AI Summary & Key Takeaways
AI InsightAs a result of the research, it was determined that 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. As covariates in the study, gender, mathematics self-efficacy scores, and common item scores were used. The distributions were obtained for all score groups. Hellinger Distance was calculated to obtain the distances between 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 Item Response Theory. The study was conducted on Canadian and Italian samples of Programme for International Student Assessment (PISA) 2012. PARSCALE and IRTEQ were used for classical methods, and R was used for Bayesian nonparametric model. When gender, mathematics self-efficacy scores, and common item scores were used as covariates in the model, distance values of obtained equated scores to target test scores were close to each other, but their distributions were different. The closest distribution to target test scores was achieved when gender and mathematics self-efficacy scores were used together as covariates in the model, and the farthest distributions were obtained from item response theory methods. As a result of the research, it was determined that the model is more informative than the classical methods.