Analyzing The Maximum Likelihood Score Estimation Method With Fences In Ca-Mst
Sahin, Melek Gulsah
Ozturk, Nagihan Boztunc
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New statistical methods are being added to the literature as a result of scientific developments each and every day. This study aims at investigating one of these, Maximum Likelihood Score Estimation with Fences (MLEF) method, in ca-MST. The results obtained from this study will contribute to both national and international literature since there is no such study on the applicability of MLEF method in ca-MST. In line with the aim of this study, 48 conditions (4 module lengths (5-10-15-20) x 2 panel designs (1-3; 1-3-3) x 2 ability distribution (normal-uniform) x 3 ability estimation methods (MLEFMLE-EAP) were simulated and the data obtained from the simulation were interpreted with correlation, RMSE and AAD as an implication of measurement precision; and with conditional bias calculation in order to show the changes in each ability level. This study is a post-hoc simulation study using the data from TIMSS 2015 at the 8th grade in mathematics. “xxIRT” R package program and MSTGen simulation software tool were used in the study. As a result, it can be said that MLEF, as a new ability estimation method, is superior to MLE method in all conditions. EAP estimation method gives the best results in terms of the measurement precision based on correlation, RMSE and AAD values, whereas the results gained via MLEF estimation method are pretty close to those in EAP estimation method. MLE proves to be less biased in ability estimation, especially in extreme ability levels, when compared to EAP ability estimation method.