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What is Stratified Random Sampling- Insights from Various Authors’ Perspectives

by liuqiyue

What is Stratified Random Sampling according to Authors?

Stratified random sampling is a sampling technique that involves dividing the population into subgroups or strata based on certain characteristics or attributes. Each stratum represents a distinct segment of the population that shares similar characteristics. According to various authors, stratified random sampling is a valuable method in research and data collection, as it ensures that the sample is representative of the entire population and allows for more accurate and reliable results.

In the field of statistics, authors have emphasized the importance of stratified random sampling in providing a comprehensive understanding of the population under study. This method is particularly useful when the population is diverse and can be categorized into different groups based on specific attributes. By dividing the population into strata, researchers can ensure that each subgroup is adequately represented in the sample, thereby reducing the likelihood of bias and increasing the generalizability of the findings.

According to authors such as Kish (1965), stratified random sampling is beneficial because it allows for the selection of samples that are more representative of the population as a whole. This is achieved by ensuring that the strata are internally homogeneous and that the sample size within each stratum is proportional to the size of the stratum in the population. By doing so, researchers can obtain a more accurate estimate of the population parameters and make more informed decisions based on the data collected.

Furthermore, authors like Särndal, Swensson, and Wretman (1992) have highlighted the advantages of stratified random sampling in terms of precision and efficiency. They argue that this method can lead to a more precise estimation of population parameters, as it allows for the reduction of sampling error. Additionally, stratified random sampling can be more efficient in terms of time and resources, as it enables researchers to focus their efforts on specific subgroups that are of particular interest.

In conclusion, according to authors, stratified random sampling is a valuable technique that ensures the representativeness of a sample and provides more accurate and reliable results. By dividing the population into strata and selecting samples proportionally, researchers can obtain a comprehensive understanding of the population and make more informed decisions based on the data collected. This method is particularly useful in diverse populations and can lead to more precise and efficient estimation of population parameters.

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