Examples of using Non-probability sampling in English and their translations into Malay
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Non-probability sampling was employed in this study.
The sampling plan to be use will be non-probability sampling.
Non-probability sampling method is used in this study.
The sampling method used for this thesis is non-probability sampling.
Third era Non-probability sampling Computer-administered Surveys linked to big data sources.
The alternative to probability sampling is non-probability sampling.
Third era 2000- present Non-probability sampling Computer-administered Surveys linked to other data.
Moving beyond quota sampling, more modern approaches to controlling the non-probability sampling process are now possible.
But, non-probability sampling has a terrible reputation among social scientists and statisticians.
The digital age is making probability sampling in practice harder andis creating new opportunities for non-probability sampling.
There are, in fact, many varieties of non-probability sampling, and these methods of data collection are becoming increasingly common in the digital age.
Despite these debates, I think there are two reasons why the timeis right for social researchers to reconsider non-probability sampling.
The simplest example of a partially controlled non-probability sampling process is quota sampling, a technique that goes back to the early days of survey research.
Going forward, if you are trying todecide between using a probability sampling approach and a non-probability sampling approach you face a difficult choice.
The main difference between probability and non-probability sampling is that with probability sampling everyone in the population has a known probability of inclusion.
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.
The type of sampling that was used for this study was the non-probability sampling method whilst the type of technique that was used was the homogenous purposive sampling technique.
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).
Although things are notyet 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).
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 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 secondreason 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).
Non-probability samples need not automatically lead to something like the Literary Digest fiasco.
With non-probability samples, weights can undo distortions caused by the assumed sampling process.
Not all non-probability samples are the same.
These new approachescan be used with either probability samples or non-probability samples.
These new approachescan be used with either probability samples or non-probability samples.
Now, I will show how thatsame idea can be applied to non-probability samples.
This headline was based in part on estimates from non-probability samples(Mosteller 1949; Bean 1950; Freedman, Pisani, and Purves 2007).
Unfortunately, when working with non-probability samples, we don't know how the sample was selected.