Joint Modeling of Compensatory Multidimensional Item Responses and Response Times

Abstract

Computer-based testing (CBT) is becoming increasingly popular in assessing test-takers' latent abilities and making inferences regarding their cognitive processes. In addition to collecting item responses, an important benefit of using CBT is that response times (RTs) can also be recorded and used in subsequent analyses. To better understand the structural relations between multidimensional cognitive attributes and the working speed of test-takers, this research proposes a joint-modeling approach that integrates compensatory multidimensional latent traits and response speediness using item responses and RTs. The joint model is cast as a multilevel model in which the structural relation between working speed and accuracy are connected through their variance-covariance structures. The feasibility of this modeling approach is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme. The results indicate that integrating RTs increased model parameter recovery and precision. In addition, Program of International Student Assessment (PISA) 2015 mathematics standard unit items are analyzed to further evaluate the feasibility of the approach to recover model parameters.

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