Stratified random sampling is an important tool in statistics and survey design. Stratified random sampling involves dividing a population, or set of respondents, into multiple layers or strata, such that all members of a given stratum share similar characteristics. Stratified random sampling is a type of probability sampling, in which respondents are randomly selected to form the sample population.

The goal of stratified random sampling is to obtain a sample population that accurately reflects the demographics, characteristics, opinions, and behavior of the entire population being studied. By stratifying a population, researchers are able to more accurately target specific groups, which can produce more accurate results. This type of sampling is particularly useful for difficult populations to survey, such as members of a homogeneous profession or socio-economic group.

The stratified random sampling process involves first selecting strata and then randomly selecting individual respondents from each strata. Strata are created based on the characteristics and demographics of the population that the researcher deems relevant. Examples of characteristics and demographics used to create strata may include age, gender, educational level, geographic region, and racial identity. Proportional stratified random sampling involves taking equal size random samples from each of the strata, while in a disproportionate stratified random sampling, the size of the sample from each strata may be different.

Once the strata and sample sizes have been determined, the researchers will randomly select individuals from each stratum. Stratified random sampling differs from simple random sampling because in the latter, each member of the population has an equal chance of being selected. However, in stratified random sampling, members of each stratum have an equal chance of being selected. The final sample may not be an exact reflection of the entire population, but it should accurately reflect the characteristics of the population at large.

Overall, stratified random sampling allows researchers to obtain a sample population that is most representative of the entire population. This type of sampling involves dividing the population into homogeneous groups called strata, and then randomly selecting samples from each stratum in order to obtain a more accurate picture of the population. Stratified random sampling is a powerful tool for researchers, especially for more difficult populations to survey, and can provide invaluable results.