Examples of using Big data sources in English and their translations into Marathi
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Big data sources tend to have ten characteristics;
Surveys linked to big data sources(section 3.6).
Big data sources are both found and designed;
Table 2.1: Studies of unexpected events using always-on big data sources.
Big data sources can be loaded with junk and spam.
Table 2.3: Examples of natural experiments using big data sources.
Big data sources do not mean the end of survey research.
Table 2.1: Studies of unexpected events using always-on big data sources.
Far from distinctive, many big data sources have information that is sensitive.
Big data sources are everywhere, but using them for social research can be tricky.
Figure 3.12: Two ways to combine big data sources and survey data. .
But big data sources also enable researchers to do empirically driven theorizing.
Finally, I will describe two strategies for combining survey data and big data sources.
First, increasingly corporate big data sources come from digital devices in the physical world.
Thus, for those who are good atasking certain types of research questions, big data sources can be very fruitful.
Second, big data sources can enable improved measurement for policy through nowcasting.
As these examples illustrate, corporate big data sources are about more than just online behavior.
Finally, big data sources can help researchers make causal estimates without running experiments.
Even though, from the perspective of researchers, big data sources are“found,” they don't just fall from the sky.
In some cases, big data sources enable you to do this counting relatively directly(as in the case of New York Taxis).
The sensitive nature of this information is part of the reason that big data sources are often inaccessible(described above).
To conclude, many big data sources are not representative samples from some well-defined population.
Based on the ideas in this chapter,I think that there are three main ways that big data sources will be most valuable for social research.
Most big data sources are incomplete, in the sense that they don't have the information that you will want for your research.
That is, researchers need to understand the characteristics of big data sources- both good and bad- and then figure out how to learn from them.
To conclude, many big data sources are drifting because of changes in who is using them, in how they are being used, and in how the systems work.
Nowcasting projects such as Google FluTrends also show what can happen if big data sources are combined with more traditional data that were created for the purposes of research.
Big data sources and surveys are complements not substitutes so as the amount of big data increases, I expect that the value of surveys will increases as well.
Some researchers believe that big data sources, especially online sources, are pristine because they are collected automatically.
When thinking about big data sources, many researchers immediately focus on online data created and collected by companies, such as search engine logs and social media posts.