Revision summary
Simple random and systematic sampling require a usable frame. Stratification ensures analytically important minorities are represented. Cluster and multistage designs suit dispersed regional surveys but need design-effect adjustment. Purposive sampling selects information-rich cases; theoretical sampling develops concepts. Snowball sampling reaches hidden groups but carries network bias. Anthropological sampling should combine transparent inference, ethnographic depth, non-response analysis and ethics.
Model answer
Introduction
Sampling selects cases from a defined population so that fieldwork is feasible. Its quality depends not on one universally best technique but on fit among research question, sampling frame, mobility, social boundaries and the inference claimed.
Body
Probability designs
- Simple random sampling gives every listed unit a known equal chance; suitable for a complete village household register, but costly over dispersed terrain.
- Systematic sampling takes every kth unit after a random start; efficient in settlement lists, unless periodic ordering creates bias.
- Stratified sampling samples within relevant strata such as sex, age, caste or ecological zone. It ensures small subgroups are represented and can improve precision.
- Cluster and multistage sampling first select villages or census blocks, then households and persons. This is practical for regional nutrition or morbidity surveys, though intra-cluster similarity enlarges sampling error.
Probability designs support estimable sampling error and population inference, provided non-response and frame omissions are addressed.
Non-probability and adaptive designs
Purposive sampling selects information-rich ritual specialists, healers or fossil collections; quota sampling fills categories without random selection; convenience sampling is fast but weak for generalisation. Snowball sampling, associated with referral chains, reaches stigmatised or hidden groups such as migrants, sex workers or drug users, but over-represents network hubs. Respondent-driven sampling attempts to model referral probabilities under strong assumptions. Theoretical sampling in grounded theory selects new cases to develop emerging concepts until saturation.
Classic intensive ethnography—Malinowski in the Trobriands or M. N. Srinivas in Rampura—often chose a site purposively, then used a census, genealogy and case studies within it. Statistical representativeness and ethnographic depth answer different questions.
Gatekeeper access, seasonal absence, household definitions, refusal and digital exclusion can bias any design. Tribal or vulnerable populations require community consent and protection from re-identification; oversampling a small group must not become extractive surveillance.
Flow diagram
flowchart TD
Q[Research question] --> P{Population frame exists?}
P -->|yes| PR[Probability sample]
PR --> R[Random stratified or multistage]
P -->|no or hidden| NP[Non-probability or adaptive]
NP --> N[Purposive snowball theoretical]
R --> I[Population inference]
N --> D[Depth or network access]
Conclusion
Use random, stratified or multistage designs when estimating population patterns; use purposive, theoretical or network designs when mechanisms, rare expertise or hidden populations are the object. Triangulation and honest limits matter more than calling every sample representative.
Quick related
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Is a large convenience sample representative?
No. Size reduces random error but cannot repair systematic selection bias.
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Can one study mix sampling techniques?
Yes. A multistage household survey can be combined with purposive life histories, provided each supports a distinct claim.
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