Examples of using Big data sources in English and their translations into Marathi
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Computer
Most big data sources are incomplete, in the sense that they don't have the information that you will want for your research.
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.
As I described in chapter 2, most big data sources are inaccessible to researchers.
Most social scientists are already familiar with the process of cleaning large-scale social survey data, but cleaning big data sources seems to be more difficult.
system drift make it hard to use big data sources to study long-term trends.
The remainder of the chapter begins by arguing that big data sources will not replace surveys
But, as was described in chapter 2, big data sources may not be accurate, they may not
Going forward, I expect that nowcasting studies that combine big data sources with researcher-collected data will enable companies
Once you realize some treatment has been assigned randomly, big data sources can provide the outcome data that you need in order to compare the results
These four examples all show that a powerful strategy in the future will be to enrich big data sources, which are not collected for research,
These four examples all show that a powerful strategy in the future will be to enrich big data sources, which are not created for research, with additional information
including big data sources, surveys, experiments,
One thing that is clear, however, is that if you are forced to work with non-probability samples or nonrepresentative big data sources(think back to Chapter 2), then there is a strong reason to
In this case, the Social Security Administration is the always-on big data source.
Population drift, usage drift, and system drift make it hard to use big data source to study long-term trends.
Measurement is much less likely to change behavior in big data sources.
Two approaches that especially benefit from big data sources are natural experiments and matching.
Like natural experiments, matching is a design that also benefits from big data sources.
In the next section, I will describe ten common characteristics of big data sources.
In particular, I will focus on big data sources created by companies and governments.