UGC NET Questions (Paper – 1)

Purposive sampling involves the deliberate selection of participants or cases that are especially knowledgeable or experienced regarding the phenomenon of interest. It is widely used in qualitative research where depth and insight are more important than representativeness. The researcher uses judgment to choose cases that can provide rich and relevant information. Thus, selecting information-rich cases matches the definition of purposive sampling.

Option A:
Simple random sampling gives each member of the population an equal probability of selection, with no emphasis on special knowledge or richness of information. It is aimed at representativeness rather than particular insight.

Option B:
Systematic sampling selects every kth element from an ordered list following a random start and does not focus on individuals’ information richness. Its purpose is efficient probability sampling, not purposive selection.

Option C:
Cluster sampling chooses intact groups such as schools or villages as primary units, often for logistical reasons. It can be random but does not inherently involve selecting units because they are especially informative about the phenomenon.

Option D:
Purposive sampling allows the researcher to focus on extreme cases, typical cases or key informants who can illuminate central issues deeply, matching the emphasis on information-rich cases in the stem and making this option correct.

Purposive sampling, also called judgmental sampling, involves selecting units based on the researcher’s judgment about which cases will be most informative or relevant to the research question. It is commonly used in qualitative studies where depth of understanding is more important than statistical representativeness. The researcher may choose typical cases, extreme cases or key informants. Because the stem describes selecting information-rich cases for in-depth study, it clearly refers to purposive sampling.

Option A:
Simple random sampling gives every element of the population an equal chance of selection using random methods such as tables or generators. It is not based on the researcher’s judgment about which cases are most informative. Therefore, simple random does not fit the description in the stem.

Option B:
Convenience sampling selects units that are easiest to access, such as friends, students in a class or people in a nearby location, regardless of their information richness. Although convenient, it can lead to biased samples and is conceptually different from purposive selection of particularly informative cases. Hence, convenience is not the correct answer.

Option C:
Purposive sampling is guided by theoretical and practical considerations concerning which cases can shed the most light on the phenomenon, and it is often combined with qualitative methods like interviews or observations. This targeted selection aligns exactly with the idea of information-rich cases highlighted in the stem, making purposive the proper completion.

Option D:
Snowball sampling involves asking initial participants to refer others in their networks, which is useful for hard-to-reach populations. While it can also lead to information-rich participants, the defining feature is chain referral, not direct judgmental selection by the researcher. Thus, snowball sampling is not the best label for the situation described.