Latent Class Analysis for Estimating an Unknown Population Size – with Application to Censuses

Baffour Bernard (), Brown James J. () and Smith Peter W.F. ()
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Baffour Bernard: School of Demography, Australian National University, Canberra, Australia.
Brown James J.: School of Mathematical and Physical Sciences, University of Technology Sydney (UTS), Sydney, Australia.
Smith Peter W.F.: Department of Social Statistics and Demography, University of Southampton, Southampton, United Kingdom.

Journal of Official Statistics, 2021, vol. 37, issue 3, 673-697

Abstract: Estimation of the unknown population size using capture-recapture techniques relies on the key assumption that the capture probabilities are homogeneous across individuals in the population. This is usually accomplished via post-stratification by some key covariates believed to influence individual catchability. Another issue that arises in population estimation from data collected from multiple sources is list dependence, where an individual’s catchability on one list is related to that of another list. The earlier models for population estimation heavily relied upon list independence. However, there are methods available that can adjust the population estimates to account for dependence among lists. In this article, we propose the use of latent class analysis through log-linear modelling to estimate the population size in the presence of both heterogeneity and list dependence. The proposed approach is illustrated using data from the 1988 US census dress rehearsal.

Keywords: Capture-recapture; latent class analysis; log-linear models (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:3:p:673-697:n:3

DOI: 10.2478/jos-2021-0030

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