Estimation of logistic regression parameters for complex survey data: simulation study based on a real data

In complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much...

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Autores: Iparragirre Letamendia, Amaia, Barrio Beraza, Irantzu, Aramendi, Jorge, Arostegui Madariaga, Inmaculada
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/72872
Acceso en línea:http://hdl.handle.net/10810/72872
Access Level:acceso abierto
Palabra clave:complex survey data
sampling weights
logistic regression
estimation of model parameters
real data based simulation study
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spelling Estimation of logistic regression parameters for complex survey data: simulation study based on a real dataIparragirre Letamendia, AmaiaBarrio Beraza, IrantzuAramendi, JorgeArostegui Madariaga, Inmaculadacomplex survey datasampling weightslogistic regressionestimation of model parametersreal data based simulation studyIn complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much discussion among researchers in different felds. We aim to contribute to this debate by means of a real data based simulation study in the framework of logistic regression models. In order to study their performance, three methods have been considered for estimating the coeffcients of the logistic regression model: a) the unweighted model, b) the weighted model, and c) the unweighted mixed model. The results suggest the use of the weighted logistic regression model is superior, showing the importance of using sampling weights in the estimation of the model parameters.Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco [IT1456-22]; the Ministry of Science and Innovation through BCAM Severo Ochoa accreditation [CEX2021- 001142-S / MICIN / AEI / 10.13039/501100011033] and through project [PID2020- 115882RB-I00 / AEI / 10.13039/501100011033] funded by Agencia Estatal de Investigación; the Basque Government through the BERC 2022-2025 program and the University of the Basque Country UPV/EHU by by grant [PIF18/213] that supported the work of AIInstitut d'Estadística de Catalunya (Idescat)202520252024info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/72872reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/info:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00/https://www.idescat.cat/sort/sort481/48.1.2.Iparragirre-etal.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work), you may not use the work for commercial purposes and you may not alter, transform, or build upon the work.oai:addi.ehu.eus:10810/728722026-06-18T09:23:17Z
dc.title.none.fl_str_mv Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
title Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
spellingShingle Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
Iparragirre Letamendia, Amaia
complex survey data
sampling weights
logistic regression
estimation of model parameters
real data based simulation study
title_short Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
title_full Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
title_fullStr Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
title_full_unstemmed Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
title_sort Estimation of logistic regression parameters for complex survey data: simulation study based on a real data
dc.creator.none.fl_str_mv Iparragirre Letamendia, Amaia
Barrio Beraza, Irantzu
Aramendi, Jorge
Arostegui Madariaga, Inmaculada
author Iparragirre Letamendia, Amaia
author_facet Iparragirre Letamendia, Amaia
Barrio Beraza, Irantzu
Aramendi, Jorge
Arostegui Madariaga, Inmaculada
author_role author
author2 Barrio Beraza, Irantzu
Aramendi, Jorge
Arostegui Madariaga, Inmaculada
author2_role author
author
author
dc.subject.none.fl_str_mv complex survey data
sampling weights
logistic regression
estimation of model parameters
real data based simulation study
topic complex survey data
sampling weights
logistic regression
estimation of model parameters
real data based simulation study
description In complex survey data, each sampled observation has assigned a sampling weight, indicating the number of units that it represents in the population. Whether sampling weights should or not be considered in the estimation process of model parameters is a question that still continues to generate much discussion among researchers in different felds. We aim to contribute to this debate by means of a real data based simulation study in the framework of logistic regression models. In order to study their performance, three methods have been considered for estimating the coeffcients of the logistic regression model: a) the unweighted model, b) the weighted model, and c) the unweighted mixed model. The results suggest the use of the weighted logistic regression model is superior, showing the importance of using sampling weights in the estimation of the model parameters.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/72872
url http://hdl.handle.net/10810/72872
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/CEX2021-001142-S/
info:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00/
https://www.idescat.cat/sort/sort481/48.1.2.Iparragirre-etal.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya (Idescat)
publisher.none.fl_str_mv Institut d'Estadística de Catalunya (Idescat)
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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