Stratified sampling is a probability sampling method where researchers divide a population into distinct subgroups, called strata, based on shared characteristics, and then randomly sample from each subgroup.
This method ensures that specific segments of the population are adequately represented in the sample, which can lead to more accurate and reliable research findings, especially when the population is diverse.
How it Works
The process of stratified sampling involves a few key steps:
- Divide into Strata: The entire population is first separated into mutually exclusive subgroups (strata). These subgroups are formed based on common characteristics that subjects share, such as:
- Age group (e.g., 18-24, 25-34, 35-44)
- Gender
- Race or ethnicity
- Educational attainment
- Income level
- Geographic location
- Random Sample from Each Stratum: Once the population is divided into these strata, a random sample is then drawn from each individual stratum. This sampling within each stratum is typically done using another probability sampling method, such as simple random sampling or systematic sampling.
Why Use Stratified Sampling?
- Representative Samples: It helps create a more representative sample by ensuring that all relevant subgroups are included in the study, preventing over-representation or under-representation of any particular group.
- Reduced Sampling Error: By focusing on specific subgroups, it can reduce the variability within each stratum, leading to more precise estimates for the overall population.
- Efficiency: It can be more efficient than simple random sampling when dealing with diverse populations, as it guarantees coverage of important segments.
This method is particularly useful when researchers need to analyze data by subgroup or when certain subgroups are small and might be missed in a simple random sample.
