AI Summary & Key Takeaways
AI InsightAs a result of the research, it was determinedthat the model is more informative than the classical methods.
📌 3 Key Takeaways
Abstract
This research is based on obtaining equated scores by using covariates in the Bayesian nonparametric model. Ascovariates in the study, gender, mathematics self-efficacy scores, and common item scores were used. Thedistributions were obtained for all score groups. Hellinger Distance was calculated to obtain the distancesbetween the distributions of equated scores by using covariates and the distribution of the target test scores.These distances were compared with the distributions of equated scores obtained from methods based on ItemResponse Theory. The study was conducted on Canadian and Italian samples of Programme for InternationalStudent Assessment (PISA) 2012. PARSCALE and IRTEQ were used for classical methods, and R was used forBayesian nonparametric model. When gender, mathematics self-efficacy scores, and common item scores wereused as covariates in the model, distance values of obtained equated scores to target test scores were close toeach other, but their distributions were different. The closest distribution to target test scores was achieved whengender and mathematics self-efficacy scores were used together as covariates in the model, and the farthestdistributions were obtained from item response theory methods. As a result of the research, it was determinedthat the model is more informative than the classical methods.