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Sampling and Measurement - Other Probability Sampling Methods

4 important questions on Sampling and Measurement - Other Probability Sampling Methods

What is systematic random sampling and what is the difference from a simple random sample?

A systematic random sample first selects a subject at random from the first k names in the sampling frame, and secondly selects every kth subject listed after that one. k stands for skip number, which you receive by dividing the population size by the sample size.
With a simple random sample, two subjects listed next to each other on the sampling frame list can both appear in the sample in contrary to a systematic random sample.

What is stratified random sampling and when is it called a proportional or a disproportional sample?

A stratified random sample divides the population into separate groups, called strata, and then selects a simple random sample from each stratum.
A proportional sample has the same strata proportions in the sample as in the population.
A disproportional sample differs from the population. This is useful when the population size for a stratum is very small.

When would you use cluster sampling and how does it work?

Cluster sampling is useful when a complete listing of the population is not available.
First, the population has to be divided into a large number of clusters, such as city blocks. Select a simple random sample of the clusters and use the subjects in those clusters as the sample.

How does multistage sampling work?

Multistage sampling uses combinations of sampling methods. For example:

Select a random sample out of all countries (treated as clusters).
Within each country selected, take a cluster random sample of square-block regions.
Within each region selected, take a systematic random sample of every 10th house.
Within each house selected, select one adult at random for the sample.