Identify the correct combination of probability sampling:
(A) Multistage Cluster — Random — Systematic Random — Stratified
(B) Random — Systematic — Multistage — Stratified
(C) Random — Systematic — Stratified — Multistage
(D) Systematic — Stratified — Random — Multistage
Correct Ans: (C)
Explanation:
Probability sampling ensures every member of the population has a known, non-zero chance of being selected, guaranteeing the sample represents the population. Several types of probability sampling exist, each offering unique benefits.
Random sampling is the most basic form. It gives every individual an equal chance of selection, ensuring fairness. However, it may overlook subgroups within a population.
Systematic sampling, on the other hand, selects every nth individual from a list, starting at a random point. While it is easier to implement than random sampling, it may introduce bias if the list follows a pattern.
Stratified sampling divides the population into distinct subgroups, or strata, based on shared characteristics like age or gender. Then, researchers select samples from each stratum, ensuring accurate representation of all relevant subgroups.
Multistage sampling combines various sampling techniques. Typically, researchers use cluster sampling in the first stage and follow it up with other methods like random or stratified sampling. This approach is particularly useful for large populations spread across a wide geographical area.
Therefore, the correct combination of probability sampling methods is Random — Systematic — Stratified — Multistage. By employing this combination, researchers ensure they select a comprehensive and representative sample effectively.
