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To do this, we used an inductive, open-coding process in which new codes were created from terms, phrases, or ideas in the text.
A total of 28 codes were created.
The codes were created based on the content of the items themselves.
Stricter building codes were created in Florida in the aftermath of Hurricane Andrew.
Randomization codes were created by the study statistician, and randomization occurred at the time of first vaccination.
Codes were created for each theme, with codes reflecting directionality of a comment (i.e., whether it was a positive or negative statement).
Similar(18)
Tax codes are created to ensure that all of us contribute to the greater good.
The classification codes are created in a nested structure (hierarchical tree).
For each simulation, different codes are created, and one hybrid fingerprint is generated and decoded for each code.
The classification codes are created in a nested structure: 473 classes at the highest level and 168,743 codes at the lowest (most detailed) level.
A final group of organisational codes was created to help the research team to gain a sense of comments that were not directly relevant to the research aims.
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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