Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach

Volume: 53, Issue: 6, Pages: 2631 - 2649
Published: May 23, 2021
Abstract
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 multilevel data, conventional methods for multilevel MI often do not properly take the nonlinear associations between the variables into account. In...
Paper Details
Title
Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach
Published Date
May 23, 2021
Volume
53
Issue
6
Pages
2631 - 2649
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