Data Fit Comparison of Mixture Item Response Theory Models and Traditional Models

Year 2018, , 301 - 313, 19.05.2018

Seher Yalçın

https://doi.org/10.21449/ijate.402806

Cited By: 4

Abstract

The
purpose of this study is to determine the best IRT model [Rasch, 2PL, 3PL, 4PL
and mixed IRT (2 and 3PL)] for the science and technology subtest of the
Transition from Basic Education to Secondary Education (TEOG) exam, which is carried
out at national level, it is also aimed to predict the item parameters under
the best model. This study is a basic research as it contributes to the
information production which is fundamental for test development theories. The
study group of the research is composed of 5000 students who were randomly
selected from students who participated in TEOG exam in 2015. The analyses were
carried out on 17 multiple choice items in TEOG science and technology subtest.
When model fit indices were evaluated, the MixIRT model with two parameters and three
latent classes was found to fit the data best. According to this model, when
the difficulties and discrimination averages of the items are taken into
account, it can be expressed that items are moderately difficult and discriminative for students in latent
class-1; the items are considerably easy and able to slightly distinguish the students in  latent class-2; the items are difficult to
the students in the third latent class and they can slightly distinguish the
students in this group.

Keywords

Item response theory, Latent class models, Mixed item response theory, model-data fit

Data Fit Comparison of Mixture Item Response Theory Models and Traditional Models

Abstract

The purpose of this study is to determine the best IRT model [Rasch, 2PL, 3PL, 4PL and mixed IRT (2 and 3PL)] for the science and technology subtest of the Transition from Basic Education to Secondary Education (TEOG) exam, which is carried out at national level, it is also aimed to predict the item parameters under the best model. This study is a basic research as it contributes to the information production which is fundamental for test development theories. The study group of the research is composed of 5000 students who were randomly selected from students who participated in TEOG exam in 2015. The analyses were carried out on 17 multiple choice items in TEOG science and technology subtest. When model fit indices were evaluated, the MixIRT model with two parameters and three latent classes was found to fit the data best. According to this model, when the difficulties and discrimination averages of the items are taken into account, it can be expressed that items are moderately difficult and discriminative for students in latent class-1; the items are considerably easy and able to slightly distinguish the students in latent class-2; the items are difficult to the students in the third latent class and they can slightly distinguish the students in this group.

Keywords

Item response theory, Latent class models, Mixed item response theory, Model-data fit

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