The general kink factor: everything or nothing turns people on
Emil O. W. Kirkegaard
Intro
Many areas of human activity, beliefs or interests have general covariance. Following the factor analytic approach (as opposed to the network approach), this is taken to mean that there is a common cause, a general behavioral trait, belief/predisposition or interest. Sexual kinks are no exception to this. However, it is generally difficult to get a hold of the relevant data, so the topic has remained underexplored.
Init
Setup.
options(digits = 2)
library(pacman)
p_load(kirkegaard)Data
Data are from Dawson et al 2014:
Little research has been conducted to examine paraphilic sexual interests in nonclinical samples. The little that exists suggests that atypical sexual interests are more common in men than in women, but the reasons for this difference are unknown. In this study, we explored the prevalence of paraphilic interests in a nonclinical sample of men and women. We expected that men would report greater arousal (or less repulsion) toward various paraphilic acts than women. We also examined putative correlates of paraphilias in an attempt to explain the sex difference. In all, 305 men and 710 women completed an online survey assessing sexual experiences, sexual interests, as well as indicators of neurodevelopmental stress, sex drive, mating effort, impulsivity, masculinity/femininity, and socially desirable responding. As expected, significant sex differences were found, with men reporting significantly less repulsion (or more arousal) to the majority of paraphilic acts than women. Using mediation analysis, sex drive was the only correlate to significantly and fully mediate the sex difference in paraphilic interests. In other words, sex drive fully accounted for the sex difference in paraphilic interests. The implications of these findings for understanding the etiology of atypical sexual interests are discussed.
Authors refused to share their person-level data (I think I asked but can’t find the email), but they reported correlation matrices for both sexes for aggregate kink interest measures.
#correlation matrix, by sex
dawson = read.csv("dawson_data.csv", row.names = 1) %>% as.matrix
#by sex
(dawson_men = MAT_half2full(dawson, diag = T))## Voyeurism Exhibitionism Scatologia Fetishism Transvestic
## Voyeurism 1.000 0.47 0.33 0.205 0.21
## Exhibitionism 0.472 1.00 0.46 0.188 0.29
## Scatologia 0.330 0.46 1.00 0.209 0.23
## Fetishism 0.205 0.19 0.21 1.000 0.33
## Transvestic 0.211 0.29 0.23 0.327 1.00
## Frotteurism 0.461 0.41 0.43 0.237 0.29
## Sadism 0.302 0.35 0.29 0.360 0.30
## Masochism 0.274 0.31 0.28 0.379 0.33
## Biastophilia 0.359 0.38 0.37 0.211 0.31
## Urophilia 0.262 0.32 0.26 0.206 0.30
## Scatophilia 0.101 0.12 0.14 0.076 0.17
## Hebephilia 0.235 0.32 0.25 0.153 0.24
## Pedophilia 0.179 0.25 0.24 0.161 0.23
## Zoophilia 0.068 0.19 0.13 0.105 0.27
## Frotteurism Sadism Masochism Biastophilia Urophilia
## Voyeurism 0.46 0.30 0.27 0.36 0.26
## Exhibitionism 0.41 0.35 0.31 0.38 0.32
## Scatologia 0.43 0.29 0.28 0.37 0.26
## Fetishism 0.24 0.36 0.38 0.21 0.21
## Transvestic 0.29 0.30 0.33 0.31 0.30
## Frotteurism 1.00 0.27 0.24 0.37 0.24
## Sadism 0.27 1.00 0.61 0.52 0.29
## Masochism 0.24 0.61 1.00 0.37 0.29
## Biastophilia 0.37 0.52 0.37 1.00 0.29
## Urophilia 0.24 0.29 0.29 0.29 1.00
## Scatophilia 0.11 0.15 0.13 0.14 0.32
## Hebephilia 0.33 0.16 0.14 0.31 0.17
## Pedophilia 0.29 0.17 0.11 0.27 0.21
## Zoophilia 0.12 0.17 0.15 0.20 0.28
## Scatophilia Hebephilia Pedophilia Zoophilia
## Voyeurism 0.101 0.23 0.18 0.068
## Exhibitionism 0.117 0.32 0.25 0.188
## Scatologia 0.143 0.25 0.24 0.132
## Fetishism 0.076 0.15 0.16 0.105
## Transvestic 0.165 0.24 0.23 0.266
## Frotteurism 0.112 0.33 0.29 0.124
## Sadism 0.153 0.16 0.17 0.171
## Masochism 0.130 0.14 0.11 0.150
## Biastophilia 0.136 0.31 0.27 0.197
## Urophilia 0.319 0.17 0.21 0.281
## Scatophilia 1.000 0.21 0.33 0.294
## Hebephilia 0.212 1.00 0.47 0.200
## Pedophilia 0.325 0.47 1.00 0.183
## Zoophilia 0.294 0.20 0.18 1.000(dawson_women = MAT_half2full(dawson, lower = F, diag = T))## Voyeurism Exhibitionism Scatologia Fetishism Transvestic
## Voyeurism 1.000 0.521 0.47 0.203 0.30
## Exhibitionism 0.521 1.000 0.44 0.136 0.24
## Scatologia 0.467 0.443 1.00 0.166 0.26
## Fetishism 0.203 0.136 0.17 1.000 0.31
## Transvestic 0.296 0.241 0.26 0.312 1.00
## Frotteurism 0.507 0.523 0.42 0.195 0.29
## Sadism 0.345 0.306 0.31 0.266 0.34
## Masochism 0.326 0.278 0.27 0.255 0.29
## Biastophilia 0.419 0.378 0.36 0.174 0.28
## Urophilia 0.133 0.195 0.14 0.151 0.19
## Scatophilia 0.062 0.057 0.15 0.054 0.12
## Hebephilia 0.206 0.214 0.18 0.151 0.24
## Pedophilia 0.146 0.206 0.15 0.083 0.15
## Zoophilia 0.130 0.163 0.10 0.152 0.19
## Frotteurism Sadism Masochism Biastophilia Urophilia
## Voyeurism 0.507 0.345 0.326 0.419 0.133
## Exhibitionism 0.523 0.306 0.278 0.378 0.195
## Scatologia 0.418 0.306 0.267 0.365 0.144
## Fetishism 0.195 0.266 0.255 0.174 0.151
## Transvestic 0.289 0.336 0.292 0.281 0.186
## Frotteurism 1.000 0.346 0.311 0.405 0.160
## Sadism 0.346 1.000 0.655 0.412 0.209
## Masochism 0.311 0.655 1.000 0.390 0.195
## Biastophilia 0.405 0.412 0.390 1.000 0.191
## Urophilia 0.160 0.209 0.195 0.191 1.000
## Scatophilia 0.043 0.076 0.062 0.099 0.294
## Hebephilia 0.185 0.166 0.172 0.296 0.159
## Pedophilia 0.179 0.119 0.114 0.170 0.137
## Zoophilia 0.181 0.137 0.167 0.185 0.091
## Scatophilia Hebephilia Pedophilia Zoophilia
## Voyeurism 0.062 0.206 0.146 0.130
## Exhibitionism 0.057 0.214 0.206 0.163
## Scatologia 0.155 0.182 0.147 0.101
## Fetishism 0.054 0.151 0.083 0.152
## Transvestic 0.125 0.242 0.149 0.193
## Frotteurism 0.043 0.185 0.179 0.181
## Sadism 0.076 0.166 0.119 0.137
## Masochism 0.062 0.172 0.114 0.167
## Biastophilia 0.099 0.296 0.170 0.185
## Urophilia 0.294 0.159 0.137 0.091
## Scatophilia 1.000 0.061 0.058 0.099
## Hebephilia 0.061 1.000 0.400 0.339
## Pedophilia 0.058 0.400 1.000 0.217
## Zoophilia 0.099 0.339 0.217 1.000Structure
#overall factor
fa_men = fa(dawson_men)
fa_women = fa(dawson_women)
#plot together
fa_plot_loadings(list("Men" = fa_men, "Women" = fa_women))
GG_save("figs/loadings.png")