Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity
By Joerg Drechsler. 2015
79 citations recorded by the source corpus.
Subfields: Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7eb428f6c0bf99b89b7a05498567e08909fb6f9b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Sociology and Political Science
- O. Lüdtke
- Craig K. Enders
- Francis L. Huang
- Bethany Rittle-Johnson
- Andrew S. Belasco
- Herbert W. Marsh
- Ulrich Trautwein
- Ming Ming Chiu
In Statistics and Probability
- O. Lüdtke
- Alexander Robitzsch
- Craig K. Enders
- S. Grund
- Victoria Savalei
- O. Schilling
- Peter M. Bentler
- J. Peugh
Citations in the corpus
- Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, background): “A great deal of recent methodological work has focused on missing data handling methods for multilevel data structures (Drechsler, 2015; Enders et al., 2016; Enders et al., 2016; Goldstein, Bonnet, & Rocher, 2007; Goldstein, Carpenter, Kenward, & Levin, 2009; Grund, Lü;dke, & Robitzsch, 2016;…”
- Multiple Imputation of Missing Data for Multilevel Models (2018, background): “In multilevel data, it is important that the imputation model takes the multilevel structure into account (e.g., Andridge, 2011; Drechsler, 2015).”