In stratified random sampling, researchers subdivide the population into subgroups (strata) based on shared characteristics before selecting the final sample. It offers a way to ensure subgroups are adequately represented and can be particularly useful when dealing with heterogeneous populations.
Here’s a deeper dive into stratified random sampling:
Core Idea:
Imagine a bag of mixed candies. You want a representative sample that reflects the proportion of chocolate, sour, and fruity candies in the entire bag. Stratified sampling achieves this by:
- Dividing the candies (population) into strata (chocolate, sour, fruity).
- Taking random samples from each stratum to create the final mix (sample).
The Process:
- Define Strata: Identify relevant characteristics (e.g., age, gender, income) to divide the population into subgroups (strata).
- Proportional Allocation (Optional): Determine the ideal proportion of the sample to come from each stratum based on their size in the population.
- Sample Within Strata: Use a simple random sampling or another probability method to select participants from each stratum.
Advantages:
- Increased Representativeness: By ensuring subgroups are included, stratified sampling provides a more accurate picture of the population, especially when subgroups have distinct characteristics relevant to the study.
- Subgroup Analysis: The data can be analyzed for each stratum separately, allowing for insights into how different subgroups respond or behave.
Example:
Imagine studying smartphone usage habits. You could stratify the population by age group (e.g., 18-24, 25-34, 35+) and then randomly select participants from each age group to ensure all age demographics are represented in the final sample.
When to Use Stratified Sampling:
- Heterogeneous Population: If the population has distinct subgroups that might be under-represented with simple random sampling, stratification helps ensure their inclusion.
- Subgroup Analysis is Important: When understanding how different subgroups within the population behave is a key research objective.
Things to Consider:
- Choosing Strata: Select strata based on characteristics relevant to the research question.
- Sample Size Within Strata: Ensure sufficient sample size within each stratum for meaningful analysis. Small subgroups might require oversampling to get enough data.
- Increased Complexity: Stratified sampling can be more complex to design and implement compared to simple random sampling.
By understanding stratified random sampling, researchers can create samples that better reflect the makeup of the population and gain deeper insights into subgroup variations.