As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the Effort-Moderated IRT (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., 2PL) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (assumption #1) and is unrelated to the underlying ability of examinees (assumption #2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of assumption #1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating assumption #2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating assumption #2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared to the 2PL model.
Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality
