Of course, your choice of sampling technique will depend on your goals, budget, and desired level of accuracy. Stratified random sampling is one of four probability sampling techniques: Simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It allows them to quickly obtain a sample population that best represents the entire population being studied. Stratified random sampling is typically used by researchers when trying to evaluate data from different subgroups or strata. Random samples are then selected from each stratum and can be compared against each other to reach specific conclusions.įor example, a researcher might want to know the correlation between income and education - they could use stratified random sampling to divide the population into strata and take a random sample from it. Stratified random sampling (also known as proportional random sampling and quota random sampling) is a probability sampling technique in which the total population is divided into homogenous groups (strata) to complete the sampling process.Įach stratum (the singular for strata) is formed based on shared attributes or characteristics - such as level of education, income and/or gender. We’re going to highlight what it is, how you can use it to your advantage, and several best-practice tips to help you get going.įree eBook: How to determine sample size Definition - what is stratified random sampling? In this article, we’re going to focus on one in particular: stratified random sampling. No two methods are the same and some are more complicated than others. Of course, each varies in accuracy, reliability, and efficiency. Simple sampling, systematic sampling, quota sampling, cluster sampling - there are numerous methods for designing a sample to represent your population of interest. When it comes to statistical surveys and getting the data you need, there’s no shortage of sampling techniques you can use.
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