Separating between‐ and within‐cluster covariate effects by using conditional and partitioning methods

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Intermediate and advanced topics in multilevel logistic regression analysis (2017, background): “Ignoring such a correlation can lead to inconsistent estimation of the regression parameters [20].”
Misspecifying the Shape of a Random Effects Distribution: Why Getting It Wrong May Not Matter (2011, background): “First, Neuhaus and McCulloch (2006) show that use of conditional likelihood methods and methods that separate covariates into between and within components eliminate or reduce bias for the within-cluster covariate in the situation where the random effects are associated with the covariates.”
A general multilevel SEM framework for assessing multilevel mediation. (2010, unknown): “…same way that few indicators and low communalities can bias relationships among latent variables in factor analysis, few Level-1 units and a low intraclass correlation (ICC) can bias Between effects (Asparouhov & Muthe´n, 2006; Lu¨dtke et al., 2008; Mancl et al., 2000; Neuhaus & McCulloch, 2006).”