Systematic and unequal sampling, HT-estimator
19 important questions on Systematic and unequal sampling, HT-estimator
What is a serious bias that can occur in quota sampling?
Why can estimates from quota sampling be biased (compared to probability sampling)?
Can systematic sampling give a representative sample of the dataset?
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Why is a replicated systematic sample used?
How does a replicated systematic sample work?
F is the size of the zone.
rF is the length of the interval with which the systematic sample is taken.
Then, the sampling fraction is 1/rF
The length of each replication is n / r = N / rF
Sample n = N/F
Sampling fraction f = 1/F = r/rF
2. For each of r replications, compute the statistic.
These statistics are combined to compute the final sample statistic.
(The Horvitz-Thompson estimator cannot be used to compute the variance estimation! )
What is required for systematic replicated sampling?
Why can an SRS have a higher standard error than a systematic sample?
What is an advantage of unequal probability sampling?
How can a sample with unequal probabilities being drawn?
What are four reasons for post stratification weighting?
2. Under-coverage,
3. Unit non-response.
4. To adjust the weighted sample distribution for key variables of interest to make it conform to a known population distribution.
What does a sampling weight represent?
How is a sample weight computed?
What is the Horvitz-Thompson estimator?
When is the Horvitz-Thompson estimator particularly useful?
What is a self-weighting sample?
What are three reasons why samples are rarely self-weighting?
2. The selected sample often has deficiencies including non-response and non-coverage.
3. The need for precise estimates for domains and special subpopulations often requires oversampling these domains.
What are three possibilities for estimation for unequal probability sampling?
2. The estimate can be estimated with the use of an augmentation variable, say X.
3. Use sampling with replacement.
For which kind of estimations do sample elements need to be weighted by the reciprocal of their selection probabilities and for which not?
For estimating means and proportions, the weights need only be proportional to the reciprocals of the selection probabilities.
How can a non-response adjusted weight be defined?
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