Examples of using Non-probability in English and their translations into Vietnamese
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Online panels can use either probability sampling or non-probability sampling.
This headline was based in part on estimates from non-probability samples(Mosteller 1949; Bean 1950; Freedman, Pisani, and Purves 2007).
These new approachescan be used with either probability samples or non-probability samples.
Because of these long-term trends, I think that non-probability sampling will become increasingly important in the third era of survey research.
Despite these debates, I think there are two reasons why the timeis right for social researchers to reconsider non-probability sampling.
One possible reason for these differences is that non-probability samples have improved over time.
These newer methods are different enough from the methods that caused problems in the past that Ithink it makes sense to think of them as“non-probability sampling 2.0.”.
In general, there is a cost-error trade-off with non-probability sampling being lower cost but higher error.
At the same time that there has been growing difficulties for probability sampling methods,there has also been exciting developments in non-probability sampling methods.
Again, this is another example of the dangers of raw, unadjusted non-probability samples and is reminiscent of the Literary Digest fiasco.
Although non-probability online panels are already being used by social researchers(e.g., the CCES), there is still some debate about the quality of estimates that come from them(Callegaro et al. 2014).
Using this analysis strategy,Wang and colleagues were able to use the XBox non-probability sample to very closely estimate the overall support that Obama received in the 2012 election(Figure 3.5).
As I said earlier, non-probability samples are viewed with great skepticism by many social researchers, in part because of their role in some of the most embarrassing failures in the early days of survey research.
Although things are not yet settled, I expect that the third era ofsurvey research will be characterized by non-probability sampling, computer-administered interviews, and the linkage of surveys to big data sources(table 3.1).
There are a variety of styles of non-probability sampling methods, but the one thing that they have in common is that they cannot easily fit in the mathematical framework of probability sampling(Baker et al. 2013).
These techniques using auxiliary information are particularly important because, as I will show later, auxiliary information is critical formaking estimates from probability samples with nonresponse and from non-probability samples.
But, it does mean that when comparing non-probability samples to probability samples in practice, we must keep in mind that both depend on assumptions and auxiliary information in order to produce estimates.
Researchers are now in the process of creating the third era of survey research that willmost likely be characterized by 1 non-probability sampling, 2 computer-administrated interviews, and 3 linking survey data to other data.
As I said earlier, non-probability samples are viewed with great skepticism by social scientists, in part because of their role in some of the most embarrassing failures in the early days of survey research.
In conclusion,social scientists and statisticians are incredibly skeptical of inferences from these non-probability samples, in part because they are associated with some embarrassing failures of survey research such as the Literary Digest poll.
Both non-probability samples and probability samples vary in their quality, and currently it is likely the case that most estimates from probability samples are more trustworthy than estimates from non-probability samples.
This framework enables us to understand new approaches to representation-in particular, non-probability samples(section 3.4)- and new approaches to measurement- in particular, new ways of asking questions to respondents(section 3.5).
It is worth learning a bit more about their approach because it builds intuition about post-stratification, and the particular version Wang andcolleagues used is one of the most exciting approaches to weighting non-probability samples.
This framework enables us to understand new approaches to representation-in particular, non-probability samples(section 3.4)- and new approaches to measurement- in particular, new ways of asking questions to respondents(section 3.5).
Non-probability sampling methods have a terrible reputation among social researchers and they are associated with some of the most dramatic failures of survey researchers, such as the Literary Digest fiasco(discussed earlier) and“Dewey Defeats Truman,” the incorrect prediction about the US presidential elections of 1948(figure 3.6).
Researchers face a difficult choice between probability sampling methods in practice- which are increasingly expensive andfar from the theoretical results that justify their use- and non-probability sampling methods- which are cheaper and faster, but less familiar and more varied.
Sometimes, researchers have found that probability samples and non-probability samples yield estimates of similar quality(Ansolabehere and Schaffner 2014), but other comparisons have found that non-probability samples do worse(Malhotra and Krosnick 2007; Yeager et al. 2011).
These newer methods are different enough from the methods that caused problems in the past that I think it makes sense to think of them as“non-probability sampling 2.0.” The second reason why researchers should reconsider non-probability sampling is because probability sampling in practice are become increasingly difficult.
In fact, non-probability sampling is associated with some of the most dramatic failures of survey researchers, such as the Literary Digest fiasco(discussed earlier) and the incorrect prediction about the US presidential elections of 1948(“Dewey Defeats Truman”)(Mosteller 1949; Bean 1950; Freedman, Pisani, and Purves 2007).