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An Analysis of Differential Bundle Functioning in Multidimensional Tests Using the SIBTEST Procedure
Özdogan, Didem; Kelecioglu, Hülya – International Journal of Assessment Tools in Education, 2022
This study aims to analyze the differential bundle functioning in multidimensional tests with a specific purpose to detect this effect through differentiating the location of the item with DIF in the test, the correlation between the dimensions, the sample size, and the ratio of reference to focal group size. The first 10 items of the test that is…
Descriptors: Correlation, Sample Size, Test Items, Item Analysis
Jang, Yoonsun; Kim, Seock-Ho; Cohen, Allan S. – Journal of Educational Measurement, 2018
This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two-dimensional data were generated under varying conditions. The two-dimensional data sets were analyzed with one- to five-class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model…
Descriptors: Item Response Theory, Simulation, Correlation, Multidimensional Scaling
Feuerstahler, Leah; Wilson, Mark – Journal of Educational Measurement, 2019
Scores estimated from multidimensional item response theory (IRT) models are not necessarily comparable across dimensions. In this article, the concept of aligned dimensions is formalized in the context of Rasch models, and two methods are described--delta dimensional alignment (DDA) and logistic regression alignment (LRA)--to transform estimated…
Descriptors: Item Response Theory, Models, Scores, Comparative Analysis
Finch, Holmes – Practical Assessment, Research & Evaluation, 2022
Researchers in many disciplines work with ranking data. This data type is unique in that it is often deterministic in nature (the ranks of items "k"-1 determine the rank of item "k"), and the difference in a pair of rank scores separated by "k" units is equivalent regardless of the actual values of the two ranks in…
Descriptors: Data Analysis, Statistical Inference, Models, College Faculty

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