What is a Stratified Sample?
A stratified sample is a type of probability sampling technique used in statistical analysis. It is used when the population being studied is divided into different subgroups, or strata, and the researcher wants to ensure that each stratum is adequately represented in the sample. This type of sampling helps to reduce sampling error and make the results of a study more accurate.
How Does Stratified Sampling Work?
In stratified sampling, the population is divided into subgroups or strata, based on some shared characteristics. The researcher then randomly selects a proportionate number of individuals from each stratum in order to create the sample. This ensures that each stratum is represented in the sample in the same proportion as it is in the population. For example, if a population is composed of 40% men and 60% women, the sample should also contain 40% men and 60% women. Stratified sampling is often used when the population is highly heterogeneous, or when the researcher wants to ensure that certain subgroups are adequately represented in the sample.
Examples of Stratified Sampling
Stratified sampling can be used in a variety of research contexts. Here are a few examples:
- In a survey of political attitudes, the population is divided into strata based on political party affiliation.
- In a study of the effects of a new drug, the population is divided into strata based on age, gender, and medical condition.
- In a survey of consumer preferences, the population is divided into strata based on income level, age, and geographical location.
Advantages and Disadvantages of Stratified Sampling
Stratified sampling is a useful technique for reducing sampling error and ensuring that certain subgroups are adequately represented in a sample. However, it does have some drawbacks. Stratified sampling can be time consuming and expensive, since a larger sample size is often needed to adequately represent all the strata. It also requires a greater degree of knowledge about the population, since the researcher must have sufficient information to accurately divide the population into strata.
Conclusion
Stratified sampling is a useful technique for reducing sampling error and ensuring that each stratum in a population is adequately represented in a sample. However, it can be time consuming and expensive, and requires a greater degree of knowledge about the population in order to accurately divide it into strata.
References
https://en.wikipedia.org/wiki/Stratified_sampling https://www.tutorialspoint.com/sampling_techniques/stratified_sampling.htm https://www.statisticshowto.datasciencecentral.com/stratified-sampling-definition/ https://www.simplypsychology.org/stratified-sampling.html