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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. When item separation was large,
Hong Jiao +3 more
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The Impact of Multidimensionality on Extraction of Latent Classes in Mixture Rasch Models
Journal of Educational Measurement, 2018 AbstractThis 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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Multivariate and Mixture Distribution Rasch Models by von Davier & Carstensen
Applied Measurement in Education, 2008 The Rasch model may be seen as elegantly simple. Its simplicity comes from its characterizing an item in terms of a single parameter, the item's location on a continuum, and also locating the perso...
R. J. De Ayala
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Measuring Middle Grades Teachers' Understanding of Rational Numbers with the Mixture Rasch Model
The Elementary School Journal, 2010 Abstract We report the development of a multiple-choice instrument that measures the mathematical knowledge needed for teaching arithmetic with fractions, decimals, and proportions. In particular, the instrument emphasizes the knowledge needed to reason about such arithmetic when numbers are embedded in problem situations.
Andrew Izsák +3 more
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The Impact of Various Class-Distinction Features on Model Selection in the Mixture Rasch Model
The Journal of Experimental Education, 2016 ABSTRACTThe purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations.
Inhee Choi, Insu Paek, Sun‐Joo Cho
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Multivariate and Mixture Distribution Rasch Models
, 2007 Matthias von Davier, Claus H. Carstensen
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An Application of a Structured Mixture Rasch Model to Computer Adaptive Data (Poster 4)
AERA 2022, 2022 Meredith Langi
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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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