Stratified Sampling
Stratified Sampling is a probability sampling method where the population is divided into homogeneous subgroups or strata, and samples are then randomly selected from each stratum. This method ensures that each subgroup of the population is adequately represented within the sample, thereby reducing sampling error and increasing the precision of the results compared to simple random sampling.
Key Features:
- Stratification: The population is divided into strata based on shared attributes or characteristics. These could be geographic, demographic, or any other relevant criteria.
- Proportional Allocation: Samples from each stratum are selected proportionally to the size of the stratum in the population. This is known as proportionate stratified sampling.
- Disproportional Allocation: In some cases, samples might be taken in disproportionate sizes to the strata's population to ensure adequate representation of smaller or underrepresented groups (disproportionate stratified sampling).
- Random Sampling: Within each stratum, units are selected using simple random sampling techniques.
History and Context:
The concept of Stratified Sampling has roots in the early 20th century when statisticians began to realize the benefits of ensuring representation across different segments of a population. One of the earliest formal discussions can be traced back to the work of William Gosset, who under the pseudonym "Student," developed techniques to improve statistical accuracy in brewing experiments. However, it was not until the mid-20th century that stratified sampling became widely recognized as a distinct method:
- In the 1930s, Jerzy Neyman formalized the theory behind stratified sampling, providing mathematical foundations for its use. His work, "On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection" (1934), is seminal in this field.
- During World War II, stratified sampling was extensively used in survey sampling for military and industrial purposes, highlighting its efficiency in dealing with diverse populations.
- Post-war, the method gained prominence in fields like market research, public opinion polling, and social sciences, where precise representation of sub-groups was crucial.
Application:
Stratified Sampling is used in various fields:
- Public Health: To ensure that health surveys represent various socio-economic groups, age, gender, etc.
- Education: For assessing student performance across different educational levels or institutions.
- Business: Market research to understand consumer behavior across different demographics.
Advantages:
- Reduces sampling error by ensuring representation of all strata.
- Provides greater precision in estimates for each stratum.
- Can be more cost-effective if strata are chosen wisely, reducing the sample size needed for the same level of precision.
Challenges:
- Requires accurate information about the population structure to define strata properly.
- Can be time-consuming and expensive to implement if the stratification criteria are complex.
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