Purposive sampling information rich – UGC NET Question

Purposive sampling involves deliberately selecting participants or cases that are expected to yield the most relevant, detailed or insightful information for the research question. The researcher uses judgement based on theory, experience or expert knowledge to choose these cases. This method is common in qualitative studies where depth of understanding is more important than statistical representativeness. Hence, selecting information-rich cases for in-depth study is accurately termed purposive sampling.

Option A:
In purposive sampling, the researcher might choose key informants, typical cases or extreme cases depending on the study’s objectives. The emphasis is on relevance and richness of data rather than on probability-based selection. These characteristics correspond closely to the description in the stem.

Option B:
Random sampling gives each element of the population a known, usually equal, chance of selection and is used primarily in quantitative research for statistical generalisation. It does not rely on the researcher’s judgement about which cases are information-rich. Therefore, random sampling is not the correct completion.

Option C:
Quota sampling involves filling predetermined numbers of participants in specified categories, such as age or gender, often using convenience procedures. Although it may achieve some diversity, it is not focused primarily on depth or richness of information, so quota sampling does not match the stem.

Option D:
Systematic sampling selects every kth unit from an ordered list after a random start, and is a probability sampling method. It is driven by a mechanical rule rather than by the researcher’s judgement about information richness, making it inappropriate as an answer here.