Примери за използване на Probability sampling на Английски и техните преводи на Български
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The alternative to probability sampling is non-probability sampling.
Currently, the dominant theoretical approach to representation is probability sampling.
However, perfect probability sampling basically never happens in the real world.
The first era of survey research was characterized by area probability sampling and face-to-face interviews.
Probability Sampling with Applications to Sample Surveys and Probability in Science and Engineering.
Online panels can use either probability sampling or non-probability sampling. .
Currently, the dominant approach to sampling for social research is probability sampling.
However, well-done non-probability sampling can produce better estimates than poorly-done probability sampling.
Some people find it surprising that this is true even when there is perfectly executed probability sampling.
Second era 1960- 2000 Random digit dialing(RDD) probability sampling Telephone Stand-alone surveys.
Figure 3.6: Probability sampling in practice and non-probability sampling are both large, heterogeneous categories.
The approach I have just described- andthat I describe mathematically in the technical appendix- falls squarely within the classical probability sampling framework.
When the cost ortime involved in the probability sampling is too high, marketing researchers will take non-probability samples.
The Horvitz-Thompson estimator is extremely useful because it leads to unbiased estimates for any probability sampling design(Horvitz and Thompson 1952).
Over time, the differences between probability sampling in theory and probability sampling in practice have been increasing.
For example, by using non-probability methods theCooperative Congressional Election Study(CCES) is able to have roughly 10 times more participants than earlier studies using probability sampling.
The second reason why researchers should reconsider non-probability sampling is because probability sampling in practice are become increasingly difficult.
In probability sampling, all members of the target population have a known, nonzero probability of being sampled, and all people who are sampled respond to the survey.
Sometimes researchers want a quick and rigid rule(e.g.,always use probability sampling methods), but it is increasingly difficult to offer such a rule.
In particular, probability sampling has been getting hard to do in practice, and non-probability sampling has been getting faster, cheaper, and better.
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 magic of true probability sampling is to rule out problems on both measured and unmeasured characteristics(a point that is consistent with our discussion of matching for causal inference from observational studies in Chapter 2).
Going forward, if you are trying to decide between using a probability sampling approach and a non-probability sampling approach you face a difficult choice.
Classic book-length treatment of standard probability sampling and estimation are Lohr(2009)(more introductory) and Särndal, Swensson, and Wretman(2003)(more advanced).
I will start by introducing probability sampling, then move to probability sampling with nonresponse, and finally, non-probability sampling.
The main difference between probability andnon-probability sampling is that with probability sampling everyone in the population has a known probability of inclusion.
In conclusion, this section has provided a model for probability sampling with non-response and shown the bias that nonresponse can introduce both without and with post-stratification adjustments.
Although both approaches were used in the early days of sampling, probability sampling has come to dominate, and many social researchers are taught to view non-probability sampling with great skepticism.
There are a variety of styles of non-probability sampling methods, butthe one thing that they have in common is that they cannot easily fit in the mathematical framework of probability sampling(Baker et al. 2013).
Thus, it is important to distinguish between probability sampling in theory,which has strong theoretical guarantees, and probability sampling in practice, which offers no such guarantees and depends on a variety of statistical adjustments.