Have a personal or library account? Click to login
Latent Class Analysis for Estimating an Unknown Population Size – with Application to Censuses Cover

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

Open Access
|Sep 2021

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.

Language: English
Page range: 673 - 697
Submitted on: Apr 1, 2019
|
Accepted on: Jul 1, 2020
|
Published on: Sep 13, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Bernard Baffour, James J. Brown, Peter W.F. Smith, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.