Σύγκριση μεθόδων

	Dual-Systems Estimation of Irregular Migration	Network Scale-Up Method
Πεδίο	Migration Studies	Migration Studies
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2003	1998
Δημιουργός	Peter van der Heijden, Maarten Cruyff, and colleagues; Dankmar Böhning	Peter Killworth, Christopher McCarty, H. Russell Bernard, and colleagues
Τύπος	Administrative-record linkage and population-size-estimation pipeline	Indirect network-based size-estimation pipeline for hidden populations
Θεμελιώδης πηγή	van der Heijden, P. G. M., Cruyff, M., & van Houwelingen, H. C. (2003). Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model. Statistica Neerlandica, 57(3), 289-304.	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.
Εναλλακτικές ονομασίες	Dual-System Estimation, Multiple-Systems Estimation of Irregular Migration, Capture-Recapture for Irregular Migrants, Truncated-Poisson Population Estimation	NSUM, Scale-Up Method, Aggregate Relational Data Method, Known-Population Network Estimation
Συναφείς	3	3
Σύνοψη	Dual-systems estimation, the two-list special case of capture-recapture, estimates how many irregular migrants exist by exploiting the overlap between administrative records that each capture only part of the population. The logic is borrowed from wildlife ecology: tag animals on one trapping occasion, see how many tagged animals reappear on a second, and the rate of overlap reveals how many were never caught at all. Applied to migration, the 'traps' become administrative lists — police apprehension records, hospital registers, deportation files — and the people who appear on no list are the quantity to be estimated. Van der Heijden, Cruyff, and colleagues showed in their 2003 Statistica Neerlandica paper that even a single police register can support estimation through the truncated-Poisson model, by using how often individuals are recorded to infer how many were never recorded. Böhning, van der Heijden, and Bunge's 2018 monograph consolidated the modern toolkit for the social and medical sciences, with explicit treatment of the two assumptions that make or break the method: that lists are not too strongly dependent and that the population is not too heterogeneous in its chance of being captured. The output is a defensible estimate of an irregular-migrant total that, by definition, no register sees in full.	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.