Satisficing in Self-Completion Modes: Theoretical Understanding, Assessment, Prevention and Consequences for Data Quality |
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| Coordinator 1 | Dr Daniil Lebedev (GESIS - Leibniz Institute for the Social Sciences, Germany) |
| Coordinator 2 | Dr May Doušak (University of Ljubljana, Slovenia) |
Self-completion surveys, which are increasingly preferred over face-to-face modes, present unique challenges. Rising costs, declining response rates, and interviewer effects make face-to-face surveys less viable. However, self-completion modes (web, mail, or mixed) introduce their own data-quality related challenges. Without an interviewer, respondents face a higher cognitive load, which can lead to satisficing – providing suboptimal answers – especially among those with lower cognitive ability or motivation. This behaviour increases measurement error and lowers data quality.
Survey methodologists have developed various indicators to assess response quality by detecting undesired respondent behaviour, such as straightlining and acquiescence, along with paradata-measured response styles like speeding, multitasking, motivated misreporting, and others. Questions assessing respondents' subjective enjoyment, cognitive effort, and time investment also help identify satisficing propensity. These tools can be used for detection and prevention through immediate feedback or adaptive survey designs based on survey data, paradata, or probing questions.
This session focuses on theoretical advancements in understanding satisficing and response styles in self-completion surveys, whether computer-assisted or paper-based, research on survey data-based indicators, paradata, and probing questions for assessing and preventing satisficing. Developing and implementing satisficing propensity models and tools and evaluating satisficing's impact on data quality in self-completion modes are also key topics. Contributions may address these areas and related research topics.
Keywords: satisficing, response styles, self-completion, web surveys, mail surveys, paradata, data quality indicators, probing questions, experiments, motivation, cognitive effort, cognitive ability, respondent engagement