Examples of using Post-stratification in English and their translations into Slovak
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This approach has deep connections to three large areas in statistics- model-based post-stratification(Little 1993),
However, if researchers can adjust for the biases in the sampling process(e.g., post-stratification) or control the sampling process somewhat(e.g., sample matching),
It is worth learning a bit more about their approach because it builds intuition about post-stratification, and the particular version Wang
which is important to understand because it is closely related to the estimation procedure called post-stratification.
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.
colleagues used an approach that combined multilevel regression and post-stratification, so they called their strategy multilevel regression with post-stratification
then post-stratification completely breaks down.
colleagues used an approach that combined multilevel regression and post-stratification, so they called their strategy multilevel regression with post-stratification
other adjustment approaches(e.g., post-stratification).
the most common method for adjusting for non-response is post-stratification as described above.
(2015) uses a technique called multilevel regression and post-stratification(MRP, sometimes called“Mister P”)
using post-stratification will produce unbiased estimates if everyone in New York has the same probability of participating
However, amplified asking has deep connections to three large areas in statistics- model-based post-stratification(Little 1993),
even if they do post-stratification adjustments, a result that has been demonstrated empirically by Abraham,
then there is a strong reason to believe that estimates made using post-stratification and related techniques will be better than unadjusted, raw estimates.
In addition to post-stratification, other techniques for working with non-probability samples-
Roughly, post-stratification helps correct for an imbalanced sample by bringing in auxiliary information about the sizes of the groups.
When using post-stratification to make estimates from their non-probability sample,
Construct a situation where can post-stratification can decrease the quality of estimates.
It turns out that the bias of the post-stratification estimator is.