Results 1 to 10 of about 50 (48)
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. Under this modeling framework, students can be characterized by both their location on a continuous latent ...
Ming-Chi Tseng, Wen-Chung Wang
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Model Selection for Multilevel Mixture Rasch Models [PDF]
Applied Psychological Measurement, 2018 Mixture item response theory (MixIRT) models can sometimes be used to model the heterogeneity among the individuals from different subpopulations, but these models do not account for the multilevel structure that is common in educational and psychological data.
Sedat Sen, Allan S. Cohen, Seock-Ho Kim
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Flexible Rasch Mixture Models with Packagepsychomix [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. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estimation ...
Frick, Hannah +3 more
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Regularized Mixture Rasch Model
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 the interpretability of model parameters.
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A Mixture Rasch Model Analysis of Mathematics Achievement
Kastamonu Eğitim Dergisi, 2023 Purpose: This study aims to determine distinct latent classes in 8th-grade students' mathematics achievement Design: The research study group consisted of 435 students who received the first booklet at the TIMSS 2015 8th grade mathematics achievement test. A mixture of Rasch model analysis was used to find the best-fitting model. Findings:
YILDIRIM HOŞ, Halime +1 more
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Statistical inference on group Rasch mixture network models
Stat, 2022 In a two‐mode network, the nodes are divided into two types (primary nodes and secondary nodes), and connections exist only between nodes of different types. In reality, in such a two‐mode network, one‐mode network connections may also exist among primary nodes, and these two kinds of networks are usually not independent and coexistent.
Yuhang Long, Tao Huang
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Strong Gaussian approximation of the mixture Rasch model [PDF]
Bernoulli, 2019 We consider the famous Rasch model, which is applied to psychometric surveys when n persons under test answer m questions. The score is given by a realization of a random binary (n,m)-matrix. Its (j,k)th component indicates whether or not the answer of the jth person to the kth question is correct.
Liese, Friedrich +2 more
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Different Approaches to Covariate Inclusion in the Mixture Rasch Model [PDF]
Educational and Psychological Measurement, 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.
Tongyun, Li +2 more
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Rasch Mixture Models for DIF Detection [PDF]
Educational and Psychological Measurement, 2014 Rasch mixture models can be a useful tool when checking the assumption of measurement invariance for a single Rasch model. They provide advantages compared to manifest differential item functioning (DIF) tests when the DIF groups are only weakly correlated with the manifest covariates available.
Frick, Hannah +2 more
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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, Yanyan Sheng
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