Advertisement

International Journal of Public Health

, Volume 60, Issue 8, pp 937–943 | Cite as

Diverse recruitment strategies result in different participation percentages in a web-based study, but in similar compliance

  • Manas K. AkmatovEmail author
  • Nicole Rübsamen
  • Anja Schultze
  • Yvonne Kemmling
  • Nadia Obi
  • Kathrin Günther
  • Wolfgang Ahrens
  • Frank Pessler
  • Gérard Krause
  • Rafael T. Mikolajczyk
Original Article

Abstract

Objectives

We compared participation and compliance with a web-based data collection on infections among population-based samples recruited in different ways.

Methods

Individuals were recruited from participants in the German National Cohort study (Group A, n = 279) or persons who were invited to this study but did not participate (Group B, n = 53). A third group was invited to the web-based study only (Group C, n = 145).

Results

Response varied among groups between 3 % (B), 11 % (C) and 61 % (A), but compliance was similar (81–85 %). Response did not differ by age and sex. Compliance was lower among the youngest and oldest participants. In addition, participants currently not employed were more likely to have better compliance. Semi-parametric group-based modelling identified three distinct compliance trajectories; “poor compliance” (8 %), “improving compliance” (14 %) and “very good compliance” (78 %).

Conclusions

Participation differed among modes of recruitment, but compliance was similar among groups and notably high. Different recruitment approaches can be used and collected data can be combined to achieve greater sample sizes for longitudinal web-based studies.

Keywords

Response Compliance Recruitment strategies Longitudinal study Non-responders Population-based study Second-stage non-response Web-based study German National Cohort 

Notes

Acknowledgments

This project was conducted in the context of the Pretest studies (Pretest 2) of the German National Cohort (http://www.nationale-kohorte.de). These were funded by the Federal Ministry of Education and Research (BMBF), project number 01ER1203, and supported by the Helmholtz Association as well as by the participating universities and Institutes of the Leibniz Association. We also gratefully acknowledge the contribution to data collection by the study personnel in Hamburg, Hannover and Bremen.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The study was approved by the Ethics Committees of the State Boards of Physicians of the German Federal States of Bremen, Hamburg, and Lower Saxony. All subjects gave written informed consent before entering the study.

References

  1. Bexelius C, Merk H, Sandin S, Nyren O, Kuhlmann-Berenzon S, Linde A, Litton JE (2010) Interactive voice response and web-based questionnaires for population-based infectious disease reporting. Eur J Epidemiol 25(10):693–702CrossRefPubMedGoogle Scholar
  2. Boshuizen H, Viet A, Picavet H, Botterweck A, van Loon A (2006) Non-response in a survey of cardiovascular risk factors in the Dutch population: determinants and resulting biases. Public Health 120(4):297–308CrossRefPubMedGoogle Scholar
  3. Duffy B, Smith K, Terhanian G, Bremer J (2005) Comparing data from online and face-to-face surveys. Int J Market Res 47(6):615–639Google Scholar
  4. Fan W, Yan Z (2010) Factors affecting response rates of the web survey: a systematic review. Comput Hum Behav 26(2):132–139CrossRefGoogle Scholar
  5. Hosmer D, Lemeshow S (2000) Logistic regression models for the analysis of correlated data. Applied logistic regression. Wiley, New YorkCrossRefGoogle Scholar
  6. Jones B, Nagin D, Roeder K (2001) A SAS procedure based on mixture models for estimating developmental trajectories. Socio Meth Res 29(3):374–393CrossRefGoogle Scholar
  7. Midanik LT, Greenfield TK (2010) Reports of alcohol-related problems and alcohol dependence for demographic subgroups using interactive voice response versus telephone surveys: the 2005 US National Alcohol Survey. Drug Alcohol Rev 29(4):392–398PubMedCentralCrossRefPubMedGoogle Scholar
  8. Nagelhout GE, Willemsen MC, Thompson ME, Fong GT, van den Putte B, de VH (2010) Is web interviewing a good alternative to telephone interviewing? Findings from the International Tobacco Control (ITC) Netherlands survey. BMC Public Health 10:351PubMedCentralCrossRefPubMedGoogle Scholar
  9. Owen J, Bantum E, Criswell K, Bazzo J, Gorlick A, Stanton A (2013) Representativeness of two sampling procedures for an internet intervention targeting cancer-related distress: a comparison of convenience and registry samples. J Behav Med 37(4):630–641PubMedCentralCrossRefPubMedGoogle Scholar
  10. Pealer L, Weiler R, Pigg R Jr, Miller D, Dorman S (2001) The feasibility of a web-based surveillance system to collect health risk behavior data from college students. Health Educ Behav 28(5):547–559CrossRefPubMedGoogle Scholar
  11. Raziano D, Jayadevappa R, Valenzula D, Weiner M, Lavizzo-Mourey R (2001) E-mail versus conventional postal mail survey of geriatric chiefs. Gerontologist 41(6):799–804CrossRefPubMedGoogle Scholar
  12. Russell C, Boggs D, Palmer J, Rosenberg L (2010) Use of a web-based questionnaire in the Black Women’s Health Study. Am J Epidemiol 172(11):1286–1291PubMedCentralCrossRefPubMedGoogle Scholar
  13. Schmitz, C (2007) LimeSurvey. The open source survey application. http://www.limesurvey.org. Accessed 5 February 2015
  14. Turner CF, Villarroel MA, Rogers SM, Eggleston E, Ganapathi L, Roman AM, Al-Tayyib A (2005) Reducing bias in telephone survey estimates of the prevalence of drug use: a randomized trial of telephone audio-CASI. Addiction 100(10):1432–1444CrossRefPubMedGoogle Scholar
  15. van Loon A, Tijhuis M, Picavet H, Surtees P, Ormel J (2003) Survey non-response in the Netherlands: effects on prevalence estimates and associations. Ann Epidemiol 13(2):105–110CrossRefPubMedGoogle Scholar
  16. Volken T (2013) Second-stage non-response in the Swiss health survey: determinants and bias in outcomes. BMC Public Health 13:167PubMedCentralCrossRefPubMedGoogle Scholar
  17. Wichmann HE, Kaaks R, Hoffmann W, Jockel KH, Greiser KH, Linseisen J (2012) The German National Cohort. Bundesgesundheitsbl 55(6–7):781–787CrossRefGoogle Scholar

Copyright information

© Swiss School of Public Health (SSPH+) 2015

Authors and Affiliations

  • Manas K. Akmatov
    • 1
    • 4
    Email author
  • Nicole Rübsamen
    • 1
  • Anja Schultze
    • 1
  • Yvonne Kemmling
    • 1
  • Nadia Obi
    • 2
  • Kathrin Günther
    • 3
  • Wolfgang Ahrens
    • 3
  • Frank Pessler
    • 4
  • Gérard Krause
    • 1
  • Rafael T. Mikolajczyk
    • 1
  1. 1.Department of EpidemiologyHelmholtz Centre for Infection ResearchBrunswickGermany
  2. 2.University Cancer Center HamburgUniversity Medical Center Hamburg-EppendorfHamburgGermany
  3. 3.Department of Epidemiological Methods and Etiologic ResearchLeibniz Institute for Prevention Research and Epidemiology-BIPSBremenGermany
  4. 4.TWINCORE Centre for Experimental and Clinical Infection ResearchHannoverGermany

Personalised recommendations