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Specification of random effects in multilevel models: a review

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  • Leonardo Grilli
  • Carla Rampichini

Abstract

The analysis of highly structured data requires models with unobserved components (random effects) able to adequately account for the patterns of variances and correlations. The specification of the unobserved components is a key and challenging task. In this paper, we first review the literature about the consequences of misspecifying the distribution of the random effects and the related diagnostic tools; we then outline the main alternatives and generalizations, also considering some issues arising in Bayesian inference. The relevance of suitably structuring the unobserved components is illustrated by means of an application exploiting a model with heteroscedastic random effects. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:3:p:967-976
    DOI: 10.1007/s11135-014-0060-5
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    Cited by:

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    2. Sandra De Iaco & Sabrina Maggio, 2022. "Using multilevel models to evaluate the attitude of separate waste collection in young people," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 77-95, April.
    3. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
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    5. Shuwen Hu & You-Gan Wang & Christopher Drovandi & Taoyun Cao, 2023. "Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 681-711, June.

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