Results 141 to 150 of about 1,099 (177)
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Mixture Rasch model for guessing group identification
AIP Conference Proceedings, 2013Several alternative dichotomous Item Response Theory (IRT) models have been introduced to account for guessing effect in multiple-choice assessment. The guessing effect in these models has been considered to be itemrelated. In the most classic case, pseudo-guessing in the three-parameter logistic IRT model is modeled to be the same for all the subjects
Hoo Leong Siow +2 more
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A Mixture Rasch Model–Based Computerized Adaptive Test for Latent Class Identification
Applied Psychological Measurement, 2012This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback–Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions.
Hong Jiao
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Spurious Latent Classes in the Mixture Rasch Model
Journal of Educational Measurement, 2011Jonathan Templin, Allan S Cohen
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A mixture Rasch facets model for rater’s illusory halo effects
Behavior Research Methods, 2022A rater's overall impression of a ratee's essay (or other assessment) can influence ratings on multiple criteria to yield excessively similar ratings (halo effect). However, existing analytic methods fail to identify whether similar ratings stem from homogeneous criteria (true halo) or rater bias (illusory halo).
Kuan-Yu Jin, Ming Ming Chiu
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Mixture Distribution Rasch Models
1995This chapter deals with the generalization of the Rasch model to a discrete mixture distribution model. Its basic assumption is that the Rasch model holds within subpopulations of individuals, but with different parameter values in each subgroup. These subpopulations are not defined by manifest indicators, rather they have to be identified by applying ...
Jürgen Rost, Matthias von Davier
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A Mixture Rasch Model With a Covariate
Applied Psychological Measurement, 2013Mixtures of item response theory (IRT) models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying ...
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Mixture-Distribution and HYBRID Rasch Models
2007This chapter provides an overview of mixture-distribution Rasch models (RMs) and HYBRID RMs and their extensions. Discrete mixture-distribution IRT models assume that the observed data were drawn from an unobservable mixture of populations. Within each of these populations, a different item response model may hold (HYBRID models), or models with ...
Matthias von Davier, Kentaro Yamamoto
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Testing the Rasch model by means of the mixture fit index
British Journal of Mathematical and Statistical Psychology, 2006proposed a new index of fit for contingency table analysis. Using the overparametrized two‐component mixture, where the first component with weight 1− w represents the model to be tested and the second component with weight w is unstructured, the ...
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The Impact of Multidimensionality on Extraction of Latent Classes in Mixture Rasch Models
Journal of Educational Measurement, 2018AbstractThis 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
Yoonsun Jang +2 more
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Loglinear Multivariate and Mixture Rasch Models
2007In this chapter, Rasch models (RMs) are derived from a stochastic subject model. Fixed-effects RMs are shown to be equivalent to loglinear models with raw-score variables; random-effects RMs are equivalent to loglinear models with latent class variables. Within the larger framework of loglinear models, various extensions of the RM can be formulated. We
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