Quota sampling requires the researcher to identify relevant categories, such as male and female or different age groups, and decide how many respondents are needed in each. Field workers then select convenient individuals until the quotas for each category are filled. While this ensures some diversity, it does not use random selection, so it remains a non-probability method. Therefore, the technique described in the stem is known as quota sampling.
Option A:
Cluster sampling is a probability method involving selection of intact groups such as schools or villages. It does not focus on filling pre-set category counts with conveniently available individuals. Thus, it is not the correct answer.
Option B:
Purposive sampling selects cases judged to be especially informative or typical based on the researcher’s judgment. It does not rely on fixed numerical quotas for categories, so it does not match the description.
Option C:
Option C, quota sampling, is widely used in market research and opinion polls when a full sampling frame is unavailable. It imitates some features of stratification but without randomisation. This matches the idea of filling predetermined numbers in categories, making it correct.
Option D:
Systematic sampling is a probability method selecting every kth unit from a list. It does not aim to satisfy numerical quotas in predefined categories using convenience selection. Therefore, it is not appropriate here.
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