Stratified random sampling is a probability sampling method that involves dividing a population into subgroups or strata based on specific characteristics and then independently selecting samples from each stratum. This method is designed to ensure that the sample is representative of the diversity within the population, especially when there are identifiable subgroups with distinct characteristics.
Advantages of stratified random sampling include:
- Increased precision: Stratified random sampling often results in more precise estimates of population parameters than simple random sampling, primarily when significant variability within strata exists.
- Ensures representation: By including all relevant subgroups, this method ensures that the sample is representative of the entire population, even if some subgroups are small.
- Improved comparisons between strata: The structure of stratified random sampling allows for meaningful comparisons between different strata, providing insights into variations across subgroups.
Some challenges of stratified random sampling include:
- Stratification criteria: Choosing appropriate criteria for stratification is crucial, and the success of the method depends on the relevance and accuracy of these criteria.
- Complexity: Implementing stratified random sampling can be more complex than simple random sampling, particularly in organizing and managing strata.
- Availability of information: Reliable information on the characteristics used for stratification must be available for the entire population.
- Resource requirements: Stratified random sampling may require more resources and effort, especially when the population is large and diverse.
List of recommended resources #
For a broad overview #
This blog by Qualtrics XM gives a clear guide on how to use the method of stratified random sampling along with examples and FAQs regarding the technique.
This blog on Scribbr by Lauren Thomas gives an overview of the stratified sampling method, a type of probability sampling, with a step-by-step guide on how to conduct sampling using this method.
For in depth understanding #
This book by Steven K. Thompson as part of Wiley Series in Probability and Statistics gives an up-to-date and in-depth understanding of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations.
This course handout by IGNOU gives a clear, easy-to-access understanding of stratified random sampling, the principles of stratification, its properties as well as a comparison with simple random sampling.
Case study #
This paper by A.A. Bartolucci, S.J. Bae and K.P. Singh estimates average environmental pollution concentrations from fertilization components and their variance using stratified random sampling.
This article considers stratified random sampling on continuously arriving data streams and statically stored data sets. The authors present S-VOILA, a practical streaming algorithm for stratified random sampling over the entire stream that is locally variance-optimal.