12/10/2023 0 Comments Stratified randomStratified sampling is one of the most common forms of non-probability sampling. Stratified sampling is a type of probability sampling where the population is divided into mutually exclusive subgroups. Once the subgroups are identified and weighted, the sample is drawn randomly from each subgroup. Sampling weight is the size of the subgroup relative to the total population. This formula includes the number of cases in the strata, the desired sample size, and the proportion of cases in each strata. They are equal probability proportionate to size (EPTS) sampling and disproportionate stratified sampling.Įqual probability proportionate to size (EPTS) sampling divides the population into mutually exclusive and collectively exhaustive strata (meaning they cannot be subdivided further and are exhaustive because they include all possible members of that subgroup), and assigns a sampling weight to each stratum before selecting the sample.ĭisproportional stratified sampling (DPS) is a type of sampling that uses a proportional formula to determine the sampling weights for the strata. There are a couple different types of stratified random sampling. The advantage of this approach is that it reduces sampling variability because the researcher uses the same sample for every subgroup. The researcher can also use cluster sampling to select the sample, which means he can survey households or geographical areas instead of a sample of individuals. This means members of the first subgroup have a greater chance of selection than members of the other two subgroups because there are fewer people in the first subgroup. He then divides the population of the country according to these subgroups and randomly selects a sample from each stratum. He has identified three mutually exclusive and collectively exhaustive subgroups: income less than $25,000, $25,000-$50,000, and $50,000 or more. Imagine a researcher is conducting a survey to learn about the income levels of various groups in the country. To understand how a stratified random sampling formula works, let’s take a look at an example. This type of probability sampling helps avoid disproportionate sampling in your stratum sample size. In stratified random sampling, members of the population belonging to each stratum have a greater chance of selection than those who do not. In simple random sampling, all members of the population have an equal chance of selection, regardless of the characteristics they possess. A stratified random sample differs from simple random sampling in that it first partitions the population into mutually exclusive and collectively exhaustive strata based on relevant identifiable characteristics and then selects a sample from each stratum through probability sampling. Probability sampling is then used to select the random sample from each stratum. Each possible sample is therefore equally likely.Ī stratified random sample is a type of statistical sampling in which a population divides into mutually exclusive and collectively homogeneous strata. It involves random selections of data from a whole population.
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