Assessing logistic regression applied to respondent-driven sampling studies: a simulation study with an application to empirical data
The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attribu...
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Veröffentlicht in: | International journal of social research methodology 2023-05, Vol.26 (3), p.319-333 |
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description | The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option. |
doi_str_mv | 10.1080/13645579.2022.2031153 |
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Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option.</description><identifier>ISSN: 1364-5579</identifier><identifier>EISSN: 1464-5300</identifier><identifier>DOI: 10.1080/13645579.2022.2031153</identifier><language>eng</language><publisher>Abingdon: Routledge</publisher><subject>Attributes ; Bayesian Statistics ; hard-to-reach populations ; Infections ; logistic regression ; Performance Based Assessment ; Recruitment ; Regression (Statistics) ; Respondent-driven sampling ; Sampling ; Scaling ; Simulation ; simulation studies ; statistical methods</subject><ispartof>International journal of social research methodology, 2023-05, Vol.26 (3), p.319-333</ispartof><rights>2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2022</rights><rights>2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial – No Derivatives License http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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subjects | Attributes Bayesian Statistics hard-to-reach populations Infections logistic regression Performance Based Assessment Recruitment Regression (Statistics) Respondent-driven sampling Sampling Scaling Simulation simulation studies statistical methods |
title | Assessing logistic regression applied to respondent-driven sampling studies: a simulation study with an application to empirical data |
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