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The first round of data collection generated ideas for concepts and the second round provided rank orderings.
Increased data collection generated a need for the development of complex database systems and software that, could analyze larger data sets.
The schedule was revised as data collection generated new issues and areas to be explored.
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The final translated items used for data collection were generated through consensus on the wording, clarity and cultural equivalence of items (refer to supporting file, File S1, for the translated scale).
A statistician blinded to the identity of the practices and not involved in the recruitment of practices or patients or in data collection, computer generated the randomisation schedule.
The final translated items used for data collection were generated through consensus on the wording, clarity and cultural equivalence of items.
The trial demonstrated that the standardised data collection tool generated a substantial amount of high-quality data, which met the trial objectives, and was therefore thought to perform well overall [ 14].
This is called theoretical sampling - "the process of data collection for generating theory whereby the analyst jointly collects, codes, and analyses his data and decides what data to collect next and where to find them, in order to develop his theory as it emerges" [ 16, 17].
This is due to the manner in which Nagios performs data collection: it generates a schedule and linearly iterates through that schedule until all polling has been completed for that time period.
A member of the research team not involved with data collection will generate the randomisation list.
An individual not involved with data collection will generate separate randomization lists for each participating site.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com