Fitting a mixture rasch model to visual sequential processing memory sub-dimension of ASIS: The role of covariates

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International Electronic Journal of Elementary Education, vol.13, no.2, pp.277-285, 2020 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.26822/iejee.2021.190
  • Journal Name: International Electronic Journal of Elementary Education
  • Journal Indexes: Scopus, IBZ Online, EBSCO Education Source, ERIC (Education Resources Information Center), Directory of Open Access Journals
  • Page Numbers: pp.277-285
  • Keywords: Concomitant/covariate variable, Latent class, Mixture rasch model, Visual processing memory
  • Anadolu University Affiliated: Yes


© 2020 Published by KURA Education & Publish-ing. This is an open access article under the CC.Advanced Item Response Theory (IRT) practices serve well in understanding the nature of latent variables which have been subject to research in various disciplines. In the current study, 7-12 aged 2536 children’s responses to 20-item Visual Sequential Processing Memory (VSPM) sub-test of Anadolu-Sak Intelligence Scale (ASIS) were analyzed with Mixture Rasch Model (MRM). In the first phase of the study, concomitant (covariate) variables were not used. In the second phase, age and gender were added to the model, and then the two models were compared in terms of fit indices, the number of latent classes and the distribution of item difficulties in the latent classes. The results of the study suggested that there were three latent classes in both models; however, the latter model had a better fit compared to the former model. In addition, the latent classes in both models had similar characteristics, and the distributions of item difficulties in the latent classes were also quite similar in both models while they had some differences in some aspects. The sizes of identical latent classes in both models varied between 15% and 30%. The results of the current study are expected to provide a deeper insight to researchers studying measurement theory and/or intelligence measurement.