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The measure is scaled so that the minimum possible score is defined as 0 and the maximum possible score as 100 with higher scores indicating better QOL.
Secondly, it demonstrates the importance of ensuring that the translated version of a measure is scaled in an equivalent way as the original version.
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The measure was scaled by 42 so that it represents AUC per day and this was transformed by log10.
MNF – measure was scaled by division by 100, and squared due to a left-skewed distribution.
RMS – measure was scaled by multiplying by 10,000, and transformed by taking its natural logarithm due to having a right-skewed distribution.
Each of these measures is scaled with low scores indicating poor QoL.
The measures are scaled based on their respective standard errors [60].
For the Subject method, the vectors of activation measures were scaled to mean 0, standard deviation 1, and a single coefficient was estimated for each path, similarly to a previous study of the angular gyrus in dyslexia [4].
The items on both measures were scaled and scored using the recommended EORTC procedures (Fayers et al, 2001).
This heterogeneity measure is scale invariant with respect to multiplying the connectivity by a scalar.
The measured data is scaled relative to maximal measured value + standard deviation and therefore has arbitrary units.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com