Related Experiment Video
Updated: Jan 12, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias
Isabella Hernandez1, Velia Nuñez2, Lorena Reynaga2
1University of Southern California, Los Angeles, CA, USA.
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.
More Related Videos
09:00Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
Published on: August 16, 2024
09:03Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias
Published on: February 29, 2020
Related Concept Videos
Stereotypes, Prejudice, and Discrimination
Surveys
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Confirmation Biases
Bias in Epidemiological Studies
Stereotype Content Model