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	Disproportional Cluster Sampling	Viktad stickprovsdragning
Ämnesområde	Surveymetodik	Surveymetodik
Familj	Process / pipeline	Process / pipeline
Ursprungsår	Mid-20th century (formalised 1950s–1965)	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)
Upphovsperson	Leslie Kish; William G. Cochran	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)
Typ	Probability sampling design	Probability sampling design
Ursprungskälla	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471489009	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	disproportionate cluster sampling, unequal-probability cluster sampling, variable-rate cluster sampling, non-proportional cluster sampling	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling
Närliggande	6	6
Sammanfattning	Disproportional cluster sampling is a probability-based survey design in which naturally occurring groups (clusters) are selected as primary sampling units, but the number of clusters or elements drawn from each group is not proportional to that group's share of the population. By deliberately over- or under-sampling certain clusters, researchers gain analytic flexibility and precision where it matters most, at the cost of requiring post-hoc weighting for population-level inference.	Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient.