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This study proposes the use of multilevel modeling, which tested intervention effects at specific time periods.
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.
The use of multilevel modeling allowed examination of clinic level attributes of test uptake.
Other strengths include the assessment of a wide-range of cognitive and health outcomes, a sample drawn from a wide range of noise exposure levels, adjustment for a wide-range of individual confounding socioeconomic factors, and the use of multilevel modeling to take school- and individual-level variation into account.
This demands the use of multilevel modeling, which would calculate the standard errors more accurately and reduce the chance of misestimating the significance of variables, as some of the assumptions inherent in traditional regression methods are not valid for nested data [ 26].
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Several authors have suggested the use of multilevel models for the analysis of data from single case designs.
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.
In the last ten years the use of multilevel models has burgeoned in epidemiology.
The use of multilevel modelling is necessary as it controls for a number of design elements within the study.
More suggestions(15)
use of multivariate modeling
use of spatial modeling
use of multiscale modeling
use of mathematical modeling
use of novel modeling
use of kinetic modeling
use of supplementary modeling
use of different modeling
use of distinct modeling
use of statistical modeling
use of compartmental modeling
use of numerical modeling
use of toxicokinetic modeling
use of hierarchical modeling
use of computational modeling
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