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

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.

Robust Statistical Estimation for Capture-Recapture Using Administrative Data

“…Given λ cps (123) = 0, each of the two-factor effects (i.e., λ cp (12) , λ cs (13) , and λ ps (23) ) is unaffected by the level of the third variable, such that (e.g., Baffour et al 2021;Bartlett 1935;Fienberg 1972), from which µ  000 follows. It can be seen that the effect of the no-three-way interaction assumption for the TSE is analogous to the independence assumption for the DSE.…”

“…where μ true^ c p s = n c p s (e.g., Baffour et al 2021; Bartlett 1935; Fienberg 1972), from which μ true^ 000 follows. It can be seen that the effect of the no-three-way interaction assumption for the TSE is analogous to the independence assumption for the DSE.…”

Evolution of the person census and the estimation of population counts in New Zealand, United Kingdom, Italy and Israel

“…The coverage survey may be yearly or less frequently, and the sample size may be relatively large or small. • Latent class modelling, where the in-and out-of-scope status of a person is treated as a latent variable that is unobserved [37,38]. A notable difference to prediction modelling is that labelled observations are not required, and the model can be fitted to population registers directlyhence, updated over time.…”

Preface

“…A similar topic is tackled in the second article in this group (Baffour et al 2021). This article focuses on methods for dealing with unobserved heterogeneity across individuals present on multiple lists used for applying capture-recapture methods for estimating population sizes.…”