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Which is the correct estimate of the effective sample size?
The effective sample size (ESS) is an estimate of the sample size required to achieve the same level of precision if that sample was a simple random sample. Mathematically, it is defined as n/D, where n is the sample size and D is the design effect.
When to use un-weighted sample size and design effect?
The un-weighted sample size divided by the supplied extra design effect is used in all statistical inference. Note that if a respondent has a missing or non-positive weight, they are excluded from the analysis and the sample size. When weight calibration is used, this assumption is equivalent to deff = Sample size / sum of weights.
When to use sampling weights in a survey?
Sampling weights are used to correct for the over-representation or under-representation of key groups in a survey. For example, if 51% of a population are female, but a sample is only 40% female, then weighting is used to correct for this imbalance.
How to calculate Deff for sample size and design effect?
deff = 1. Statistical inference is conducted under the assumption that the weights are frequency weights (see [1]), where the frequency weights are the supplied weights divided by the supplied extra design effect. deff = Sample size / sum of weights.
How is the sample size related to the design effect?
In 1965, Leslie Kish defined it as the original sample size divided by the design effect to reflect the variance from the current sampling design as compared to what would be if the sample was a simple random sample . Then the mean of this distribution is estimated by the mean of the sample:
How does random sampling affect the sample size?
Ideally, choice of one participant should not affect the chance of another’s selection (hence we try to select the sample randomly – thus, it is important to note that random sampling does not describe the sample or its size as much as it describes how the sample is chosen).
How does Kish’s effective sample size effect work?
If the data has been weighted (the weights don’t have to be normalized, i.e. have their sum equal to 1 or n, or some other constant), then several observations composing a sample have been pulled from the distribution with effectively 100% correlation with some previous sample. In this case, the effect is known as Kish ‘s Effective Sample Size