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R package hmi: a convenient tool for hierarchical multiple imputation and beyond

Author

Listed:
  • Speidel, Matthias

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Drechsler, Jörg

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Jolani, Shahab

    (Maastricht University)

Abstract

"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))

Suggested Citation

  • Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:201816
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    References listed on IDEAS

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    1. Patrick Royston, 2007. "Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring," Stata Journal, StataCorp LP, vol. 7(4), pages 445-464, December.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    4. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    5. Gartner, Hermann & Rässler, Susanne, 2005. "Analyzing the changing gender wage gap based on multiply imputed right censored wages," IAB-Discussion Paper 200505, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    6. repec:mpr:mprres:6195 is not listed on IDEAS
    7. Wickham, Hadley, 2007. "Reshaping Data with the reshape Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i12).
    8. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    9. Schenker, Nathaniel & Raghunathan, Trivellore E. & Chiu, Pei-Lu & Makuc, Diane M. & Zhang, Guangyu & Cohen, Alan J., 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 924-933, September.
    10. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    11. Gurprit Grover & Vinay K. Gupta, 2015. "Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 817-827, April.
    12. Jian Zhu & Trivellore E. Raghunathan, 2015. "Convergence Properties of a Sequential Regression Multiple Imputation Algorithm," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1112-1124, September.
    13. H. Schneeweiss & J. Komlos & A. Ahmad, 2010. "Symmetric and asymmetric rounding: a review and some new results," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(3), pages 247-271, September.
    14. Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
    15. Jeff Larrimore & Richard V. Burkhauser & Shuaizhang Feng & Laura Zayatz, 2008. "Consistent Cell Means for Topcoded Incomes in the Public Use March CPS (1976-2007)," NBER Working Papers 13941, National Bureau of Economic Research, Inc.
    16. Jingchen Liu & Andrew Gelman & Jennifer Hill & Yu-Sung Su & Jonathan Kropko, 2014. "On the stationary distribution of iterative imputations," Biometrika, Biometrika Trust, vol. 101(1), pages 155-173.
    17. Su, Yu-Sung & Gelman, Andrew & Hill, Jennifer & Yajima, Masanao, 2011. "Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i02).
    18. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    19. Drechsler, Jörg & Kiesl, Hans, 2014. "Beat the heap - an imputation strategy for valid inferences from rounded income data," IAB-Discussion Paper 201402, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    20. S. Zinn & A. Würbach, 2016. "A statistical approach to address the problem of heaping in self-reported income data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 682-703, March.
    21. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
    22. Carpenter, James R. & Goldstein, Harvey & Kenward, Michael G., 2011. "REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i05).
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    More about this item

    Keywords

    Bundesrepublik Deutschland ; Datengewinnung ; Fehler ; Imputationsverfahren ; Datenfusion ; lineares Modell ; Mehrebenenanalyse ; Software ; IAB-Haushaltspanel;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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