What type of sampling allows every individual a chance of being selected?

Random sampling is the method that ensures every individual in a population has an equal chance of being selected. This technique is essential for producing a representative sample that accurately reflects the larger population, which is vital in social research to minimize bias and increase the generalizability of the results.

In random sampling, every member of the population is identifiable, and a selection process is implemented, often using random number generators or other randomization techniques, to choose individuals for the sample. This randomness helps to achieve a sample that is not influenced by the characteristics of individuals within the population.

Other sampling methods, while useful in their contexts, do not provide every individual with an equal chance of selection. For instance, stratified sampling involves dividing the population into subgroups and then randomly sampling from those subgroups, which might unintentionally exclude individuals outside those categories. Systematic sampling selects individuals based on a fixed interval from a randomized starting point, which can introduce patterns that bias the selection. Cluster sampling involves dividing the population into clusters and then randomly selecting clusters to sample from, potentially overlooking others. Therefore, while all these methods have benefits depending on the research objectives, random sampling is the gold standard for ensuring equal selection probability.