The sampling method is the process used to pull samples from the population. Simple random samples and stratified random samples are both common methods for obtaining a sample. A simple random sample is used to represent the entire data population and randomly selects individuals from the population without any other consideration. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics.
Therefore, a stratified sampling strategy will ensure that members from each subgroup are included in the data analysis. Simple random sampling is a statistical tool used to describe a very basic sample taken from a data population. This sample represents the equivalent of the entire population.
The simple random sample is often used when there is very little information available about the data population, when the data population has far too many differences to divide into various subsets, or when there is only one distinct characteristic among the data population.
For instance, a candy company may want to study the buying habits of its customers in order to determine the future of its product line. If there are 10, customers, it may use choose of those customers as a random sample. It can then apply what it finds from those customers to the rest of its base. Statisticians will devise an exhaustive list of a data population and then select a random sample within that large group. In this sample, every member of the population has an equal chance of being selected to be part of the sample.
They can be chosen in two ways:. Using simple random sampling allows researchers to make generalizations about a specific population and leave out any bias. This can help determine how to make future decisions. That way, the candy company from the example above can use this tool to develop a new candy flavor to manufacture based on the current tastes of the customers.
But keep in mind, these are generalizations, so there is room for error. After all, it is a simple sample. Those customers may not have an accurate representation of the tastes of the entire population. Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets.
These groups are based on certain criteria, then randomly choose elements from each in proportion to the group's size versus the population. This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population.
But the researchers must ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is mutually exclusive.
Overlapping strata would increase the likelihood that some data are included, thus skewing the sample. The candy company may decide to use the random stratified sampling method by dividing its customers into different age groups to help make determinations about the future of its production.
Portfolio managers can use stratified random sampling to create portfolios by replicating an index such as a bond index. Jason Jason 1 1 1 silver badge 1 1 bronze badge. Add a comment. Active Oldest Votes. Third stage: 50 elementary schools from total yy elementary schools in the selected ZZZ county in the second stage Fourth stage: 10 students from each selected school. Improve this answer.
Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Featured on Meta. As an analogy, you can think of your sample as an aquarium and your population as the ocean.
What is the advantage of selecting a simple random sample? It is one of several methods statisticians and researchers use to extract a sample from a larger population; other methods include stratified random sampling and probability sampling. The advantages of a simple random sample include its ease of use and its accurate representation of the larger population.
What is a sample quizlet? A sample is selection of a portion of the population to represent the entire population. Researchers usually sample from the target population. Researchers usually sample from the accessible population but should identify the target population to which they want to generalize their results. What are the two primary requirements to make samples as representative of the population?
Typically, representative sample characteristics are focused on demographic categories. Some examples of key characteristics can include sex, age, education level, socioeconomic status, and marital status. Generally, the larger the population being examined, the more characteristics that may arise for consideration. What is the major difference between a population and a target population? Basically, target population also known as theoretical population is the group to whom we wish to generalize our findings.
Study population also known as accessible population is the actual sampling frame, from which we randomly drew our sample. Which sampling method is best? Survey Sampling Methods. Random sampling is the purest form of probability sampling. Systematic sampling is often used instead of random sampling. Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. What is an example of stratified sampling?
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 18—29, 30—39, 40—49, 50—59, and 60 and above. What is the purpose of stratified random sampling?
Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied. Stratified random sampling involves dividing the entire population into homogeneous groups called strata. How do you conduct stratified random sampling? The process for performing stratified sampling is as follows:.
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