Online Program

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Thursday, October 19
Thu, Oct 19, 2:45 PM - 3:50 PM
Aventine Ballroom E
Speed Session 1

Predicting Breakoffs in Web Surveys (303744)

*Felicitas Mittereder, University of Michigan, ISR 
Brady West, University of Michigan, ISR 

Keywords: Paradata, Web Survey, Response Behavior, Projection

Due to recent general shifts in survey data collection modes from mail to web, respondents who break off from a web survey prior to completing it have become a more prevalent problem in data collection. Given the (already) lower response rate in web surveys compared to more traditional modes, it is crucial to keep as many diverse respondents in the web survey as possible to prevent breakoff bias, maintaining high data quality and producing accurate survey estimates. As a first step of preventing and reducing breakoffs, this study aims to predict breakoff timing on a question level. We analyze data from an annual online survey on sustainability conducted by the Institute for Social Research at the University of Michigan. This study will make use of survey data, along with rich paradata and accessible administrative information from the sampling frame. In addition to well-known factors associated with breakoffs such as answering device (e.g. mobile vs. PC) we investigate previous response behavior like speeding and item nonresponse to predict breakoff probability for each respondent on a question level using logistic regression and survival analyses.