What sampling method should be implemented when a sampling frame is unavailable?
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Answer: B. Cluster sampling
Explanation
Cluster sampling bypasses the need for individual lists by grouping populations into accessible natural units. Researchers can randomly select entire groups when individual member lists are completely missing or outdated. This method saves immense resources when studying large populations spread across wide geographic areas. A sampling frame serves as the complete operational list of every single element in the population. Cluster sampling divides the target population into distinct groups, often based on geographical boundaries. Random selection is applied to the groups themselves rather than every individual person inside them.
National health surveys often randomly select specific neighborhoods or schools when comprehensive citizen lists are absent. Educational researchers sample entire classrooms instead of listing every individual student across the entire district. Cost efficiency makes cluster sampling essential for large scale demographic studies in developing regions. Simple random sampling requires an exhaustive list of every population member to ensure equal selection probability. Systematic sampling depends on a structured list with a fixed interval between chosen subjects. Stratified sampling demands complete subpopulation lists to properly divide elements into distinct homogeneous layers.
- Subject
- Mathematics
- Category
- Statistics
- Topic
- —
- Difficulty
- medium
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