. 2025 Oct 31;25(1):3708.

doi: 10.1186/s12889-025-25038-4.

Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias

Isabella Hernandez¹, Velia Nuñez², Lorena Reynaga², Kennedy Stewart², Ixel Hernandez-Castro¹, Luis E Maldonado¹, Karina Corona¹, Max Aung¹, Emily A Knapp³, Garrett Fuselier³, Christian Douglas⁴, Carmen Velez Vega⁵, Elissa Faro⁶, Rachel Morello Frosch⁷, Johnnye Lewis⁸, Lisa A Croen⁹, Anne Lang Dunlop¹⁰, Jody Ganiban¹¹, Kate Keenan¹², Theresa Bastain^{13

14}; Environmental influences on Child Health Outcomes

Collaborators, Affiliations

Collaborators

Environmental influences on Child Health Outcomes:
Peterson, Parsons, Kannan, Fennell, Sumner, Du, Teitelbaum, Wright, Alshawabkeh, Aschner, Trasande, Camargo, Dabelea, Deoni, Duarte, Dunlop, Elliott, Ferrara, Gern, Breton, Hertz-Picciotto, Hipwell, Karagas, Karr, Lester, Leve, MacKenzie, Weiss, McEvoy, Lyall, O'Connor, Oken, O'Shea, Kerver, Herbstman, Schantz, Stanford, Trasande, Wright

Affiliations

¹ University of Southern California, Los Angeles, CA, USA.
² California State University, Northridge, Northridge, CA, USA.
³ Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ RTI International, Research Triangle Park, NC, USA.
⁵ University of Puerto Rico, San Juan, Puerto Rico, USA.
⁶ University of Iowa, Iowa City, Iowa, USA.
⁷ University of California, Berkeley, Berkeley, CA, USA.
⁸ University of New Mexico, Albuquerque, NM, USA.
⁹ Kaiser Permanente Northern California, Pleasanton, CA, USA.
¹⁰ Emory University, Atlanta, GA, USA.
¹¹ George Washington University, Washington, DC, USA.
¹² University of Chicago, Chicago, IL, USA.
¹³ University of Southern California, Los Angeles, CA, USA. bastain@usc.edu.
¹⁴ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1845 N. Soto Street, M/C 9237, Los Angeles, CA, 90032, USA. bastain@usc.edu.

PMID: 41174740
PMCID: PMC12577174
DOI: 10.1186/s12889-025-25038-4

Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias

Isabella Hernandez et al. BMC Public Health. 2025.

. 2025 Oct 31;25(1):3708.

doi: 10.1186/s12889-025-25038-4.

Authors

Collaborators

Environmental influences on Child Health Outcomes:
Peterson, Parsons, Kannan, Fennell, Sumner, Du, Teitelbaum, Wright, Alshawabkeh, Aschner, Trasande, Camargo, Dabelea, Deoni, Duarte, Dunlop, Elliott, Ferrara, Gern, Breton, Hertz-Picciotto, Hipwell, Karagas, Karr, Lester, Leve, MacKenzie, Weiss, McEvoy, Lyall, O'Connor, Oken, O'Shea, Kerver, Herbstman, Schantz, Stanford, Trasande, Wright

Affiliations

¹ University of Southern California, Los Angeles, CA, USA.
² California State University, Northridge, Northridge, CA, USA.
³ Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ RTI International, Research Triangle Park, NC, USA.
⁵ University of Puerto Rico, San Juan, Puerto Rico, USA.
⁶ University of Iowa, Iowa City, Iowa, USA.
⁷ University of California, Berkeley, Berkeley, CA, USA.
⁸ University of New Mexico, Albuquerque, NM, USA.
⁹ Kaiser Permanente Northern California, Pleasanton, CA, USA.
¹⁰ Emory University, Atlanta, GA, USA.
¹¹ George Washington University, Washington, DC, USA.
¹² University of Chicago, Chicago, IL, USA.
¹³ University of Southern California, Los Angeles, CA, USA. bastain@usc.edu.
¹⁴ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1845 N. Soto Street, M/C 9237, Los Angeles, CA, 90032, USA. bastain@usc.edu.

PMID: 41174740
PMCID: PMC12577174
DOI: 10.1186/s12889-025-25038-4

Abstract

Background: Questionnaires for research that involve diverse populations require inclusive language. There are few guidelines to assist researchers in minimizing social and cultural biases in data collection materials; such biases can result in harm and negatively impact data integrity.

Methods: We describe an approach to evaluating language in data collection forms reflecting racial, ethnic, heteronormative, and gender bias using the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study (EWC) as a case study. The 245 data collection forms were used by 69 cohorts in the first seven years of the (ECHO)-wide Cohort Study (EWC). A diverse panel of reviewers (n = 5) rated all forms; each form also was rated by a second student. Items identified as reflecting bias were coded as to the specificity of the bias using nine categories (e.g., racial bias, heteronormative assumptions) following whole panel discussion. We provide recommendations for conducting inclusive research to the scientific community.

Results: Thirty-six percent (n = 88) of the data collection forms were identified as containing biased language. In total, 137 instances of bias were recorded, eight instances of racial or ethnic bias, 56 instances of bias related to sex, gender identity and sexual orientation and 73 instances of bias related to universal assumptions. Seventy-three percent (n = 64) of forms with biased language are validated measures. The review culminated in recommended revisions to forms used by ECHO and the general scientific community.

Conclusion: Adverse health outcomes disproportionately affect marginalized populations. Utilizing culturally and socially conscious research materials that are inclusive of various identities and experiences is necessary to help remediate these disparities. Our review finds compelling evidence of bias in many widely used data collection instruments. Recommendations for conducting more inclusive science are discussed.

Keywords: Bias; Data collection; Inclusive language.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approvals were obtained from Institutional Review Boards at each participating cohort site, with informed consent obtained from primary caregivers and child assent when appropriate. Select de-identified data from the ECHO Program are available through NICHD’s Data and Specimen Hub (DASH). Information on study data not available on DASH, such as some Indigenous datasets, can be found on the ECHO study DASH webpage. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

**Fig. 1**
Analysis of Biased Language in Data Collection Forms: Subcategories and Instances. Of the 260 total data collection forms used in the initial 7-year cycle of the ECHO-wide Cohort Data Collection Protocol (EWCP), 15 were excluded due to computer adaptive testing (CAT) or exclusive data abstraction use. This left 245 participant-facing forms for review. Among these, 88 forms exhibited biased language across nine subcategories, with 33 containing multiple instances, totaling 137 instances. Eight instances of racial or ethnic bias, 56 instances related to sex, gender identity, and sexual orientation bias, and 73 instances of universal assumptions were identified

See this image and copyright information in PMC

References

1. Burlew AK, Peteet BJ, McCuistian C, Miller-Roenigk BD. Best practices for researching diverse groups. Am J Orthopsychiatry. 2019;89(3):354–68. - PubMed
1. Devakumar D, Selvarajah S, Abubakar I, Kim SS, McKee M, Sabharwal NS, et al. Racism, xenophobia, discrimination, and the determination of health. Lancet. 2022;400(10368):2097–108. - PubMed
1. Selvarajah S, Corona Maioli S, Deivanayagam TA, de Morais Sato P, Devakumar D, Kim SS, et al. Racism, xenophobia, and discrimination: mapping pathways to health outcomes. Lancet. 2022;400(10368):2109–24. - PubMed
1. What is Health Equity? Centers for Disease Control and Prevention: Office of Minority Health & Health Equity (OMHHE); 202: https://www.cdc.gov/healthequity/whatis/index.html.
1. Williams DR, Rucker TD. Understanding and addressing racial disparities in health care. Health Care Financ Rev. 2000;21(4):75–90. - PMC - PubMed

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Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias

Collaborators

Affiliations

Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources