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Specification of random effects in multilevel models: a review

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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.

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Authors and Affiliations

  1. Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’ - University of Florence, Florence, Italy

    Leonardo Grilli & Carla Rampichini

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  1. Leonardo Grilli
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  2. Carla Rampichini
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Correspondence to Carla Rampichini.

Additional information

This research was supported by the FIRB 2012 project “Mixture and latent variable models for causal inference and analysis of socio-economic data”, Grant No. RBFR12SHVV_003.

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Grilli, L., Rampichini, C. Specification of random effects in multilevel models: a review. Qual Quant 49, 967–976 (2015). https://doi.org/10.1007/s11135-014-0060-5

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