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Publikation Nr. 3124 - Details

Yavuz Temel, G., Machunsky, M., Rietz, C. & Okropiridze, D. (2022). Investigating Subscores of VERA 3 German Test based on IRT/MIRT Models, Frontiers in Education, 7.

URL: https://www.frontiersin.org/articles/10.3389/feduc.2022.801372/full
DOI: https://doi.org/10.3389/feduc.2022.801372


Abstract
In this study, the psychometric properties of the listening and reading subtests of the German VERA 3 test were examined using Item Response Theory (IRT) and Multidimensional Item Response Theory (MIRT) models. Listening and reading subscores were estimated using unidimensional Rasch, 1PL, and 2PL models, and total scores on the German test (listening reading) were estimated using unidimensional and multidimensional IRT models. Various MIRT models were used, and model fit was compared in a cross-validation study. The results of the study showed that unidimensional models of the reading and listening subtests and the German test provided a good overall model-data fit, however, multidimensional models of the subtests provided a better fit. The results demonstrated that, although the subtest scores also fit adequately independently, estimating the scores of the overall test with a model (e.g., bifactor) that includes a general factor (construct) in addition to the subfactors significantly improved the psychometric properties of the test. A general factor was identified that had the highest reliability values; however, the reliabilities of the specific factors were very low. In addition to the fit of the model data, the fit of the persons with IRT/MIRT models was also examined. The results showed that the proportion of person misfit was higher for the subtests than for the overall tests, but the overfit was lower. NA-German students, who did not speak German all-day, had the highest proportion of misfits with all models.


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Art der Begutachtung: Peer Review(Keine Angabe)
Print: Nein
Online: Ja, mit Open Access aber ohne CC-Lizenz
Datenmedium: Nein