![]() The research team is studying the average GPAs for the different trades. They took a random selection of 2,000 trade school students out of 10.5 million students in the state. Here’s an example of stratified random sampling:Ī research team performed a study on the GPAs of trade school students across the state of California. Related: How To Calculate Statistical Significance (Plus What It Is and Why It's Important) Example of stratified random sampling This presents a stratified random sampling of the original population. Use that sampling method on each of the subgroups to create a sample. Take random samples of the subgroupsĬhoose a probability sampling method, such as systematic sampling or random selection. Decide on a total sample size that's large enough to draw statistical conclusions for each stratum. While subgroups that are less represented in the population are less represented in the sample, subgroups more represented in the population are also more represented in the sample. ![]() The next step is to make sure the sample size for each subgroup is proportionate to the entire population. Make sure there’s no overlap, that each stratum is mutually exclusive and that they include the entire population. Assign each member to a specific subgroup. Split the population into subgroupsĪfter defining the population and subgroups, collect a list with information for each member of the population. Keep in mind you can only place each member into one subgroup. Subgroups must be mutually exclusive, have no overlapping and include the entire population. You can use multiple characteristics to define subgroups, such as race and gender. Then, divide this population into clearly defined subgroups. Start by defining the population where you plan to take your sample. Here are four steps for performing a stratified random sampling: 1. Related: How To Calculate the Necessary Sample Size for Your Survey or Study How to perform stratified random sampling This lets you estimate the statistical measures for each subgroup and ensures you represent strata in the sample proportionally. You can only place each member of the population into one subgroup, or stratum, and then randomly sample each using a probability sampling method like random selection. Researchers define strata based on shared characteristics or attributes that fit the purposes of their research, such as defining subgroups by gender, race, location, education level or socioeconomic status. ![]() ![]() By taking a proportional random sample of a population that’s subdivided into strata, the sampling is more precise and a better representation of the total population. Learning how to use stratified random sampling can help you analyze strata relationships more easily, whether as a researcher or statistician. ![]()
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