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Journals / International Journal of Assessment Tools in Education / 2026 / Volume: 5 Issue: 2

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

Seher Yalçın(Ankara Üniversitesi)0000-0003-0177-6727
Pages
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Publication Date
2018-05-19
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:The study group of the research is composed of 5000 students who were randomly selected from students who participated in TEOG exam in 2015.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:Item response theory · Latent class models · Mixed item response theory

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.

Özet

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.

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Cite This Article

Yalçın, S. (2018). Data Fit Comparison of Mixture Item Response Theory Models and Traditional Models. International Journal of Assessment Tools in Education, Volume: 5 Issue: 2. https://doi.org/10.21449/ijate.402806

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