IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v6y2016i4p2158244016668220.html
   My bibliography  Save this article

Multiple Imputation of Multilevel Missing Data

Author

Listed:
  • Simon Grund
  • Oliver Lüdtke
  • Alexander Robitzsch

Abstract

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan , and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.

Suggested Citation

  • Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2016. "Multiple Imputation of Multilevel Missing Data," SAGE Open, , vol. 6(4), pages 21582440166, October.
    • Handle: RePEc:sae:sagope:v:6:y:2016:i:4:p:2158244016668220
    DOI: 10.1177/2158244016668220
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244016668220
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2158244016668220?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Jerome P. Reiter, 2007. "Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data," Biometrika, Biometrika Trust, vol. 94(2), pages 502-508.
    3. 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.
    4. Joseph L. Schafer, 2003. "Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 19-35, February.
    5. Anne C. Black & Ofer Harel & D. Betsy McCoach, 2011. "Missing data techniques for multilevel data: implications of model misspecification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1845-1865, September.
    6. 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).
    7. 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.
    8. Harvey Goldstein & James R. Carpenter & William J. Browne, 2014. "Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 553-564, February.
    9. Consentino, Fabrizio & Claeskens, Gerda, 2010. "Order selection tests with multiply imputed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2284-2295, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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].
    2. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2023. "Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 454-489, August.
    3. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    4. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    5. Yajuan Si & Jerome P. Reiter, 2013. "Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 499-521, October.
    6. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    7. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    8. 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).
    9. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
    10. 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).
    11. Kristian Kleinke & Jost Reinecke & Cornelia Weins, 2021. "The development of delinquency during adolescence: a comparison of missing data techniques revisited," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 877-895, June.
    12. repec:cup:judgdm:v:15:y:2020:i:5:p:798-806 is not listed on IDEAS
    13. Thelma Dede Baddoo & Zhijia Li & Samuel Nii Odai & Kenneth Rodolphe Chabi Boni & Isaac Kwesi Nooni & Samuel Ato Andam-Akorful, 2021. "Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation," IJERPH, MDPI, vol. 18(16), pages 1-26, August.
    14. 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.
    15. Yulia V. Marchenko & Jerome P. Reiter, 2009. "Improved degrees of freedom for multivariate significance tests obtained from multiply imputed, small-sample data," Stata Journal, StataCorp LP, vol. 9(3), pages 388-397, September.
    16. Rashid, S. & Mitra, R. & Steele, R.J., 2015. "Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 84-96.
    17. repec:jss:jstsof:45:i01 is not listed on IDEAS
    18. A.Y. Kombo & H. Mwambi & G. Molenberghs, 2017. "Multiple imputation for ordinal longitudinal data with monotone missing data patterns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 270-287, January.
    19. Jesús Rascón & Wildor Gosgot Angeles & Manuel Oliva-Cruz & Miguel Ángel Barrena Gurbillón, 2022. "Wind Characteristics and Wind Energy Potential in Andean Towns in Northern Peru between 2016 and 2020: A Case Study of the City of Chachapoyas," Sustainability, MDPI, vol. 14(10), pages 1-11, May.
    20. 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.
    21. Lenka Vargová & Ľubica Zibrínová & Gabriel Baník, 2020. "The way of making choices: Maximizing and satisficing and its relationship to well-being, personality, and self-rumination," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 798-806, September.
    22. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:6:y:2016:i:4:p:2158244016668220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.