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Measuring Gender in Comparative Survey Research

Published online by Cambridge University Press:  09 June 2025

Oscar Castorena
Affiliation:
Vanderbilt University, USA
Eli G. Rau
Affiliation:
Tecnologico de Monterrey, Mexico
Valerie Schweizer-Robinson
Affiliation:
Vanderbilt University, USA
Elizabeth J. Zechmeister
Affiliation:
Vanderbilt University, USA
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Abstract

As societal conceptions of gender have evolved, so too have survey-based approaches to the measurement of gender. Yet, most research innovations and insights regarding the measurement of gender come from online or phone surveys in the Global North. We focus on face-to-face surveys in the Global South, specifically in the Latin America and Caribbean (LAC) region. Through in-person interviews, an online experiment, and survey experiments, we identify and assess an open-ended approach to incorporating respondent-provided gender identity in face-to-face interviews. Our results affirm that the measure is comparatively effective in minimizing discomfort and does not have substantial consequences for data quality across a diverse set of LAC countries. We discuss the potential traveling capacity of our approach and identify paths for further research on best practices in recording interviewee gender in face-to-face surveys in the LAC region and beyond.

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Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of American Political Science Association
Figure 0

Figure 1 Attrition and Overall NonresponseTreatment effects are estimated from linear regressions, controlling for interviewer ID. The estimated effects are presented with 95% confidence intervals.

Figure 1

Figure 2 Average Treatment EffectsTreatment effects are estimated from linear regressions, controlling for age, urban/rural residence, interviewer ID, and whether any other individuals were present at the time of interview. The estimated effects are presented with 95% confidence intervals.

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