Stratified sampling is a statistical method used when the population is heterogeneous, or diverse, but can be partitioned into different subgroups or strata that are more homogeneous. Each subgroup is a stratum (plural: strata), and ideally, there is less variation within each stratum than across the whole population.
Stratified sampling works by dividing the population into distinct groups based on characteristics such as age, income, education level, race, or geographic location. A separate simple random sample is then drawn from each stratum. This method aims to ensure that each subgroup is adequately represented in the sample, which can provide a more accurate representation of the population as a whole.
Here are the steps involved in stratified sampling:
1. **Identifying Strata**: The first step in stratified sampling is to identify the different strata. This could be based on a variety of factors like demographic characteristics, geographic location, socioeconomic status, etc.
2. **Determining Sample Sizes**: Once the strata are identified, you need to determine the size of the sample from each stratum. This could be proportional (i.e., the size of the sample from each stratum is proportional to the size of the stratum in the population) or equal (i.e., the same number of observations is taken from each stratum, regardless of the size of the stratum in the population).
3. **Sampling**: Then, a simple random sample is drawn from each stratum. This ensures that each member of the stratum has an equal chance of being selected in the sample.
4. **Data Analysis**: The final step is to analyze the collected data. When analyzing the data, it is essential to take into account the stratified design.
Stratified sampling can provide more precise estimates than simple random sampling, especially when the strata are well-defined and truly represent distinct subsections of the population. It ensures that groups which could be overlooked in a simple random sample get representation in the study.