The Q-Matrix Anchored Mixture Rasch Model [PDF]
Frontiers in Psychology, 2021 Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds.
Ming-Chi Tseng, Wen-Chung Wang
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Regularized Mixture Rasch Model [PDF]
Information, 2022 The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders ...
Alexander Robitzsch
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Setting a standard for low reading proficiency: A comparison of the bookmark procedure and constrained mixture Rasch model. [PDF]
PLoS ONE, 2021 In order to draw pertinent conclusions about persons with low reading skills, it is essential to use validated standard-setting procedures by which they can be assigned to their appropriate level of proficiency.
Tabea Feseker +2 more
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Different Approaches to Covariate Inclusion in the Mixture Rasch Model. [PDF]
Educ Psychol Meas, 2016 The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning.
Li T, Jiao H, Macready GB.
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Mixture Rasch Model with Main and Interaction Effects of Covariates on Latent Class Membership [PDF]
International Journal of Assessment Tools in Education, 2019 Covariateshave been used in mixture IRT models to help explain why examinees are classedinto different latent classes. Previous research has considered manifestvariables as covariates in a mixture Rasch analysis for prediction of groupmembership.
Tugba Karadavut +2 more
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Flexible Rasch Mixture Models with Package psychomix [PDF]
Journal of Statistical Software, 2012 Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data.
Hannah Frick +3 more
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Investigation of the effect of parameter estimation and classification accuracy in mixture IRT models under different conditions [PDF]
International Journal of Assessment Tools in Education, 2022 This study aims to examine the effects of mixture item response theory (IRT) models on item parameter estimation and classification accuracy under different conditions.
Hakan Yavuz Atar +1 more
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A Mixture Rasch Model Analysis of Data from a Survey of Novice Teacher Core Competencies
International Journal of Contemporary Educational Research, 2023 Although the Rasch model is used to measure latent traits like attitude or ability where there are multiple latent structures within the dataset it is best to use a technique called the Mixture Rasch Model (MRM) which is a combination of a Rasch model ...
Turker Toker, Kent Seıdel
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A Comparison of Latent Class Analysis and the Mixture Rasch Model Using 8th Grade Mathematics Data in the Fourth International Mathematics and Science Study (TIMSS-2011) [PDF]
International Journal of Assessment Tools in Education, 2021 This study provides a comparison of the results of latent class analysis (LCA) and mixture Rasch model (MRM) analysis using data from the Trends in International Mathematics and Science Study – 2011 (TIMSS-2011) with a focus on the 8th-grade mathematics ...
Kathy Green, Turker Toker
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Comparing two maximum likelihood algorithms for mixture Rasch models
Behaviormetrika, 2019 The mixture Rasch model is gaining popularity as it allows items to perform differently across subpopulations and hence addresses the violation of the unidimensionality assumption with traditional Rasch models. This study focuses on comparing two common maximum likelihood methods for estimating such models using Monte Carlo simulations. The conditional
Yevgeniy Ptukhin, Sheng Yanyan
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