Stability of estimation item parameter in IRT dichotomy considering the number of participants

Abstract

This research is related to item response theory (IRT) which is needed to measure the goodness of a test set, while item parameter estimation is needed to determine the technical properties of a test item. Stability of item parameter estimation is conducted to determine the minimum sample that can be used to obtain good item parameter estimation results. The purpose of this study is to describe the effect of the number of test takers on the stability of item parameter estimation with the Bayes method (expected a posteriori, EAP) on dichotomous data. This research is an exploratory descriptive research with a bootstrap approach using the EAP method. The EAP method is performed by modifying the likelihood and function to include prior information about the participant's 9 score. Bootstrapping on the original data is done to take bootstrap samples. with ten different sample sizes of 100, 150, 250, 300, 500, 700, 1,000, 1,500, 2,000, 2,500 were then replicated ten times and grain parameter estimation was performed. Each sample data with ten replications was calculated Root Mean Squared Difference (RMSD) value. The results showed that the 2PL model was chosen as the best model. The RMSD value obtained proves that many test participants affect the stability of item parameter estimation on dichotomous data with the 2PL model. The minimum sample to ensure the stability of item parameter estimates with the 2PL model is 1,000 test participants.

Keywords

• stability
• item parameter estimation
• item response theory
• eap
• bootstrapping