1. bookVolume 37 (2021): Issue 3 (September 2021)
    Special Issue on Population Statistics for the 21st Century
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 13 Sep 2021
Page range: 673 - 697
Received: 01 Apr 2019
Accepted: 01 Jul 2020
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
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

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