Which sampling method maintains the same proportion of individuals as they appear in major subgroups of the population?

Proportional Stratified Sampling is the correct choice because this method involves dividing the population into distinct subgroups, known as strata, and then ensuring that the sample from each stratum is representative of the proportion of that stratum in the overall population. For instance, if a population consists of 60% females and 40% males, using proportional stratified sampling would mean that the sample selected would also be composed of approximately 60% females and 40% males.

This approach is particularly beneficial when researchers aim to ensure that varied characteristics within the population are adequately represented in the sample. It allows for better comparisons among subgroups since each subgroup is included in proportion to its size in the overall population.

In contrast, while stratified sampling does involve subdividing the population into strata, it does not necessarily maintain the sample proportions consistent with the population proportions; it can involve equal representation from each stratum regardless of their size in the population. Cluster sampling focuses on groups (clusters) instead of individuals and may not reflect the overall population distribution accurately, while random sampling does not account for any subgroups at all, so it may lead to over-representation or under-representation of certain segments of the population.