Cross-Classification Multilevel Logistic Models in Psychometrics. Van den Noortgate, W., De Boeck, P., & Meulders, M. Journal of Educational and Behavioral Statistics, 28(4):369–386, December, 2003. ![Cross-Classification Multilevel Logistic Models in Psychometrics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In IRT models, responses are explained on the basis of person and item effects. Person effects are usually defined as a random sample from a population distribution. Regular IRT models therefore can be formulated as multilevel models, including a within-person part and a between-person part. In a similar way, the effects of the items can be studied as random parameters, yielding multilevel models with a within-item part and a between-item part. The combination of a multilevel model with random person effects and one with random item effects leads to a cross-classification multilevel model, which can be of interest for IRT applications. The use of cross-classification multilevel logistic models will be illustrated with an educational measurement application.
@article{van_den_noortgate_cross-classification_2003,
title = {Cross-{Classification} {Multilevel} {Logistic} {Models} in {Psychometrics}},
volume = {28},
issn = {1076-9986, 1935-1054},
url = {http://journals.sagepub.com/doi/10.3102/10769986028004369},
doi = {10.3102/10769986028004369},
abstract = {In IRT models, responses are explained on the basis of person and item effects. Person effects are usually defined as a random sample from a population distribution. Regular IRT models therefore can be formulated as multilevel models, including a within-person part and a between-person part. In a similar way, the effects of the items can be studied as random parameters, yielding multilevel models with a within-item part and a between-item part. The combination of a multilevel model with random person effects and one with random item effects leads to a cross-classification multilevel model, which can be of interest for IRT applications. The use of cross-classification multilevel logistic models will be illustrated with an educational measurement application.},
language = {en},
number = {4},
urldate = {2021-04-21},
journal = {Journal of Educational and Behavioral Statistics},
author = {Van den Noortgate, Wim and De Boeck, Paul and Meulders, Michel},
month = dec,
year = {2003},
keywords = {HLM, hierarchical linear and nonlinear models, Hierarchical Generalized Linear Model, HGLM, IRT, Item Response Theory, Linear-Logistic Test Model, LLTM, SAS, hierarchical IRT model, sirt},
pages = {369--386},
}
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