Revision summary
Random sampling gives known chances of selection from a frame. It raises reliability through repeatable rules and estimable variance. It raises validity for population totals by cutting convenience bias. It is not random assignment and not a guarantee of construct validity. NFHS and NSS illustrate the gain; hidden groups and meaning studies still need other designs.
Model answer
Introduction
Random sampling gives each unit in a defined population a known, non-zero chance of selection. It is praised for reliability and validity because it reduces selection bias and supports inference from sample to population. It is not a magic wand for every sociological question.
Body
What random sampling is
- Simple random, systematic, stratified, and cluster designs are probability designs when the chance is known.
- A sampling frame, such as a Census list or a household listing for NFHS, is the practical condition.
Reliability
- Reliability is consistency of a procedure. Random rules can be repeated by another team with the same frame, which interviewer whim cannot.
- Variance can be estimated. Quotas and convenience samples hide that uncertainty.
- Stratification, as in rural–urban or sex cells, can raise precision for the same size, which is a reliability gain in the estimate.
Validity of inference
- External validity of a population estimate is stronger when non-coverage and non-response are managed. Random selection attacks the first bias of picking friends.
- Internal validity of a causal claim is a different issue. Random sampling is not random assignment. Emile Durkheim’s suicide study was not a random sample of persons, yet it still reasoned about rates.
- Construct validity still depends on concepts. Max Weber’s meaning is not guaranteed by a random draw of poorly worded items.
Why the claim is only “more”
- Hidden populations, as in some informal labour, have no frame. Snowballing may be more valid for that object.
- George Herbert Mead’s interaction studies need cases, not a national percentage.
- Robert K. Merton’s middle-range work can use both: a probability survey for rates, a case for mechanism.
- Indian official practice: Census as frame-maker, NSS and NFHS as probability household surveys. Caste-association office networks still need other designs.
Limits to advertise
- Non-response, outdated frames, and clustered designs that ignore design effects produce false precision.
- A random sample of households can still miss women’s work if the questionnaire is androcentric, as Ann Oakley-type critiques show.
Flow diagram
flowchart TD FRAME[Sampling frame] --> RS[Random selection] RS --> REL[Repeatable estimates] RS --> VAL[Population inference] REL --> RATE[Rates Census NFHS] HIDDEN[No frame] --> OTHER[Purposive snowball]
Conclusion
Random sampling has more reliability as a repeatable selection rule and more validity for population inference because chance, not convenience, chooses cases. It does not by itself validate meaning, causation, or a bad concept. Use it for rates; do not use it as a certificate for every method.
Quick related
Students also ask
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Is a large convenience sample almost random?
No. Size without a chance mechanism repeats the same bias more loudly.
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Does random sampling make research scientific?
It makes one kind of inference stronger. Theory, measurement, and ethics still decide the rest.
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