Separation of individual‐level and cluster‐level covariate effects in regression analysis of correlated data

By M. Begg, M. Parides. Statistics in Medicine, 2003

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Application of random effects to the study of resource selection by animals. (2006, background): “In the presence of multiple hierarchies, random effects become even more important (Ten Have, Kunselman & Tran 1999; Begg & Parides 2003).”
Neighborhood Socioeconomic Disadvantage and 30 Day Rehospitalizations: An Analysis of Medicare Data (2014, methodology): “It is possible that hospitals that serve predominantly disadvantaged neighborhoods provide different quality of care than hospitals that serve predominantly nondisadvantaged neighborhoods, and this had an influence on our findings (43).”
On the Unnecessary Ubiquity of Hierarchical Linear Modeling (2017, background): “However, if researchers are not interested in explicitly understanding the variability of the random effects, then random effects are not required to estimate the population-averaged contextual effect (Begg & Parides, 2003; Berkhof & Kampen, 2004; Snijders & Bosker, 2011, p. 106).”
Modeling Clustered Data with Very Few Clusters (2016, background): “Contextual effects can be modeled with GEE (Begg & Parides, 2003; Berkhof & Kampen, 2004); however, the interpretation can only be made marginally.”
Longitudinal Study of Automatic Face Recognition (2018, background): “, age or elapsed time) exhibits variability within, but also between individuals; models which assume that the within- and betweenindividual effects are equal do not properly estimate either of these effects [23], [26], [27], [28].”