Impact of Parameter Predictability and Joint Modeling of Response Accuracy and Response Time on Ability Estimates

To maintain test quality, a large supply of items is typically desired. Automatic item generation can result in a reduction in cost and labor, especially if the generated items have predictable item parameters and thus possibly reducing or eliminating the need for empirical tryout. However, the effect of different levels of item parameter predictability on the accuracy of trait estimation using item response theory models is unclear. If predictability is lower, adding response time as a collateral source of information may mitigate the effect on trait estimation accuracy. The present study investigates the impact of varying item parameter predictability on trait estimation accuracy, along with the impact of adding response time as a collateral source of information. Results indicated that trait estimation accuracy using item family model-based item parameters differed only slightly from using known item parameters. Somewhat larger trait estimation errors resulted from using cognitive complexity features to predict item parameters. Further, adding response times to the model resulted in more accurate trait estimation for tests with lower item difficulty levels (e.g., achievement tests). Implications for item generation and response processes aspect of validity are discussed.