Stratified Random Sampling
Stratified random sampling is a sampling technique that divides the population into smaller groups, called strata, based on shared characteristics. Then, a random sample is drawn from each stratum, ensuring that the sample proportions of each stratum are proportional to their prevalence in the population.
Process:
- Stratification: Divide the population into distinct strata based on shared characteristics, such as age groups, genders, socioeconomic status, or geographical regions.
- Sample Selection: Randomly select a sample of units from each stratum. The number of units selected from each stratum is proportional to its size in the population.
- Combining Samples: Combine the samples from all strata to form a final sample that represents the entire population.
Advantages:
- Increased Precision: Stratified sampling can provide more precise estimates than simple random sampling, as it reduces sampling variability by ensuring a more representative sample from different groups.
- Enhanced Representativeness: The sample is more likely to reflect the diversity of the population, as it includes a proportional number of units from each stratum.
- Reduced Bias: Stratified sampling can reduce bias, as it minimizes the influence of sampling errors on specific groups.
Disadvantages:
- Stratification Complexity: Stratification can be complex, especially with large populations and many strata.
- Sample Size: The number of units required for each stratum may increase the overall sample size.
- Strata Imbalance: If one stratum has a very low population, it may be difficult to draw a sufficient sample from that group.
Examples:
- Sampling students from different age groups in a school.
- Selecting voters from different political parties in a election poll.
- Collecting data on housing prices in different neighborhoods.
In summary, stratified random sampling is a sampling technique that increases precision, enhances representativeness, and reduces bias by dividing the population into strata and selecting a random sample from each stratum. However, it can be more complex than simple random sampling, and the sample size may increase.
FAQs
What is stratified random sampling?
It’s a method where the population is divided into subgroups (strata), and a random sample is taken from each group.
What is the difference between simple random sampling and stratified random sampling?
Simple random sampling selects randomly from the entire population, while stratified random sampling divides the population into groups and samples from each group.
What is an example of stratified random sampling?
A survey where students are divided by grade level, and a random sample is taken from each grade.
What is the difference between systematic random sampling and stratified random sampling?
Systematic sampling selects at regular intervals, while stratified sampling selects randomly from subgroups.
What is stratified randomization?
It’s a process where participants are grouped by characteristics and then randomly assigned to different treatments.
