2017
DOI: 10.1177/1094428117703686
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Multiple Imputation of Missing Data for Multilevel Models

Abstract: Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present paper, based on theoretical arguments and computer simulations, we pro… Show more

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Cited by 167 publications
(175 citation statements)
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References 62 publications
(116 reference statements)
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“…To evaluate changes on relevant outcome measurements from baseline to discharge and discharge to follow‐up, we used multilevel models, conducted in R using the nlme package (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, ). To handle high levels of missing data, we used full information maximum likelihood estimation, which is considered a robust method for generation of model estimates with missing data (Allison, ) and generates estimates similar to multiple imputation methods (Ferro, ; Grund, Lüdtke, & Robitzsch, ; Peyre, Leplege, & Coste, ). Multilevel models included repeated measurements of dependent variables (EDE‐Q, binge eating and purging frequency, %EBW, BDI‐II, STAI‐Trait) nested within participants.…”
Section: Methodsmentioning
confidence: 99%
“…To evaluate changes on relevant outcome measurements from baseline to discharge and discharge to follow‐up, we used multilevel models, conducted in R using the nlme package (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, ). To handle high levels of missing data, we used full information maximum likelihood estimation, which is considered a robust method for generation of model estimates with missing data (Allison, ) and generates estimates similar to multiple imputation methods (Ferro, ; Grund, Lüdtke, & Robitzsch, ; Peyre, Leplege, & Coste, ). Multilevel models included repeated measurements of dependent variables (EDE‐Q, binge eating and purging frequency, %EBW, BDI‐II, STAI‐Trait) nested within participants.…”
Section: Methodsmentioning
confidence: 99%
“…Recent multilevel research demonstrated that multiple imputation is preferable over listwise deletion (Grund, Lüdtke and Robitzsch, 2016, 2018). We applied a reversed multiple imputation procedure for missing level‐1 data with the mice package (van Buuren and Groothuis‐Oudshoorn, 2011), taking into account the multilevel structure (Grund, Lüdtke and Robitzsch, 2018). Following Graham, Olchowski and Gilreath (2007), 20 datasets were imputed.…”
Section: Methodsmentioning
confidence: 99%
“…Finally, in line with other published studies, we may not have done enough to minimize the proportion of nonresponses. A recent study proposed advanced solutions for missing data to maximize the use of data in analysis [70]. We suggest that future studies could apply for processing missing data by S Grund (2018) [70] to achieve a better quality of research design and a robust conclusion.…”
Section: Limitations and Future Studymentioning
confidence: 98%