Revision summary
Non-probability sampling gives no known chance of selection. Convenience, purposive, quota, snowball, and theoretical sampling fit pilots, types, hidden groups, and saturation. Gig networks, SHGs, and caste associations are typical Indian uses. Census and NFHS remain the designs for rates. Name the bias; do not sell a quota as a random national fact.
Model answer
Introduction
Non-probability sampling does not give every unit a known chance of selection. It is not a failed census. It is a set of techniques used when a sampling frame is missing, when the population is hidden, or when the aim is theoretical saturation rather than a population estimate.
Body
Convenience and volunteer samples
- The researcher takes those who are at hand: a college class, a clinic queue, or an online form.
- Condition of use: pilot tests, student exercises, and rapid reconnaissance when bias is admitted.
- Example: a first sketch of gig-app riders waiting at a city hub. It cannot stand in for all informal labour in the Census.
Purposive or judgment sampling
- Cases are chosen because they illustrate a type: a successful SHG, a mill town, a mixed-caste panchayat.
- Condition of use: case study and expert selection when the type, not the proportion, is the object.
- Max Weber’s ideal type logic sits here: the case is chosen for theoretical clarity.
Quota sampling
- Interviewers fill cells by sex, age, or caste until a quota is met, without random draws inside the cell.
- Condition of use: rapid opinion work when a frame is costly, as in some market and exit-poll designs.
- Limit: interviewers pick the easiest persons in the quota, so hidden groups stay hidden.
Snowball or chain referral
- One respondent names the next. Networks of migrants, users of a banned substance, or caste-association office-bearers can be reached this way.
- Condition of use: hidden or rare populations with no list. Bias follows the first seeds and the dense core of the network.
- Example: mapping a platform of gig workers through WhatsApp groups, or tracing a caste association’s office network.
Theoretical sampling
- Barney Glaser and Anselm Strauss select the next case to develop a category, and stop at saturation.
- Condition of use: grounded theory and ethnography, not a NFHS-style estimate of anaemia.
- Example: adding a male unpaid-care case after women’s SHG talk has saturated a theme.
When not to use them
- Official rates of literacy, fertility, or vote share need probability designs like the Census and NFHS.
- Mixing a snowball story with a national percentage without warning is a method error.
Flow diagram
flowchart TD NP[Non-probability] --> CV[Convenience] NP --> PU[Purposive] NP --> QU[Quota] NP --> SN[Snowball] NP --> TH[Theoretical saturation] FR[No frame hidden group] --> NP EST[Population estimate] --> PR[Probability Census NFHS]
Conclusion
Convenience, purposive, quota, snowball, and theoretical sampling are legitimate when the frame is absent, the group is hidden, or the aim is a type. They are the wrong tools for population totals. State the condition, show the bias, and do not dress a chain of names as a Census.
Quick related
Students also ask
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How do qualitative and quantitative methods supplement each other in sociological enquiry?
Next question on this syllabus topic (2021 · Q3(a)). View answer →
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Is non-probability unscientific?
It is unscientific as a pretence of random inference. It is scientific as a designed way to reach types and hidden groups.
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Can snowballing study caste?
It can map an association’s network. It cannot give the caste composition of India; that needs a frame such as the Census.
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