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Kim, Jinho; Wilson, Mark – Educational and Psychological Measurement, 2020
This study investigates polytomous item explanatory item response theory models under the multivariate generalized linear mixed modeling framework, using the linear logistic test model approach. Building on the original ideas of the many-facet Rasch model and the linear partial credit model, a polytomous Rasch model is extended to the item…
Descriptors: Item Response Theory, Test Items, Models, Responses
Torres Irribarra, David; Diakow, Ronli; Freund, Rebecca; Wilson, Mark – Grantee Submission, 2015
This paper presents the Latent Class Level-PCM as a method for identifying and interpreting latent classes of respondents according to empirically estimated performance levels. The model, which combines elements from latent class models and reparameterized partial credit models for polytomous data, can simultaneously (a) identify empirical…
Descriptors: Item Response Theory, Test Items, Statistical Analysis, Models
Castellano, Katherine E.; Duckor, Brent; Wihardini, Diah; Telléz, Kip; Wilson, Mark – Teacher Education Quarterly, 2016
With the adoption by most states of the Common Core State Standards (CCSS) for English language arts and literacy and for mathematics (CCSS Initiative, 2010a, 2010b) comes major changes in public education that will affect instructional practice, curriculum, and assessment across the nation. Heritage, Walqui, and Linquanti (2015) argued that the…
Descriptors: Elementary School Mathematics, Mathematics Teachers, Teacher Certification, Language Usage
De Boeck, Paul; Cho, Sun-Joo; Wilson, Mark – Applied Psychological Measurement, 2011
The models used in this article are secondary dimension mixture models with the potential to explain differential item functioning (DIF) between latent classes, called latent DIF. The focus is on models with a secondary dimension that is at the same time specific to the DIF latent class and linked to an item property. A description of the models…
Descriptors: Test Bias, Models, Statistical Analysis, Computation

Wilson, Mark; Moore, Stephen – Australian Journal of Education, 1989
A guide to the use of microcomputer statistical analysis packages for loglinear analysis describes the application of one popular package to a straightforward problem and critically surveys features available in four of the leading packages. Survey findings and suggestions for further development are included. (Author/MSE)
Descriptors: Computer Oriented Programs, Computer Software, Mathematical Models, Media Selection