The correlation between tabooness and sexual interest was statistically significant at p < 0.01.

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I have a nagging suspicion that “taboo-ness” here is not just a correlation, but a source of bias (specifically, response bias.) Instead of graphing by it, could you try correcting for it? You’d get, I think, something like “interest given complete disinhibition,” which seems useful for predicting e.g. fetish demand.

(I’d also like to try a few other things with the data; would it be possible to get a raw CSV to play with? Reconstructing the X/Y-axis-mapped variables is easy enough, but the color-mapped variable is a bit harder.)

[…] Sexualtaboos by AellaGirl – A graph of sexual fetishes. The axes are “taboo-ness” and “reported interest”. Taboo correlated negatively with interest (p < 0.01). Lots of fetishes are included and the sample size is pretty large. […]

I have a nagging suspicion that “taboo-ness” here is not just a correlation, but a source of bias (specifically, response bias.) Instead of graphing by it, could you try correcting for it? You’d get, I think, something like “interest given complete disinhibition,” which seems useful for predicting e.g. fetish demand.

(I’d also like to try a few other things with the data; would it be possible to get a raw CSV to play with? Reconstructing the X/Y-axis-mapped variables is easy enough, but the color-mapped variable is a bit harder.)

What did you use to make that chart? It’s different from the charts in previous posts.

[…] Sexualtaboos by AellaGirl – A graph of sexual fetishes. The axes are “taboo-ness” and “reported interest”. Taboo correlated negatively with interest (p < 0.01). Lots of fetishes are included and the sample size is pretty large. […]

If trans people were counted in the chart gender data, would futa be the pinkest one?

Interesting and surprising in many cases.

I agree some of them may be missnumbered