Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data

Ideally, survey respondents read and understand survey instructions, questions, and response scales, and provide answers that carefully reflect their beliefs, attitudes, or knowledge. However, respondents may also arrive at their responses using cues or heuristics that facilitate the production of a response, but diminish the targeted information content. We use eye-tracking data as covariates in a Bayesian switching-mixture model to identify different response behaviors at the item-respondent level. The model distinguishes response behaviors that are predominantly influenced either positively or negatively by the previous response, and responses that reflect respondents' preexisting knowledge and experiences of interest. We find that controlling for multiple types of adaptive response behaviors allows for a more informative analysis of survey data and respondents.