Multiple imputation of binary multilevel missing not at random data





Faculty/Professorship: Statistics and Econometrics  
Author(s): Hammon, Angelina ; Zinn, Sabine
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2021
Pages: 547-564
Source/Other editions: Journal of the Royal Statistical Society: Series C (Applied Statistics). 69 (2020), 3, S. 547-564 - ISSN: 0035-9254
is version of: 10.1111/rssc.12401
Year of first publication: 2020
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
DOI: 10.1111/rssc.12401
URN: urn:nbn:de:bvb:473-irb-498234
Abstract: 
We introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first equation of the model defines the regression model for the missing data mechanism. The second equation specifies the regression model of the variable to be imputed. The non-random selection of the binary data is mapped by correlations between the error terms of the two regression models. Hierarchical data structures are modelled by random intercepts in both equations. To fit the novel imputation model we use maximum likelihood and adaptive Gauss–Hermite quadrature. A comprehensive simulation study shows the overall performance of the approach.We test its usefulness for empirical research by applying it to a common problem in social scientific research: the emergence of educational aspirations. Our software is designed to be used in the R package mice.
GND Keywords: Fehlende Daten ; Imputationstechnik
Keywords: Fully conditional specification; Missingness not at random; Multilevel data; Multiple imputation; Selection model
DDC Classification: 300 Social sciences, sociology & anthropology  
RVK Classification: MR 2100   
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/49823
Release Date: 28. April 2021

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