This website is using cookies to ensure you get the best experience possible on our website.
More info: Privacy & Cookies, Imprint
Stratified sampling is a statistical technique that divides the total population into distinct homogeneous groups, or strata. A sample is then taken from each stratum to obtain a representative sample of the overall population. The implementation of stratified sampling in practice usually involves the following steps:
Defining the Strata: Identify the relevant characteristics or variables by which you want to divide the overall population into homogeneous groups. For example, you could stratify by age, gender, income, or geographic location.
Determining sample size: Decide how many elements you want to draw from each stratum. The sample size can be proportional to the size of the stratum or other criteria to ensure that each stratum is adequately represented.
Random sampling within strata: Run a random sample within each stratum to obtain the required number of elements. This can be done by simply drawing random numbers or using computerized random number generators.
Collect data: Collect the relevant data for each selected item in the sample. Make sure you document the data carefully and maintain attribution to the appropriate strata for later analysis.
Analyze Data: Analyze the collected data using statistical methods and techniques appropriate to your research goals. Note the stratum of each element to extrapolate the results to the total population and derive accurate estimates.
Stratified sampling is a powerful tool for ensuring that different subgroups of a population are adequately represented in a sample. This method can reduce distortion and provide more meaningful results.