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Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel
BACKGROUND: Knowing about predictors of attrition in a panel is important to initiate early measures against loss of participants. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation. METHODS: We used data from t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580321/ https://www.ncbi.nlm.nih.gov/pubmed/28859617 http://dx.doi.org/10.1186/s12874-017-0408-3 |
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author | Rübsamen, Nicole Akmatov, Manas K. Castell, Stefanie Karch, André Mikolajczyk, Rafael T. |
author_facet | Rübsamen, Nicole Akmatov, Manas K. Castell, Stefanie Karch, André Mikolajczyk, Rafael T. |
author_sort | Rübsamen, Nicole |
collection | PubMed |
description | BACKGROUND: Knowing about predictors of attrition in a panel is important to initiate early measures against loss of participants. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation. METHODS: We used data from the HaBIDS panel that was designed to investigate knowledge, attitudes, and practice regarding infections in the German general population. HaBIDS was divided into two phases: an initial phase when some participants could choose their preferred mode of participation (paper-and-pencil or online) and an extended phase when participants were asked to become members of an online panel that was not limited regarding its duration (i.e. participants initially preferring paper questionnaires switched to online participation). Using competing risks regression, we investigated two types of attrition (formal withdrawal and discontinuation without withdrawal) among online participants, separately for both phases. As potential predictors of attrition, we considered sociodemographic characteristics, physical and mental health as well as auxiliary information describing the survey process, and, in the extended phase, initial mode preference. RESULTS: In the initial phase, higher age and less frequent Internet usage predicted withdrawal, while younger age, higher stress levels, delay in returning the consent form, and need for receiving reminder emails predicted discontinuation. In the extended phase, only need for receiving reminder emails predicted discontinuation. Numbers of withdrawal in the extended phase were too small for analysis. Initial mode preference did not predict attrition in the extended phase. Besides age, there was no evidence of differential attrition by sociodemographic factors in any phase. CONCLUSIONS: Predictors of attrition were similar in both phases of the panel, but they differed by type of attrition (withdrawal vs. discontinuation). Sociodemographic characteristics only played a minor role for both types of attrition. Need for receiving a reminder was the strongest predictor of discontinuation in any phase, but no predictor of withdrawal. We found predictors of attrition, which can be identified already in the early phase of a panel so that countermeasures (e.g. special incentives) can be taken. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0408-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5580321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55803212017-09-07 Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel Rübsamen, Nicole Akmatov, Manas K. Castell, Stefanie Karch, André Mikolajczyk, Rafael T. BMC Med Res Methodol Research Article BACKGROUND: Knowing about predictors of attrition in a panel is important to initiate early measures against loss of participants. We investigated attrition in both early and late phase of an online panel with special focus on preferences regarding mode of participation. METHODS: We used data from the HaBIDS panel that was designed to investigate knowledge, attitudes, and practice regarding infections in the German general population. HaBIDS was divided into two phases: an initial phase when some participants could choose their preferred mode of participation (paper-and-pencil or online) and an extended phase when participants were asked to become members of an online panel that was not limited regarding its duration (i.e. participants initially preferring paper questionnaires switched to online participation). Using competing risks regression, we investigated two types of attrition (formal withdrawal and discontinuation without withdrawal) among online participants, separately for both phases. As potential predictors of attrition, we considered sociodemographic characteristics, physical and mental health as well as auxiliary information describing the survey process, and, in the extended phase, initial mode preference. RESULTS: In the initial phase, higher age and less frequent Internet usage predicted withdrawal, while younger age, higher stress levels, delay in returning the consent form, and need for receiving reminder emails predicted discontinuation. In the extended phase, only need for receiving reminder emails predicted discontinuation. Numbers of withdrawal in the extended phase were too small for analysis. Initial mode preference did not predict attrition in the extended phase. Besides age, there was no evidence of differential attrition by sociodemographic factors in any phase. CONCLUSIONS: Predictors of attrition were similar in both phases of the panel, but they differed by type of attrition (withdrawal vs. discontinuation). Sociodemographic characteristics only played a minor role for both types of attrition. Need for receiving a reminder was the strongest predictor of discontinuation in any phase, but no predictor of withdrawal. We found predictors of attrition, which can be identified already in the early phase of a panel so that countermeasures (e.g. special incentives) can be taken. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0408-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-31 /pmc/articles/PMC5580321/ /pubmed/28859617 http://dx.doi.org/10.1186/s12874-017-0408-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Rübsamen, Nicole Akmatov, Manas K. Castell, Stefanie Karch, André Mikolajczyk, Rafael T. Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title | Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title_full | Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title_fullStr | Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title_full_unstemmed | Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title_short | Factors associated with attrition in a longitudinal online study: results from the HaBIDS panel |
title_sort | factors associated with attrition in a longitudinal online study: results from the habids panel |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580321/ https://www.ncbi.nlm.nih.gov/pubmed/28859617 http://dx.doi.org/10.1186/s12874-017-0408-3 |
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