The Impact of Covariate Variables on Kernel Equating under the Non-equivalent Groups

Abstract

This study aims to use covariate variables correlated with the test scores instead of common items for non-equivalent groups with covariates (NEC) design in kernel equating. This study used the 2016 Monitoring and Evaluation of Academic Skills Project in Turkey. The study used data from 6,000 students, randomly selected from the Turkish Ministry of National Education’s current student data. Three thousand of the students took form A, and 3,000 of them took form B. The data include mathematics test scores and consist of 18 items, nine of which are the first items, and nine of which are anchor items. The equated scores from the NEC design were compared with equated scores from the non-equivalent group (NEAT) design. From the equating results, the root mean squared difference (RMSD) and standard error of equating (SEE) values were calculated. The results showed that NEC design could produce lower standard errors compared with the NEAT design, and the least RMSD was provided by NEAT PSE methods and NEC methods. The general result of this research is that test forms can be equated using covariates when there are no anchor items.