Researchersuse stratified random sampling to obtain a sample population that bestrepresents the entire population being studied. Its advantages includeminimizing sample selection bias (a type of bias caused by choosing non-randomdata from statistical analysis) and it ensures each subgroup within thepopulation receives proper representation within the sample. The disadvantagesof stratified sampling include that several conditions must be met for it to beused properly. Researchers must identify every member of a population beingstudied and classify each of them into one, subpopulation. Finding anexhaustive and definitive list of an entire population is the first challenge. The other challenge is accurately sorting each member of the population into asingle stratum.
This example is fairly simple; undergraduate, graduate, maleand female are clearly defined groups. In other situations, however it is farmore difficult. Imagine bringing defining characteristics such as race, ethnicity or religion into play. The sorting process becomes more difficult, rendering stratified random sampling an ineffective method.
