An Example of Stratified Random Sampling
Let’s take an example to get a better understanding of how stratified random sampling works. Say that the researcher wants to find out about how the cosmetic consumption pattern varies amongst air hostesses in different age brackets, employed with a particular airline.
Now if the majority of the air hostesses fall in the 22-26 years age group, a random sampling will not be able to ensure that the air hostesses in other age groups are sufficiently covered. Let’s say the distribution of the air hostesses across the different age groups is something like:
- 18-22 years – 6%
- 22-24 years – 58%
- 24-28 years – 20%
- 28-32 years – 12%
- 32-36 years – 4%.
This distribution of the population has been graphically presented in the above image.
If a random sample were to be drawn from this population, it is very likely that representatives from minority groups i.e. 18-22 years and 32-36 years may be covered in very small numbers or may get completely left out. This would cause the research to go futile, as it defects the main objective of studying cosmetic use across different age groups. In keeping with the stratified random sampling approach if a sample size of 100 is to be surveyed, at least 6 air hostesses from the 18-22 age group and 4 from the 32-36 age group will be included in the survey. Once these numbers have been identified respondents can be picked up randomly from each stratum.
Thus, it’s evident from the example that using stratified random sampling, where clearly identifiable subgroups exist, lends more objectivity to the research and delivers results with higher precision for making better business decisions.