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Multilevel models are suitable to control for correlated scores within higher level units (e.g., lessons or students) and allow conceptual differences of measures at different levels of analysis.
Multilevel modelling allows for the examination of variability in outcomes between individuals as well as between higher level units.
There is a need for data that allow the analysis of individuals and higher level units simultaneously, preferably over time.
The MLA approach has specifically been developed for these situations, where units on which variables are measured are nested within larger (higher level) units; such as patients within GP practices, or GPs within a country's health care system.
These populations are assumed to project to higher level units in the visual hierarchy where faster changes in neuronal activity are engendered in the context of higher precision (for details, see Feldman and Friston 2010).
The latter method was used in order to avoid excluding the higher level units since they performed major operations such as caesarean sections which were more likely to expose patients and health care workers (HCWs) to nosocomial infections.
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In longitudinal cluster randomized clinical trials (cluster-RCT), subjects are nested within a higher level unit such as clinics and are evaluated for outcome repeatedly over the study period.
Balance of baseline characteristics is particularly important to establish for cluster trials given the (higher level) unit of randomisation.
In order to not lose too many level two units, we choose to use only 10 individuals per higher level unit in this study.
It clearly points out the failed assumption that the history of components is congruent with the history of the higher level unit to which it belongs.
Note that a multi-level analysis approach could have been applied to these data, as the young people attending centres are clustered within the higher level unit of the different centres that they attended [ 21].
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