Keywords

data quality, survey research, self-report data, online survey

Abstract

Some participants of online surveys engage in extreme answering behavior while generating responses (i.e., they respond too fast or too slow) relative to population norms. Here, we demonstrate how participants’ navigation behaviors can be used to potentially identify such responses. We administered an online survey where students (who were earlier instructed to complete a task) report lenience scores towards non-appropriate behavior while completing the task. We draw on cognitive dissonance theory to posit that failure to follow instruction predicts lenience scores. We then created different datasets by excluding data from participants flagged by our metrics and generated predictive models. We found that model performance improves by removing data from flagged participants, indicating a reduction in noise from the dataset. Despite demonstrating the effectiveness of our approach, we encourage researchers to exercise caution and elaborate on the limitations of our approach and future avenues of research.

Original Publication Citation

Kumar, M., Valacich, J. S., Jenkins, J. L., and Kim, D. (2022) “Too Fast? Too Slow? A Novel Approach for Identifying Extreme Response Behavior in Online Surveys” 21st Annual Pre-ICIS Workshop on HCI Research in MIS” Proceedings of the Twenty-First Annual Workshop on HCI Research in MIS, Copenhagen, Denmark, December 11.

Document Type

Conference Paper

Publication Date

2022

Publisher

Proceedings of the Twenty-First Annual Workshop on HCI Research in MIS

Language

English

College

Marriott School of Business

Department

Information Systems Management

University Standing at Time of Publication

Full Professor

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