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21 - Indirect sampling for hard-to-reach populations

Published online by Cambridge University Press:  05 September 2014

By
Pierre Lavallée
Edited by
Roger Tourangeau ,
Brad Edwards ,
Timothy P. Johnson ,
Kirk M. Wolter  and
Nancy Bates
Pierre Lavallée
Affiliation:
Statistics Canada
Roger Tourangeau
Affiliation:
Westat Research Organisation, Maryland
Brad Edwards
Affiliation:
Westat Research Organisation, Maryland
Timothy P. Johnson
Affiliation:
University of Illinois, Chicago
Kirk M. Wolter
Affiliation:
University of Chicago
Nancy Bates
Affiliation:
US Census Bureau
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Summary

Introduction

In survey sampling, some populations are hard to reach because they happen to be hard to survey. Their relative rareness and the absence of a suitable sampling frame are two main reasons for this. As mentioned by Kalton and Anderson (1986): “The initial consideration in designing a sample for a rare population is whether there exists a separate frame for that population. If a separate frame exists, is available for sample selection and is deemed adequate, the sample may be selected from it using standard methods and no problems arise.” When no sampling frame is available for the desired target population, one might then choose a sampling frame that is indirectly related to the targeted rare population. We can then speak of two populations UA and UB that are related to one another. We wish to produce an estimate for the population UB by selecting a sample from the population UA for which a sampling frame is available and using the existing links between the two populations. This sampling process is referred to as indirect sampling (Lavallée, 2002, 2007). Producing estimates in the context of indirect sampling can be difficult to achieve if the links between UA and UB are not one-to-one. A solution for this is to use the generalized weight share method (GWSM).

The population UB can be the hard-to-survey population itself or it can be a population that contains it as a subpopulation. Fortunately for the statistician, it turns out that hard-to-reach populations can often be found by surveying clusters. This is the case, for example, with infectious diseases (Thompson, 1992). In this chapter then, we will assume that the population UB is partitioned into clusters. Selection of clusters will then be performed through the indirect sampling process. Since sampling clusters rather than individual units might allow easier tracking of units that are part of the hard-to-reach populations, we can foresee considerable reductions in costs since a large part of the costs are related to the identification of the hard-to-reach populations. As well, cluster sampling allows for the production of results at the cluster level itself, in addition to the units.

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Hard-to-Survey Populations , pp. 445 - 467
Publisher: Cambridge University Press
Print publication year: 2014

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References

Ardilly, P., & Le Blanc, D. (2001). Sampling and weighting a survey of homeless persons: a French example. Survey methodology, 27(1), 109–18.Google Scholar
Birnbaum, Z. W., & Sirken, M. G. (1965). Design of sample surveys to estimate the prevalence of rare diseases: three unbiased estimates. Vital and Health Statistics, 2(11), 1–8.Google Scholar
Casady, R. J., & Sirken, M. G. (1980). A multiplicity estimator for multiple frame sampling. In Joint Statistical Meetings Proceedings, Survey Research Methods Section (pp. 601–05). Alexandria, VA: American Statistical Association.Google Scholar
Coleman, J. S. (1958). Relational analysis: the study of social organization with survey methods. Human Organization, 17(4), 28–36.CrossRefGoogle Scholar
Deville, J. C., & Maumy-Bertrand, M. (2006). Extension of the indirect sampling method and its application to tourism. Survey Methodology, 32(2), 177–86.Google Scholar
Ernst, L. (1989). Weighting issues for longitudinal household and family estimates. In Kasprzyk, D., Duncan, G., Kalton, G., & Singh, M. P. (eds.), Panel Surveys (pp. 135–59). New York: John Wiley & Sons.Google Scholar
Frank, O. (1977). Survey sampling in graphs. Journal of Statistical Planning and Inference, 1(3), 235–64.CrossRefGoogle Scholar
Frank, O. (1978). Sampling and estimation in large social networks. Social Networks, 1, 91–101.CrossRefGoogle Scholar
Frank, O., & Snijders, T. (1994). Estimating the size of hidden populations using snowball sampling. Journal of Official Statistics, 10, 53–67.Google Scholar
Goel, S., & Salganik, M. J. (2010). Assessing respondent-driven sampling. Proceedings of the National Academy of Sciences, 107(15), 6743–47.CrossRefGoogle ScholarPubMed
Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–70.CrossRefGoogle Scholar
Hartley, H. O. (1962). Multiple frame surveys. In Joint Statistical Meetings Proceedings, Social Statistics Sections (pp. 203–06). Alexandria, VA: American Statistical Association.Google Scholar
Hartley, H. O. (1974). Multiple frame methodology and selected applications. Sankhya, Series C, 36, 99–118.Google Scholar
Heckathorn, D. D. (1997). Respondent driven sampling: a new approach to the study of hidden populations. Social Problems, 44(2), 174–99.CrossRefGoogle Scholar
Heckathorn, D. D. (2002). Respondent driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Social Problems, 49(1), 11–34.CrossRefGoogle Scholar
Heckathorn, D. D. (2007). Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociological Methodology, 37, 151–208.CrossRefGoogle Scholar
Heckathorn, D. D., & Jeffri, J. (2001). Finding the beat: using respondent-driven sampling to study jazz musicians. Poetics, 28, 307–29.CrossRefGoogle Scholar
Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. In Journal of the American Statistical Association, 47, 663–85.CrossRefGoogle Scholar
Huang, H. (1984). Obtaining cross-sectional estimates from a longitudinal survey: experiences of the income survey development program. In Joint Statistical Meetings Proceedings, Survey Research Methods Section (pp. 670–75). Alexandria, VA: American Statistical Association.Google Scholar
Kalton, G., & Anderson, D. W. (1986). Sampling rare populations. Journal of the Royal Statistical Society: Series A (General), 149, Part 1, 65–82.CrossRefGoogle Scholar
Lavallée, P. (1995). Cross-sectional weighting of longitudinal surveys of individuals and households using the weight share method. Survey Methodology, 21(1), 25–32.Google Scholar
Lavallée, P. (2002). Le sondage indirect, ou la méthode généralisée du partage des poids. Éditions de l’Université de Bruxelles (Belgique) et Éditions Ellipses (France), 215 pages.
Lavallée, P. (2007). Indirect Sampling. New York: Springer.CrossRefGoogle Scholar
Levy, P. S. (1977). Optimum allocation in stratified random network sampling for estimating the prevalence of attributes in rare populations. Journal of the American Statistical Association, 72(360), 758–63.Google Scholar
Lohr, S. L. (2011). Alternative survey sample designs: sampling with multiple overlapping frames. Survey Methodology, 37(2), 197–213.Google Scholar
Lohr, S. L., & Rao, J. N. K. (2000). Inference from dual frame surveys. Journal of the American Statistical Association, 95, 271–80.CrossRefGoogle Scholar
Lohr, S. L., & Rao, J. N. K. (2006). Estimation in multiple-frame surveys. Journal of the American Statistical Association, 101(475), 1019–30.CrossRefGoogle Scholar
Lu, X., Bengtsson, L., Britton, T., Camitz, M., Kim, B. J., Thorson, A., et al. (2012). The sensitivity of respondent-driven sampling. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175, Part 1, 191–216.CrossRefGoogle Scholar
Mecatti, F. (2004). Center sampling: a strategy for surveying difficult-to-sample populations. In Proceedings of the Statistics Canada Symposium 2004 [online archive]. Ottawa, Ontario: Statistics Canada.Google Scholar
Mecatti, F. (2007). A single frame multiplicity estimator for multiple frame surveys. Survey Methodology, 33(2), 151–57.Google Scholar
Salganik, M. J. (2006). Variance estimation, design effects and sample size calculations for respondent driven sampling. Journal of Urban Health, 83(Suppl. 7), 98–112.CrossRefGoogle ScholarPubMed
Salganik, M. J., & Heckathorn, D. D. (2004). Sampling and estimation in hidden populations using respondent-driven sampling. Social Methodology, 34, 193–239.CrossRefGoogle Scholar
Sanders, L. L., & Kalsbeek, W. D. (1990). Network sampling as an approach to sampling pregnant women. In Joint Statistical Meetings Proceedings, Survey Research Methods Section (pp. 326–31). Alexandria, VA: American Statistical Association.Google Scholar
Särndal, C.-E., Swensson, B., & Wretman, J. (1992). Model Assisted Survey Sampling. New York: Springer-Verlag.CrossRefGoogle Scholar
Sirken, M. G. (1970a). Household surveys with multiplicity. Journal of the American Statistical Association, 65(329), 257–66.CrossRefGoogle Scholar
Sirken, M. G. (1970b). Survey strategies for estimating rare health attributes. In Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability (pp. 135–44). Berkeley: University of California Press.Google Scholar
Sirken, M. G. (1972). Stratified sample surveys with multiplicity. Journal of the American Statistical Association, 67(337), 224–27.CrossRefGoogle Scholar
Sirken, M. G. (2004). Network sample surveys of rare and elusive populations: a historical review. In Proceedings of the Methodological Symposium 2004 [online archive]. Ottawa, Ontario: Statistics Canada.Google Scholar
Sirken, M. G., & Levy, P. S. (1974). Multiplicity estimation of proportions based on ratio of random variables. Journal of the American Statistical Association, 69(345), 68–73.CrossRefGoogle Scholar
Skinner, C. J., & Rao, J. N. K. (1996). Estimation in dual frames surveys with complex designs. Journal of the American Statistical Association, 91, 349–56.CrossRefGoogle Scholar
Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–59.CrossRefGoogle Scholar
Thompson, S. K. (1991a). Stratified adaptive cluster sampling. Biometrika, 78(2), 389–97.CrossRefGoogle Scholar
Thompson, S. K.(1991b). Adaptive cluster sampling: designs with primary and secondary units. Biometrics, 47, 1103–15.CrossRefGoogle Scholar
Thompson, S. K. (1992). Sampling. New York: John Wiley & Sons.Google ScholarPubMed
Thompson, S. K. (2002). Sampling (2nd edn.). New York: John Wiley & Sons.Google ScholarPubMed
Thompson, S. K. (2006). Targeted random walk designs. Survey Methodology, 32(1), 11–24.Google Scholar
Thompson, S. K. (2011). Adaptive network and spatial sampling. Survey Methodology, 37(2), 183–96.Google Scholar
Thompson, S. K., & Seber, G. A. (1996). Adaptive Sampling. New York: John Wiley & Sons.Google Scholar
Volz, E., & Heckathorn, D. D. (2008). Probability based estimation theory for respondent driven sampling. Journal of Official Statistics, 24(1), 79–97.Google Scholar
Wolter, K. M. (2007). Introduction to variance estimation (2nd edn.). New York: Springer.Google Scholar

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