The irony of it is that better planning before data collection can only take days!pic.twitter.com/10GRwiWIVE
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Even more irony in data analysts often not being involved to talk about the structure/contents of the organizations data collection.
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So true! I spent 3 years re-cleaning the Coronary Drug Project data, and the NIH has now spent 3 more years & counting checking & finalizing the data cleaning for public release.
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Any teaching resources you'd recommend specifically on data cleaning/QC or even data collection/database structure strategies to reduce mistakes?
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Who forgot to rename and re-label the reverse coded items?! Why is 98% of our high functioning sample depressed?pic.twitter.com/OSDe8a6CGX
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“Use reverse coding”, they said. “It’ll help with validity”, they said.
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It's literally my entire job in data management. And so much of it is self-inflicted in the study design stage.
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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In industry, they have dedicated people/teams to do data cleaning. In academia, these are mostly absent, and it is often not covered in grant proposals (or is it?). So guess how well data cleaning is done in academic research... fortunately some academics do care!
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Ah, my sweet summer child. “Teams in industry dedicated to data cleaning”
. Signed, someone from Industry - Još 2 druga odgovora
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