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Missing Data in the Analysis of Multilevel and Dependent Data

In: Dependent Data in Social Sciences Research

Author

Listed:
  • Simon Grund

    (University of Hamburg)

  • Oliver Lüdtke

    (Leibniz Institute for Science and Mathematics Education
    Centre for International Student Assessment)

  • Alexander Robitzsch

    (Leibniz Institute for Science and Mathematics Education
    Centre for International Student Assessment)

Abstract

Multilevel and other types of dependent data are often incomplete, and the treatment of missing data can be particularly challenging in these types of data. The past years have seen a significant increase in both the number and scope of statistical methods for incomplete multilevel data, which includes imputation-based methods such as multiple imputation (MI) and model-based methods such as maximum-likelihood or Bayesian estimation (MLE or BE). The purpose of this chapter is to provide an overview of the different methods that have been recommended for handling missing data in multilevel analysis and to discuss the features of multilevel data that need to be considered when these methods are used. In this context, we discuss what options the different methods provide for accommodating the structure and analysis of multilevel data, and we illustrate their application in a series of simulated examples. Finally, we also review the availability of imputation- and model-based methods in statistical software and provide guidance for their application in practice.

Suggested Citation

  • Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2024. "Missing Data in the Analysis of Multilevel and Dependent Data," Springer Books, in: Mark Stemmler & Wolfgang Wiedermann & Francis L. Huang (ed.), Dependent Data in Social Sciences Research, edition 2, chapter 0, pages 289-324, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-56318-8_12
    DOI: 10.1007/978-3-031-56318-8_12
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