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On the performance of different regularization methods in bifactor-(S-1) models with explanatory variables: Caveats, recommendations, and future directions

MPS-Authors
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Ernst,  Maximilian S.       
Center for Lifespan Psychology, Max Planck Institute for Human Development, Max Planck Society;

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Peikert,  Aaron       
Center for Lifespan Psychology, Max Planck Institute for Human Development, Max Planck Society;
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

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Brandmaier,  Andreas M.       
Center for Lifespan Psychology, Max Planck Institute for Human Development, Max Planck Society;
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

External Resource

https://doi.org/10.1080/10705511.2022.2140664
(Publisher version)

https://osf.io/4nz6j/
(Research data)

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Citation

Friemelt, B., Bloszies, C., Ernst, M. S., Peikert, A., Brandmaier, A. M., & Koch, T. (2023). On the performance of different regularization methods in bifactor-(S-1) models with explanatory variables: Caveats, recommendations, and future directions. Structural Equation Modeling, 30(4), 560-573. doi:10.1080/10705511.2022.2140664.


Cite as: https://hdl.handle.net/21.11116/0000-000B-6C45-F
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