How to validate a dataset? Six steps.
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Datajournalists and scientists use similar insights to validate an external dataset. Although the interpretation may differ. As presented @ICA2013. Steps mentioned are based on the insights of 20 scientists and datajournalists.
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How to validate a dataset? Six steps.
- 1. never trust a scientist datajournalist dataset Tilburg University -‐ data journalism
- 2. Tilburg University -‐ data journalism
- 3. missing data, no value stored “I need to solve this” Tilburg University -‐ data journalism
- 4. missing data, no value stored “I need to solve this” missing data, no value stored “I need to write a story about this” Tilburg University -‐ data journalism
- 5. forreporters.com/andrew-lehren/ Tilburg University -‐ data journalism
- 6. scientist to journalist: “You twist everything” Tilburg University -‐ data journalism
- 7. journalist to scientist: “Your articles are useless” Tilburg University -‐ data journalism
- 8. “I am right” Tilburg University -‐ data journalism
- 9. can I trust (and use) this dataset? Tilburg University -‐ data journalism
- 10. “Trustworthiness and data management are vital to the success of qualitative studies … There is a lack of scientific literature regarding the structures and processes for managing large qualitative data sets.” (White, Oelken, Friesen, 2012) Tilburg University -‐ data journalism
- 11. “A simple answer to objective reporting is the kind of reporting that uses relevant and reliable sources which is not bias or slanted to a certain party.” Ibrahim, Pawanteh, Kee (2011) Tilburg University -‐ data journalism
- 12. question: how to validate a dataset? Tilburg University -‐ data journalism
- 13. check the data source what are his/her/its intentions? Tilburg University -‐ data journalism
- 14. what is the citation index of the data owner? do other journalists cite the data owner? Tilburg University -‐ data journalism
- 15. check the data Tilburg University -‐ data journalism
- 16. benefit do I need this? do I need to use it? Tilburg University -‐ data journalism
- 17. check data gathering? clarification of the data? Tilburg University -‐ data journalism
- 18. check data gathering? is this correct? clarification of the data? do I understand? Tilburg University -‐ data journalism
- 19. missing data what is wrong? what is the story? Tilburg University -‐ data journalism
- 20. missing data what is wrong? I need to solve what is the story? I need to write Tilburg University -‐ data journalism
- 21. trouble? TEST! CALL! Tilburg University -‐ data journalism
- 22. I need more sources! (do I?) give me data give me humans Tilburg University -‐ data journalism
- 23. I need more sources! (do I?) give me data check consistency give me humans check my story Tilburg University -‐ data journalism
- 24. same steps different interpretation Tilburg University -‐ data journalism
- 25. “Dear datajournalist, Please take a look at the research method yourself and act a bit more like a scientist.” Tilburg University -‐ data journalism
- 26. “Dear scientist, Try to avoid intellectual arrogance. There are other people who are just as smart.” Tilburg University -‐ data journalism
- 27. “practice what you preach” Tilburg University -‐ data journalism
- 28. scientists check the source (citation) check the data check benefit check data gathering TEST! more data sources data journalists check the source (citation) check the data check benefit check clarification CALL! more human sources Tilburg University -‐ data journalism
Notes de l'éditeur
- NameWork at university – work as a writer / data journalistSomewhere in between – I do research something with a scientific goals and soething with a journalustic aim
- If you are in between – it is interesting that the worlds of social science and datajournalism in the field are sometimes really different – but sometimes notIf we take fo example this dataset – which is the dataset Andrew Lehren from te New York Times used in Pullitzer prize winner story about the New York Marathon you can see a blind spot
- … if a scientist sees this, in gereneral his first reponse it that the dataset is technically not right. There us some missing data. A problem which needs to be solved
- While, if a journalist sees a white spot, he is really interested in the story behind the missing data. Why is the data missing?
- In this case, both appriaches were all right; some runners missed checkpointBut also some technical flaws
- If I talk about journalists with scientists not always as ethustaistic as they could be- They can’t de al with data – they use data in a superficial
- Journalists – scietists are really egocentric – and their stories are not useful for the real world. They just do research to please themselves and their collegues at university
- At least o eon thing they agree; they assume they aee both right
- Because I live in both worlds, I am interested to see the real differences or notAnd one of the differences or not, is how scnetists as well astdatajournalists decide if they trust and use a dataset or not. And what I would like to discuss today is really just a startig point of this topic
- So if you dig into the literature of the trustworthiness of data from the perspective of a scientists – you will find a broad variety of articles in different different scietif field. Anf it’s not easy to dtect a specific line in the ariety of articles n all these different field. And there is a lack in specific guidelines how scinetists determine the trustworthiness a scientist
- And if you readscientifartciles about what makes a datasettrustworthy for journalists – you will find nothinhYou will only find general readings about the trustwothiness of a news source and general. Like the main principles of Gans. And a dataset could simply be one of these news sources. But on a literature level. Its is hard to compare
- So, with no clear starting oint, it seemed right to start with a very general question. And that’s what I did. I asked ten of me scirntif as well a
- Are the intentions of any influence on the dataset?
- So they both use their collegues as peers
- Using a dataaet from another source is not really common in social science -
- Experiments – case study