title: Methods for Evaluating Respondent Attrition in Web-Based Surveys.
abstract: Electronic surveys are convenient, cost effective, and increasingly popular tools for collecting information. While the online platform allows researchers to recruit and enroll more participants, there is an increased risk of participant dropout in Web-based research. Often, these dropout trends are simply reported, adjusted for, or ignored altogether.
  
  
  
  To propose a conceptual framework that analyzes respondent attrition and demonstrates the utility of these methods with existing survey data.
  
  
  
  First, we suggest visualization of attrition trends using bar charts and survival curves. Next, we propose a generalized linear mixed model (GLMM) to detect or confirm significant attrition points. Finally, we suggest applications of existing statistical methods to investigate the effect of internal survey characteristics and patient characteristics on dropout. In order to apply this framework, we conducted a case study; a seventeen-item Informed Decision-Making (IDM) module addressing how and why patients make decisions about cancer screening.
  
  
  
  Using the framework, we were able to find significant attrition points at Questions 4, 6, 7, and 9, and were also able to identify participant responses and characteristics associated with dropout at these points and overall.
  
  
  
  When these methods were applied to survey data, significant attrition trends were revealed, both visually and empirically, that can inspire researchers to investigate the factors associated with survey dropout, address whether survey completion is associated with health outcomes, and compare attrition patterns between groups. The framework can be used to extract information beyond simple responses, can be useful during survey development, and can help determine the external validity of survey results.
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Camille J
authors.identifier: 0000-0002-0984-0909
authors.initials: CJ
authors.lastname: Hochheimer
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Roy T
authors.identifier: 0000-0002-5866-2598
authors.initials: RT
authors.lastname: Sabo
authors.affiliation: Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Alex H
authors.identifier: 0000-0003-4060-9155
authors.initials: AH
authors.lastname: Krist
authors.affiliation: Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Teresa
authors.identifier: 0000-0003-3515-650X
authors.initials: T
authors.lastname: Day
authors.affiliation: Tompkins-McCaw Library, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: John
authors.identifier: 0000-0001-5331-5151
authors.initials: J
authors.lastname: Cyrus
authors.affiliation: Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Steven H
authors.identifier: 0000-0001-9384-033X
authors.initials: SH
authors.lastname: Woolf
mesh_terms: D000328:Adult; D000368:Aged; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D020407:Internet; D008297:Male; D008875:Middle Aged; D010352:Patient Dropouts; D011795:Surveys and Questionnaires
keywords: electronic health records; patient dropouts; surveys and questionnaires