Recovering crossed random effects in mixed-effects models using model averaging
Author(s) / Creator(s)
Martínez-Huertas, José Ángel
Olmos, Ricardo
Abstract / 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.
Keyword(s)
mixed-effects models crossed random effects random effects model averaging Akaike weights Bayesian model averaging AIC BICPersistent Identifier
Date of first publication
2022-12-23
Journal title
Methodology
Volume
18
Issue
4
Page numbers
298–323
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Martínez-Huertas, J., & Olmos, R. (2022). Recovering crossed random effects in mixed-effects models using model averaging. Methodology, 18(4), 298-323. https://doi.org/10.5964/meth.9597
-
meth.v18i4.9597.pdfAdobe PDF - 1.75MBMD5: defdf643c4f0e0f7384f06d0c7d82c14
-
There are no other versions of this object.
-
Author(s) / Creator(s)Martínez-Huertas, José Ángel
-
Author(s) / Creator(s)Olmos, Ricardo
-
PsychArchives acquisition timestamp2023-01-23T14:06:47Z
-
Made available on2023-01-23T14:06:47Z
-
Date of first publication2022-12-23
-
Abstract / DescriptionRandom 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.en_US
-
Publication statuspublishedVersion
-
Review statuspeerReviewed
-
CitationMartínez-Huertas, J., & Olmos, R. (2022). Recovering crossed random effects in mixed-effects models using model averaging. Methodology, 18(4), 298-323. https://doi.org/10.5964/meth.9597en_US
-
ISSN1614-2241
-
Persistent Identifierhttps://hdl.handle.net/20.500.12034/7990
-
Persistent Identifierhttps://doi.org/10.23668/psycharchives.12449
-
Language of contenteng
-
PublisherPsychOpen GOLD
-
Is version ofhttps://doi.org/10.5964/meth.9597
-
Keyword(s)mixed-effects modelsen_US
-
Keyword(s)crossed random effectsen_US
-
Keyword(s)random effectsen_US
-
Keyword(s)model averagingen_US
-
Keyword(s)Akaike weightsen_US
-
Keyword(s)Bayesian model averagingen_US
-
Keyword(s)AICen_US
-
Keyword(s)BICen_US
-
Dewey Decimal Classification number(s)150
-
TitleRecovering crossed random effects in mixed-effects models using model averagingen_US
-
DRO typearticle
-
Issue4
-
Journal titleMethodology
-
Page numbers298–323
-
Volume18
-
Visible tag(s)Version of Recorden_US