A Study on the Identification of Latent Classes Using Mixture Item Response Theory Models: TIMSS 2015 Case

Fatıma Münevver Saatçi̇oğlu,Hakan Yavuz Atar

doi:10.17275/per.20.41.7.3

Fatıma Münevver Saatçi̇oğlu, Hakan Yavuz Atar

Open Access

https://doi.org/10.17275/per.20.41.7.3

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Abstract

This study examined the existence of latent classes in TIMSS 2015 data from three countries, Singapure, Turkey and South Africa, were analyzed using Mixture Item Response Theory (MixIRT) models (Rasch, 1PL, 2PL and 3PL) on 18 multiple-choice items in the science subtest. Based on the findings, it was concluded that the data obtained from TIMSS 2015 8th grade science subtest have a heterogeneous structure consisting of two latent classes. When the item difficulty parameters in two classes were examined for Singapore, it was determined that the items were considerably easy for the students in Class 1 and the items were easy for the students in Class 2. When the item difficulty parameters in two classes were examined for Turkey, it was found that the items were easy for the students in Class 1 and the items were difficult for the students in Class 2. When the item difficulty parameters in two classes were examined for South Africa, it was ascertained that the items were a bit easy for the students in Class 1 and the items were considerably difficult for the students in Class 2. The findings were discussed in the context of the assumption of parameter invariance and test validity.

Highlights

Accurate understanding and analysis of data in education and related fields are important to obtain reliable and valid measurements and evaluations
This study examined the existence of latent classes in TIMSS 2015 data from three countries, Singapure, Turkey and South Africa, were analyzed using Mixture Item Response Theory (MixIRT) models
Standard IRT models are used for calibration of item parameters and scaling of individual performances in international large-scale assessments such as TIMSS and PISA (Martin et al, 2016)

Summary

Introduction

Accurate understanding and analysis of data in education and related fields are important to obtain reliable and valid measurements and evaluations. In particular results from international large-scale assessments guide the process of education to be more efficient and allow the academic achievements of student groups in one country to be compared with those in other countries (Cook, 2006). A method based on student samples from all participating countries and calibrations of the Item Response Theory (IRT) is implemented to ensure comparability of scores in international large-scale assessments. This method ensures that each participating country contributes an equal amount to the calibration of item parameters (Oliveri & von Davier, 2011). A generalized partial credit model is used for polytomous scored constructed-response items (Martin et al, 2016)

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