AI Summary & Key Takeaways
AI InsightIt is concluded that Kernel equatingmethods are relatively more the most appropriate equating method methodthan traditional equating methods.
📌 3 Key Takeaways
Abstract
In this study, the equated score results of the kernel equating (KE)method compared with the results of traditional equating methods—equipercentile and linear equating and 9th grade 2009 ÖBBS Form B of SocialSciences and 2009 ÖBBS Form D of Social Sciences was used under anequivalent groups (EG) design. Study sample consists of 16.249 studentstaking booklets B and another 16.327 students taking D in that test. Theanalysis of the test forms was carried out in four steps. First, descriptivestatistics were calculated for the data and then it was checked whether the dataobtained from the two booklets satisfy the equating conditions. In the secondstep, the booklets were equated according to methods. Lastly, the errors foreach equating methods were calculated. Kernel equating results were nearlysame to the results from the corresponding traditional equating methods. InKernel equating, when parameter h was selected as optimal, equated scoresprovided almost identical results as traditional equipercentile equating. Whenit was selected large, this time the equated scores provided results almostidentical to traditional linear equating. It is concluded that Kernel equatingmethods are relatively more the most appropriate equating method methodthan traditional equating methods.