Stratified Sampling

Stratified sampling is a statistical sampling technique used in research and data analysis to ensure that a sample is representative of a population with certain characteristics or attributes. It involves dividing the population into subgroups or strata based on specific criteria and then selecting a random sample from each stratum. This approach is useful when you want to ensure that each subgroup is adequately represented in the sample, which can lead to more accurate and reliable results.

To give a quick example here: For research, the target market is split into two strata based on gender, where there are 2,000 males and 6,000 females. Then, for a sampling fraction of ¼, 500 males and 1,500 females will be selected in the final sample population

Here’s how stratified sampling works:

  1. Population Stratification: Identify the relevant characteristics or attributes that you want to ensure are represented in your sample. These could be demographic factors, geographical location, income levels, or any other variable of interest.
  2. Divide into Strata: Divide the population into mutually exclusive and exhaustive subgroups or strata based on the chosen criteria. Each individual or item in the population should belong to one and only one stratum. This ensures that there is no overlap between strata, and the entire population is covered.
  3. Random Sampling: Within each stratum, use random sampling techniques to select a representative sample. This can involve simple random sampling, systematic sampling, or other methods, depending on the size and structure of the stratum.
  4. Combine Samples: Once samples are collected from each stratum, combine them to create the final stratified sample. The size of each stratum’s sample can be proportional to the size of the stratum in the overall population, or it can be adjusted to ensure that the strata of greater interest are well-represented.

The key advantages of stratified sampling include:

  1. Increased Precision: Stratified sampling can provide more precise and accurate estimates, especially when there is substantial variation in the population across the strata.
  2. Representative Samples: By ensuring representation from all strata, the sample is more likely to be representative of the entire population.
  3. Enhanced Comparisons: Researchers can make meaningful comparisons between subgroups within the population, as each stratum has its own sample.
  4. Reduced Sampling Bias: It helps minimize the potential bias that might arise when using simple random sampling on a heterogeneous population.

However, stratified sampling can be more complex and time-consuming than simple random sampling, and it may not be the most appropriate technique for every situation. It is best used when the population exhibits significant variability across the strata of interest, and when it’s important to draw inferences about each stratum separately.

What are the examples of stratified sampling? A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.