Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning
Sun-Joo Cho () and
Amanda P. Goodwin
Additional contact information
Sun-Joo Cho: Vanderbilt University’s Peabody College
Amanda P. Goodwin: Vanderbilt University’s Peabody College
Psychometrika, 2017, vol. 82, issue 3, No 14, 846-870
Abstract:
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 data; doubly multilevel data; generalized linear mixed models; learning; psycholinguistic data; word learning (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11336-016-9496-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:psycho:v:82:y:2017:i:3:d:10.1007_s11336-016-9496-y
Ordering information: This journal article can be ordered from
http://www.springer. ... gy/journal/11336/PS2
DOI: 10.1007/s11336-016-9496-y
Access Statistics for this article
Psychometrika is currently edited by Irini Moustaki
More articles in Psychometrika from Springer, The Psychometric Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().