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Multilevel modelling was used to identify variations between alternative sets of subjective and automated zone design neighbourhoods, which incorporated different boundaries and different scales.
Multilevel modelling was adjusted for cluster (centre) and individual effects.
Multilevel modelling was used to predict decision satisfaction.
Multilevel modelling was used to analyze the data.
Multilevel modelling was used to analyse the relationship between these variables.
Poisson multilevel modelling was conducted of deaths as standardised mortality ratios.
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If standard multilevel modelling is used without incorporating such a sampling design, the estimators of parameters may be biased.
The results of multilevel modelling are summarized in Table 4.
Multilevel modelling is a statistical technique that can be used to correct for this clustering.
All the independent variables included in multilevel modelling were significant at the bivariate stage.
The use of multilevel modelling is necessary as it controls for a number of design elements within the study.
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