Stratified Random Sampling
Stratified random sampling is a method of sampling that involves the division of a population into smaller subgroups known as strata. In stratified random sampling, or stratification, the strata are formed based on members’ shared attributes or characteristics, such as income or educational attainment. Stratified random sampling has numerous applications and benefits, such as studying population demographics and life expectancy.Stratified random sampling is also called proportional random sampling or quota random sampling.
Stratified Random Sampling
Definition: Stratified random sampling is a sampling method that involves dividing a population into smaller subgroups, known as strata. In stratified random sampling, strata are formed based on members' shared attributes or characteristics, such as income or education level. Samples within each stratum are selected through random sampling.
Origin: The concept of stratified random sampling originated in statistics, first proposed in the early 20th century to improve the representativeness and accuracy of samples. As statistics evolved, this method became widely used in social sciences, market research, and public health.
Categories and Characteristics: Stratified random sampling can be divided into proportional stratified sampling and non-proportional stratified sampling.
- Proportional Stratified Sampling: The sample size for each stratum is proportional to the stratum's size in the population. This method ensures the representativeness of the sample.
- Non-Proportional Stratified Sampling: The sample size for each stratum does not necessarily match the stratum's proportion in the population, often used for specific research purposes.
- Improved sample representativeness
- Reduced sampling error
- Suitable for heterogeneous populations
Specific Cases:
- Case 1: In a national survey on education levels, researchers stratify the population by region (e.g., urban, suburban, and rural) and then randomly sample within each stratum. This ensures that the education levels of each region are accurately represented.
- Case 2: In market research, a company wants to understand consumer reactions to a new product across different income levels. Researchers stratify the population by income level and then randomly sample within each stratum. This helps the company better understand the needs and preferences of different income groups.
Common Questions:
- How to determine stratification criteria? Stratification criteria should be based on the research objectives and the heterogeneous characteristics of the population, such as demographic or behavioral traits.
- What is the difference between stratified random sampling and simple random sampling? Stratified random sampling improves sample representativeness by stratifying the population, while simple random sampling does not consider population heterogeneity.
