Porovnať metódy

	Multiplicity Sampling of Migrant Stock	Network Scale-Up Method
Odbor	Migration Studies	Migration Studies
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1970	1998
Tvorca	Monroe G. Sirken	Peter Killworth, Christopher McCarty, H. Russell Bernard, and colleagues
Typ	Network-based survey design and weighting estimator for rare populations	Indirect network-based size-estimation pipeline for hidden populations
Pôvodný zdroj	Sirken, M. G. (1970). Household Surveys with Multiplicity. Journal of the American Statistical Association, 65(329), 257-266.	Bernard, H. R., Hallett, T., Iovita, A., Johnsen, E. C., Lyerla, R., McCarty, C., Mahy, M., Salganik, M. J., & Stroup, S. (2010). Counting Hard-to-Count Populations: The Network Scale-Up Method for Public Health. Sexually Transmitted Infections, 86(Suppl 2), ii11-ii15.
Ďalšie názvy	Network Sampling of Migrants, Multiplicity Survey of Emigrants, Sirken Multiplicity Estimator, Relative-Report Migrant Sampling	NSUM, Scale-Up Method, Aggregate Relational Data Method, Known-Population Network Estimation
Príbuzné	3	3
Zhrnutie	Multiplicity sampling, introduced by Monroe Sirken in 1970, is a survey design for counting rare and hard-to-reach populations by letting respondents report not only about themselves but about eligible relatives living elsewhere. For migration research the appeal is direct: emigrants and dispersed migrants are, by definition, absent from the sampling frame of the place that wants to count them, so an ordinary household survey misses them. Under multiplicity sampling a sampled household reports its migrant relatives — say, children or siblings who have moved abroad — according to an explicit counting rule, which dramatically raises the effective coverage of the rare group because many households can each contribute reports. The price of this expanded reach is that the same migrant may be reportable by several households, so each reported migrant must be weighted by the inverse of the number of households eligible to report them, the migrant's 'multiplicity.' Sirken showed that this multiplicity-weighted estimator is unbiased and that, by enlarging the set of reporters, it can sharply reduce the sampling variance for rare populations compared with conventional surveys.	The network scale-up method (NSUM) estimates the size of a hidden population — such as undocumented migrants or members of a stigmatized group — by asking ordinary people in a general survey how many members of that population they personally know. Developed by Killworth, McCarty, Bernard, and colleagues and formalized in their 1998 Evaluation Review paper, it rests on a simple bookkeeping idea: if you know roughly how many people each respondent knows in total, and you observe how many of those acquaintances belong to the hidden group, you can scale that fraction up to the whole society. The trick to recovering the total acquaintance count is to ask about several groups whose sizes are already known — people named Michael, nurses, women who gave birth last year — and use the responses to calibrate each respondent's personal-network size. Bernard and colleagues' 2010 review brought the method into mainstream public-health surveillance and emphasized two crucial corrections: transmission bias, because people often do not know which of their acquaintances belong to a hidden group, and barrier effects, because the hidden group may be socially clustered away from typical respondents. For migration research NSUM is attractive precisely because it never requires contacting migrants directly; it infers their numbers from the social fabric of the wider population.