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REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types

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  • Carpenter, James R.
  • Goldstein, Harvey
  • Kenward, Michael G.

Abstract

Multiple imputation is becoming increasingly established as the leading practical approach to modelling partially observed data, under the assumption that the data are missing at random. However, many medical and social datasets are multilevel, and this structure should be reflected not only in the model of interest, but also in the imputation model. In particular, the imputation model should reflect the differences between level 1 variables and level 2 variables (which are constant across level 1 units). This led us to develop the REALCOM-IMPUTE software, which we describe in this article. This software performs multilevel multiple imputation, and handles ordinal and unordered categorical data appropriately. It is freely available on-line, and may be used either as a standalone package, or in conjunction with the multilevel software MLwiN or Stata.

Suggested Citation

  • 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).
    • Handle: RePEc:jss:jstsof:v:045:i05
    DOI: http://hdl.handle.net/10.18637/jss.v045.i05
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    Cited by:

    1. 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.
    2. repec:jss:jstsof:45:i01 is not listed on IDEAS
    3. Leckie, George & Charlton, Chris, 2013. "runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i11).
    4. Oya Kalaycioglu & Andrew Copas & Michael King & Rumana Z. Omar, 2016. "A comparison of multiple-imputation methods for handling missing data in repeated measurements observational studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 683-706, June.
    5. Manuel Gomes & Richard Grieve & Richard Nixon & Edmond S.‐W. Ng & James Carpenter & Simon G. Thompson, 2012. "Methods For Covariate Adjustment In Cost‐Effectiveness Analysis That Use Cluster Randomised Trials," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1101-1118, September.
    6. Josse, Julie & Husson, François, 2016. "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i01).
    7. H. Christoph Steinhardt & Jan Delhey, 2020. "Socio-Economic Modernization and the “Crisis of Trust” in China: A Multi-level Analysis of General and Particular Trust," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(3), pages 923-949, December.
    8. Tampubolon, Gindo & Hanandita, Wulung, 2014. "Poverty and mental health in Indonesia," Social Science & Medicine, Elsevier, vol. 106(C), pages 20-27.
    9. Marquez, Jose & Qualter, Pamela & Petersen, Kimberly & Humphrey, Neil & Black, Louise, 2022. "In a lonely place: Neighbourhood effects on loneliness among adolescents," SocArXiv hzer5, Center for Open Science.
    10. Manuel Gomes & Nils Gutacker & Chris Bojke & Andrew Street, 2016. "Addressing Missing Data in Patient‐Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance," Health Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 515-528, May.
    11. Manuel Gomes & Nils Gutacker & Chris Bojke & Andrew Street, 2014. "Addressing missing data in patient-reported outcome measures (PROMs): implications for comparing provider performance," Working Papers 101cherp, Centre for Health Economics, University of York.
    12. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2016. "Multiple Imputation of Multilevel Missing Data," SAGE Open, , vol. 6(4), pages 21582440166, October.
    13. 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].
    14. Shane A Kavanagh & Julia M Shelley & Christopher Stevenson, 2018. "Is gender inequity a risk factor for men reporting poorer self-rated health in the United States?," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    15. Stephen A. Mistler & Craig K. Enders, 2017. "A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data," Journal of Educational and Behavioral Statistics, , vol. 42(4), pages 432-466, August.

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