#internet school

The Urantia Book – Internet School

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Okay, so. I’m a trans woman, and also a qualified Internet Sociologist ™, so I have a whole lot of experience and thoughts about gender fields in surveys and data capture forms. In fact, producing “best practice” advice for gender fields is one of my pipe dream projects for a paper. So here’s a short essay that I recently wrote to explain the issue to another sociologist.

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The purpose of a survey is to capture data – relevant data. The purpose of each question and each field is also to capture relevant data, obviously. Now both the researcher and (more importantly) the participant have limited supplies of energy, attention, and goodwill. This is reflected most obviously in response rates: most, or many, people, encounter a survey and decide they can’t be bothered to fill it out before they even see the first question. But you also get attrition where people start surveys and don’t finish them, because they get bored, or because the questions are unclear or too hard to answer quickly. This is why I stressed that every question has to be relevant: questions are a limited resource. That’s also obvious – it’s why we don’t ask participants for their entire life stories. (I actually *did* have a participant tell me her life story in an interview once, and it took five hours).

  Therefore, the first questions you have to ask yourself when you add a field to your survey is: “what am I trying to capture here, and why is it relevant to my study?”.  And when capturing demographics, the first question is almost always a gender field. Unfortunately, almost nobody reflects on why they’re including it. If they did, the honest answer is would be “because it allows me to neatly split my data in two, and then I can run a whole host of really easy statistical tests that will let me say something seemingly insightful to pad out my paper.”

  Now often capturing gender is relevant, because doing so will let your analysis make some good feminist claims backed up by data, which is nice. But another problem here is that capturing gender doesn’t really allow you to split your dataset neatly into two subpopulations the way everyone always does. The main reason for this is that nonbinary, agender, and many other types of people exist. Accommodating this in survey design is actually pretty easy. First decide whether capturing gender is really relevant. If it’s not, either make the field optional or omit it altogether. As for what options to include: at a bare minimum, “female,” “male,” and “other”. Even better, include “prefer not to say,” a write-in field, and also make the options not mutually exclusive.

  But won’t having all these options ruin your nice easy analysis? Not really. When discussing data in the aggregate, it doesn’t matter, of course – and you have more respondents and more data to work with, and therefore can make stronger claims, because you didn’t scare away nonbinary people by preventing them from answering the very first question. That is to say, part of the purpose of writing a sensible gender question is to encourage and increase response rates – and nonbinary and gender-nonconforming people turning around and walking away at the first sign of a two-option gender field is literally a meme in the community right now.

  Even if it causes extra work for part of your analysis, you now have more data for all the other fields because you turned non-participants into participants. Plus, you achieved this by respecting individuals’ identities, while acknowledging their existence and equality with others. Indeed, one of the main reasons I recommend including nonbinary genders on surveys is a matter of representation: every single other respondent – most of whom will be cis and binary - will see those terms in use, possibly even for the first time, and perhaps we can thereby make some small progress to increasing awareness and acceptance of minorities. I can only hope.

  As mentioned before, splitting the data by gender is often just busywork, but if you really desperately do have to produce data on “men” and “women”, you have some options. The first is to just omit other genders from this one section of your analysis, which is not ideal from the perspective of completeness or representation of minorities. If you have a large enough number of participants in the other categories, you may collapse them into an “other” category, or report on all categories separately. In some specific cases in sociology, it may be relevant and justified to collapse your categories down to “men” and “non-men”. For certain studies, this will meaningfully reflect the dominant power dynamic in our unfortunately patriarchal society.

  The remaining issue we’re skirting around here is, “how do I report on transgender people in my survey?”. In almost all cases, whether or not a participant is trans is not relevant data – so it doesn’t need to be captured. Trans women are women, trans men are men. In many cases, especially data capture for medical purposes, segregating trans people is actually harmful to the quality of the data – and to the patients themselves.** This is because people falsely believe that being trans implies certain things that are by no means true: knowing that someone is trans tells you absolutely nothing about their hormone levels, or their genitals, or practically anything else medically relevant, and can therefore lead to dangerous misapprehensions.

  But in sociology at least, being trans will sometimes be very relevant indeed: because it’s a highly stigmatised identity, with far-reaching effects on individuals’ social situation and interactions. So when we do need to ask if a participant is trans, how do we do it? The easiest answer – again, only if it’s relevant because trans people get sick of being asked if they’re trans(!) – is to have a separate question, “do you consider yourself to be transgender?”. With either a “prefer not to say” choice, or with the entire question optional if it’s not absolutely fundamental to the study.

  In many cases, however, the reason whether the participant is trans is relevant is that the research is targeted specifically at LGBTQ individuals as a whole. In that case, whether the participant is L, G, B, or Q will also be relevant. So, separate from the gender field, you can simply ask: “Which aspects of the LGBTQ community do you identify with? Tick all that apply”. (I am a trans lesbian, for example, and it’s truly depressing how many people assume we don’t exist). Having a write-in “other” field is also sensible here, as the acronym is imperfect – for example, many people consider “queer” to be a slur - and lots of identities aren’t specifically covered within it. But that’s a topic for another essay.

In general:

Don’t ask or report on anything if it’s not relevant to your research questions.

Don’t make questions mandatory if they don’t have to be.

Don’t make options mutually exclusive if they’re not.

Include “other” and write-in fields when you know your options aren't exhaustive.

Regarding gender:

Writing your gender field sensibly will improve both the quality of your data and increase response rates.

Nonbinary genders exist, and splitting a dataset by gender is often less relevant or insightful than you’d think

“Trans” is not a gender of its own. It’s an adjective. Trans women are women, trans men are men, and most of the time that’s all you need to know.

Avoid the terms “male to female” and “female to male”, as many trans people find this outdated and offensive (because they imply that they “used to be” or “are really” the gender they were forcibly assigned at birth).

Not everyone likes the acronyms LGBTQ, QUILTBAG, GSM, etc. Some participants will consider certain terms to be slurs, and others will be perfectly fine with them.

 ******

 (*the worst I have seen is a mandatory field with three mutually exclusive options: “male”, “female,” and “transgender”. Which is both incredibly offensive for what it implies, and will also ruin your dataset for obvious reasons.)

 (**I’ve seen questions along the lines of “if you are transgender, please only tick your birth gender”. This is wrong for so many reasons, not least because it tells trans women they’re ~really men~, and because there’s no such thing as “birth gender”, and because of all the reasons listed above).

Guising is a Scottish term for trick-or-treating which comes from the word disguise - because the children wear costumes.

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