Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/222432
Authors: 
Hammon, Angelina
Zinn, Sabine
Year of Publication: 
2020
Citation: 
[Journal:] Journal of the Royal Statistical Society: Series C (Applied Statistics) [ISSN:] 1467-9876 [Volume:] 69 [Issue:] 3 [Pages:] 547-564
Abstract: 
Summary 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.
Subjects: 
Fully conditional specification
Missingness not at random
Multilevel data
Multiple imputation
Selection model
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version

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