My kink survey design methodology

This is a very detailed, very simple step-by-step walkthrough of my process for creating my currently-running fetish survey. It’s meant to be easily accessible/understandable to people with little familiarity with research, to explain my reasoning for my decisions, and to outline the limitations. I plan to refer to this as part of my methodology when I start publishing results.

If you haven’t taken the survey yet, please do it before reading this article, so that your answers aren’t influenced by my description of the process!

(General note: I use ‘kink’ and ‘fetish’ interchangeably. What I actually mean is something like a broad “sexual interest”, which encompasses very basic stuff like “I like having penis-in-vagina sex” and also things people consider damaging and pathological, like pedophilia. I am not trying to make any particular point, I’m not attached to the language, I just don’t really care about the official distinction between those terms for my current research purposes. My curiosity is anything and everything that turns people on, whether obligate or not, vanilla or taboo.)

How I Built It

I received funding to research fetishes, so I was like cool, let’s run a study to find out stuff I’m curious about.

I have some questions like, did people who are interested in BDSM get abused more in childhood? (Other research seems to suggest yes, but I wanted to replicate, and know details - what kinds of BDSM? What kind of abuse? Does the effect hold across gender? etc.)

So easy - I run a bunch of questions asking about BDSM and childhood abuse. But if I’m already getting people to take a survey, I might as well ask them questions about some more kinks. What if abuse actually correlates with a whole set of fetishes, not just BDSM? I should ask about other fetishes too! But if I did a bunch of little surveys, I’d have to include all the stuff I’m testing each time - like ask people their gender and are you into BDSM? And then the next survey, I’d ask what’s your gender and are you into feet? This is inefficient, and duplicates a lot of time answering questions.

So, for efficiency, I should include all the questions I want in one survey, and then just cross check all the stuff I’m curious about, with each other.

Cool. So in order to do this, I need to write down all of the hypotheses-testing-questions I have that I think might correlate with an interesting thing in fetish-land (like, how clean was your childhood home? Was your father absent when you were growing up? What’s your gender?), and then all the stuff in fetish land (are you into feet? bestiality? choking people?).

Simple. Easy. I was like, this’ll take me a week or two. Somewhere beneath the earth’s crust, Satan laughed.

First, let’s get a list of fetishes - all of them. I can figure out how to categorize or whittle down later, let’s just see what we’re working with.

I pulled from fetish lists across the internet - from humansexmap and a few others I can’t re-find the links to. I pulled from lists on wikipedia and googling for buzzy articles about weird fetishes you’ve never heard of. I compiled all of them into a big spreadsheet, deleted obvious duplicates, and ended up with around 850 fetishes.

This was too much. I couldn’t just ask people to rate how much they liked a fetish, 850 times! Nobody would finish that survey.

If you’re unfamiliar with how this stuff works, basically the more people you get to take your survey, the more you can trust the conclusions you draw from it. If I ask ten random people off the street if they like cheese, and 6/10 do, then I can maybe guess that 60% of the US population likes cheese, though it’s a pretty weak guess - maybe by total coincidence I just happened to pick a lot of cheese eaters. But if I ask ten thousand random people off the street if they like cheese, and six thousand of them do, then my guess that 60% of the total US population likes cheese will be way more confident, because it’s probably not total coincidence.

So okay, I need to ask a lot of people - a high sample size - in order to have confident guesses. This is why having a shorter survey is important - more people quit surveys if they’re longer.

But there’s another reason I wanted to have a high sample size - if I’m asking about a lot of questions, then I’m going to be checking a lot of correlations between those questions, and the more correlations you check, the more likely it is that you’ll find ones that are “accidental.”

For example, let’s say you’re going to do a study about food preference, and you’re planning to tell me all the most exciting findings. If you ask ten random people on the street if they like cheese, and 9/10 of them say yes, you might be like wow, people in general probably really like cheese.

But let’s say you go out and ask ten random people on the street if they like cheese, and milk, and steak, and a hundred other random foods. Then you look at their answers, see that nobody fully agrees on any food except for one - 10/10 of them like pineapple! This is the exciting finding you were hoping for - so you go “Breaking news - 100% of surveyed responders like pineapple!”

Probably what actually happened is that, if you’re picking 10 people, by total random chance they’ll all have some specific food taste in common. If you ask them about a hundred foods, eventually you’ll find that random coincidence. And this doesn’t mean the rest of the population likes pineapple - you just asked people until you found a coincidence!

This is mostly what people mean by ‘p-hacking’; you ask a group of people a whole bunch of things, and then because you asked them so many things, you’ll eventually stumble on a random coincidence, and then you can report this like a big finding. This is why a lot of published research fails.

And because I’m trying to build a survey that’s asking about a whole lot of things - so many fetishes and hypothesis questions - I need to be super careful about this, because the more things you ask about, the easier it is to find something by random coincidence. In order to guard against mistaking a random coincidence for a meaningful indicator when I’m asking a bunch of questions, I need to ask a lot of people. If you ask ten thousand people what their favorite foods are, it’s super unlikely that 100% of them will like pineapple just by coincidence - even if you asked them about hundreds of foods!

So I’m in a pickle: I need to ask people about a lot of stuff, but I also need to ask a lot of people.

I came up with two solutions:

1. Give people an organic incentive to finish the survey - results at the end that tell them about themselves and their ranking compared to other responders. This should also cause them to share the results with others, and make their friends want to take it.

2. Hide a lot of the survey questions - for example, only ask about bondage-related questions if someone says they’re into bondage. If we can collapse down full sections of the fetish questions with a “no I’m not into anything like that”, this shortens the survey a lot.

Cool. I started writing the survey, but quickly ran into a problem with #2 - categorizing the fetishes was really hard.

For example, let’s take the fetishes “rapeplay”, “spread eagle ties”, and “choking.” All of these seem to be vaguely combined - something about power dynamics? But one of them has to do with explicit nonconsent play (rapeplay), whereas spread eagle ties and choking could be done both consensually. And maybe some people were into choking, but for more sensory reasons - cutting off blood flow can be really erotic, even if it’s not really about nonconsent or bondage.

Basically, people can be into the same fetish for very different reasons, so it’s hard to know what one individual fetish is about, and thus it’s very hard to categorize it in a way everyone will universally understand.

Before doing this I’d done some long-form interviews with people with obscure fetishes. I asked them questions to find out what exactly about the thing they liked, turned them on? Was it the emotional state? If so, are different situations with the same emotional state also erotic? Was it the actual concrete action/object that was arousing? If so, what about scenarios where the actual action/object was shifted slightly? Etc.

With these interviews I learned there was a way greater diversity of sexual arousal than I’d fully grasped. I would often make guesses about what I thought was behind their fetish, only to end up completely wrong.

And so if I was going to categorize 850 fetishes into topical categories that would be widely understood and agreed on…. this was a daunting task.

I tried a few things - reading internet forums to try to directly understand fetish clusters. I tried using other people’s fetish categorization systems, I tried squinting really hard at my big list and willing an organic cluster to appear. This stumped me for a while, and I lost some motivation.

I was avoiding the labor-intensive thing I didn’t want to do: go through my spreadsheet and tag each of the 850 fetishes into categories. But nothing else worked, and so eventually, grudgingly, this is what I did.

I needed to “discover” categories, and so I figured I would generate categories for each fetish individually, and then look at the total tags afterwards to see if any information came out of it.

As in: I’d pick a fetish, like “anal sex,” and then think, “what categories would this fit in?” Probably something like “vanilla” (it’s a pretty common interest), maybe “body parts” (cause it’s focused on a specific aspect of the body), and maybe “power dynamics” (anal sex tends to be associated with more dominant stuff) and also a bit of “sadomasochism” (given that painial is an entire category of porn)?

So I generated columns with these categories and tagged anal sex in all of them. I then went on to the next fetish - and if it seemed to share any overlap of category with anal, I just used that category - and if none of the preexisting categories seemed to capture an important element about it, then I made a new category and put a tag there too.

Many fetishes I didn’t know much about - sploshing for example, where people are interested in getting covered in mud or cake or messy things. I had no idea how to tag this - is it about humiliation? Is it meant to trigger disgust, or is it more about the sheer sensory aspect of wet sploshiness on your skin? I had to go research these, read people’s accounts of being into them on reddit, and watch some related porn to figure out how the hell to tag them.

Doing this for 850 fetishes took forever.

But I ended up with 41 organically-created categories - and I could see how popular the categories were, and how many categories held fetishes that didn’t have a lot of overlap with other categories. Using this information, I found the categories to eliminate - “intensity” was one category, for example, that both didn’t have a ton of stuff in it, and the stuff in it was pretty easily moved into other categories.

This got me down to thirty categories, hallelujah.

I ended up putting the thirty categories in two questions that presented them as checkboxes - “check all that you’re into”, the first question being more common categories (like ‘humiliation’), and the second being uncommon stuff (like transformations). I used data from my previous surveys to judge rough frequency.

So much of my effort went into making the survey easy-to-navigate; I didn’t want any one question to have too much information that would force people to stop and read, so I tried to minimize that as best as I could. Building this survey felt mostly like a series of annoying decisions about information flow and efficient communication, because I was trying to cram so much information into such a tiny space. The little decisions were important, because little slow-downs would accumulate over the course of a very long survey, thus reducing my sample size, which was the most important thing.

I ended up making sacrifices. I wanted to get a degree of interest for each individual fetish (if you’re into body parts, rank on a scale of how much you’re into feet? elbows? hair? tongues?), but a brand new question for each fetish made the survey way too long. I ended up going with a crude two-part system; I’d ask “are you into body parts?” and if they said yes, I’d ask them “how much are you into body parts?” And then I’d give them a list of a bunch of body parts and tell them to select all they were into. After they did this, I gave them a second question which presented all of those body parts they’d just selected, and then asked - which of these is the most erotic to you?

This means on the back end, I got a few bits of info - I can represent a “1” for everything they’re into, and a “2” for the single thing they’re most into. It’s primitive, but it cut down massively on the survey length. It took a lot of experimentation for me to decide on this method.

Not all sections of the survey used this method - for example if you’re into bondage, you get asked thee additional questions - how much are you into light bondage? How much are you into medium bondage? heavy bondage? I chose to expand some sections out from checkboxes into full individual questions if they seemed like big, meaningful splits in the way someone experienced a fetish.

I wasn’t able to do very in-depth research for every single thing on my list, so I didn’t get everything quite right. I did spot-check different sections with people who had corresponding fetishes, but not all. But I viewed this as mostly okay; because this survey was so broad, my goal was to use it to get a good birds-eye-view of the landscape, and then if a section looked promising I could do later surveys to zoom in, if I needed to.

I did eliminate or combine fetishes I felt were close enough; for example I condensed all the different types of gags into just two gag preferences (funnel gags and all other gags).

Fetishes can be about different things - like I mentioned earlier, they can be about the emotions involved, or the concrete scenario. I decided to ask two sets of things - a list of more concrete fetishes, and also a list of mental states, to try to attack this from both angles.

Through several rounds of feedback, I built a question pair that asked users to check boxes for a (not-too-long) list of mental states they found erotic; one question was about them, and the other about other people. Choosing the items to go on the list and in what types of clustering was not easy - I was trying to keep the list short and easy, but also thorough; so do you put ‘shyness’ with ‘embarrassment’? I kept presenting the list to testers and getting “You’re missing this important emotion” and then me throwing up my hands and reshuffling the list. I ended up with 22:

“Eagerness or desire, Wildness or primalness, Irritation or annoyance, Ambivalence or disinterest, Numbness or disassociation, Calmness or serenity, Safety or warmth, Love or romance, Shyness or nervousness, Powerlessness or vulnerability, Hesitation or reluctance, Shock or surprise, Fear or trepidation, Humiliation or worthlessness, Embarrassment or shame, Grief or regret, Despair or horror, Disgust or revulsion, Anger or tension, Hate or disdain, Cruelty or brutality, Power or smugness”

(I did not anticipate that the majority of effort on this whole damn project would just be spent figuring out how to categorize things. For a while most of my messages to friends was annoying stuff like ‘do you think ‘powerfulness’ counts as an emotion or should I just call it smugness, are they even meaningfully different’)

Survey structure

Ultimately, my survey ended up with 302 questions in a structure that looks like this (questions were presented mostly in this order, though some sections were conditional on previous responses)

1. Demographic information (gender, age, relationship style, etc.) (9 questions)

2. IQ question (1 question)

3. Sexual history/romantic status (2 questions)

4. Sexual assault (giving and receiving) (2 questions)

5. Political views (2 questions)

6. Height/weight (2 questions)

7. Upbringing stuff - how repressive (1 question)

8. Age of first masturbation/sex (2 questions)

9. Religious upbringing, what religion, how serious was it, etc. (5 questions)

10. Misc upbringing class/cleanliness/responsibility (3 questions)

11. Childhood abuse/discipline (10 questions)

12. Birth order (2 questions)

13. Big 5, pulled official big5 questions (10 questions)

14. Powerlessness (4 questions)

15. Attachment styles/mental illness (2 questions)

16. Self-rated attractiveness (1 question)

17. What emotions are erotic, self/other (2 questions)

18. Sexual interest narrowness/knowledge (2 question)

19. Porn use (6 questions)

20. General sex q; arousal via partner, experiment level, etc. (6 questions)

21. Consent/dominance (2 questions)

22. Ephebophilia question (1 question)

23. Quantity of identification (1 question)

24. Preferred energy level (1 question)

25. Vanilla sex questions (7 questions)

26. Autogynephilia questions (4 questions)

27. Control over fetish (2 questions)

28. Preferred boob size (1 question)

29. Abnormal body parts (9 questions)

30. Age (nonstandard) (13 questions)

31. Appearance states (5 questions)

32. Bestiality/creatures creations (8 questions)

33. Bodily secretions (15 questions)

34. Body parts (4 questions)

35. Bondage (7 questions)

36. Brutal/violent (6 questions)

37. Clothing (8 questions)

38. Creepy/horror (8 questions)

39. Dirtiness/disgust/messiness (5 questions)

40. Eagerness questions (10 questions)

41. Exhibitionism/voyeurism (5 questions)

42. Genderplay (6 questions)

43. Gentleness (4 questions)

44. Humiliation (5 questions)

45. Incest (9 questions)

46. Mental alteration (6 questions)

47. Multiple partners (4 questions)

48. Mythical/fictional creatures (4 questions)

49. Nonconsent (5 questions)

50. Objects (nonstandard) (4 questions)

51. Power dynamics and D/s (10 questions)

52. Reproduction (4 questions)

53. Roles questions (8 questions)

54. Sadomasochism questions (12 questions)

55. Sensory questions (4 questions)

56. Toys questions (5 questions)

57. Transformations (9 questions)

58. Vore (4 questions)

59. Survey end: survey test #, honesty, and directory (3 questions)

At the beginning of the survey, I included some practical instruction stuff (like survey time), but also some instructions for how to answer the questions. I tried to make them easy to read (spaced the out through the survey in short segments) and I included a lot of repetition, but still I got the impression a low percentage of people actually read them. My goal was to provide a calibration for what it meant to find things erotic, and above all to ask about fantasy, not in-person practice. The text I used was:

“I ask about a lot of sensitive, upsetting, or delicate topics. Please keep in mind you are *fully anonymous*, and that I do *not* interpret any of the answers as an indication you might cause any harm to yourself or others in real life. I plan on analyzing answers in aggregate and posting any cool findings!”

“Our sexual fantasies are different from what we do in real life. I'm going to ask about some things that might seem cruel if actually done; being aroused by a taboo erotic fantasy does /not/ mean you would violate consent in real life. Please answer according to your ideal fantasies, even if you would never actually act on it.”

“In this survey, I'll ask about a bunch of specific elements. Answer for each element in isolation. For example, if you are really into peeing onto clowns in bondage, then answer yes for 'peeing' and 'clowns' and 'bondage,' even if it's only mostly erotic when they're all combined. “

“There's a lot of fetishes out there! To narrow them down, here's a list of things you might be into. Check any that you're *definitely* into. Do not check things that "might be kinda hot in the right circumstances. A good test is "have you searched porn of this thing", "have you masturbated to fantasies about this thing", or "have you asked a sexual partner to participate in this thing"

For the questions themselves, I almost entirely used the word “erotic”, as opposed to “hot,” “sexual,” or “arousing”; I wanted a word that encompassed a lot of ways sexuality could be experienced. For example, I was afraid “arousing” would be interpreted as genital activation, when maybe some people have sexual fetishistic fascinations even when their penis isn’t hard. I also tried to use identical wording across all the “is this erotic” questions, with only a few exceptions.

For back-end weight, I weighted the degree of answer non-linearly; “not arousing”=0, “slightly arousing”=1, “somewhat arousing”=2, “moderately arousing”=3, “very arousing”=5, and “extremely arousing”=8.

The scale I use will impact my findings; you can see that for each level of interest, the number increases by one, but for stronger levels of interest (very and extremely), the numbers increase by more than one. In previous research, when I test out a linear scale (a regular increase in value, like just +1 each time), this gets me different results than when I test nonlinear (for example, some values increase not in a regular way). I generally understand why this is, but I don’t deeply understand why, and this is one area I’d like to learn more about. For this survey I decided to use a partially nonlinear increase, under an intuition that interest in sexual fetishes look nonlinear. I could be wrong here, and I can easily change these numbers in the future if I do turn out to be wrong.

But I do want to sort of “precommit” to a nonlinear way of measuring this, so that I don’t end up checking both ways of measuring and picking the one that generates a cooler correlation. I don’t deeply understand if “picking the one that looks better” is a bad idea, but I suspect it is. Because this is very easy to change later on, there’s little downside and I’m okay with proceeding without deep knowledge here.

Scoring

I then built and ran two other surveys, these much simpler - one to rate how taboo people found various fetishes, and another to rank how kinky they thought various fictional and historical characters were.

I’d already run a version of the first one - how taboo do you find this fetish? - several years ago, but I’d only tested forty fetishes - here I needed to test way more. I included around 350 fetishes in this survey, and let people answer as many as they wanted, the questions they got randomized. I also separated out some fetishes that I thought were more gender based - for example, maybe people viewed dressing up in lingerie as more taboo for men than for women!

I used the data from this survey (got around 1400 responses for each fetish) to plug in a scoring system into my original big fetish survey. It was a pretty detailed and elaborate scoring, including weighting your answers differently depending on if you were male or female. This in itself added on a lot more time and a lot of complexity into the survey build itself.

(For more details about this: I had to make a lot of decisions about how to weight scores. For example, how should I weight someone being ‘slightly into’ bestiality vs ‘extremely’ into bestiality? Should ‘sightly’ into bestiality be equivalent to ‘really’ into feet? What about the super-taboo ones; if someone is only into necrophilia, should they get a higher score than someone who was moderately into every other, less taboo fetish? Should I average scores from only the questions answered, or include scores from questions not-answered, somehow? Should a pedophile who’s also into generic anal sex be scored more or less taboo than a pedophile who’s into literally nothing else? I agonized over this for a long time.)

I had to remind myself that the way I built scoring was not going to impact the data I was actually looking at, and it was okay if it wasn’t perfect. Nevertheless, I’m pretty proud of my final result here. I added the ‘how kinky is this character?’ results in, so that you’d get a name equivalent for your score. I figured this would help increase incentive to take the survey - people like knowing “who” they are, what image they map to, in some sort of concrete, fun way.

I also included an option (at the end, to avoid people taking the survey feeling deanonymized) for people to leave their emails, if they wanted to be added to a directory of kinky people later on.

Testing the survey took a while; I was using Guided Track for the first time, which took a while to learn. Thankfully Spencer Greenberg, its creator, responded to nearly all of my questions inhumanly promptly; I also got a lot of help from Ronny Fernandez. Guided Track uses a very simple, easy-to-learn programming language, but this was my first time with any sort of programming, so I made a lot of basic mistakes and it took several weeks for me to fix all the bugs. And there were a lot of them, because my survey was huge, had tons of conditions, and complicated scoring. I had a small group of volunteer testers who kept retaking the survey for me, god bless them, may they have many descendants.

(I strongly recommend Guided Track if you want to do any sort of complicated survey design and are willing to handle a bit of a learning curve; I had tested out a ton of other platforms and Guided Track by far has the best power-to-effort ratio)

I slowly increased the size of each round of testing as people kept pointing out bugs and giving me feedback. Eventually - I think it took me about 10 rounds? - I felt the survey was ready, and I published it for good.

I published it by posting it on both of my twitter accounts, on my reddit page, and on my Instagram and Facebook. I anticipated that most of the responses would come from friends sharing their results on their own accounts. I think this is probably true; normally when I share less-designed-to-be-viral surveys, I get around 3-4k responses. But now, the survey has been up for about two weeks, and has around 12k responses; about 80% of them male. My personal goal is to get 20k responses, with at least 5k of them female.

I’m also currently experimenting with buying responses from Positly (also by Spencer!), a service where you can pay random people to take your survey.

I’ve started learning python in order to process the data (before this I’d use google spreadsheets (with mega thanks to Nate Soares for building google sheets programs for me to use to help do more complicated crunching), but this data is far too big for google sheets to handle.

Limitations

1. It’s possible that the incentive to take the survey - a quiz where you get a score at the end - caused people to slightly modify their answers in an attempt to get the score they want. I anticipate this increased the average scores slightly.

2. The quiz aspect also increases the chance that people might retake the survey in order to try to get a different result. My guess is the length of the survey probably reduces this problem a bit, as people should be discouraged from taking the survey a second time if it’s so long.

   1. To help address this, I included a question at the end of the survey asking if this was the first time they took the survey, and eliminated everyone who said no.

3. Selection bias: my followers probably are not too representative of the general population. Followers of my main twitter account tend to skew heavily male, white, 25-35 years old, western-world, in STEM, roughly evenly distributed across the political spectrum (though more heavily libertarian), and very sex-friendly. The followers on most of my other platforms (around 500k total) followed me when I was posting pornographic content to market my Onlyfans. The content I posted was pretty generically vanilla, and distributed across a wide variety of minor kinks (e.g., the subreddits r/palegirls, r/breedingmaterial).
   I’m anticipating the greatest skew here coming from disproportionately sampling people who are sex-positive or expose themselves to internet pornography more regularly than others.

   1. In buying responses from Positly, I plan to compare users from there to my current sample, so I can tell exactly how much and where my sample deviates from the normal population. I also am hoping to buy responses from a few target countries like India and China.

   2. I also included a question asking people where they came from; this should help me identify exactly how much my source is impacting the kind of demographics I’m getting.

4. Length of the survey might somehow meaningfully filter respondents; as in, it’s possible people with less time in their day, or with less patience, would be less likely to complete the survey, thus skewing the responses.

   1. I’m not too worried about this one, because it seems kind of unclear about how this would meaningfully impact responses. I’m still going to keep an eye on it, though!

5. Wording is always a potential issue; often after running a survey I find out that respondents were interpreting a question differently than I meant it. This is something I pay close attention to in pilot runs, asking people to tell me if any wording was confusing.

   1. In general I think I’m much better at this than most; while I’ve run surveys before, most of my expertise comes from tweeting thousands of twitter polls over several years with regular, immediate feedback about how people misunderstand and misinterpret the questions I’ve asked. Getting precise, clearly-interpreted wording is probably my biggest strength as a survey designer. It’s still hard though, and impossible to totally eliminate error.

6. Some people submit bad data for fun! There will always be some amount of noise.

   1. I asked people how honest they were answering the survey, and am removing people who answered “not at all” or “slightly.” I also have some duplicate questions, and plan to use these to remove everyone who answered very contradictory. I also am throwing out answers that look pretty suspicious - like anyone who answered “age 100, weight 400 pounds, height 7”, etc.

7. I failed to ask some questions I wish I had in hindsight - I wish I’d asked more about emotional neglect as a child, as well as how obligate fetishes were. I did try very hard to incorporate the obligate spectrum on a detailed level, but it ended up putting too much length into the survey. Still, I wish I’d maybe included a crude question like “In general, for your more niche interests, how easily can you find sexual satisfaction without them?”

8. I might be losing some good data based on my survey structure. My structure is such that you only get asked questions about specific fetishes if you select the overarching category. It’s possible people did not select categories that included fetishes people would have said yes to. If this was systematic in a specific way, like if I drastically miscategorized a specific fetish such that everyone interested in that fetish failed to pick the right category, then this would reflect on results.

   1. I tried to fix this a bit by including 3-4 examples in the categories. I selected the examples very carefully, often in response to feedback, in order to show a representative range of fetishes inside that category.

      

Hypothesis

Mostly I’m looking for find surprise correlations in areas I didn’t expect, but I do have one main hypothesis I want to test, mainly that:

Not all fetishes are created equal: or, causation of a fetish (as well as other factors, such as fetish age onset) varies depending on the fetish. My prediction is that the answer to the question “when does a fetish begin” or “does it come out of trauma” or “is it genetic” depend on the type of fetish we’re asking about; that some fetishes will see high association with these answers, and others with none.

Of course there’s a lot of other stuff I’m vaguely curious about but don’t have strong guesses; you can see most of what I’m asking in my above list of questions in the “survey structure” section. Birth order, height, personality, political leaning, gender, location, so many more - all of these things I’m super curious about for their potential correlations with fetishes.

Because I’m learning python in conjunction with looking at the data, I plan on publishing results one subcategory (or one question!) at a time.

If you’d like to place bets on predictions about my findings, I’m going to be making prediction markets on Manifold, so you can win internet money (just for charity now, I guess?) by predicting what I’ll find.