce equivalent item characteristics. Results show striking similarities between methods across multiple psychometric item properties. These findings suggest that the Recalculating method may offer an efficient alternative for items with sufficient option choices. We discuss implementation considerations and present our experimental design and analytical approach as a framework that other testing programs can adapt to evaluate whether the Recalculating method is appropriate for their specific contexts.