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Using linear modelling, a two-level hierarchical modelling approach was used to explore which factors independently affected 'overall satisfaction' scores.
Having fitted the TCH-only model, Asner and Mascaro's modelling approach was followed (Asner and Mascaro 2014).
In most cases, a nonlinear mixed modelling approach was used with models fitted using the NLMIXED procedure in Version 9.3 of the SAS software (SAS Institute Inc. 2011).
Whenever a two-step modelling approach was used, the true model was only weakly supported by the data, as AIC values were never 2.0 or less.
The partial least squares structural equation modelling approach was used to test the model.
A forward-stepwise modelling approach was used to construct multivariate logistic regression models [ 23, 25]; correlation was accounted for with methods described above.
A modelling approach was used based on the Urban Forest Effects Model (UFORE) and a modified version.
A flexible modelling approach was adopted, and variables were retained in the model if they had P<0.1.
The modelling approach was then developed to establish the sub unit-cell model.
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Within this framework, a micro-modelling approach was considered.
Finite element analysis was used to determine the biophysical environment, and a lattice-modelling approach was used to simulate cell activity.
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