AI Summary & Key Takeaways
AI InsightThe study results showed that the bias of the estimated slipping and guessing parameters from both 4PL IRT and DINA models were reasonably small in general although the estimated slipping and guessing parameters were more biased when datasets were analyzed through the 4PL IRT model rather than the DINA model (i.e., the average bias for both guessing and slipping parameters = .00 from DINA model, but .08 from 4PL IRT model).
📌 3 Key Takeaways
Abstract
In an achievement test, the examinees with the required knowledge and skill on a test item are expected to answer the item correctly while the examinees with a lack of necessary information on the item are expected to give an incorrect answer. However, an examinee can give a correct answer to the multiple-choice test items through guessing or sometimes give an incorrect response to an easy item due to anxiety or carelessness. Either case may cause a bias estimation of examinee abilities and item parameters. 4PL IRT model and the DINA model can be used to mitigate these negative impacts on the parameter estimations. The current simulation study aims to compare the estimated pseudo-guessing and slipping parameters from the 4PL IRT model and the DINA model under several study conditions. The DINA model was used to simulate the datasets in the study. The study results showed that the bias of the estimated slipping and guessing parameters from both 4PL IRT and DINA models were reasonably small in general although the estimated slipping and guessing parameters were more biased when datasets were analyzed through the 4PL IRT model rather than the DINA model (i.e., the average bias for both guessing and slipping parameters = .00 from DINA model, but .08 from 4PL IRT model). Accordingly, both 4PL IRT and DINA models can be considered for analyzing the datasets contaminated with guessing and slipping effects.