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. 2025 Jul 18;57(8):231.
doi: 10.3758/s13428-025-02730-2.

Dropout analysis: A method for data from Internet-based research and dropR, an R-based web app and package to analyze and visualize dropout

Ulf-Dietrich Reips  1 , Annika T Overlander  2 , Matthias Bannert  3
Affiliations

Dropout analysis: A method for data from Internet-based research and dropR, an R-based web app and package to analyze and visualize dropout

Ulf-Dietrich Reips et al. Behav Res Methods. .

Abstract

With Internet-based research, non-response such as lack of responses to particular items and dropout have become interesting dependent variables due to highly voluntary participation and large numbers of participants (Reips, 2000, 2002b). In this article, we develop and discuss the methodology of using and analyzing dropout in Internet-based research, and we present dropR, an R package and web service (web application) to analyze and visualize dropout. The web app was written in R using Shiny, a free software environment for statistical computing and graphics. Among other features, dropR turns input from datasets into accessible and publication-ready visual displays of dropout curves. It calculates parameters relevant to dropout analysis, such as chi-square values and odds ratios for points of difference, initial drop, and percent remaining in stable states. It provides Kaplan-Meier survival statistics and tests survival curve differences. With automated inferential components, it identifies critical points in dropout and critical differences between dropout curves for different experimental conditions (Kolmogorov-Smirnov and rho-family statistics) and produces related statistical copy. Requiring no programming knowledge, dropR is provided as a free web application at https://dropr.eu and for programmers as an R package (under a cost free general public license, GPL-3, https://cran.r-project.org/web/licenses/GPL-3 ) from researchers for researchers. All code and materials are openly available on GitHub ( https://github.com/iscience-kn/dropR ).

Keywords: Atrittion; Dropout; Mortality; Non-response; R package; Shiny; Survival analysis; Web app; iScience.

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Conflict of interest statement

Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Open Practices: Materials and all source code are available at https://github.com/iscience-kn/dropR . Author Note: This research was first presented at the 4th conference of the European Survey Research Association (ESRA), July 18–22, 2011, Lausanne, Switzerland, in the symposium “Internet-based research: New horizons”, at the 44th annual meeting of the Society for Research in Psychology (SCiP), Long Beach, November 20, 2014, and at the 7th Webdatanet conference, Salamanca, Spain, May 27, 2015. The dropR package was also presented at the user! Conference in Salzburg, Austria, July 9, 2024. Correspondence concerning this article should be addressed to Ulf-Dietrich Reips, reips@uni-konstanz.de, University of Konstanz, Department of Psychology, Box 31, 78457 Konstanz, Germany. Competing interests: The authors have no competing interests to declare that are relevant to the content of this article.

Figures

Fig. 1
Fig. 1
Dropout rates by the method of recruitment. Note. Dropout rates resulting from an experimental manipulation of recruitment of participants and setting: No dropout in the laboratory, after traditional recruitment, but on the Internet, both when recruited via flyer or via links on websites (figure adapted from Reips, , based on data from Neuhaus, 2002).
Fig. 2
Fig. 2
Initial page shown in the dropR application
Fig. 3
Fig. 3
Upload tab in the dropR application
Fig. 4
Fig. 4
Example of dropout analysis for four experimental conditions. Note. The dropout curves shown here were created using the default settings in the dropR web app. The data used is the demo dataset with fictitious data available in the package as dropR::demodata or in the app by checking the “Use demo data” box in the Upload tab. It shows dropout rates for four experimental conditions (11, 12, 21, 22) over 52 questions as well as the overall dropout curve (“total”)
See this image and copyright information in PMC

References

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