What is Stratified Random Sampling?

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Stratified random sampling is a method of sampling that involves the division of a population into smaller subgroups known as strata. In stratified random sampling, or stratification, the strata are formed based on members’ shared attributes or characteristics, such as income or educational attainment. Stratified random sampling has numerous applications and benefits, such as studying population demographics and life expectancy.Stratified random sampling is also called proportional random sampling or quota random sampling.

Definition

Stratified random sampling is a sampling method that involves dividing a population into smaller subgroups known as strata. In stratified random sampling or stratified sampling, strata are formed based on members' shared attributes or characteristics, such as income or education level. Stratified random sampling is also known as proportional random sampling or quota random sampling.

Origin

The concept of stratified random sampling originated from the development of statistics, particularly in the early 20th century, as the demand for more precise data analysis increased. Its purpose is to enhance the representativeness of the sample by conducting random sampling within each stratum.

Categories and Features

Stratified random sampling can be divided into proportional stratified sampling and non-proportional stratified sampling. Proportional stratified sampling means that the sample size from each stratum is proportional to the stratum's size in the overall population, whereas non-proportional stratified sampling does not have to follow this proportion. The main feature of stratified sampling is its ability to improve the representativeness and accuracy of the sample, especially when the population has distinct stratification characteristics.

Case Studies

In market research, stratified random sampling is often used to survey consumer behavior. For example, a large retail company might stratify its customers based on age, income, and geographic location, then randomly select samples from each stratum to better understand the purchasing habits of different customer groups.

In public health research, stratified random sampling is used to study the health status of different populations. Researchers might stratify the population by age, gender, and race to ensure sufficient samples in each stratum to analyze the impact of specific health issues.

Common Issues

A common issue with stratified random sampling is how to properly define and divide the strata. If the strata are poorly defined, it can lead to sample bias. Additionally, implementing stratified sampling may require more resources and time to collect and analyze data.

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