Cargando…
Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach
Multilevel models often include nonlinear effects, such as random slopes or interaction effects. The estimation of these models can be difficult when the underlying variables contain missing data. Although several methods for handling missing data such as multiple imputation (MI) can be used with mu...
Autores principales: | Grund, Simon, Lüdtke, Oliver, Robitzsch, Alexander |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613130/ https://www.ncbi.nlm.nih.gov/pubmed/34027594 http://dx.doi.org/10.3758/s13428-020-01530-0 |
Ejemplares similares
-
Multiple imputation methods for missing multilevel ordinal outcomes
por: Dong, Mei, et al.
Publicado: (2023) -
Flexible imputation of missing data
por: van Buuren, Stef
Publicado: (2018) -
Characterization of the synthetic cannabinoid MDMB-CHMCZCA
por: Weber, Carina, et al.
Publicado: (2016) -
Substantive model compatible multilevel multiple imputation: A joint modeling approach
por: Quartagno, Matteo, et al.
Publicado: (2022) -
Maximum likelihood estimation of a social relations structural equation model
por: Nestler, Steffen, et al.
Publicado: (2020)