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‘I Just Don’t Trust Them’: Reasons for Distrust and Non-Disclosure in Demographic Questionnaires for Individuals in STEM

Author

Listed:
  • Maria Goldshtein

    (Learning Engineering Institute, Arizona State University, Tempe, AZ 85287, USA)

  • Erin K. Chiou

    (Human Systems Engineering, Arizona State University, Mesa, AZ 85212, USA)

  • Rod D. Roscoe

    (Learning Engineering Institute, Arizona State University, Tempe, AZ 85287, USA
    Human Systems Engineering, Arizona State University, Mesa, AZ 85212, USA)

Abstract

Demographic data pertain to people’s identities and behaviors. Analyses of demographic data are used to describe patterns and predict behaviors, to inform interface design, and even institutional decision-making processes. Demographic data thus need to be complete and correct to ensure they can be analyzed in ways that reflect reality. This study consists of interviews with 40 people in STEM and addresses how causes of relational (dis)trust in demographic data collection contribute to pervasive problems of missing and incorrect responses and disobliging responses (e.g., non-disclosure, false responses, attrition, and hesitancy to use services). The findings then guide a preliminary set of recommendations for cultivating trustworthiness based on recent developments in trust theory and designing for responsive and trustworthy systems. Specifically, we explore how demographic questionnaire design (e.g., item construction and instructions) can communicate necessary reassurances and transparency for users. The ongoing research provides interview-based recommendations for improving the quality and completeness of demographic data collection. This research adds to other recommendations on improving demographic questionnaires.

Suggested Citation

  • Maria Goldshtein & Erin K. Chiou & Rod D. Roscoe, 2024. "‘I Just Don’t Trust Them’: Reasons for Distrust and Non-Disclosure in Demographic Questionnaires for Individuals in STEM," Societies, MDPI, vol. 14(7), pages 1-21, June.
    • Handle: RePEc:gam:jsoctx:v:14:y:2024:i:7:p:105-:d:1425577
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    References listed on IDEAS

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    1. Knighton, James & Buchanan, Brian & Guzman, Christian & Elliott, Rebecca & White, Eric & Rahm, Brian, 2020. "Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: exploring the roles of topography, minority populations, and political dissimilarity," LSE Research Online Documents on Economics 105761, London School of Economics and Political Science, LSE Library.
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