A brief conceptual tutorial of multilevel analysis in social epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon

By J. Merlo, B. Chaix, Min Yang, J. Lynch, L. Råstam. Journal of Epidemiology and Community Health, 2005

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Climate Change-Induced Migration in Coastal Bangladesh? A Critical Assessment of Migration Drivers in Rural Households under Economic and Environmental Stress (2019, methodology): “This is confirmed by the intercorrelation coefficient (ICC, which takes on values between 0 and 1) in the empty model, thereby quantifying this aspect [59].”
Intermediate and advanced topics in multilevel logistic regression analysis (2017, background): “Furthermore, it also prevents the study of the association between specific hospital characteristics and the individual outcome [6].”
Nested by design: model fitting and interpretation in a mixed model era (2013, background): “This practice is unfortunate, because the estimators for the randomeffect variances allow important biological insight (Merlo et al. 2005a,b,c, 2006).”
A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena (2006, background): “In multilevel linear regression analysis it is easy to partition the variance between different levels and compute measures of clustering that provide intuitive information for capturing contextual phenomena.”