title: Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning.
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.
authors.affiliation: Vanderbilt University's Peabody College, Nashville, TN, USA. sj.cho@vanderbilt.edu.
authors.forename: Sun-Joo
authors.initials: SJ
authors.lastname: Cho
authors.affiliation: Vanderbilt University's Peabody College, Nashville, TN, USA.
authors.forename: Amanda P
authors.initials: AP
authors.lastname: Goodwin
keywords: binary longitudinal data; doubly multilevel data; generalized linear mixed models; learning; psycholinguistic data; word learning