Eksempler på brug af Amplified asking på Engelsk og deres oversættelser til Dansk
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I call this type of combination amplified asking.
Amplified asking and enriched asking Figure 3.10.
Approaches from multiple imputuation(Rubin 2004)might help capture uncertainty in estimates from amplified asking.
The amplified asking estimates were more timely, substantially cheaper, and more granular.
But, on the other hand, there is not yet a strong theoretical basis for this kind of amplified asking.
Amplified asking follows a basic recipe that can be tailored to your particular situation.
But, on the other hand, at this time, there is not a strong theoretical basis for this kind of amplified asking.
First, the amplified asking estimates were more timely, substantially cheaper, and more granular.
In this section, I will describe two approaches that are useful and distinct, andI will call them enriched asking and amplified asking figure 3.12.
In amplified asking(Section 3.6.1) the digital traces are used to amplify the survey data.
This characteristic-only indirect interest in the big data source-makes amplified asking different from embedded asking, which I described earlier.
Regarding amplified asking, this approach is not as new as it might appear from how I have described it.
In addition to the ethical issues regarding accessing the digital trace data, amplified asking could also be used to infer sensitive traits that people might not choose to reveal in a survey Kosinski, Stillwell, and Graepel 2013.
Amplified asking using a predictive model to combine survey data from a few people with a big data source from many people.
For ideas about how to do amplified asking better, I would recommend learning more about multiple imputation Rubin 2004.
In amplified asking, a researcher uses a predictive model to combine a small amount of survey data with a big data source in order to produce estimates at a scale or granularity that would not be possible with either data source individually.
One ethical issue regarding amplified asking is that it can be used to infer sensitive traits that people might not choose to reveal in a survey as described in Kosinski, Stillwell, and Graepel 2013.
In amplified asking, the digital traces are not of any direct interest other than their ability to help extract more value from the survey data.
Regarding amplified asking, this approach is not as new as it might appear from how I have described it.
Further, the amplified asking approach does not yet have good ways to quantify uncertainty around its estimates.
Further, the amplified asking approach does not yet have good ways to quantify uncertainty around its estimates.
In general, amplified asking will probably have high fixed costs and low variable costs similar to those of digital experiments see chapter 4.
One example of amplified asking comes from the work of Joshua Blumenstock, who wanted to collect data that would help guide development in poor countries.
However, amplified asking has deep connections to three large areas in statistics-model-based post-stratification(Little 1993), imputation(Rubin 2004), and small-area estimation(Rao and Molina 2015)-and so I expect that progress will be rapid.