Using the Theory of Satisficing to Evaluate the Quality of Survey Data
By Scott Barge, Hunter Gehlbach. Research in Higher Education, 2011
164 citations recorded by the source corpus.
Increasingly colleges and universities use survey results to make decisions, inform research, and shape public opinion. Given the large number of surveys distributed on campuses, can researchers reasonably expect that busy respondents will diligently answer each and every question? Less serious respondents may “satisfice,” i.e., take short-cuts to conserve effort, in a number of ways—choosing the same response every time, skipping items, rushing through the instrument, or quitting early. In this paper we apply this satisficing framework to demonstrate analytic options for assessing respondents’ conscientiousness in giving high fidelity survey answers. Specifically, we operationalize satisficing as a series of measurable behaviors and compute a satisficing index for each survey respondent. Using data from two surveys administered in university contexts, we find that the majority of respondents engaged in satisficing behaviors, that single-item results can be significantly impacted by satisficing, and that scale reliabilities and correlations can be altered by satisficing behaviors. We conclude with a discussion of the importance of identifying satisficers in routine survey analysis in order to verify data quality prior to using results for decision-making, research, or public dissemination of findings.
Subfields: Economics and Econometrics, Organizational Behavior and Human Resource Management, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/1cd11a6063936b8bba678f4eda30f05db66d0171
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Citations in the corpus
- Applying Social Psychology to Prevent Careless Responding during Online Surveys (2018, background): “…of CR need to be researched, including: environmental distractions (Carrier, Cheever, Rosen, Benitez, & Chang, 2009), multitasking (Zwarun & Hall, 2014), boredom, and other internal states of low motivation all of which can lead to satisficing when completing surveys (Barge & Gehlbach, 2012).”
- Too Fast, too Straight, too Weird: Non-Reactive Indicators for Meaningless Data in Internet Surveys (2019, background): “First, the standard deviation (SD) was chosen to indicate straightlining with minor deviations (Barge & Gehlbach, 2012).”