Resources for Developing Inclusive Demographic Survey Questions

Hello! I would be really interested in any resources anyone may have around guides for writing inclusive demographic survey questions. I am trying to develop a standardized demographic bank that people on our team can pull from when developing surveys. Obviously it wouldn’t be fully comprehensive and people would need to adjust as needed, but I would like to make sure the starting tool is as inclusive and instructional as possible. I have pulled some information from the Census, HRC, IDEA Council, GRI standards, and ORARC. But I am specifically looking for examples of demographic questions/response options, resources that go into more depth about how to think about wording questions, etc. Any tools/resources that anyone is willing to share would be greatly appreciated.

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We wrote a guide for our stakeholders after consulting with many experts, some in our stakeholders’ field of computing education research, but many not. This)may or may not be useful to you, but throwing it out there just in case… NCWIT Demographics Guide_Read Me - Google Docs

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This is very helpful. I will definitely look through how we can use this as a starting point. Thank you so much!

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Hi Alida,

Have you come across this tip sheet from Harvard’s Office of Regulatory Affairs and Research Compliance?

Hope this helps!

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Hi Alida! I work at NAMI (National Alliance on Mental Illness) and we’ve put together a document explaining our demographic data collection/management philosophy, what we collect, why, what words we chose to use, etc.

Hope this helps with your work! I’d love to see what you come up with (if you can share) because there’s always room for growth :slight_smile:

https://www.nami.org/getmedia/5adbaa46-26ff-4704-b9ac-f5c01ad3f8ed/Core-Demographic-Questions-Explainer-Field

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Yes, I did! And it was helpful. Thank you.

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Thanks for sharing this @LizNorton. This is extremely helpful, even from just a how to present it to people perspective. I don’t know if my organization will let me share the final product publicly but I may be able to share a draft of it at some point. I will keep you posted!

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Hey Liz! I’m working on gathering research about best practices when writing gender categories. I am a nonbinary person working in the field of human research via museums and other cultural/science orgs.

Setting aside the options beyond the binary for a moment, I have always used Male/ Female (seeing these as gender categories related to masculine and feminine expressions) instead of Man/ Woman (which I’ve seen as overarching identities). However, I have increasingly seen the latter being used.

It seems like both options are used by professionals. For example, Harvard’s Office of Regulatory Affairs and Research Compliance posted earlier in this chat use Male/Female, but Reimagine Gender uses Man/Woman. I would like to be updated on what the thought or research is going into this!

Is this based on research, and if so, can you point me to it? @Heather I’m also interested about the best practices you use, or any insight your team might have.

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Hey Madeleine! Excellent question.

I used the following resources to inform whether we asked for “Male/Female” or “Man (Boy) / Woman (Girl)”.

Man/Woman seemed to be more common, but I haven’t seen research that says, “Use this and not that.”

I think often people are more used to using the terms Men/Women than Males/Females, so we chose to use “more common” Man/Woman to make it easier for respondents to quickly process the answer options.

Hope this helps!

Liz

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Hey @LizNorton and @MCPope great discussion! Thank you for so many useful links.

In terms of figuring out the best categories to use for any given project or context, for me, the essential step is to understand specifically what the work is trying to understand. So many times we collect social identity data, including gender to use as a proxy for something similar, often an experience rather than an actual identity.

If it’s possible to measure the experience, then that is what I recommend.

If that’s not possible, then the specific categories that are best are usually the ones that reflect the chosen identity expression of the people you’re collecting data on.

We’re having Alex Kapitan, the Radical Copywriter as a special guest on Talking Data Equity this Friday. I’ll be interested to see what they think about this.

Also, depending on where you are in your process it can be really useful to take a step back and look at the broader purpose of any identity question “standardization”.

Here are a few questions we usually ask ourselves when quantitative data’s innate pressure towards standardization rears its head:

  • Why do you want to collect this information?

  • Is the demographic/identity data what you want to know or is it a proxy for something else?

  • Are you trying to understand a type of person or a type of lived experience?

  • Are the benefits of standardizing your system going to be felt equally for people in each of the categories you create?

  • Are the downsides to standardizing your categories going to be felt equally across the people in your system?

  • Who does standardizing make this process better for: people working the data, consuming the data products, or the people providing the data?

Answers to these can help to provide a more solid foundation to the “why” behind how you ask these questions.

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Uhm wow. I love these questions. I have literally copy-pasted them into a note document so I can reference them later. Thank you for sharing them.

I’d be interested in knowing how you approach thinking about how to best balance the need to standardize for people working with the data (for example standardizing demographic questions so that multiple data sets across projects can be compared at some point) vs. what may be best for the specific project/participants?

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Hi @Alida great question. Sorry for the slow reply - I’m catching up :person_cartwheeling:t2:

There are a lot of different parts to this question and they’re things I think about a lot.

First of all, having really standard data definitely makes it easier for data workers. And 9 times out of 10, my opinion is going to be that data workers should work harder rather than trying to collect data which makes being a data worker easier. However, this is not, of course, always true in all settings.

Second, the question of whether demographic data should be standardized across data sets is a really tricky one when thinking about it from a data equity lens. Standardizing demographic data, particularly things like ethnic and racial background, sexual orientation, and gender can be extremely important in building a population-level evidence base about people who aren’t getting what they need in terms of health care, education, access to opportunity, etc. It also makes it much easier and sometimes more reliable to track changes (hopefully progress) over time.

However, of course, standardized demographic data is always going to prioritize certain groups and not be a good fit for lots of people. And if all our data is standardized, these folks are never going to see evidence that reflects their lived experience.

There are pros and cons. The relative weight of the pros and cons is going to depend on the purpose of the data collection exercise, what other data is available, and who we are trying to develop meaning for in the project.

We talk about this often in our Talking Data Equity sessions because it’s such a big deal.

Both Marieka and Naomi shared their viewpoints on this recently. If you have other folks you’d like to see as guests on Talking Data Equity, let me know!

Heather! You are always so helpful. Thank you for providing feedback and sharing resources (as always). Can’t tell you how much it means.

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Thank you so much Alida. I’d love to hear how your next step goes in the project.

Wendy… Its been a couple of months since you shared this document and I can’t tell you how useful it has been in helping me develop a demographic survey bank of my own. I am working on finalizing what I have so far but will try to get permission to share it with everyone else here afterwards. Gotta pay it forward!

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Thank you so much @Alida we’d love to see it when you can share it!