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Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms

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  • Harvey Goldstein
  • James R. Carpenter
  • William J. Browne

Abstract

type="main" xml:id="rssa12022-abs-0001"> The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:553-564
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Citations

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    Cited by:

    1. 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.
    2. William J. Browne, 2022. "A celebration of Harvey Goldstein’s lifetime contributions: Memories of working with Harvey Goldstein on multilevel modelling methods and applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 753-758, July.
    3. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    4. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2016. "Multiple Imputation of Multilevel Missing Data," SAGE Open, , vol. 6(4), pages 21582440166, October.
    5. Li, Haocheng & Shu, Di & He, Wenqing & Yi, Grace Y., 2019. "Variable selection via the composite likelihood method for multilevel longitudinal data with missing responses and covariates," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 25-34.
    6. Ben Weidmann & Luke Miratrix, 2021. "Missing, presumed different: Quantifying the risk of attrition bias in education evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 732-760, April.
    7. Adrian Byrne & Natalie Shlomo & Tarani Chandola, 2023. "Multilevel modelling approach to analysing life course socioeconomic status and understanding missingness," Review of Evolutionary Political Economy, Springer, vol. 4(2), pages 275-297, July.
    8. Timothy Hayes, 2019. "Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 625-641, October.

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