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A multilevel model was estimated by including a group-level predictor (here, refers to stations) and individual (refers to measurement at the same location).
A multilevel model was generated to control for violations of the assumption of independence (multiple rear-seated passengers traveling in the same vehicle) through the use of the GLIMMIX procedure, with the group-level variable VIN number, to adjust for clustering of passengers in vehicles.
A final multilevel model was used to account for the potential hierarchical structure of the data, similar to previous studies of vehicle crashes that accounted for the hierarchical nature of crash data (Jones and Jørgensen 2003; Gkritza and Mannering 2008; Kim et al. 2007).
The multilevel model was applied to the villager dataset alone, and to the villager and village datasets combined.
Second, a multilevel model was employed for the substantive statistical analyses, in which villagers were nested within villages.
A multilevel model was utilized to assess the effects of whz, age, haemoglobin, CRP and iron sufficiency markers on isotope incorporation independent of day of administration.
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Changing patterns of diversity projected from the multilevel model are very similar to the patterns of diversity projected from Maxent.
Levels within the multilevel model were patient (level 1) within practice (level 2).
Most of the prior distributions of hyperparameters used in our multilevel model were not conjugate.
Levels within the multilevel model were persons (level 1) within geographical area (LSOA, level 2).
For specific hospital interpretation or hospital ranking the multilevel model is recommended [ 54, 55].
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