ESRA 2027 sessions by theme

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Session Details

Rising costs, declining response rates, and interviewer effects make face-to-face surveys less viable. However, self-completion modes (web, mail, or mixed), which are increasingly preferred over face-to-face modes, introduce their own data-quality related challenges. Without an interviewer, respondents face a higher cognitive load as well as lack motivation to provide accurate answers, which can lead to satisficing – completing suboptimally or skipping entirely some or all of the cognitive steps of answering a questions. This behaviour increases nonresponse, measurement error and lowers data quality in general.

Survey methodologists have developed various indicators to assess response quality by detecting undesired respondent behaviour, such as straightlining and acquiescence, alongside 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.