#multigender math

The multigender survey was conducted to collect information about people who identify as multiple genders, or were questioning whether they identified as multiple genders. Over the course of a month, it received 743 responses. 

This summary of the results will cover

The gender labels that participants used

The number and fluidity of participants' genders

The pronouns and honorifics participants preferred

The types of gendered terms (man, woman, person, etc) participants preferred

The orientation labels that participants used

The use of multiple or combined orientation labels

The combination of orientation labels and gender labels that participants used

Other miscellaneous questions: Names, gender modality, intersex identity, plural identity

Written responses to "Share anything else about your multigender identity that you find relevant"

Questioning

The first question asked participants to indicate whether they were multigender or were questioning. 1196 participants (81.03%) identified as multigender, while 280 (18.97%) were questioning whether they identified as multigender. 

Gender categories & combinations

Every gender given was sorted into one of five categories: Fully male, fully female, partially male, partially female, and not binary at all. The following data describes which combination of gender categories people had. 

Note on categorization:  Although not all people who are men or boys identify with the term “male,” and not all people who are women or girls identify with the term “female,” men and boys were categorized as fully male and women and girls were categorized as fully female for ease of simplicity. People who identified as “everything” or “all genders” were categorized as both fully male and fully female. Demiboys and demigirls, as well as other gender microlabels that were connected to maleness or femaleness, were categorized as partially male and partially female. Genders not at all connected to maleness or femaleness were categorized as “not binary.” It was impossible to know how each person considered themself beyond the options they selected and the labels written into the “other” option, but that information was used to find the closest likely option.  Every gender is unique, and it is very simplistic to divide gender into these categories, but this simplicity is necessary for useful data. Otherwise, I would have nearly 1500 different categories.

All participants were divided into one of the following nine categories:

Fully male and fully female (532 participants, 36.04%)

Partially male and partially female (110 participants, 7.45%)

Fully male and partially female (134 participants, 9.08%)

Partially male and fully female (29 participants, 1.96%)

Fully male and not binary (288 participants, 19.51%)

Partially male and not binary (89 participants, 6.03%)

Fully female and not binary (68 participants, 4.61%)

Partially female and not binary (62 participants, 4.20%)

No binary genders at all (164 participants, 11.11%)

[id: a pie chart representing the above data, in the colors of the gilbert baker pride flag]

To display the number of binary genders that each participant had, this data was divided into larger categories of “both binary genders,” “one binary gender,” and “neither binary gender.” This was done twice. In one version, “binary genders” only included people who were fully that gender. In the other, “binary genders” included people who were fully or partially that gender. 

Both binary genders, fully (532 participants, 36.04%)

1 binary gender, fully (540 participants, 36.59%)

Neither binary gender (404 participants, 27.37%)

[id: a pie chart representing the above data, in the colors of the trans pride flag]

Both binary genders, partially or fully (805 participants, 54.54%)

1 binary gender, partially or fully (507 participants, 34.35%)

Neither binary gender at all (164 participants, 11.11%)

[id: a pie chart representing the above data, in the colors of the trans pride flag]

Multigender people who were, in some capacity, both male and female made up the majority of respondents, but it was not a very strong majority. This is an indication that multigender identities not composed of a male/masculine gender and a female/feminine gender are more common than many people would likely expect, based on an informal tumblr poll.

Gender modality

Gender modality refers to the relationship between someone’s gender identity and what they were assigned at birth. The most common gender modalities are cisgender and transgender, but for many multigender people, it is more complicated than that. This question asked “Which of these gender modality terms do you identify with?” and allowed for participants to select multiple options.

A majority of participants identified as trans, with 1197 people selecting that option (81.10%). Additionally, 90 participants (6.10%) identified as cis. 

Gender modality terms beyond trans and cis were also popular among participants. The most common of these terms was transfemmasc, an identity referring to people who identify as both transmasculine and transfeminine. 212 participants (14.36%) identified as transfemmasc. 70 participants (4.74%) identified as isogender, an identity referring to people who are neither transgender nor cisgender, and 82 participants (5.56%) identified as both transgender and cisgender. 164 participants (11.11%) did not identify with any gender modality terms.

In a question regarding whether participants identify as nonbinary, the options given were

Yes, nonbinary describes my gender identity overall (388 participants, 26.29%)

Yes, nonbinary is one of the genders I identify with (556 participants, 37.67%)

No, but I don’t mind being referred to as such (275 participants, 18.63%)

No, and being referred to as such is misgendering (182 participants, 12.33%)

There was also an “other” option, where participants could write their answer if it did not fit one of the four given. These responses were divided into four additional categories.

Nonbinary is both my overall gender identity, and one of the genders I identify with (25 participants, 1.96%)

The nonbinary label is technically correct, but I prefer not to use it (23 participants, 1.56%)

Whether I use the nonbinary label depends on context (15 participants, 1.02%)

Other (12 participants, 0.81%)

These results were compared to data regarding whether participants identified as fully male and fully female, as one full binary gender, or as neither binary gender.

This analysis found that participants who were both male and female were significantly less likely than participants overall to identify as nonbinary, either as an overall identity or as a single gender (297 participants, 55.83% of participants who were both male and female, compared to 63.96% of total participants). They were also significantly more likely than participants overall to feel misgendered by the label “nonbinary” (94 participants, 17.67% of participants who were both male and female).