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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

机译:使用广义线性混合模型进行双重多级二元纵向数据建模学习:测量和解释词学习的应用

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摘要

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.
机译:在青少年实验研究中,当单词学习得到指导的支持时,单词知识结果往往是从复杂的数据结构中收集的,例如单词知识的多个方面、多层次读者数据、多层次项目数据、纵向设计和多组。这项研究说明了如何使用广义线性混合模型来测量和解释如此复杂的数据的单词学习。这个应用程序的结果提供了比简单模型更深入的单词知识理解,并表明单词知识是多维的,取决于单词特征和教学上下文。

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