Примери за използване на Post-stratification на Английски и техните преводи на Български
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The key to post-stratification is to form the right groups.
It turns out that the bias of the post-stratification estimator is.
In fact, as we will see below,both approaches basically rely on the same estimation method: post-stratification.
Construct a situation where can post-stratification can decrease the quality of estimates.
One common technique for utilizing auxiliary information is post-stratification.
A classic book-length treatment of post-stratification and related methods is Särndal and Lundström(2005).
Given this fact,researchers often want to create a huge number of groups for post-stratification.
A classic book-length treatment of post-stratification and related methods is Särndal and Lundström(2005).
There are two ways that I like to think about making the bias small in each post-stratification group.
Just because post-stratification worked well in this particular case, there is no guarantee that it will work well in other cases.
In particular, they used a more sophisticated form of the post-stratification I told you about.
The main idea of post-stratification is to use auxiliary information about the target population to help improve the estimate that comes from a sample.
Thus, the overall bias will be small if the bias in each post-stratification group is small.
In particular, they used post-stratification, a technique that is also widely used to adjust probability samples that have coverage errors and non-response.
Correct for the nonrepresentativeness of your sample using post-stratification or some other technique.
Bethlehem(2015) argues that the performance of sample matching will actually be similar to other sampling approaches(e.g., stratified sampling) andother adjustment approaches(e.g., post-stratification).
Correct for the non-representativeness of your sample using post-stratification or some other technique.
One example of a design that can lead to unequal probabilities of inclusion is stratified sampling,which is important to understand because it is closely related to the estimation procedure called post-stratification.
But, there are three important points to keep in mind about post-stratification, all of which make it seem more promising.
But chapter 3(Asking questions)shows that these problems are potentially addressable using post-stratification.
Bethlehem(2010) extends many of the above derivations about post-stratification to include both nonresponse and coverage errors.
In conclusion, this section has provided a model for probability sampling with non-response andshown the bias that nonresponse can introduce both without and with post-stratification adjustments.
(2015) uses a technique called multilevel regression and post-stratification(“Mr. P.”) that allows researchers to estimate group means even when there are many, many groups.
But, Chapter 3(Asking questions)shows that these problems are potentially addressable using post-stratification and sample matching.
Thus, Wang and colleagues used an approach that combined multilevel regression and post-stratification, so they called their strategy multilevel regression with post-stratification or, more affectionately,“Mr. P.”.
The reason that this assumption is needed for probability samples in practice is that probability samples have non-response, andthe most common method for adjusting for non-response is post-stratification as described above.
This weighting procedure is called post-stratification, and the idea of weighing should remind you of the example in Section 3.4.1 where respondents from Rhode Island were given less weight than respondents from California.
Therefore, in practice,researchers doing sample matching also perform some kind of post-stratification adjustment to make estimates.
In other words, thinking back to our example,using post-stratification will produce unbiased estimates if everyone in New York has the same probability of participating and everyone in Alaska has the same probability of participating and so on.
Therefore, to estimate the support in each group they used a technique called multilevel regression with post-stratification, which researchers affectionately call Mr. P.