Stratified sampling is a probability sampling technique that divides a population into smaller subgroups, known as strata, before selecting samples from each stratum. This method ensures that the final sample is representative of the entire population, making it a powerful tool in statistics and research.
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How Stratified Sampling Works
 Define the population: Clearly identify the group you want to study.
 Choose stratification variables: Select characteristics that divide your population into nonoverlapping subgroups (e.g., age, gender, income).
 If there are no subgroups, consider other sampling techniques.
 Divide the population into strata: Create homogeneous subgroups based on the chosen variables.
 Determine sample size: Decide how many samples you need from each stratum.
 Use a technique like this or the Random Sampler addon.
 Select samples: Use random sampling within each stratum to choose participants.
Advantages
 Improves representation of minority subgroups
 Increases precision for a given sample size
 Allows for different sampling techniques in different strata
 Enables analysis of interstrata variations
Types
 Proportionate stratified sampling: The sample size for each stratum is proportional to the stratum’s size in the population.
 Disproportionate stratified sampling: Sample sizes vary based on analytical goals or stratum characteristics.
When to Use Stratified Sampling
 The population has distinct, nonoverlapping subgroups, as shown in the example spreadsheet above.
 Subgroup proportions in the population are known
 You need to study specific subgroups in detail
 There’s high variability between subgroups but low variability within them
Limitations
 Requires knowledge of appropriate stratification variables
 It can be complex and timeconsuming
 It may be challenging if population members belong to multiple strata
Video Tutorial
Conclusion
Stratified sampling is a valuable technique for researchers seeking accurate representation of diverse populations. By understanding its principles and applications, you can leverage this method to enhance the validity and reliability of your studies across various fields, from social sciences to market research.
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