Stratified sampling

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In statistics, stratified sampling is a method of sampling from a population.

When sub-populations vary considerably, it is advantageous to sample each subpopulation (stratum) independently. Stratification is the process of grouping members of the population into relatively homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then random or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population.

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Stratified sampling strategies

A real-world example of using stratified sampling would be for a political survey. If the respondents needed to reflect the diversity of the population, the researcher would specifically seek to include participants of various minority groups such as race or religion, based on their proportionality to the total population as mentioned above. A stratified survey could thus claim to be more representative of the population than a survey of simple random sampling or systematic sampling.

Similarly, if population density varies greatly within a region, stratified sampling will ensure that estimates can be made with equal accuracy in different parts of the region, and that comparisons of sub-regions can be made with equal statistical power. For example, in Ontario a survey taken throughout the province might use a larger sampling fraction in the less populated north, since the disparity in population between north and south is so great that a sampling fraction based on the provincial sample as a whole might result in the collection of only a handful of data from the north.

Randomized stratification can also be used to improve population representativeness in a study.

Disadvantages

It is not useful when there are no similar subgroups. It cannot be used when amount of data in subgroups is not equal but total data in a subgroup are of equal importance as it gives more importance to subgroups with more data.

Practical example

In general the size of the sample in each stratum is taken in proportion to the size of the stratum. This is called proportional allocation. Suppose that in a company there are the following staff:

  • male, full time: 90
  • male, part time: 18
  • female, full time: 9
  • female, part time: 63
  • Total: 180

and we are asked to take a sample of 40 staff, stratified according to the above categories.

The first step is to find the total number of staff (180) and calculate the percentage in each group.

  • % male, full time = 90 / 180 = 50%
  • % male, part time = 18 / 180 = 10%
  • % female, full time = 9 / 180 = 5%
  • % female, part time = 63 / 180 = 35%

This tells us that of our sample of 40,

  • 50% should be male, full time.
  • 10% should be male, part time.
  • 5% should be female, full time.
  • 35% should be female, part time.

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