Comparar métodos

	Amostragem por Conglomerados Desproporcional	Amostragem Estratificada Desproporcional
Área	Metodologia de survey	Metodologia de survey
Família	Process / pipeline	Process / pipeline
Ano de origem	Mid-20th century (formalised 1950s–1965)	1934
Autor original	Leslie Kish; William G. Cochran	Jerzy Neyman
Tipo	Probability sampling design	Probability sampling design
Fonte seminal	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
Outros nomes	disproportionate cluster sampling, unequal-probability cluster sampling, variable-rate cluster sampling, non-proportional cluster sampling	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling
Relacionados	6	6
Resumo	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.	Disproportional stratified sampling divides the population into mutually exclusive strata and deliberately draws different proportions from each stratum — oversampling small or analytically important subgroups and undersampling large ones. Post-hoc weighting restores population-level representativeness when overall estimates are needed. First formalised by Jerzy Neyman in 1934, it is the standard approach when subgroup-level precision matters as much as total-population estimates.