Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
Abstract: 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 si.... https://meth.psychopen.eu/index.php/meth/article/view/9597
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging ; volume:18 ; number:4 ; day:22 ; month:12 ; year:2022
Methodology ; 18, Heft 4 (22.12.2022)
- Creator
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Martínez-Huertas, José Ángel
Olmos, Ricardo
- DOI
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10.5964/meth.9597
- URN
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urn:nbn:de:101:1-2023012104143006000616
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Martínez-Huertas, José Ángel
- Olmos, Ricardo
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