Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging

Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averagi...

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Detalhes bibliográficos
Autores: Olmos, Ricardo, Martínez Huertas, José Ángel
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12598
Acesso em linha:https://hdl.handle.net/20.500.14468/12598
Access Level:acceso abierto
Palavra-chave:mixed-effects models
crossed random effects
random effects
model averaging
Akaike weights
Bayesian model averaging
AIC
BIC
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spelling Recovering Crossed Random Effects in Mixed-Effects Models Using Model AveragingOlmos, RicardoMartínez Huertas, José Ángelmixed-effects modelscrossed random effectsrandom effectsmodel averagingAkaike weightsBayesian model averagingAICBICRandom effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.PsychOpene-Spacio UNED20242024-05-2020222022-12-2220222022-12-22journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12598reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0oai:e-spacio.uned.es:20.500.14468/125982026-06-06T12:38:31Z
dc.title.none.fl_str_mv Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
title Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
spellingShingle Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
Olmos, Ricardo
mixed-effects models
crossed random effects
random effects
model averaging
Akaike weights
Bayesian model averaging
AIC
BIC
title_short Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
title_full Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
title_fullStr Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
title_full_unstemmed Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
title_sort Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
dc.creator.none.fl_str_mv Olmos, Ricardo
Martínez Huertas, José Ángel
author Olmos, Ricardo
author_facet Olmos, Ricardo
Martínez Huertas, José Ángel
author_role author
author2 Martínez Huertas, José Ángel
author2_role author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv mixed-effects models
crossed random effects
random effects
model averaging
Akaike weights
Bayesian model averaging
AIC
BIC
topic mixed-effects models
crossed random effects
random effects
model averaging
Akaike weights
Bayesian model averaging
AIC
BIC
description Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-12-22
2022
2022-12-22
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12598
url https://hdl.handle.net/20.500.14468/12598
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv PsychOpen
publisher.none.fl_str_mv PsychOpen
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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