Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Muestreo aleatorio simple proporcional× | Muestreo Ponderado× | |
|---|---|---|
| Campo | Metodología de encuestas | Metodología de encuestas |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | Mid-20th century (formalized ~1950s–1970s) | 1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator) |
| Autor original≠ | William G. Cochran and survey statisticians (classical probability sampling tradition) | Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework) |
| Tipo≠ | Probability sampling technique | Probability sampling design |
| Fuente seminal | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Alias≠ | proportional SRS, probability-proportional simple random sampling, proportional random sampling | probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling |
| Relacionados | 6 | 6 |
| Resumen≠ | Proportional simple random sampling is a probability-based sampling technique in which units are drawn at random from each subgroup of the population in numbers proportional to each subgroup's share of the total population. This ensures the resulting sample mirrors the population's composition across key subgroups, while retaining the randomness and unbiasedness of simple random sampling within each group. | 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. |
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