Voorbeelden van het gebruik van Non-probability in het Engels en hun vertalingen in het Nederlands
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Unadjusted non-probability samples are likely to produce bad estimates.
Now, I will show how that same idea can be applied to non-probability samples.
Non-probability sampling is however widely used in qualitative research.
Second, there have been many developments in the collection and analysis of non-probability samples.
Probability samples and non-probability samples are not that different in practice;
estimation in probability and non-probability samples.
With non-probability samples, weights can undo distortions caused by the assumed sampling process.
weighting-specifically Mr. P.-seems to do a good job correcting the biases in non-probability data;
We should not have an irrational aversion to non-probability methods because of errors that happened a long time ago.
they can also weight responses from non-probability samples.
However, the second lesson is that non-probability samples, when weighted properly,
In fact, I will argue that the whole concept of"representativeness" is not helpful for thinking about probability and non-probability samples.
For a more pessimistic view of non-probability sampling methods see the the AAPOR Task Force on Non-probability Sampling Baker et al.
statistical adjustments-specifically Mr. P.-seem to do a good job correcting the biases in non-probability data;
For a more pessimistic view of non-probability sampling methods see the AAPOR Task Force on Non-Probability Sampling Baker et al.
probability samples and non-probability samples are not as different as many researchers believe.
The simplest example of a partially controlled non-probability sampling process is quota sampling,
Both non-probability samples and probability samples vary in their quality, and currently it is likely the case that most estimates from probability samples are more trustworthy than estimates from non-probability samples.
is if you are forced to work with non-probability samples, then there is strong reason to believe that adjusted estimates will be better than non-adjusted estimates.
Sometimes researchers have found that probability samples and non-probability samples yield estimates of similar quality(Ansolabehere and Schaffner 2014), but other comparisons have found that non-probability samples do worse Malhotra and Krosnick 2007;
statisticians are incredibly skeptical of inferences from these non-probability samples, in part because they are associated with some embarrassing failures of survey research such as the Literary Digest poll.
For a meta-analysis on the effect of weighting to reduce bias in non-probability samples, see Table 2.4 in Tourangeau,
When using post-stratification to make estimates from their non-probability sample, Wang