Survey non-response in an internet-mediated, longitudinal autism research study
- 1Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, Maryland, USA
- 2Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, Maryland, USA
- 3Division of Health Science Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- 4Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, Maryland, USA
- 5Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Correspondence to Dr Paul Law, Department of Medical Informatics, Kennedy Krieger Institute, 3825 Greenspring Avenue, Painter Building, 1st Floor, Baltimore, MD 21211, USA;
Contributors PL is principal investigator for the Interactive Autism Network (IAN). As such, he was responsible for collection of the data employed in the present study. He also provided the original conception of the study. CC contributed to the collection of data and design of instruments within IAN. LK and PLwere both responsible for the data analyses. LK created the first draft of the manuscript. HL contributed to the manuscript through assistance with crafting of research questions, critical edits, and review of results from the statistical analyses and figures. All authors contributed to the design of the study and provided substantial edits to the current manuscript.
- Received 19 January 2012
- Accepted 24 March 2012
- Published Online First 26 April 2012
Objective To evaluate non-response rates to follow-up online surveys using a prospective cohort of parents raising at least one child with an autism spectrum disorder. A secondary objective was to investigate predictors of non-response over time.
Materials and Methods Data were collected from a US-based online research database, the Interactive Autism Network (IAN). A total of 19 497 youths, aged 1.9–19 years (mean 9 years, SD 3.94), were included in the present study. Response to three follow-up surveys, solicited from parents after baseline enrollment, served as the outcome measures. Multivariate binary logistic regression models were then used to examine predictors of non-response.
Results 31 216 survey instances were examined, of which 8772 or 28.1% were partly or completely responded to. Results from the multivariate model found non-response of baseline surveys (OR 28.0), years since enrollment in the online protocol (OR 2.06), and numerous sociodemographic characteristics were associated with non-response to follow-up surveys (all p<0.05).
Discussion Consistent with the current literature, response rates to online surveys were somewhat low. While many demographic characteristics were associated with non-response, time since registration and participation at baseline played the greatest role in predicting follow-up survey non-response.
Conclusion An important hazard to the generalizability of findings from research is non-response bias; however, little is known about this problem in longitudinal internet-mediated research (IMR). This study sheds new light on important predictors of longitudinal response rates that should be considered before launching a prospective IMR study.
- decision modeling
- internet-mediated research
- online survey research
- public health informatics
- survey non-response
Funding This study was funded by Autism Speaks Foundation, Simons Foundation, Kennedy Krieger Institute, and the National Institute of Mental Health as part of IAN Core Activity grants. The authors have no financial disclosures.
Competing interests None.
Ethics approval Ethics approval was provided by Johns Hopkins Medicine Institutional Review Board (JHM IRB#: NA_00002750).
Provenance and peer review Not commissioned; externally peer reviewed.