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Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo

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  • Timothy Hayes

    (Florida International University)

Abstract

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one’s imputation model must be congenial to (appropriate for) one’s intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo , to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:5:p:625-641
    DOI: 10.3102/1076998619858624
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    References listed on IDEAS

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    1. 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.
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