Analyzing the Maximum Likelihood Score Estimation Method with Fences in ca-MST

Year 2019, , 555 - 567, 05.01.2020

Melek Gülşah Şahin , Nagihan Boztunç Öztürk

https://doi.org/10.21449/ijate.634091

Cited By: 3

Abstract

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 (MLEF-MLE-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.

Keywords

MLEF, MLE, EAP, ca-MST, Ability estimation

Analyzing the Maximum Likelihood Score Estimation Method with Fences in ca-MST

Abstract

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 (MLEF-MLE-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.

Keywords

MLEF, MLE, EAP, ca-MST, Ability estimation

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