Stratified Sampling

Definition:

 Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally.
Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups.
The researcher can represent even the smallest sub-group in the population. There are two types of stratified sampling – one is proportionate stratified random sampling and another is disproportionate stratified random sampling. In the proportionate random sampling, each stratum would have the same sampling fraction. For example, you have three sub-groups with a population size of 150, 200, 250 subjects in each subgroup respectively. Now, to make it proportionate, the researcher uses one specific fraction or a percentage to be applied on its subgroups of population. The sample for first group would be 150*0.5= 75, 200*0.5=100 and 250*0.5= 125. Here the constant factor is the proportion ration for each population subset.
The only difference is the sampling fraction in the disproportionate stratified sampling technique. The researcher could use different fractions for various subgroups depending on the type of research or conclusion he wants to derive from the population. The only disadvantage to that is the fact that if the researcher lays too much emphasis on one subgroup, the result could be skewed.