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The use of multilevel modelling is necessary as it controls for a number of design elements within the study.
Moreover, the use of multilevel modelling appropriately adjusted the standard errors of the estimates for the non-independence of observations from children nested within households.
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This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods.
Several authors have suggested the use of multilevel models for the analysis of data from single case designs.
The increase in Type 1 error due to subject-level effects can be mitigated through the use of multilevel modeling techniques.
The use of multilevel modeling techniques allowed for the evaluation of moderating effects on these relationships, including those of gender, year of data collection, mean age of the sample, publication type, and type of symptomatology measured.
Over the last 20 years, veterinary epidemiologists have made great progress in dealing more effectively with random error (particularly through the use of multilevel models) but paid relatively little attention to systematic error.
The use of multilevel models corrects this by modelling the correlation between the control sets.
The use of multilevel modeling allowed examination of clinic level attributes of test uptake.
In the last ten years the use of multilevel models has burgeoned in epidemiology.
Another strength of this study was the use of multilevel models to account for, and assess the scale of the differences between GP cooperative catchment areas.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com