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The two approaches also differ in terms of interpretation of their coefficients.
Since the latter analysis approach is more restrictive, the results between both analysis approaches also differ.
The two approaches also differ in other aspects, including the robustness or sensitivity to the assumptions of correlation structures and missing mechanisms [ 2].
The two approaches also differ in their standpoint to decision guiding (immediate frame vs. long-term calculation), task context (certainty or risk vs. uncertainty), and task process (description-based vs. experience-based).
The training and validation approaches also differ and we would argue that our tripartite training, validation and testing splits of the initial data are better suited to avoid over-fitting than the GWAVA bipartite training and validation approach.
The approaches also differ significantly in computational resources required, ranging from Bustard, which is reported to be the fastest [ 18], to Alta-Cyclic and BayesCall, which requires orders of magnitudes more computational resources.
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In addition to tumor type, the methodological approaches also differed.
The two approaches also differed in terms of the proportions of children who were referred to extra care by PYHC and those who were referred to specialised care outside of PYHC.
Secondly, our approach also differs with respect to variations in journal weights.
The CSE approach also differs from previous ones in relation to the definition of what should be counted as avoided deforestation and how to estimate avoided deforestation.
The current approach also differs substantially from the approach considered by Buckley (2009), who focused on the paradox in relation to two of the PISA attitudinal subscales, the Enjoyment and Value subscales, in analyzing the PISA 2006 data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com