What Disproportionate stratified random sampling is
Disproportionate stratified random sampling is a method of sampling from a population in which the number of elements in each stratum is not proportional to the size of the population. The goal of disproportionate stratified random sampling is to ensure that each stratum is adequately represented in the sample.
Steps for disproportionate stratified random sampling:
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Identify the population to be sampled and create subpopulations, or strata, based on key characteristics.
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Determine the proportion of the sample that should come from each stratum.
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Within each stratum, randomly select the desired number of elements.
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Combine the samples from each stratum to form the final sample.
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Collect data from the sample population.
Examples
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A researcher wants to study the voting habits of people in a particular neighborhood. They use disproportionate stratified random sampling to ensure that they collect data from each socio-economic group in the area.
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An epidemiologist is studying the incidence of a certain disease in a particular region. They use disproportionate stratified random sampling to ensure that they collect data from all parts of the region, including those with higher and lower rates of the disease.
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A market research firm is conducting a survey on consumer preferences. They use disproportionate stratified random sampling to ensure that they collect data from different age groups and income levels.