Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/27577
Title: Evaluating different recruitment methods in a longitudinal survey: Findings from the pan-European PASTA project
Authors: Gaupp-Berghausen, Mailin
Raser, Elisabeth
Anaya-Boig, Esther
Avila-Palencia, Ione
de Nazelle, Audrey
DONS, Evi 
Franzen, Helen
Gerike, Regine
Götschi, Thomas
Iacorossi, Francesco
Hössinger, Reinhard
Nieuwenhuijsen, Mark
Rojas-Rueda, David
Sanchez, Julian
Smeds, Emilia
Deforth, Manja
Standaert, Arnout
Stigell, Erik
Cole-Hunter, Tom
INT PANIS, Luc 
Issue Date: 2019
Source: JOURNAL OF MEDICAL INTERNET RESEARCH, 21 (5), Art N° e11492
Status: Early View
Abstract: Background: Sufficient sample size and minimal sample bias are core requirements in empirical data analyses. Combining opportunistic recruitment with an online survey and data collection platform yields new benefits compared to traditional recruitment approaches. Objective: The objective of this paper is to report on the success of different recruitment methods to obtain participants’ characteristics, participation behavior, recruitment rates, and representativeness of the sample. Methods: A longitudinal online survey was implemented as part of the European PASTA project, which was online between November 2014 and December 2016. During this period participants in seven European cities were recruited on a rolling basis. For all cities to reach a sufficient number of adult participants a standardized guide on recruitment strategy was developed. In order to make use of the strengths and to minimize weakness a combination of different opportunistic recruitment methods was applied. In addition, the city of Oerebro applied random sampling approach. In order to reduce attrition rate and improve real-time monitoring the online platform featured a participant and a researchers` user interface and dashboard. Results: A total of 10,691 participants were recruited. Most people found out about the survey through their workplace or employer (21.5 %), outreach promotion (20.8 %), and social media (17.4 %). The average number of questionnaires filled-in per participant varied between the cities, with the highest number in Zurich (11.0 ± 0.33) and the lowest in Oerebro (4.8 ± 0.17). Collaboration with local organizations, the use of Facebook and mailing lists, and direct street recruitment were the most effective approaches in reaching a high share of participants (p = <.001). Under consideration of invested working hours Facebook (p = <.001) was one of the most time-efficient methods. Compared to the cities census data, the composition of study participants was broadly representative in terms of gender distribution, however included younger and better educated participants. Conclusions: We observed that offering a mixed recruitment approach was very effective in achieving a high participation rate. The highest attrition rate and the lowest average number of questionnaires filled-in per participant were observed in Oerebro, who also recruited participants through random sampling. The findings suggest that people that are more interested in the topic are more willing to participate and to stay in a survey than those who are selected randomly and may not have a strong connection to the research topic. Whereas direct face-to-face contacts were very effective with respect to the number of recruited participants; recruiting people through social media was not only effective, but also very time-efficient. The collected data is based on one of the largest recruited longitudinal samples with a common recruitment strategy in different European cities.
Notes: Gaupp-Berghausen, M (reprint author), Univ Nat Resources & Life Sci, Inst Transport Studies, Peter Jordan Str 82, A-1190 Vienna, Austria. mailin.gaupp-berghausen@boku.ac.at
Keywords: recruitment; longitudinal survey; opportunistic sampling; multi-central; webbased questionnaire; online survey
Document URI: http://hdl.handle.net/1942/27577
ISSN: 1438-8871
DOI: 10.2196/11492
ISI #: 000467070500001
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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