ISR Awards

Satisficing in Web Surveys: Implications for Data Quality and Strategies for Its Reduction

U.S. Census Bureau Dissertation Fellowship Program Proposal:
Satisficing in Web Surveys:
Implications for Data Quality and Strategies for Its Reduction
Doctoral Candidate: Chan Zhang
Faculty Dissertation Advisor: Frederick Conrad
Program in Survey Methodology, University of Michigan

Abstract: With the increasing use of the Web in mixed mode surveys, especially
those conducted by the Census and other federal statistical agencies, it has
become more urgent than ever to develop methods to enhance online
measurement quality. This dissertation research (which includes three studies)
focuses on respondent satisficing as a source of online measurement errors, and
an intervention approach to reduce satisficing behaviors. The first and second
studies aim to extend the scope of previous research on Web survey interventions
by investigating how the design of the intervention might affect its success in
curtailing respondent satisficing. The third study aims to improve our
understanding of satisficing behaviors by investigating patterns of satisficing
across questions and how different types of satisficing behaviors might be
associated to one another. The findings will lead to better interpretation of
satisficing behaviors and more informed designs of interventions to reduce
satisficing and enhance online measurement quality.