Examples of using Big data sources in English and their translations into Slovak
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Some researchers believe that big data sources, especially online sources,
using nonrepresentative big data sources to do out-of-sample generalizations can go very wrong.
As I'm describing these characteristics you will notice that they often arise because big data sources were not created for the purpose of research.
In some cases, big data sources enable you to do this counting relatively directly(as in the case of New York Taxis).
offer other advice for discovering useful comparisons within big data sources.
Understanding these 10 general characteristics is a helpful first step toward learning from big data sources.
Table 2.4 provides some other examples of how matching can be used with big data sources.
In fact, people who have worked with big data sources know that they are frequently dirty.
The sensitive nature of this information is part of the reason that big data sources are often inaccessible(described above).
Then, I will illustrate three research strategies that can be used to successfully learn from big data sources.
thinking about natural experiments in big data sources.
Finally, I will describe two research templates for linking survey data to big data sources(section 3.6).
Table 2.4 provides some other examples of how matching can be used with big data sources.
Thus, for those who are good at asking certain types of research questions, big data sources can be very fruitful.
If true, this would seem to severely limit what can be learned from big data sources because many of them are nonrepresentative.
but cleaning big data sources seems to be more difficult.
I think that there are three main ways that big data sources will be most valuable for social research.
Instead, it is going to teach you how to learn from big data sources(chapter 2).
As I described in chapter 2, most big data sources are inaccessible to researchers.
Most big data sources are incomplete,