Which random sampling method is best when the population has several groups and a researcher wants to ensure that each group is in the sample? Give an example.

Question

Quizplus · Accepted Answer

The Answer is The random sampling method best suited for a situation where the population has several distinct groups and the researcher wants to ensure representation from each group is stratified random sampling.

Stratified random sampling involves dividing the population into different subgroups or strata that share similar characteristics. Each subgroup is then sampled as an independent sub-population, out of which individual elements are randomly selected. This method ensures that each subgroup is proportionally represented in the final sample, which can provide more accurate and generalizable results than simple random sampling, especially when there are significant differences between groups.

Example:
Imagine a researcher is studying the job satisfaction levels of employees in a large multinational corporation that has offices in multiple countries. The researcher wants to ensure that the sample reflects the geographical diversity of the company's workforce. To achieve this, the researcher could use stratified random sampling by first dividing the population into strata based on the location of the offices (e.g., North America, Europe, Asia, etc.). Within each of these geographical strata, the researcher would then randomly select a proportionate number of employees to participate in the study. This approach would guarantee that each region is adequately represented in the sample, allowing the researcher to compare job satisfaction across different locations and to draw conclusions that are relevant to the entire company.

Which Random Sampling Method Is Best When the Population Has