IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v82y2024i2d10.1007_s40300-023-00259-y.html
   My bibliography  Save this article

Deriving a zero-truncated modelling methodology to analyse capture–recapture data from self-reported social networks

Author

Listed:
  • Mark E. Piatek

    (University of Southampton)

  • Dankmar Böhning

    (University of Southampton)

Abstract

Capture–recapture (CRC) is widely used to estimate the size (N) of hidden human populations (e.g., the homeless) from the overlap of sample units between two or more repeated samples or lists (a.k.a., capture occasions). There is growing interest in deriving CRC data from social-network data. The current paper hence explored if self-reported social networks (lists of social ties) submitted by participants from the target population could function as distinct capture occasions. We particularly considered the application of zero-truncated count distribution modelling to this type of data. A case study and follow-up simulation study focused on two methodological issues: (1) that a participant cannot be named in their own self-reported social network and hence cannot be named as many times as non-participants; and (2) positive dependence between being a participant and being named by (a social tie of) other participants. Regarding the latter, a further motivation of the simulation study was to consider the impact of using respondent-driven sampling to select participants, because all non-seed RDS participants are recruited as a social tie of another participant. Exponential random graph modelling was used to generate the simulation study’s target populations. Early comparison was also made to estimates of N from Successive Sampling.

Suggested Citation

  • Mark E. Piatek & Dankmar Böhning, 2024. "Deriving a zero-truncated modelling methodology to analyse capture–recapture data from self-reported social networks," METRON, Springer;Sapienza Università di Roma, vol. 82(2), pages 135-160, August.
    • Handle: RePEc:spr:metron:v:82:y:2024:i:2:d:10.1007_s40300-023-00259-y
    DOI: 10.1007/s40300-023-00259-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40300-023-00259-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40300-023-00259-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liqun Xi & Ray Watson & Paul S. F. Yip, 2008. "The Minimum Capture Proportion for Reliable Estimation in Capture–Recapture Models," Biometrics, The International Biometric Society, vol. 64(1), pages 242-249, March.
    2. Peter G.M. Van Der Heijden & Maarten Cruyff & Hans C. Van Houwelingen, 2003. "Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(3), pages 289-304, August.
    3. A. M. Coumans & M. Cruyff & P. G. M. Heijden & J. Wolf & H. Schmeets, 2017. "Estimating Homelessness in the Netherlands Using a Capture-Recapture Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(1), pages 189-212, January.
    4. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2017. "Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 49-79, March.
    5. Beáta Dávid & Tom Snijders, 2002. "Estimating the Size of the Homeless Population in Budapest, Hungary," Quality & Quantity: International Journal of Methodology, Springer, vol. 36(3), pages 291-303, August.
    6. Ryan T. Godwin & Dankmar Böhning, 2017. "Estimation of the population size by using the one-inflated positive Poisson model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 425-448, February.
    7. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
    8. Zwane, E. N. & van der Heijden, P. G. M., 2003. "Implementing the parametric bootstrap in capture-recapture models with continuous covariates," Statistics & Probability Letters, Elsevier, vol. 65(2), pages 121-125, November.
    9. Bilal Khan & Hsuan-Wei Lee & Ian Fellows & Kirk Dombrowski, 2018. "One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-39, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maciej Berk{e}sewicz & Katarzyna Pawlukiewicz, 2020. "Estimation of the number of irregular foreigners in Poland using non-linear count regression models," Papers 2008.09407, arXiv.org.
    2. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    3. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    4. Lanumteang, K. & Böhning, D., 2011. "An extension of Chao's estimator of population size based on the first three capture frequency counts," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2302-2311, July.
    5. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    6. Fernando Bonassi & Rafael Stern & Cláudia Peixoto & Sergio Wechsler, 2015. "Exchangeability and the law of maturity," Theory and Decision, Springer, vol. 78(4), pages 603-615, April.
    7. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    8. Dexter Cahoy & Elvira Di Nardo & Federico Polito, 2021. "Flexible models for overdispersed and underdispersed count data," Statistical Papers, Springer, vol. 62(6), pages 2969-2990, December.
    9. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
    10. Sellers, Kimberly F. & Raim, Andrew, 2016. "A flexible zero-inflated model to address data dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 68-80.
    11. Ryan T. Godwin, 2024. "One-inflated zero-truncated count regression models," Papers 2402.02272, arXiv.org.
    12. Zhou, Can & Jiao, Yan & Browder, Joan, 2019. "K-aggregated transformation of discrete distributions improves modeling count data with excess ones," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    13. Rodrigues, Josemar & Balakrishnan, N. & Cordeiro, Gauss M. & de Castro, Mário, 2011. "A unified view on lifetime distributions arising from selection mechanisms," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3311-3319, December.
    14. Rajib Dey & M. Ataharul Islam, 2017. "A conditional count model for repeated count data and its application to GEE approach," Statistical Papers, Springer, vol. 58(2), pages 485-504, June.
    15. Borges, Patrick & Rodrigues, Josemar & Balakrishnan, Narayanaswamy & Bazán, Jorge, 2014. "A COM–Poisson type generalization of the binomial distribution and its properties and applications," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 158-166.
    16. Pattiz, Brian David, 2009. "Count regression models for recreation demand: an application to Clear Lake," ISU General Staff Papers 200901010800002092, Iowa State University, Department of Economics.
    17. Haraguchi, Masahiko & Funahashi, Tomomi & Biljecki, Filip, 2024. "Assessing governance implications of city digital twin technology: A maturity model approach," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    18. Kimberly F. Sellers & Ali Arab & Sean Melville & Fanyu Cui, 2021. "A flexible univariate moving average time-series model for dispersed count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-12, December.
    19. Rodrigues, Josemar & Bazán, Jorge L. & Suzuki, Adriano K. & Balakrishnan, Narayanaswamy, 2016. "The Bayesian restricted Conway–Maxwell-Binomial model to control dispersion in count data," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 281-288.
    20. Sharad Borle & Peter Boatwright & Joseph B. Kadane & Joseph C. Nunes & Shmueli Galit, 2005. "The Effect of Product Assortment Changes on Customer Retention," Marketing Science, INFORMS, vol. 24(4), pages 616-622, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metron:v:82:y:2024:i:2:d:10.1007_s40300-023-00259-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.