Asking about Gender and Sex in Surveys

Most survey questionnaires in the social sciences, health sciences, civil engineering, city planning and so on include a demographic question about sex or gender. Traditionally, survey-based items about sex or gender have asked respondents to indicate whether they are male or female (Box 1). This method, however, has been criticized for conceptual slippage, inaccuracy and an inability to capture the complexities of gender and sex identities. Conflating birth sex and gender identity in questionnaires can lower the precision and relevance of survey research for policy development and innovation.

Box 1. The one-step method (example)
Are you male or female?
Example taken from the American National Election Survey 2008-2009 (Westbrook and Saperstein, 2015)

In a review of major U.S. social surveys, Westbrook and Saperstein (2015, p. 535) found that the current one-step method tends to “conflate sex and gender and treat the resulting conceptual muddle as a starkly dichotomous, biologically fixed, and empirically obvious characteristic.” Surveys that employ the one-step method tend to use sex and gender as interchangeable terms or leave unspecified which of the two they are asking about (Box 1). Further, this method presumes a concordance between the respondent’s birth sex and current gender identity that makes it impossible for survey analysts to differentiate between cisgender and transgender populations (see Terms: Sex and Gender).

One-step methods typically also constrict the available response options to mutually exclusive binary categories (female/male, woman/man) (Westbrook and Saperstein, 2015). By doing so, they implicitly perpetuate stereotypical conceptions of sex and/or gender, and make gender-diverse individuals who do not identify with female/male and woman/man categories invisible.

To remedy these shortcomings, researchers have developed a two-step method that measures birth sex and current gender identity separately (Deutsch et al., 2013; GenIUSS Group, 2014; Melendez et al., 2006). The two-step method has been tested in transgender populations and validated in broader North American populations, with good results (GenIUSS Group, 2014; Magliozzi et al., 2016; Reisner et al., 2014; Saperstein and Westbrook, 2018; Tate et al., 2013).

This method (Box 2) has several advantages. It allows survey researchers to infer transgender identity indirectly when a respondent’s birth sex does not match the reported gender identity (e.g. when a respondent ticks ‘male’ in the birth-sex question and ‘woman’ in the gender-identity question) (Magliozzi et al., 2016). Moreover, it expands the number of response options available to survey participants. In addition to the response categories ‘female and ‘male’, the birth-sex question in Box 2 includes ‘intersex’ and ‘sex not listed here’ (with an open response option). Likewise, the current gender-identity question lists ‘non-binary’, ‘genderqueer’ and ‘a gender identity not listed here’ (with an open response option) as possible response options.

Box 2. The two-step method
Birth sex
Adapted from Magliozzi et al. (2016) and GenIUSS Group (2014)

Note here that the response options for birth sex include the biological terms ‘female’ and ‘male’, while the question about current gender identity lists the gender terms ‘woman’ and ‘man’ (Box 2). This terminological distinction is important as it helps signal to respondents the conceptual difference between birth sex and current gender identity (see Terms: Sex and Gender). However, this distinction is not possible in languages that use a single term to refer to both sex and gender identity. For instance, the Danish, Norwegian, and Swedish languages do not have separate terms for sex and gender identity, and do not distinguish between the biological terms ‘female’ and ‘male’ and the gender terms ‘woman’ and ‘man.’ Survey researchers in these countries can, however, still make use of the two-step approach to separate birth sex and current gender identity.

Individuals who do not identify with binary gender categories (woman or man) may use a variety of terms to express their gender identity (e.g. non-binary, genderqueer, gender-nonconforming, agender) (Factor & Rothblum, 2008; Magliozzi et al., 2016; Scheuderman et al., 2019). The meaning and prevalence of these gender expressions tend to change over time and to vary by language and cultural context (Factor & Rothblum, 2008; GenIUSS Group, 2014; Jans et al., 2015).

This introduces a challenge for survey researchers. No matter how many response options a survey provides, it will never be possible to fully cover the gamut of gendered self-expressions (Magliozzi et al., 2016). This challenge can be resolved by including open-ended response options (Box 2). To allow for meaningful statistical comparison, however, survey analysts may need to subsume such open-ended expressions within broader categories before analyzing the data (Magliozzi et al., 2016).

Data from respondents who do not identify with binary gender categories or intersex individuals should not simply be removed from the analysis, but handled according to a pre-planned protocol. To allow for meaningful statistical analysis, researchers should consider oversampling non-binary and intersex individuals in the data collection process (Methods: Analyzing Gender in Health & Biomedicine; Analyzing Sex in Biomedicine).

Recently, researchers have begun to explore more fine-grained multifactorial measures of gender in survey questionnaires (Magliozzi et al., 2016, Nielsen et al., forthcoming, Pilote et al., 2013; Saperstein and Westbrook, 2018). The use of such measures may allow for more precise knowledge on specific gender-related attitudes and behaviors (see Case Study: Gender Variables for Health Research).

Works Cited

Deutsch, M. B., Green, J., Keatley, J., Mayer, G., Hastings, J., Hall, A. M., ... & Blumer, O. (2013). Electronic medical records and the transgender patient: recommendations from the World Professional Association for Transgender Health EMR Working Group. Journal of the American Medical Informatics Association, 20(4), 700-703.

Factor, R., & Rothblum, E. (2008). Exploring gender identity and community among three groups of transgender individuals in the United States: MTFs, FTMs, and genderqueers. Health Sociology Review, 17(3), 235-253.

GenIUSS Group (2014). Best practices for asking questions to identify transgender and other gender minority respondents on population-based surveys. Los Angeles: Williams Institute.

Jans, M., Grant, D., Park, R., Kil, J., Viana, J., Lordi, N., ... & Herman, J. L. (2015). Using verbal paradata monitoring and behavior coding to pilot test gender identity questions in the California Health Interview Survey: the role of qualitative and quantitative feedback. In Proceedings of the American Association for Public Opinion Research Annual Conference.

Magliozzi, D., Saperstein, A., & Westbrook, L. (2016). Scaling up: Representing gender diversity in survey research. Socius, 2, 1-11.

Melendez, R. M., Exner, T. A., Ehrhardt, A. A., Dodge, B., Remien, R. H., Rotheram-Borus, M. J., ... & National Institute of Mental Health Healthy Living Project Team. (2006). Health and health care among male-to-female transgender persons who are HIV positive. American Journal of Public Health, 96(6), 1034-1037.

Nielsen, M. W., Peragine, D., Brooks, C., Cullen, M., Einstein, G., Ioannidis, J.P.A, Neilands, T. B. (….), Schiebinger, L. (forthcoming). Gender variables for health research.

Pilote, L., & Karp, I. (2012). GENESIS-PRAXY (GENdEr and Sex determInantS of cardiovascular disease: From bench to beyond-Premature Acute Coronary SYndrome). American Heart Journal, 163(5), 741-746.

Reisner, S. L., Conron, K., Scout, N., Mimiaga, M. J., Haneuse, S., & Austin, S. B. (2014). Comparing in-person and online survey respondents in the US National Transgender Discrimination Survey: Implications for transgender health research. LGBT Health, 1(2), 98-106.

Saperstein, A. and Westbrook, L. (2018). Categorical and continuous: Testing multiple alternative measures of sex and gender in U.S. surveys. Conference paper presented at “Gender Diversity in Survey Research, 06.11.2018, University of Gothenburg.

Scheuerman, M. K., Paul, J. M., & Brubaker, J. R. (2019). How Computers See Gender: An Evaluation of Gender Classification in Commercial Facial Analysis Services. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-33.

Tate, C. C., Ledbetter, J. N., & Youssef, C. P. (2013). A two-question method for assessing gender categories in the social and medical sciences. Journal of Sex Research, 50(8), 767-776.

Westbrook, L., & Saperstein, A. (2015). New categories are not enough: Rethinking the measurement of sex and gender in social surveys. Gender & Society, 29(4), 534-560.



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