Stratified random sampling is a probability sampling method that involves dividing the population into distinct subgroups or strata based on specific characteristics or variables of interest. Within each stratum, a simple random sample is then taken. Stratified random sampling ensures that the sample is representative of different subgroups in the population, allowing for more precise estimates and comparisons within each stratum.
Here are the steps involved in conducting a stratified random sample:
1. Define the population: Clearly define the population of interest and identify the relevant characteristics or variables that you want to use for stratification. These characteristics should be related to the research objectives or variables of interest.
2. Determine the sample size: Decide on the desired sample size for the overall sample and determine how many individuals or units you want to include from each stratum. The sample size for each stratum should be determined based on factors such as the proportion of the population within each stratum and the desired precision.
3. Stratify the population: Divide the population into mutually exclusive and exhaustive strata based on the chosen characteristics or variables. Each member of the population should fit into only one stratum. Strata should be homogeneous within themselves and heterogeneous between the strata.
4. Determine the sample size within each stratum: Calculate the sample size for each stratum proportional to its size in the population. This can be done by multiplying the desired overall sample size by the proportion of the population within each stratum. Ensure that the sample size within each stratum is large enough to provide meaningful statistical results.
5. Randomly select samples from each stratum: Apply simple random sampling within each stratum to select the required number of individuals or units. This can be done using random number generators or random selection methods specifically within each stratum.
6. Combine the stratum samples: Combine the samples from each stratum to form the complete stratified random sample. The combined sample will represent the entire population, with each individual or unit having an equal chance of being selected.
Stratified random sampling allows researchers to ensure that important subgroups within the population are represented adequately in the sample. It helps increase the precision of estimates within each stratum and enables comparisons and analyses specific to different strata. Stratified sampling is particularly useful when there are significant differences or variability between subgroups in the population and when there is a desire to make inferences at the stratum level.
