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Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning

Published online by Cambridge University Press:  01 January 2025

Sun-Joo Cho  and
Amanda P. Goodwin
Sun-Joo Cho*
Affiliation:
Vanderbilt University’s Peabody College
Amanda P. Goodwin
Affiliation:
Vanderbilt University’s Peabody College
*
Correspondence should be made to Sun-Joo Cho, Vanderbilt University’s Peabody College, Nashville, TN, USA. Email: sj.cho@vanderbilt.edu. http://www.vanderbilt.edu/psychological_sciences/bio/sun-joo-cho
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Abstract

When word learning is supported by instruction in experimental studies for adolescents, word knowledge outcomes tend to be collected from complex data structure, such as multiple aspects of word knowledge, multilevel reader data, multilevel item data, longitudinal design, and multiple groups. This study illustrates how generalized linear mixed models can be used to measure and explain word learning for data having such complexity. Results from this application provide deeper understanding of word knowledge than could be attained from simpler models and show that word knowledge is multidimensional and depends on word characteristics and instructional contexts.

Keywords

binary longitudinal datadoubly multilevel datageneralized linear mixed modelslearningpsycholinguistic dataword learning
Type
Original paper
Information
Psychometrika , Volume 82 , Issue 3 , September 2017 , pp. 846 - 870
Copyright
Copyright © 2016 The Psychometric Society

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