Quota sampling non probability – UGC NET Question

Quota sampling requires the researcher to identify relevant categories, such as gender or age groups, and decide how many units from each category should be included. Participants are then selected conveniently until each quota is filled, without knowing the exact probability of selection. This makes it a non-probability technique that still attempts to mirror some characteristics of the population. Because the stem mentions filling a pre-specified number of units in categories, quota sampling is the correct term.

Option A:
Snowball sampling relies on existing participants to recruit further participants from among their acquaintances, especially in hard-to-reach populations. It does not involve setting numerical quotas for specific categories in advance, so it does not fit the description given.

Option B:
Cluster sampling is a probability method that selects intact groups like schools or villages, usually by random procedures, and is not primarily defined by filling quotas in categories. Therefore, cluster sampling is not the appropriate completion for the stem.

Option C:
Purposive sampling selects participants based on the researcher’s judgment about which cases are most informative, without necessarily specifying fixed numbers in each category. While it is also non-probability, it emphasises information-rich cases rather than quota fulfilment. Hence, purposive sampling is not the best answer.

Option D:
Quota sampling is often used in opinion polls and market research when quick, inexpensive samples are needed, though it may introduce bias due to non-random selection. Its defining feature is matching predefined category counts, which aligns directly with the stem’s description.