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The Two-step Cluster Analysis confirms that the score distribution for 35 items is optimally split up into 2 clusters, and for 15 items into 3.
We combined the 15D items into 3 and even 2 categories because the frequencies in the categories four and five were too small for statistical analyses.
Despite efforts to prioritize the items into 3 categories, the calculated cost of category A items far exceeded the amount of funds available.
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The developers of the NEI-VFQ distributed the original 52 items into 13 different domains.
When we arrange the 7 action items into 4 groups, however, we can determine a reasonably consistent prioritization.
To make this huge data set manageable for presentation, we aggregate the per capita nutrient availabilities of the 131 food items into 13 food groups by summing up nutrient values of each individual food.
Based on factor analyses Beck and coworkers divided the 15 items into 2 subscales.
We identified 16 preparedness actions for earthquake and tsunami evacuation and grouped these items into 5 progressive levels of readiness (Table 1).
Merging 4 items into 2 was a direct consequence of the active role played by respondents in the adaptation of the PACIC questionnaire.
The list was then validated by experts in a first Delphi survey round, following which the researchers translated the list items into 75 statements.
We compiled these items into 58 non-overlapping food groups, based on nutrients, common characteristics, or culinary use, as described in more detail previously.
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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