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When would you use disproportionate stratified sampling?

When would you use disproportionate stratified sampling?

This sampling approach is used when there are strata in the population of interest that are quite small but very important and they may not be adequately represented in a survey if other sampling approaches are used.

What is a disproportionate stratified random sample?

Disproportionate stratified sampling is a stratified sampling procedure in which the number of elements sampled from each stratum is not proportional to their representation in the total population. Population elements are not given an equal chance to be included in the sample.

How do you calculate disproportionate stratified sampling?

Proportionate and Disproportionate Stratification For example, if the researcher wanted a sample of 50,000 graduates using age range, the proportionate stratified random sample will be obtained using this formula: (sample size/population size) x stratum size.

What is the difference between proportionate and disproportionate stratified sampling?

If the same sampling fraction is used in each stratum this is termed ‘proportionate stratified sample’; if the sample fraction is not the same in each stratum this is termed ‘disproportionate sampling’.

What is the advantage of stratified random sampling?

In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.

Why would you use stratified sampling?

Stratified random sampling is used when the researcher wants to highlight a specific subgroup within the population. This technique is useful in such researches because it ensures the presence of the key subgroup within the sample. This allows the researcher to sample the rare extremes of the given population.