AI Summary & Key Takeaways
AI InsightThe study results showed that thebias of the estimated slipping and guessing parameters from both 4PL IRT and DINA models were reasonablysmall in general although the estimated slipping and guessing parameters were more biased when datasets wereanalyzed through the 4PL IRT model rather than the DINA model (i.e., the average bias for both guessing andslipping 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 answerthe item correctly while the examinees with a lack of necessary information on the item are expected to give anincorrect answer. However, an examinee can give a correct answer to the multiple-choice test items throughguessing or sometimes give an incorrect response to an easy item due to anxiety or carelessness. Either case maycause a bias estimation of examinee abilities and item parameters. Four-parameter logistic item response theory(4PL IRT) model and the deterministic inputs, noisy, and gate (DINA) model can be used to mitigate thesenegative impacts on the parameter estimations. The current simulation study aims to compare the estimatedpseudo-guessing and slipping parameters from the 4PL IRT model and the DINA model under several studyconditions. The DINA model was used to simulate the datasets in the study. The study results showed that thebias of the estimated slipping and guessing parameters from both 4PL IRT and DINA models were reasonablysmall in general although the estimated slipping and guessing parameters were more biased when datasets wereanalyzed through the 4PL IRT model rather than the DINA model (i.e., the average bias for both guessing andslipping parameters = .00 from DINA model, but .08 from 4PL IRT model). Accordingly, both 4PL IRT andDINA models can be considered for analyzing the datasets contaminated with guessing and slipping effects.