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What's an example of stratified random sampling?

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❶The small group used is referred to as a sample size , which is a subset of the population that is used to represent the population. He will soon find that there were almost , MBA graduates for the year.

Stratified random sampling

The Advantages of Sampling
Uses of Stratified Random Sampling
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Assume that we need to estimate average number of votes for each candidate in an election. Assume that country has 3 towns: Town A has 1 million factory workers, Town B has 2 million office workers and Town C has 3 million retirees. We can choose to get a random sample of size 60 over the entire population but there is some chance that the random sample turns out to be not well balanced across these towns and hence is biased causing a significant error in estimation.

Instead if we choose to take a random sample of 10, 20 and 30 from Town A, B and C respectively then we can produce a smaller error in estimation for the same total size of sample. 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. The reasons to use stratified sampling rather than simple random sampling include [2]. If the 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.

Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint subgroups. It would be a misapplication of the technique to make subgroups' sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup sizes or to their variances, if known to vary significantly -- e. Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling.

If subgroup variances differ significantly and the data needs to be stratified by variance, it is not possible to simultaneously make each subgroup sample size proportional to subgroup size within the total population. For an efficient way to partition sampling resources among groups that vary in their means, variance and costs, see "optimum allocation".

The problem of stratified sampling in the case of unknown class priors ratio of subpopulations in the entire population can have deleterious effect on the performance of any analysis on the dataset, e. Combining sub-strata to ensure adequate numbers can lead to Simpson's paradox , where trends that actually exist in different groups of data disappear or even reverse when the groups are combined.

The mean and standard error of stratified random sampling are given by: Foregoing the finite population correction gives:. Clearly, a student could only be classified as either male or female. No student could fit into both categories ignoring transgender issues. Furthermore, imagine extending the sampling requirements such that we were also interested in how career goals changed depending on whether a student was an undergraduate or graduate.

Since the strata must be mutually exclusive and collectively exclusive, this means that we would need to sample four strata from the population: This will increase overall sample size required for the research, which can increase costs and time to carry out the research. Attaining a complete list of the population can be difficult for a number of reasons: Even if a list is readily available, it may be challenging to gain access to that list. The list may be protected by privacy policies or require a length process to attain permissions.

There may be no single list detailing the population you are interested in. As a result, it may be difficult and time consuming to bring together numerous sub-lists to create a final list from which you want to select your sample. As an undergraduate and master's level dissertation student, you may simply not have sufficient time to do this.

Indeed, it will be more complex and time consuming to prepare this list compared with simple random sampling and systematic random sampling. Many lists will not be in the public domain and their purchase may be expensive; at least in terms of the research funds of a typical undergraduate or master's level dissertation student.

In terms of human populations as opposed to other types of populations; see the article: The basics , some of these populations will be expensive and time consuming to contact, even where a list is available. Assuming that your list has all the contact details of potential participants in the first instance, managing the different ways postal, telephone, email that may be required to contact your sample may be challenging, not forgetting the fact that your sample may also be geographical scattered.

In the case of human populations, to avoid potential bias in your sample, you will also need to try and ensure that an adequate proportion of your sample takes part in the research. This may require re-contacting non-respondents, can be very time consuming, or reaching out to new respondents. Stratified random sampling Stratified random sampling is a type of probability sampling technique [see our article Probability sampling if you do not know what probability sampling is].

Stratified random sampling explained Creating a stratified random sample Advantages and disadvantages limitations of stratified random sampling. Stratified random sampling explained Imagine that a researcher wants to understand more about the career goals of students at the University of Bath.

Creating a stratified random sample To create a stratified random sample, there are seven steps: Use a simple random or systematic sample to select your sample. STEP TWO Choose the relevant stratification If we wanted to look at the differences in male and female students, this would mean choosing gender as the stratification , but it could similarly involve choosing students from different subjects e.

STEP FOUR List the population according to the chosen stratification As with the simple random sampling and systematic random sampling techniques, we need to assign a consecutive number from 1 to NK to each of the students in each stratum.

Advantages and disadvantages limitations of stratified random sampling The advantages and disadvantages limitations of stratified random sampling are explained below. Advantages of stratified random sampling The aim of the stratified random sample is to reduce the potential for human bias in the selection of cases to be included in the sample.

Disadvantages limitations of stratified random sampling A stratified random sample can only be carried out if a complete list of the population is available. With disproportionate sampling, the different strata have different sampling fractions.

The precision of this design is highly dependent on the sampling fraction allocation of the researcher. If the researcher commits mistakes in allotting sampling fractions, a stratum may either be overrepresented or underrepresented which will result in skewed results. Check out our quiz-page with tests about:. Retrieved Sep 14, from Explorable. The text in this article is licensed under the Creative Commons-License Attribution 4. You can use it freely with some kind of link , and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations with clear attribution.

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What is 'Stratified Random Sampling'

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Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata.

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Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata.

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Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling or stratification, the strata are formed based on members' shared attributes or characteristics. A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like , , , , and 60 and above. To.

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Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics. 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.