Comparar métodos

	Amostra Ponderada	Amostragem em Múltiplos Estágios
Área	Metodologia de survey	Metodologia de survey
Família	Process / pipeline	Process / pipeline
Ano de origem	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
Autor original	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)	Leslie Kish; William G. Cochran
Tipo	Probability sampling design	Probability sampling design
Fonte seminal	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
Outros nomes	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
Relacionados	6	5
Resumo	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.	Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.