Introduction
Stratified random sampling is a method used in research to ensure that the sample represents specific subgroups within a population. It involves dividing the population into homogeneous groups or strata based on certain characteristics, and then randomly selecting samples from each stratum. This technique helps in obtaining a more accurate and representative sample compared to simple random sampling.
How Does it Work?
In stratified random sampling, the population is first divided into mutually exclusive strata based on certain variables such as age, gender, income, or location. Then, a random sample is selected from each stratum in proportion to their size in the population. This ensures that each subgroup is adequately represented in the sample.
Examples
For example, if a researcher wants to study the satisfaction levels of customers in a shopping mall, they may stratify the population based on different age groups such as 15-25, 26-40, and 41-60. They would then randomly select samples from each age group to ensure that the survey represents customers of all ages.
Case Studies
In a study on the effects of social media on teenagers’ mental health, researchers may stratify the population based on socio-economic status. By selecting samples from different economic backgrounds, the study can provide more comprehensive insights into how social media impacts teenagers from diverse backgrounds.
Advantages
- Ensures representation of all subgroups in the population
- Allows for more precise analysis of different strata
- Increases the reliability and validity of research findings
Statistics
According to a study published in the Journal of Marketing Research, researchers found that using stratified random sampling significantly improved the accuracy of their survey results compared to simple random sampling methods.
