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For the first time, the 2000 census allowed Americans to check more than one category to identify their race.
Therefore, we created a second, less-restrictive category to identify another group of subject genes.
We further analyzed each functional category to identify which proteins were the most enriched.
We counted positive, neutral, and negative connotations associated with responses to each category to identify what kinds of connotations they had.
Categories were initially developed in line with the main domains covered in the interviews but this changed following a more detailed reading of information under each category to identify emergent themes and sub-themes.
In addition, we performed sensitivity analyses isolating participants with missing PIR data in a separate category to identify whether CVD risk profiles in this group deviated markedly from participants without missing data.
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Users can create anonymous profiles, then the company says it looks at more than 50 categories to identify likely candidates for a job.
This contribution outlines few approaches, reviews some available methods and analysis categories to identify users and to derive design insights from them.
We then conducted quantitative trait association analyses using the distribution of scores in each of the categories to identify sets of SNPs that associate with symptomatic severity of each of the behaviors listed in Table S1.
Similar codes were grouped into categories to identify the main themes by framing differentiated concepts (categories).
We used stratified multilevel models across three household income categories to identify populations at risk for FI.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com