Examples of using Non-probability in English and their translations into Korean
{-}
-
Colloquial
-
Ecclesiastic
-
Ecclesiastic
-
Programming
-
Computer
Non-probability sample.
Option three, select a non-probability sample.
Non-probability sampling includes a huge variety of designs(Baker et al. 2013).
And we're going to also look at examples of non-probability samples.
First, unadjusted non-probability samples can lead to bad estimates;
These new approaches can be used with either probability samples or non-probability samples.
One form of non-probability sampling that is particular suited to the digital age is the use of online panels.
These new approaches can be used with either probability samples or non-probability samples.
In other words, in non-probability sampling methods not everyone has a known and nonzero probability of inclusion.
One possible reason for these differences is that non-probability samples have improved over time.
First, unadjusted non-probability samples can lead to bad estimates; this is a lesson that many researchers have heard before.
One possible reason for these differences is that non-probability samples have improved over time.
Despite these debates, I think there are two reasons why the time is right for social researchers to reconsider non-probability sampling.
Because of these long-term trends, I think that non-probability sampling will become increasingly important in the third era of survey research.
The digital age is making probability sampling in practice harder and is creating new opportunities for non-probability sampling.
This headline was based in part on estimates from non-probability samples(Mosteller 1949; Bean 1950; Freedman, Pisani, and Purves 2007).
In the history of sampling, there have been two competing approaches:probability sampling methods and non-probability sampling methods.
The most common moral of the story is that researchers can't learn anything from non-probability samples(i.e., samples without strict probability-based rules for selecting participants).
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 totally settled yet, I expect that the third era of survey research will be characterized by non-probability sampling and computer-administered interviews.
For example, by using non-probability methods the Cooperative Congressional Election Study(CCES) is able to have roughly 10 times more participants than earlier studies using probability sampling.
These techniques using auxiliary information are particularly important because, as I will show later, auxiliary information is critical for making estimates from probability samples with nonresponse and from non-probability samples.
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).
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).
When there are high rates of non-response-as there are in real surveys now-the actual probabilities of inclusion for respondents are not known, and thus, probability samples and non-probability samples are not as different as many researchers believe.
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).
Although things are not yet settled, I expect that the third era of survey research will be characterized by non-probability sampling, computer-administered interviews, and the linkage of surveys to big data sources(table 3.1).
A clear example of how far we have come with non-probability samples is the research by Wei Wang, David Rothschild, Sharad Goel, and Andrew Gelman(2015) that correctly recovered the outcome of the 2012 US election using a non-probability sample of American Xbox users-a decidedly nonrandom sample of Americans.
When there are high rates of non-response-as there are in real surveys now-the actual probabilities of inclusion for respondents are not known, andthus, probability samples and non-probability samples are not as different as many researchers believe.
Three areas where I expect to see exciting opportunities are(1) non-probability sampling(section 3.4),(2) computer-administrated interviews(section 3.5), and(3) linking surveys and big data sources(section 3.6).
