R package hmi: a convenient tool for hierarchical multiple imputation and beyond
Jörg Drechsler () and
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Matthias Speidel: Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]
Jörg Drechsler: Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]
No 201816, IAB Discussion Paper from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]
"Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional datasets. Nowadays multiple imputation is also applied to impute missing values in hierarchical datasets, address confidentiality concerns, combine data from different sources, or correct measurement errors in surveys. However, software developments did not keep up with these recent extensions. Most imputation software can only deal with item nonresponse in cross-sectional settings and extensions for hierarchical data - if available at all - are typically limited in scope. Furthermore, to our knowledge no software is currently available for dealing with measurement error using multiple imputation approaches. The R package hmi tries to close some of these gaps. It offers multiple imputation routines in hierarchical settings form any variable types (for example, nominal, ordinal, or continuous variables). It also provides imputation routines for interval data and handles a common measurement error problem in survey data: Biased inferences due to implicit rounding of the reported values. The user-friendly setup which only requires the data and optionally the specification of the analysis model of interest makes the package especially attractive for users less familiar with the peculiarities of multiple imputation. The compatibility with the popular mice package ensures that the rich set of analysis and diagnostic tools and post-imputation commands available in mice can be used easily once the data have been imputed." (Author's abstract, IAB-Doku) ((en))
Keywords: Imputationsverfahren; lineares Modell; Datengewinnung; Software; Mehrebenenanalyse; Fehler; Datenfusion; IAB-Haushaltspanel (search for similar items in EconPapers)
JEL-codes: C38 C83 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:iab:iabdpa:201816
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