Stratified, Cluster, and Convenience Sampling methods in research.
Stratified Sampling involves dividing the study population into relevant subgroups and drawing a sample from each subgroup based on certain characteristics. This method allows for a more precise analysis by ensuring representation from different segments of the population.
Cluster Sampling divides the population into clusters based on shared characteristics like cities or schools, then randomly selects individuals from each cluster for the survey. While cost-effective, it may not always provide the most representative results due to the clustering effect.
Convenience Sampling is a non-probabilistic method based on ease of access to participants, often used when true random sampling is challenging. This technique may lack generalizability but offers practicality in gathering data.
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