ArticlePsychometrika2022
Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data.
Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
34698978What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.