Modeling learning in doubly multilevel binary longitudinal data using generalized linear mixed models: an application to measuring and explaining word learning

DOI10.1007/s11336-016-9496-yzbMath1402.62304OpenAlexW2334468110WikidataQ31067019 ScholiaQ31067019MaRDI QIDQ1682455

Amanda P. Goodwin, Sun-Joo Cho

Publication date: 30 November 2017

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-016-9496-y

zbMATH Keywords

learninggeneralized linear mixed modelsbinary longitudinal datadoubly multilevel datapsycholinguistic dataword learning

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Measurement and performance in psychology (91E45) Applications of statistics to psychology (62P15)

Uses Software

• R
• WinBUGS
• lme4
• Hmisc
• languageR

Cites Work

• Unnamed Item
• Unnamed Item
• Modeling rule-based item generation
• Bayesian item response modeling. Theory and applications.
• A multidimensional latent trait model for measuring learning and change
• Estimating the dimension of a model
• Additive multilevel item structure models with random residuals: item modeling for explanation and item generation
• Explanatory multidimensional multilevel random item response model: an application to simultaneous investigation of word and person contributions to multidimensional lexical representations
• Parameter estimation of multiple item response profile model
• Reliability measures in item response theory: Manifest versus latent correlation functions

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