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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychol. | doi: 10.3389/fpsyg.2019.00248

Modeling Multiple Item Context Effects with Generalized Linear Mixed Models: Disentangling Item Position Effects, Block Position Effects, and Domain Order Effects

  • 1Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Germany
  • 2Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik, Christian-Albrechts-Universität zu Kiel, Germany
  • 3Institute of Educational Science, Department of Research Methods in Education, Friedrich-Schiller-Universität Jena, Germany
  • 4Centre for Educational Measurement, Faculty of Education, University of Oslo, Norway
  • 5German Institute for International Educational Research (LG), Germany

Item context effects refer to the impact of features of a test on an examinee’s item responses. These effects cannot be explained by the abilities measured by the test. Investigations typically focus on only a single type of item context effects, such as item position effects, or mode effects, thereby ignoring the fact that different item context effects might operate simultaneously. In this study, two different types of context effects were modeled simultaneously drawing on data from an item calibration study of a multidimensional computerized test (N = 1,632) assessing student competencies in mathematics, science, and reading. We present a generalized linear mixed model (GLMM) parameterization of the multidimensional Rasch model including item position effects (distinguishing between within-block position effects and block position effects), domain order effects, and the interactions between them. Results show that both types of context effects played a role, and that the moderating effect of domain orders was very strong. The findings have direct consequences for planning and applying mixed domain assessment designs.

Keywords: item position effects, item context effects, domain order effects, multidimensional item response theory, generalized linear mixed models

Received: 02 Oct 2017; Accepted: 25 Jan 2019.

Edited by:

Holmes Finch, Ball State University, United States

Reviewed by:

MARIA ANNA DONATI, Università degli Studi di Firenze, Italy
Anthony D. Albano, University of Nebraska System, United States  

Copyright: © 2019 Rose, Nagy, Nagengast, Frey and Becker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Norman Rose, University of Tübingen, Hector Research Institute of Education Sciences and Psychology, Tübingen, 72072, Germany, norman.rose@uni-tuebingen.de