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PLmixed: An R Package for Generalized Linear Mixed Models With Factor Structures

PLmixed: An R Package for Generalized Linear Mixed Models With Factor Structures Computer Program Exchange Applied Psychological Measurement 2018, Vol. 42(5) 401–402 PLmixed: An R Package The Author(s) 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav for Generalized Linear DOI: 10.1177/0146621617748326 journals.sagepub.com/home/apm Mixed Models With Factor Structures 1 2 Minjeong Jeon and Nicholas Rockwood Keywords Item response theory, factor analysis, latent variable models, MIRT, multilevel models, hierarchi- cal models Description The R (R Core Team, 2017) package PLmixed (Jeon & Rockwood, 2017) has been developed to extend the capabilities of the existing R package lme4 (Bates, Machler, Bolker, & Walker, 2015) to allow for profile-likelihood estimation of generalized linear mixed models (GLMMs) with factor structures (i.e., factor loadings, weights, item discrimination parameters), as out- lined in Jeon and Rabe-Hesketh (2012). The modeling framework is a subset of the generalized linear latent and mixed model framework (GLLAMM; Skrondal & Rabe-Hesketh, 2004), which subsumes common models used within social and behavioral science research, such as factor analysis models, item response theory (IRT) models, and multilevel models. PLmixed is espe- cially useful for estimating complex measurement models involving multilevel and crossed ran- dom effects/latent variables, which are often encountered in large-scale educational testing, multitrait–multimethod designs, and longitudinal studies. Background Several researchers have used lme4 to estimate one-parameter http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Psychological Measurement SAGE

PLmixed: An R Package for Generalized Linear Mixed Models With Factor Structures

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Publisher
SAGE
Copyright
© The Author(s) 2017
ISSN
0146-6216
eISSN
1552-3497
DOI
10.1177/0146621617748326
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Abstract

Computer Program Exchange Applied Psychological Measurement 2018, Vol. 42(5) 401–402 PLmixed: An R Package The Author(s) 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav for Generalized Linear DOI: 10.1177/0146621617748326 journals.sagepub.com/home/apm Mixed Models With Factor Structures 1 2 Minjeong Jeon and Nicholas Rockwood Keywords Item response theory, factor analysis, latent variable models, MIRT, multilevel models, hierarchi- cal models Description The R (R Core Team, 2017) package PLmixed (Jeon & Rockwood, 2017) has been developed to extend the capabilities of the existing R package lme4 (Bates, Machler, Bolker, & Walker, 2015) to allow for profile-likelihood estimation of generalized linear mixed models (GLMMs) with factor structures (i.e., factor loadings, weights, item discrimination parameters), as out- lined in Jeon and Rabe-Hesketh (2012). The modeling framework is a subset of the generalized linear latent and mixed model framework (GLLAMM; Skrondal & Rabe-Hesketh, 2004), which subsumes common models used within social and behavioral science research, such as factor analysis models, item response theory (IRT) models, and multilevel models. PLmixed is espe- cially useful for estimating complex measurement models involving multilevel and crossed ran- dom effects/latent variables, which are often encountered in large-scale educational testing, multitrait–multimethod designs, and longitudinal studies. Background Several researchers have used lme4 to estimate one-parameter

Journal

Applied Psychological Measurement – SAGE

Published: Jul 1, 2018

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