Stratified random sampling is a type of probability sampling using which a research organization can branch off the entire population into multiple non-overlapping, homogeneous groups (strata) and randomly choose final members from the various strata for research which reduces cost and improves efficiency. Members in each of these groups should be distinct so that every member of all groups get equal opportunity to be selected using simple probability. This sampling method is also called “random quota sampling”. Show
Select your respondents Age, socioeconomic divisions, nationality, religion, educational achievements and other such classifications fall under stratified random sampling. Let’s consider a situation where a research team is seeking opinions about religion amongst various age groups. Instead of collecting feedback from 326,044,985 U.S citizens, random samples of around 10000 can be selected for research. These 10000 citizens can be divided into strata according to age,i.e, groups of 18-29, 30-39, 40-49, 50-59, and 60 and above. Each stratum will have distinct members and number of members. Learn more: Demographic Segmentation 8 Steps to select a stratified random sample:
Learn more: Simple Random Sampling Types of Stratified Random Sampling:
In this approach, each stratum sample size is directly proportional to the population size of the entire population of strata. That means each strata sample has the same sampling fraction. Proportionate Stratified Random Sampling Formula: nh = ( Nh / N ) * n nh= Sample size for hth stratum Nh= Population size for hth stratum N = Size of entire population n = Size of entire sample If you have 4 strata with 500, 1000, 1500, 2000 respective sizes and the research organization selects ½ as sampling fraction. A researcher has to then select 250, 500, 750, 1000 members from the respective stratum.
Irrespective of the sample size of the population, the sampling fraction will remain uniform across all the strata. Learn more: Systematic Sampling
Sampling fraction is the primary differentiating factor between the proportionate and disproportionate stratified random sampling. In disproportionate sampling, each stratum will have a different sampling fraction. The success of this sampling method depends on the researcher’s precision at fraction allocation. If the allotted fractions aren’t accurate, the results may be biased due to the overrepresented or underrepresented strata.
Learn more: Cluster Sampling Stratified Random Sampling Examples:Researchers and statisticians use stratified random sampling to analyze relationships between two or more strata. As the stratified random sampling involves multiple layers or strata, it’s crucial to calculate the strata before calculating the sample value. Learn more: Quantitative Market Research Following is a classic stratified random sampling example: Let’s say, 100 (Nh) students of a school having 1000 (N) students were asked questions about their favorite subject. It’s a fact that the students of the 8th grade will have different subject preferences than the students of the 9th grade. For the survey to deliver precise results, the ideal manner is to divide each grade into various strata. Here’s a table of the number of students in each grade:
Calculate the sample of each grade using the stratified random sampling formula:
Learn more: Convenience Sampling Advantages of Stratified Random Sampling:
Learn more: Cluster Sampling vs Stratified Sampling When to use Stratified Random Sampling?
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What is the name of a sampling method when the population is divided into groups and then some members of each groups are randomly selected?Stratified random sample: The population is first split into groups. The overall sample consists of some members from every group. The members from each group are chosen randomly.
In what type of sampling is the population split into subgroups?In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender identity, location). Every member of the population studied should be in exactly one stratum.
When dividing a population into subgroups so that a random sample from each subgroup can be collected what type of sampling is used?To use stratified sampling, you need to be able to divide your population into mutually exclusive and exhaustive subgroups. That means every member of the population can be clearly classified into exactly one subgroup.
What is random systematic and stratified sampling?Stratified random sampling - random samples are taken from within certain categories. Stratified systematic sampling - regular samples are taken from within certain categories.
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