What kind of people are really into anime?
a study of the OKCupid (2014-2015) dataset
TL;DR: people who claim to be really into anime are more likely to be men, childless, poor, bicurious, nonwhite, racist, unathletic, bullied in high school, into Asians, kinky, unconscientious, and eccentric.
Intelligence and height were uncorrelated with liking anime.
Full list of predictors of liking anime:
Being male.
Being bisexual (regardless of sex).
Having a sexual ecounter with the same sex or wanting one.
Not being gay (within men).
Not having children.
Being young.
Having poor hygeine habits.
Being either skinny or fat.
Liking the taste of blood (surprisingly large effect — ~21% for no and ~39% for yes).
Low income (people into anime make 10,000$ less, controlling for race/sex/sexuality/age).
No association between field of employment and liking anime survives controls for sex, race, age, and income.
Being a college dropout or student.
Taking drugs.
Being a Buddhist, Muslim, or Atheist; not a Christian.
Being Asian, Hispanic, or Black; not White or Indian.
(if black or asian) not viewing race as a core part of your identity.
(if white) viewing race as a core part of your identity.
Claiming to find Asians attractive (regardless of race or sex).
Finding foreign accents sexy.
Being comfortable with racist jokes (effect is specific to men).
Claiming to be racist.
Living in Turkey, the Philippines, Sweden, Australia, Peru, or Trinidad.
Not living in DC, Germany, Illinois, Indiana, Maryland, the Netherlands, New York, Ontario, Oklahoma, or Pennsylvania.
Not having left one’s country of origin.
Non-conservative political beliefs.
Being willing to date a shorter partner (if female). Some of this is due to convariance with a preference for Asians.
Having rape fantasies (regardless of sex)
Having LGBT friends.
Not thinking that bisexual people are more likely to be unfaithful.
Using the word ‘gay’ as a prejorative.
Not claiming to have been a “cool kid” in high school.
Claiming to have been picked on a lot in school.
Being less close to your family.
Claiming to be unticklish.
Reporting lower levels of attractiveness relative to other individuals
Being into bondage.
Believing in ghosts.
Wearing a lot of black.
Disliking chivalry
Supporting restrictions on the number of children that a family can have.
Supporting nuclear energy.
Preferring to send rather than receive messages (?).
Thinking that it should not be a jailable offense to have sex with a 16 year old as a 25+ year old.
Viewing laziness as a cause of homelessness.
Thinking there should be more or less humans on the planet.
Thinking people of colour can’t be racist.
Thinking the world would be a better place if low IQ people couldn’t reproduce
Thinking seuxal assault accusations at the workplace made by women are false.
Ambivalent attitudes towards feminism (if female).
Ambivalent or positive attitudes towards feminism (if male).
Being willing to take off one’s shirt in the nightclub.
Enjoying arguing.
Claiming to be unhappy with one’s life.
Claiming to be less ambitious (d = -.25), more independent (d = .12), more romantic (d = .1) more kinky (d = .32), less optimistic (d = -.24), more into literature (d = .15), less into politics (d = -.17), having worse manners (d = -.25), more sloppy (d = .13), more indie (d = .34), more introverted (d = .13), more trusting (d = .25), more thrifty (d = .38), less organized (d = -.16), experienced in sex (d = .1), artsy (d = .21), spontaneous (d = .18), giving (d = .12), less experienced in love (d = -.18), less conventionally moral (d = -.15), less kind (d = -.14), less friendly to strangers (d = -.13), less old-fashioned (d = -.28), less energetic (d = -.21), less into exercise (d = -.23), more into math (d = .16), geeky (d = 1.21), and adventurous (d = .18).
Things that were uncorrelated with liking anime:
Nationality and location, in most cases.
Height
Believing in the existence of a correlation between race and intelligence.
Being Jewish.
Blushing easily.
IQ.
Claiming to be confident, being laid back, being into drugs, being progressive, being dominant, being submissive, arrogant, cool, have a high sex drive, honest, into science, experienced in life, greedy, capitalistic, aggressive, extraverted (surely this is wrong?), competitive, driven by love, spirited, passionate
Disliking promiscuous people.
Being into abstract art.
Being disgusted by extremely obese people
The statistics in this post are based on the OK Cupid dataset, a scraped sample of 68,371 individuals who used the OK Cupid website. Some participants answered whether they were “really into Japanese Animation (anime) movies” — 20,000 people answered and 25% said yes.
Given the large sample size, I didn’t bother engaging in significance testing, as even the smallest effects pass significance. For example, people who had LGBT friends were really into anime 24% of the time, while those who did not were really into anime 21% of the time. This difference passed statistical significance easily:
> okcdataset %>% group_by(q7085) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q7085 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2136 2154
2 Yes 0.2437 13465
3 NA 0.2824 8173
table_data <- table(q7085 = okcdataset$q7085, anime = okcdataset$anime)
> chisq.test(table_data)
Pearson's Chi-squared test with Yates' continuity correction
data: table_data
X-squared = 9.1232, df = 1, p-value = 0.002524I looked through the list of questions the participants were asked, tallied the ones that looked interesting, and ran the R code.
I charted the best results:
There was no difference in measured intelligence; I copied the method Kirkegaard uses to calculate IQ, and subset the sample to those who answered at least 5 questions:
The association did become positive after controlling for race/sex/sexuality/income:
> summary(lr)
Call:
lm(formula = anime ~ IQ + race + sex + sexuality + numincome,
data = d)
Residuals:
Min 1Q Median 3Q Max
-0.7438 -0.2992 -0.2395 0.5426 1.5853
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1647436287 0.0143720806 11.463 < 0.0000000000000002 ***
IQ 0.0233550072 0.0085601642 2.728 0.00638 **
raceAsian 0.2636851982 0.0418578199 6.300 0.0000000003187740 ***
raceBlack 0.1818156689 0.0309390413 5.877 0.0000000044034711 ***
raceHispanic / Latin 0.1393756141 0.0296596412 4.699 0.0000026680088361 ***
raceIndian -0.0666111519 0.0785596379 -0.848 0.39652
raceMiddle Eastern 0.0806213268 0.1260831090 0.639 0.52257
raceOther 0.0644259638 0.0387806243 1.661 0.09670 .
racePacific Islander 0.4396059473 0.1380291450 3.185 0.00146 **
raceSelected over 1 race 0.1395538637 0.0185229402 7.534 0.0000000000000561 ***
sexMale 0.1830984905 0.0140346037 13.046 < 0.0000000000000002 ***
sexualityBisexual 0.1663696674 0.0228315753 7.287 0.0000000000003564 ***
sexualityGay -0.0045087442 0.0246157412 -0.183 0.85467
sexualityOther 0.0630129728 0.0842310494 0.748 0.45443
numincome -0.0000012188 0.0000001102 -11.063 < 0.0000000000000002 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4358 on 6271 degrees of freedom
(22433 observations deleted due to missingness)
Multiple R-squared: 0.06421, Adjusted R-squared: 0.06212
F-statistic: 30.73 on 14 and 6271 DF, p-value: < 0.00000000000000022…Which is an artefact of controlling for income, which should not be done, as personal income does not cause IQ. It just means that the non-income variance in IQ is positively correlated with liking anime.
R code:
#Sexuality coding:
okcdataset$sexuality <- okcdataset$d_orientation
okcdataset$sexuality[okcdataset$sexuality=="Pansexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Straight, Gay"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Heteroflexible"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Homoflexible"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Lesbian"] <- "Gay"
okcdataset$sexuality[okcdataset$sexuality=="Bisexual, Pansexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Straight, Bisexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Straight, Bisexual, Heteroflexible"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Straight, Pansexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Heteroflexible, Bisexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Pansexual, Bisexual"] <- "Bisexual"
okcdataset$sexuality[okcdataset$sexuality=="Homoflexible, Heteroflexible, Pansexual, Bisexual"] <- "Bisexual"
okcdataset$sexuality[!(okcdataset$sexuality=="Bisexual" | okcdataset$sexuality=="Straight" | okcdataset$sexuality=="Gay" | is.na(okcdataset$sexuality))] <- "Other"
#Race coding:
okcdataset$race <- okcdataset$d_ethnicity
okcdataset$race[!(okcdataset$d_ethnicity=='Asian' | okcdataset$d_ethnicity=='Black' | okcdataset$d_ethnicity=='Indian' | okcdataset$d_ethnicity=='White' | okcdataset$d_ethnicity=='Hispanic / Latin' | okcdataset$d_ethnicity=='White' | okcdataset$d_ethnicity=='Middle Eastern' | okcdataset$d_ethnicity=='Pacific Islander' | okcdataset$d_ethnicity=='Other' | is.na(okcdataset$d_ethnicity))] <- 'Selected over 1 race'
Call:
lm(formula = anime ~ IQ + race + sex + sexuality, data = d)
Residuals:
Min 1Q Median 3Q Max
-0.6992 -0.2494 -0.2453 0.4451 0.9365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.102364 0.006663 15.362 < 0.0000000000000002 ***
IQ 0.002616 0.004801 0.545 0.58582
raceAsian 0.202613 0.015975 12.683 < 0.0000000000000002 ***
raceBlack 0.164589 0.016303 10.096 < 0.0000000000000002 ***
raceHispanic / Latin 0.125546 0.015795 7.949 0.00000000000000199 ***
raceIndian -0.069057 0.034661 -1.992 0.04635 *
raceMiddle Eastern 0.098434 0.052654 1.869 0.06158 .
raceOther 0.093367 0.019344 4.827 0.00000139858133607 ***
racePacific Islander 0.271782 0.069745 3.897 0.00009778746816569 ***
raceSelected over 1 race 0.122779 0.010170 12.072 < 0.0000000000000002 ***
sexMale 0.146296 0.006996 20.911 < 0.0000000000000002 ***
sexualityBisexual 0.181169 0.012360 14.657 < 0.0000000000000002 ***
sexualityGay -0.034171 0.012304 -2.777 0.00549 **
sexualityOther 0.062387 0.038580 1.617 0.10587
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4235 on 19425 degrees of freedom
(9280 observations deleted due to missingness)
Multiple R-squared: 0.046, Adjusted R-squared: 0.04536
F-statistic: 72.05 on 13 and 19425 DF, p-value: < 0.00000000000000022> okcdataset %>% group_by(gender) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
gender into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Man 0.2862 15643
2 Other 0.3417 120
3 Woman 0.1697 7296
4 NA 0.4011 733
> okcdataset %>% group_by(sexuality, gender) %>% summarise(anime_like = mean(anime, na.rm=T), sum(!is.na(anime)))
`summarise()` has grouped output by 'sexuality'. You can override using the `.groups` argument.
# A tibble: 13 × 4
# Groups: sexuality [5]
sexuality gender anime_like `sum(!is.na(anime))`
<chr> <chr> <dbl> <int>
1 Bisexual Man 0.4556 496
2 Bisexual Other 0.55 20
3 Bisexual Woman 0.3344 1259
4 Gay Man 0.2437 1149
5 Gay Other 0.125 8
6 Gay Woman 0.1841 315
7 Other Man 0.3871 62
8 Other Other 0.3077 78
9 Other Woman 0.2093 86
10 Straight Man 0.2832 13936
11 Straight Other 0.3571 14
12 Straight Woman 0.1315 5636
13 NA NA 0.4011 733
> okcdataset %>% filter(d_ethnicity=='Asian' | d_ethnicity=='Black' | d_ethnicity=='Indian' | d_ethnicity=='White' | d_ethnicity=='Hispanic / Latin' | d_ethnicity=='Middle Eastern' | d_ethnicity=='Pacific Islander' | d_ethnicity=='Other') %>% group_by(d_ethnicity) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 8 × 3
d_ethnicity into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Asian 0.3983 836
2 Black 0.3766 802
3 Hispanic / Latin 0.3481 856
4 Indian 0.1698 159
5 Middle Eastern 0.3014 73
6 Other 0.3207 555
7 Pacific Islander 0.4524 42
8 White 0.2171 16203
q12 Divide your age by 2. Have you had sex with at least that many people?
> okcdataset %>% group_by(q12) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q12 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2755 9428
2 Yes 0.2347 5671
3 NA 0.2441 8693
q13 Is a girl who's slept with 100 guys a bad person?
q14 Is a guy who's slept with 100 girls a bad person?
> okcdataset %>% group_by(q13) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q13 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2557 18274
2 Yes 0.2367 2505
3 NA 0.2605 3013
> okcdataset %>% group_by(q14) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q14 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2530 17598
2 Yes 0.2531 2940
3 NA 0.2624 3254
q51 Do you think really abstract art—like just splattered paint—can be truly brilliant?
> okcdataset %>% group_by(q51) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q51 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2470 7057
2 Yes 0.2406 8939
3 NA 0.2766 7796
q65 Do you have a problem with racist jokes?
> okcdataset %>% group_by(q65) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q65 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2926 10814
2 Yes 0.2185 10273
3 NA 0.2370 2705
q57 Do you like to argue?
> okcdataset %>% group_by(q57) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q57 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2338 14061
2 Yes 0.2850 7298
3 NA 0.2803 2433
q18781 Do you agree with the statement 'everyone's a little racist'?
> okcdataset %>% group_by(q18781) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q18781 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2390 2188
2 Yes 0.2849 2738
3 NA 0.2516 18866
q163 If a poor 20 year-old woman marries a wealthy 40 year-old man, do you assume it's for the money?
> okcdataset %>% group_by(q163) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q163 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2622 6118
2 Yes 0.2170 5866
3 NA 0.2687 11808
q177 Which better explains why most homeless people are homeless?
> okcdataset %>% group_by(q177) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q177 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Impossible odds 0.2669 1416
2 Sheer laziness 0.3141 312
3 NA 0.2526 22064
q187 In high school, were you one of the 'cool' kids?
> okcdataset %>% group_by(q187) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q187 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2628 7278
2 Yes 0.1883 2321
3 NA 0.2607 14193
q262 Most claims of sexual harrassment in the workplace are LIES made up by a scorned woman.
> okcdataset %>% group_by(q262) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
q262 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 False 0.2414 3293
2 I don't know 0.2785 1734
3 True 0.3401 147
4 NA 0.2536 18618
q348 Which do you like more? Be honest.
> okcdataset %>% group_by(q348) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q348 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Giving massages 0.2999 6750
2 Receiving massages 0.2071 8434
3 NA 0.2648 8608
q391 Are you disgusted by the extremely obese?
> okcdataset %>% filter(d_bodytype=='Average') %>% group_by(q391) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q391 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2584 1304
2 Yes 0.2483 1889
3 NA 0.2660 4357
q401 Are you very close to your family?
> okcdataset %>% group_by(q401) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q401 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2905 3993
2 Yes 0.2190 9579
3 NA 0.2732 10220
q1062 How frequently do you bathe or shower?
> okcdataset %>% group_by(q1062) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q1062 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 A couple times a week. 0.3551 704
2 At least once a day. 0.2355 14778
3 Once a week or less. 0.3830 47
4 Usually daily. I skip some. 0.2834 6446
5 NA 0.2614 1817
q1133 Do you have rape fantasies?
> okcdataset %>% group_by(q1133, gender) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
`summarise()` has grouped output by 'q1133'. You can override using the `.groups` argument.
# A tibble: 12 × 4
# Groups: q1133 [3]
q1133 gender into_anime `sum(!is.na(anime))`
<chr> <chr> <dbl> <int>
1 No Man 0.2634 12290
2 No Other 0.3194 72
3 No Woman 0.1424 4192
4 No NA 0.2143 154
5 Yes Man 0.3964 1607
6 Yes Other 0.4762 21
7 Yes Woman 0.2558 1071
8 Yes NA 0.4783 115
9 NA Man 0.3454 1746
10 NA Other 0.2963 27
11 NA Woman 0.1805 2033
12 NA NA 0.4440 464
q1379 Would you take your top off while dancing in a nightclub? (If you're a girl, assume you have a bikini top or bra on underneath)
> okcdataset %>% group_by(q1379) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 3 × 3
q1379 into_anime n
<chr> <dbl> <int>
1 No 0.2362 2405
2 Yes 0.2960 883
3 NA 0.2546 65083
> print(okcdataset %>% group_by(d_bodytype) %>% summarise(d_t = mean(dancetopless, na.rm=T), n=n()), n=40)
# A tibble: 13 × 3
d_bodytype d_t n
<chr> <dbl> <int>
1 A little extra 0.2257 5016
2 Athletic 0.3933 8167
3 Average 0.2495 18272
4 Curvy 0.2209 4925
5 Fit 0.35 8121
6 Full figured 0.1565 1941
7 Jacked 0.2778 307
8 Overweight 0.1990 1790
9 Rather not say 0.125 194
10 Skinny 0.3117 1498
11 Thin 0.2685 4315
12 Used up 0.3429 351
13 NA 0.3013 13474
> print(okcdataset %>% group_by(q1379, d_bodytype) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime))), n=40)
`summarise()` has grouped output by 'q1379'. You can override using the `.groups` argument.
# A tibble: 39 × 4
# Groups: q1379 [3]
q1379 d_bodytype into_anime `sum(!is.na(anime))`
<chr> <chr> <dbl> <int>
1 No A little extra 0.2906 265
2 No Athletic 0.1831 142
3 No Average 0.2327 679
4 No Curvy 0.1307 176
5 No Fit 0.2205 195
6 No Full figured 0.2364 110
7 No Jacked 0.2727 11
8 No Overweight 0.3235 136
9 No Rather not say 0.2 5
10 No Skinny 0.3333 45
11 No Thin 0.2593 135
12 No Used up 0.25 20
13 No NA 0.2210 181
14 Yes A little extra 0.2973 74
15 Yes Athletic 0.2619 84
16 Yes Average 0.3162 234
17 Yes Curvy 0.2281 57
18 Yes Fit 0.3429 105
19 Yes Full figured 0.2273 22
20 Yes Jacked 0 5
21 Yes Overweight 0.3939 33
22 Yes Rather not say 1 1
23 Yes Skinny 0.4286 21
24 Yes Thin 0.2115 52
25 Yes Used up 0.2 10
26 Yes NA 0.2754 69
27 NA A little extra 0.3148 2398
28 NA Athletic 0.2006 2039
29 NA Average 0.2611 6637
30 NA Curvy 0.1917 1633
31 NA Fit 0.2121 2494
32 NA Full figured 0.2013 949
33 NA Jacked 0.1848 92
34 NA Overweight 0.3388 971
35 NA Rather not say 0.3125 48
36 NA Skinny 0.3734 474
37 NA Thin 0.2318 1303
38 NA Used up 0.2932 133
39 NA NA 0.2953 1754
q7085 Do you have any gay, bisexual, or transgender friends?
> okcdataset %>% group_by(q7085) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q7085 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2136 2154
2 Yes 0.2437 13465
3 NA 0.2824 8173
q7847 Are you racist?
> okcdataset %>% group_by(q7847) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q7847 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2565 5282
2 Yes 0.3544 158
3 NA 0.2528 18352
q1401 Have you ever had a sexual encounter with someone of the same sex?
> okcdataset %>% group_by(q1401) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q1401 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No, and I would never. 0.2509 14285
2 No, but I would like to. 0.3249 1231
3 Yes, and I did not enjoy myself. 0.2869 955
4 Yes, and I enjoyed myself. 0.2627 4424
5 NA 0.2175 2897
q12955 Have you ever been outside of your own country?
> okcdataset %>% group_by(q12955) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q12955 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.3333 441
2 Yes 0.2307 2740
3 NA 0.2557 20611
q12954 Do you have a thing for foreign accents?
> okcdataset %>% group_by(q12954) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q12954 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.1992 2620
2 Yes 0.2567 6805
3 NA 0.2632 14367
q12970 How often do you brush your teeth?
> okcdataset %>% group_by(q12970) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q12970 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Once a day 0.3286 6281
2 Only on days I feel like it 0.4183 263
3 Rarely / never 0.3721 43
4 Twice or more a day 0.2196 15777
5 NA 0.2766 1428
q13669 Would you date someone shorter than you?
> okcdataset %>% group_by(q13669, gender) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
`summarise()` has grouped output by 'q13669'. You can override using the `.groups` argument.
# A tibble: 12 × 4
# Groups: q13669 [3]
q13669 gender into_anime `sum(!is.na(anime))`
<chr> <chr> <dbl> <int>
1 No Man 0.3108 148
2 No Other 0.1818 11
3 No Woman 0.1311 3317
4 No NA 0.2576 66
5 Yes Man 0.2760 14197
6 Yes Other 0.3871 93
7 Yes Woman 0.2031 2442
8 Yes NA 0.2698 63
9 NA Man 0.3952 1298
10 NA Other 0.1875 16
11 NA Woman 0.1997 1537
12 NA NA 0.4305 604
> lr <- lm(data=woman, anime ~ q13669)
> summary(lr)
Call:
lm(formula = anime ~ q13669, data = woman)
Residuals:
Min 1Q Median 3Q Max
-0.2031 -0.2031 -0.1311 -0.1311 0.8689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.131143 0.006363 20.609 < 0.0000000000000002 ***
q13669Yes 0.071970 0.009772 7.365 0.000000000000202 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3665 on 5757 degrees of freedom
(20193 observations deleted due to missingness)
Multiple R-squared: 0.009334, Adjusted R-squared: 0.009162
F-statistic: 54.24 on 1 and 5757 DF, p-value: 0.0000000000002021
> lr <- lm(data=woman, anime ~ q13669 + q92015)
> summary(lr)
Call:
lm(formula = anime ~ q13669 + q92015, data = woman)
Residuals:
Min 1Q Median 3Q Max
-0.44476 -0.19830 -0.13322 -0.09085 0.90915
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.37967 0.04258 8.918 < 0.0000000000000002 ***
q13669Yes 0.06509 0.02749 2.368 0.0181 *
q92015Black -0.28883 0.05262 -5.489 0.0000000544 ***
q92015Latino -0.23831 0.05466 -4.360 0.0000147506 ***
q92015White -0.24646 0.04342 -5.676 0.0000000194 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3763 on 790 degrees of freedom
(25157 observations deleted due to missingness)
Multiple R-squared: 0.05619, Adjusted R-squared: 0.05142
F-statistic: 11.76 on 4 and 790 DF, p-value: 0.00000000278
q15409 Do you ever use the word 'gay' as an insult or pejorative?
> okcdataset %>% group_by(q15409) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q15409 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2296 14200
2 Yes 0.2924 4237
3 NA 0.2894 5355
q85770 Is nearly seven billion living humans too many?
> okcdataset %>% group_by(q85770) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q85770 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No, there can never be too many humans. 0.2949 156
2 No, this is just about right. 0.25 264
3 No, we need more to make the world better. 0.3182 88
4 Yes, there are too many humans on Earth. 0.2824 1087
5 NA 0.2524 22197
q86217 Can people of color be racist?
> okcdataset %>% group_by(q86217) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q86217 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No. 0.2984 677
2 Yes. 0.2382 15356
3 NA 0.2823 7759
q92015 Not to be racist but which ethnicity do you find to be most attractive?
> okcdataset %>% group_by(q92015) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q92015 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Asian/Indian 0.4591 697
2 Black 0.2870 338
3 Latino 0.2821 585
4 White 0.2469 2041
5 NA 0.2466 20131
q156914 Are you Jewish?
> okcdataset %>% filter(d_ethnicity=='White') %>% group_by(q156914) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q156914 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2137 14481
2 Yes 0.1898 822
3 NA 0.2967 900
q212813 Which best describes your political beliefs?
> okcdataset %>% group_by(q212813) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q212813 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Centrist 0.2452 4091
2 Conservative / Right-wing 0.1668 1445
3 Liberal / Left-wing 0.2307 9296
4 Other 0.3047 6942
5 NA 0.2706 2018
q313640 Were you picked on a lot in school?
> okcdataset %>% group_by(q313640) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q313640 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2036 12507
2 Yes 0.3251 8708
3 NA 0.2612 2577
q26144 Would it bother you if your boss was a minority, female, or gay?
> okcdataset %>% group_by(q26144) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q26144 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2510 7485
2 Not really, but maybe 0.2432 111
3 Not sure 0.3125 64
4 Yes, one or more of those types bother me 0.3571 42
5 NA 0.2554 16090
q35594 Compared to those who are entirely straight or entirely gay, do you think bisexuals are more or less likely to be unfaithful?
> okcdataset %>% group_by(q35594) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q35594 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Bisexuals are less likely to be unfaitful. 0.2759 29
2 Bisexuals are more likely to be unfaithful. 0.1491 161
3 I don't know. 0.2417 393
4 It makes no difference. 0.2788 2249
5 NA 0.2527 20960
q37708 The idea of gay and lesbian couples having children is:
> okcdataset %>% group_by(q37708) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q37708 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Acceptable. 0.2563 20360
2 Not acceptable. 0.1816 1316
3 NA 0.2798 2116
q219 Gay marraige: should it be legal?
> okcdataset %>% group_by(q219) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q219 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2047 1602
2 Yes 0.2541 19693
3 NA 0.2875 2497
q2 Breast implants?
> okcdataset %>% group_by(q2) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q2 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 more cool than pathetic 0.2637 5309
2 more pathetic than cool 0.2362 13346
3 NA 0.2916 5137
q60826 How ticklish are you?
> okcdataset %>% group_by(q60826) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q60826 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Extremely. 0.2464 3997
2 Just a little bit. 0.2519 4184
3 Not at all. 0.2857 1365
4 Somewhat. 0.2342 6618
5 NA 0.2715 7628
q156913 Are you Christian?
q156915 Are you a Buddhist?
q156916 Are you a Muslim?
q156917 Are you an Atheist?
> okcdataset %>% group_by(q156913) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q156913 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2785 15383
2 Yes 0.1973 6579
3 NA 0.2557 1830
> okcdataset %>% group_by(q156917) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q156917 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2376 9654
2 Yes 0.2788 6943
3 NA 0.2530 7195
> okcdataset %>% group_by(q156915) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q156915 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2377 19877
2 Yes 0.3631 931
3 NA 0.3311 2984
> okcdataset %>% group_by(q156916) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q156916 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2429 6212
2 Yes 0.3837 86
3 NA 0.2577 17494
>
q280 Do you like the taste of blood?
> okcdataset %>% group_by(q280) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q280 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2112 15113
2 Yes 0.3876 3124
3 NA 0.2965 5555
q476 Do you wear a lot of black?
> okcdataset %>% group_by(q476) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 3 × 3
q476 into_anime n
<chr> <dbl> <int>
1 No 0.2069 6730
2 Yes 0.2823 7195
3 NA 0.2636 54446
q49468 In terms of height, how do you compare to others of your sex?
> okcdataset %>% group_by(q49468) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 4 × 3
q49468 into_anime n
<chr> <dbl> <int>
1 I am about average. 0.2470 7235
2 I am shorter. 0.2203 2558
3 I am taller. 0.2402 5231
4 NA 0.2664 53347
q49714 Should governments control population by placing legal limits on childbearing?
> okcdataset %>% group_by(q49714) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 5 × 3
q49714 into_anime n
<chr> <dbl> <int>
1 No, for other reasons. 0.2472 1760
2 No, that is against my spiritual beliefs. 0.22 200
3 No, that violates human rights. 0.2243 4890
4 Yes. 0.3280 1800
5 NA 0.2557 59721
q18125 Do you believe that there exists a statistical correlation between race and intelligence?
> okcdataset %>% group_by(q18125) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 3 × 3
q18125 into_anime n
<chr> <dbl> <int>
1 No 0.2482 23968
2 Yes 0.2596 2012
3 NA 0.2757 42391
q16 Should sex with someone 16 years old be a jailable offense, if you're 25 or older?
> okcdataset %>% group_by(q16) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q16 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.3177 2024
2 Yes 0.2458 4223
3 NA 0.2490 17545
q29 Would you rather:
> okcdataset %>% group_by(q29) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
q29 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 avoid bondage all together 0.2013 4933
2 be tied up during sex 0.2581 6618
3 do the tying 0.2896 8665
4 NA 0.2349 3576
q45 Do you believe in ghosts?
> okcdataset %>% group_by(q45) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q45 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2242 10457
2 Yes 0.2776 7662
3 NA 0.2783 5673
> okcdataset %>% group_by(d_bodytype) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 13 × 3
d_bodytype into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 A little extra 0.3120 2737
2 Athletic 0.2018 2265
3 Average 0.2603 7550
4 Curvy 0.1870 1866
5 Fit 0.2176 2794
6 Full figured 0.2054 1081
7 Jacked 0.1852 108
8 Overweight 0.3386 1140
9 Rather not say 0.3148 54
10 Skinny 0.3722 540
11 Thin 0.2336 1490
12 Used up 0.2822 163
13 NA 0.2879 2004
> okcdataset %>% group_by(d_drugs) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
d_drugs into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Never 0.2404 17068
2 Often 0.3354 158
3 Sometimes 0.3147 2024
4 NA 0.2765 4542
> okcdataset %>% group_by(d_education_phase) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
d_education_phase into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Dropped out of 0.3054 1611
2 Graduated from 0.2256 13778
3 Working on 0.3185 4028
4 NA 0.2665 4375
> okcdataset %>% filter(d_orientation=='Asexual' | d_orientation=='Bisexual' | d_orientation=='Straight' | d_orientation=='Gay' | d_orientation=='Lesbian' | d_orientation=='Heteroflexible' | d_orientation=='Queer') %>% group_by(d_orientation) %>% summarise(into_anime = mean(anime, na.rm=T), n=n())
# A tibble: 7 × 3
d_orientation into_anime n
<chr> <dbl> <int>
1 Asexual 0.5 8
2 Bisexual 0.3700 4769
3 Gay 0.2308 3401
4 Heteroflexible 0.3333 29
5 Lesbian 0 6
6 Queer 0.04762 50
7 Straight 0.2396 57774
> print(okcdataset %>% group_by(race, gender) %>% summarise(into_anime = mean(anime, na.rm=T), n=n()), n=60)
`summarise()` has grouped output by 'race'. You can override using the `.groups` argument.
# A tibble: 28 × 4
# Groups: race [10]
race gender into_anime n
<chr> <chr> <dbl> <int>
1 Asian Man 0.4536 485
2 Asian Other 0 1
3 Asian Woman 0.3229 350
4 Black Man 0.4519 489
5 Black Other 0.4 5
6 Black Woman 0.2565 308
7 Hispanic / Latin Man 0.3881 621
8 Hispanic / Latin Other 0 1
9 Hispanic / Latin Woman 0.2436 234
10 Indian Man 0.1955 133
11 Indian Woman 0.03846 26
12 Middle Eastern Man 0.2830 53
13 Middle Eastern Woman 0.35 20
14 Other Man 0.3455 385
15 Other Other 0.3333 3
16 Other Woman 0.2635 167
17 Pacific Islander Man 0.5333 30
18 Pacific Islander Woman 0.25 12
19 Selected over 1 race Man 0.3909 1522
20 Selected over 1 race Other 0.4444 18
21 Selected over 1 race Woman 0.2409 685
22 White Man 0.2511 11165
23 White Other 0.3333 81
24 White Woman 0.1388 4957
25 NA Man 0.2724 760
26 NA Other 0.2727 11
27 NA Woman 0.1508 537
28 NA NA 0.4011 733
> okcdataset %>% group_by(d_offspring_current) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
d_offspring_current into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 - 0.2672 2665
2 kids 0.1511 953
3 NA 0.2575 20174
> print(okcdataset %>% group_by(d_job) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime))), n=100)
# A tibble: 22 × 3
d_job into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Administration 0.2685 607
2 Art / Music / Writing 0.2774 1453
3 Banking / Finance 0.1555 669
4 Construction 0.2535 501
5 Education 0.1536 1341
6 Entertainment / Media 0.2646 854
7 Hospitality 0.2720 614
8 Law 0.1645 389
9 Management 0.1722 987
10 Medicine 0.2053 1062
11 Military 0.3624 149
12 Other 0.2593 4053
13 Politics / Government 0.1844 450
14 Rather not say 0.2584 298
15 Retired 0.1986 146
16 Sales / Marketing 0.2512 1278
17 Science / Engineering 0.2463 1283
18 Student 0.3386 1589
19 Technology 0.3046 2436
20 Transportation 0.2347 409
21 Unemployed 0.3672 177
22 NA 0.2724 3047
q15634 Do you like to be the center of attention?
> okcdataset %>% group_by(q15634) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 4 × 3
q15634 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Not really, no 0.2505 8464
2 Yes, always 0.2539 445
3 Yes, sometimes 0.2258 8704
4 NA 0.2997 6179
> lr <- lm(data=okcdataset %>% filter(gender=='Woman'), anime ~ q65 + race)
> summary(lr)
Call:
lm(formula = anime ~ q65 + race, data = okcdataset %>% filter(gender ==
"Woman"))
Residuals:
Min 1Q Median 3Q Max
-0.4114 -0.1508 -0.1318 -0.1318 0.8682
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.34745 0.02201 15.782 < 0.0000000000000002 ***
q65Yes -0.01899 0.01013 -1.875 0.060822 .
raceBlack -0.07069 0.03109 -2.274 0.023024 *
raceHispanic / Latin -0.10904 0.03429 -3.180 0.001481 **
raceIndian -0.33424 0.08086 -4.134 0.0000362 ***
raceMiddle Eastern 0.06394 0.09885 0.647 0.517744
raceOther -0.06514 0.03789 -1.719 0.085586 .
racePacific Islander -0.05902 0.14288 -0.413 0.679547
raceSelected over 1 race -0.09208 0.02629 -3.502 0.000465 ***
raceWhite -0.19665 0.02236 -8.793 < 0.0000000000000002 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3736 on 5772 degrees of freedom
(20170 observations deleted due to missingness)
Multiple R-squared: 0.0274, Adjusted R-squared: 0.02588
F-statistic: 18.07 on 9 and 5772 DF, p-value: < 0.00000000000000022
> lr <- lm(data=okcdataset %>% filter(gender=='Man'), anime ~ q65 + race)
> summary(lr)
[1] "p_conf"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.01 0.04 0.1
Multivariate (Mahalanobis) distance between groups
[1] 0.044
r equivalent of difference between two means
data
0.02
[1] "p_laidback"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.09 -0.01 0.07
Multivariate (Mahalanobis) distance between groups
[1] 0.01
r equivalent of difference between two means
data
0
[1] "p_drug"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.08 0.02 0.11
Multivariate (Mahalanobis) distance between groups
[1] 0.016
r equivalent of difference between two means
data
0.01
[1] "p_lit"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.11 0.15 0.19
Multivariate (Mahalanobis) distance between groups
[1] 0.15
r equivalent of difference between two means
data
0.06
[1] "p_progress"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.15 -0.02 0.11
Multivariate (Mahalanobis) distance between groups
[1] 0.019
r equivalent of difference between two means
data
-0.01
[1] "p_roman"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.06 0.11 0.15
Multivariate (Mahalanobis) distance between groups
[1] 0.11
r equivalent of difference between two means
data
0.05
[1] "p_dominant"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.06 0 0.06
Multivariate (Mahalanobis) distance between groups
[1] 0.00084
r equivalent of difference between two means
data
0
[1] "p_polit"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.21 -0.17 -0.13
Multivariate (Mahalanobis) distance between groups
[1] 0.17
r equivalent of difference between two means
data
-0.07
[1] "p_pure"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.03 0.07 0.11
Multivariate (Mahalanobis) distance between groups
[1] 0.069
r equivalent of difference between two means
data
0.03
[1] "p_manners"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.3 -0.25 -0.2
Multivariate (Mahalanobis) distance between groups
[1] 0.25
r equivalent of difference between two means
data
-0.11
[1] "p_submissive"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.09 -0.03 0.03
Multivariate (Mahalanobis) distance between groups
[1] 0.031
r equivalent of difference between two means
data
-0.01
[1] "p_inde"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.07 0.12 0.16
Multivariate (Mahalanobis) distance between groups
[1] 0.12
r equivalent of difference between two means
data
0.05
[1] "p_kinky"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.29 0.32 0.36
Multivariate (Mahalanobis) distance between groups
[1] 0.32
r equivalent of difference between two means
data
0.14
[1] "p_opti"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.29 -0.24 -0.19
Multivariate (Mahalanobis) distance between groups
[1] 0.24
r equivalent of difference between two means
data
-0.1
[1] "p_sloppy"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.09 0.13 0.17
Multivariate (Mahalanobis) distance between groups
[1] 0.13
r equivalent of difference between two means
data
0.06
[1] "p_indie"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.3 0.34 0.39
Multivariate (Mahalanobis) distance between groups
[1] 0.34
r equivalent of difference between two means
data
0.14
[1] "p_introvert"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.07 0.13 0.19
Multivariate (Mahalanobis) distance between groups
[1] 0.13
r equivalent of difference between two means
data
0.06
[1] "p_arro"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.01 0.06 0.1
Multivariate (Mahalanobis) distance between groups
[1] 0.056
r equivalent of difference between two means
data
0.02
[1] "p_ambi"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.29 -0.25 -0.2
Multivariate (Mahalanobis) distance between groups
[1] 0.25
r equivalent of difference between two means
data
-0.11
[1] "p_cool"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.15 -0.07 0.01
Multivariate (Mahalanobis) distance between groups
[1] 0.073
r equivalent of difference between two means
data
-0.03
[1] "p_trusting"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.2 0.25 0.29
Multivariate (Mahalanobis) distance between groups
[1] 0.25
r equivalent of difference between two means
data
0.11
[1] "p_thrift"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.33 0.38 0.43
Multivariate (Mahalanobis) distance between groups
[1] 0.38
r equivalent of difference between two means
data
0.16
[1] "p_organ"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.21 -0.16 -0.12
Multivariate (Mahalanobis) distance between groups
[1] 0.16
r equivalent of difference between two means
data
-0.07
[1] "p_sexdrive"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.05 0.08 0.12
Multivariate (Mahalanobis) distance between groups
[1] 0.084
r equivalent of difference between two means
data
0.04
[1] "p_honest"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.1 -0.01 0.08
Multivariate (Mahalanobis) distance between groups
[1] 0.009
r equivalent of difference between two means
data
0
[1] "p_expsex"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.04 0.1 0.16
Multivariate (Mahalanobis) distance between groups
[1] 0.1
r equivalent of difference between two means
data
0.04
[1] "p_artsy"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.17 0.21 0.25
Multivariate (Mahalanobis) distance between groups
[1] 0.21
r equivalent of difference between two means
data
0.09
[1] "p_scien"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0 0.04 0.08
Multivariate (Mahalanobis) distance between groups
[1] 0.044
r equivalent of difference between two means
data
0.02
[1] "p_spon"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.13 0.18 0.22
Multivariate (Mahalanobis) distance between groups
[1] 0.18
r equivalent of difference between two means
data
0.08
[1] "p_explife"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.09 -0.05 -0.01
Multivariate (Mahalanobis) distance between groups
[1] 0.051
r equivalent of difference between two means
data
-0.02
[1] "p_greed"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.02 0.06 0.15
Multivariate (Mahalanobis) distance between groups
[1] 0.064
r equivalent of difference between two means
data
0.03
[1] "p_capi"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.04 0.06 0.15
Multivariate (Mahalanobis) distance between groups
[1] 0.056
r equivalent of difference between two means
data
0.02
[1] "p_giving"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.07 0.12 0.18
Multivariate (Mahalanobis) distance between groups
[1] 0.12
r equivalent of difference between two means
data
0.05
[1] "p_explove"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.22 -0.18 -0.14
Multivariate (Mahalanobis) distance between groups
[1] 0.18
r equivalent of difference between two means
data
-0.08
[1] "p_convenmoral"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.2 -0.15 -0.1
Multivariate (Mahalanobis) distance between groups
[1] 0.15
r equivalent of difference between two means
data
-0.06
[1] "p_aggre"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.02 0.08 0.14
Multivariate (Mahalanobis) distance between groups
[1] 0.08
r equivalent of difference between two means
data
0.04
[1] "p_kind"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.19 -0.14 -0.1
Multivariate (Mahalanobis) distance between groups
[1] 0.14
r equivalent of difference between two means
data
-0.06
[1] "p_extro"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.08 -0.02 0.05
Multivariate (Mahalanobis) distance between groups
[1] 0.018
r equivalent of difference between two means
data
-0.01
[1] "p_friendstrangers"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.18 -0.13 -0.09
Multivariate (Mahalanobis) distance between groups
[1] 0.13
r equivalent of difference between two means
data
-0.06
[1] "p_oldfash"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.32 -0.28 -0.23
Multivariate (Mahalanobis) distance between groups
[1] 0.28
r equivalent of difference between two means
data
-0.12
[1] "p_comp"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.05 -0.01 0.03
Multivariate (Mahalanobis) distance between groups
[1] 0.011
r equivalent of difference between two means
data
0
[1] "p_lovedri"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.05 0.09 0.13
Multivariate (Mahalanobis) distance between groups
[1] 0.089
r equivalent of difference between two means
data
0.04
[1] "p_sprit"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.05 0.09 0.13
Multivariate (Mahalanobis) distance between groups
[1] 0.091
r equivalent of difference between two means
data
0.04
[1] "p_passion"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.18 -0.08 0.02
Multivariate (Mahalanobis) distance between groups
[1] 0.083
r equivalent of difference between two means
data
-0.03
[1] "p_energetic"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.26 -0.21 -0.17
Multivariate (Mahalanobis) distance between groups
[1] 0.21
r equivalent of difference between two means
data
-0.09
[1] "p_exer"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] -0.27 -0.23 -0.18
Multivariate (Mahalanobis) distance between groups
[1] 0.23
r equivalent of difference between two means
data
-0.1
[1] "p_logic"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.02 0.07 0.12
Multivariate (Mahalanobis) distance between groups
[1] 0.073
r equivalent of difference between two means
data
0.03
[1] "p_math"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.12 0.16 0.21
Multivariate (Mahalanobis) distance between groups
[1] 0.16
r equivalent of difference between two means
data
0.07
[1] "p_geeky"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 1.18 1.21 1.25
Multivariate (Mahalanobis) distance between groups
[1] 1.2
r equivalent of difference between two means
data
0.48
[1] "p_adven"
Call: cohen.d(x = okcdataset$traitof, group = okcdataset$anime)
Cohen d statistic of difference between two means
lower effect upper
[1,] 0.14 0.18 0.21
Multivariate (Mahalanobis) distance between groups
[1] 0.18
r equivalent of difference between two means
data
0.08
Call:
lm(formula = anime ~ q65 + race, data = okcdataset %>% filter(gender ==
"Man"))
Residuals:
Min 1Q Median 3Q Max
-0.5528 -0.2839 -0.2157 0.5866 0.8466
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.473229 0.021541 21.968 < 0.0000000000000002 ***
q65Yes -0.068225 0.007828 -8.716 < 0.0000000000000002 ***
raceBlack 0.004114 0.029873 0.138 0.89046
raceHispanic / Latin -0.064598 0.028381 -2.276 0.02286 *
raceIndian -0.251620 0.045564 -5.522 0.0000000341 ***
raceMiddle Eastern -0.147303 0.066844 -2.204 0.02756 *
raceOther -0.088391 0.032063 -2.757 0.00584 **
racePacific Islander 0.079542 0.087280 0.911 0.36213
raceSelected over 1 race -0.059775 0.024520 -2.438 0.01479 *
raceWhite -0.189346 0.021903 -8.645 < 0.0000000000000002 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4477 on 13524 degrees of freedom
(26681 observations deleted due to missingness)
Multiple R-squared: 0.02648, Adjusted R-squared: 0.02583
F-statistic: 40.87 on 9 and 13524 DF, p-value: < 0.00000000000000022
> print(cntr %>% arrange(-n), n=1000)
# A tibble: 203 × 3
d_country into_anime n
<chr> <dbl> <int>
1 CA 0.2565 2558
2 NY 0.2186 1592
3 UK 0.2946 1341
4 TX 0.2623 1155
5 FL 0.2854 820
6 IL 0.2071 763
7 PA 0.2202 754
8 NA 0.4011 733
9 WA 0.2271 722
10 MA 0.2111 597
11 OH 0.2630 521
12 MI 0.2355 484
13 Ontario 0.1870 476
14 VA 0.2268 463
15 NJ 0.2383 449
16 OR 0.2511 446
17 GA 0.2361 415
18 NC 0.2427 412
19 Germany 0.2117 411
20 Australia 0.3713 404
21 CO 0.2005 389
22 MN 0.2334 377
23 MD 0.2333 360
24 AZ 0.2659 346
25 IN 0.1981 308
26 WI 0.2175 308
27 LA 0.2782 284
28 MO 0.2509 283
29 TN 0.2407 241
30 British Columbia 0.2265 234
31 CT 0.25 200
32 Netherlands 0.1744 195
33 NV 0.2327 159
34 SC 0.2774 155
35 OK 0.1948 154
36 UT 0.2597 154
37 KY 0.2614 153
38 IA 0.2143 140
39 Sweden 0.375 136
40 KS 0.2388 134
41 DC 0.1024 127
42 Alberta 0.2308 117
43 Finland 0.2696 115
44 Denmark 0.2544 114
45 Quebec 0.2807 114
46 France 0.3125 112
47 NM 0.2857 112
48 Italy 0.2685 108
49 AL 0.2430 107
50 Ireland 0.3474 95
51 NH 0.1895 95
52 RI 0.2 95
53 Brazil 0.3820 89
54 NE 0.2093 86
55 Singapore 0.4118 85
56 AR 0.3333 81
57 Israel 0.2533 75
58 HI 0.3662 71
59 ME 0.1642 67
60 ID 0.3231 65
61 Philippines 0.4844 64
62 MS 0.2712 59
63 India 0.2105 57
64 VT 0.2105 57
65 Belgium 0.3455 55
66 Austria 0.1509 53
67 WV 0.32 50
68 Mexico 0.2889 45
69 AK 0.3636 44
70 Spain 0.2558 43
71 China 0.3333 42
72 MT 0.2857 42
73 Turkey 0.4762 42
74 DE 0.25 40
75 Japan 0.375 40
76 Switzerland 0.225 40
77 Norway 0.2564 39
78 Greece 0.3514 37
79 Manitoba 0.2188 32
80 New Zealand 0.3548 31
81 Indonesia 0.4667 30
82 Russia 0.1667 30
83 South Africa 0.3103 29
84 Portugal 0.3929 28
85 Romania 0.2143 28
86 Argentina 0.2593 27
87 Malaysia 0.1852 27
88 Nova Scotia 0.2593 27
89 South Korea 0.32 25
90 Poland 0.375 24
91 Croatia 0.2609 23
92 PR 0.3 20
93 SD 0.15 20
94 Hong Kong 0.2632 19
95 Hungary 0.3158 19
96 ND 0.2105 19
97 WY 0.3529 17
98 Czech Republic 0.0625 16
99 Saskatchewan 0.25 16
100 Bulgaria 0.2 15
101 Taiwan 0.4667 15
102 Thailand 0.2 15
103 United Arab Emirates 0.2857 14
104 New Brunswick 0.2308 13
105 Costa Rica 0.25 12
106 Serbia 0.25 12
107 Iceland 0.1111 9
108 Chile 0.5 8
109 Estonia 0.25 8
110 Peru 0.75 8
111 Saudi Arabia 0.1429 7
112 Slovenia 0.1429 7
113 Ukraine 0.2857 7
114 Vietnam 0.5714 7
115 Colombia 0.3333 6
116 Egypt 0 6
117 Latvia 0.3333 6
118 Slovakia 0 6
119 Dominican Republic 0 5
120 Malta 0.2 5
121 Venezuela 0.4 5
122 Lithuania 0.25 4
123 Luxembourg 0.25 4
124 Bahamas 0.3333 3
125 Iran 0 3
126 Lebanon 0 3
127 Macedonia 0.3333 3
128 Trinidad and Tobago 1 3
129 VI 0.6667 3
130 Bermuda 0 2
131 Cambodia 0.5 2
132 Ecuador 0 2
133 Guatemala 0 2
134 Haiti 0 2
135 Jamaica 0.5 2
136 Kenya 0 2
137 Morocco 0.5 2
138 Pakistan 0.5 2
139 Qatar 0 2
140 Tunisia 1 2
141 13 1 1
142 Algeria 0 1
143 Azerbaijan 0 1
144 Bahrain 0 1
145 Bangladesh 1 1
146 Barbados 1 1
147 Belarus 0 1
148 Bosnia and Herzegovina 0 1
149 Cyprus 0 1
150 El Salvador 0 1
151 Falkland Islands (Islas Malvinas) 1 1
152 GU 0 1
153 Honduras 1 1
154 Isle of Man 0 1
155 Jordan 0 1
156 Kazakhstan 0 1
157 Kuwait 1 1
158 Lesotho 0 1
159 Mali 0 1
160 Mongolia 0 1
161 Namibia 0 1
162 Nepal 0 1
163 Netherlands Antilles 0 1
164 Newfoundland and Labrador 0 1
165 Nicaragua 0 1
166 Nigeria 1 1
167 Oman 0 1
168 Panama 0 1
169 Paraguay 0 1
170 Prince Edward Island 0 1
171 Republic of the Congo 0 1
172 Sri Lanka 0 1
173 Uganda 0 1
174 Uruguay 0 1
175 Vanuatu 1 1
176 Zimbabwe 0 1
177 Afghanistan NaN 0
178 Albania NaN 0
179 Antigua and Barbuda NaN 0
180 Armenia NaN 0
181 Belize NaN 0
182 Bolivia NaN 0
183 Brunei NaN 0
184 Cayman Islands NaN 0
185 Democratic Republic of the Congo NaN 0
186 Georgia NaN 0
187 Ghana NaN 0
188 Guernsey NaN 0
189 Iraq NaN 0
190 Laos NaN 0
191 Liberia NaN 0
192 Macau NaN 0
193 Mauritius NaN 0
194 Moldova NaN 0
195 Myanmar NaN 0
196 Niue NaN 0
197 South Georgia and the Islands NaN 0
198 Suriname NaN 0
199 Svalbard NaN 0
200 Tajikistan NaN 0
201 Tanzania NaN 0
202 Uzbekistan NaN 0
203 West Bank NaN 0
> lr <- lm(data=okcdataset, anime ~ d_job + sex + race + d_age + numincome)
> summary(lr)
Call:
lm(formula = anime ~ d_job + sex + race + d_age + numincome,
data = okcdataset)
Residuals:
Min 1Q Median 3Q Max
-0.8970 -0.3138 -0.1907 0.5070 1.2909
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5180924508 0.0443769023 11.675 < 0.0000000000000002 ***
d_jobAdministration 0.0629525098 0.0465813652 1.351 0.17660
d_jobArt / Music / Writing -0.0217693104 0.0429890099 -0.506 0.61260
d_jobBanking / Finance -0.0540009654 0.0482599208 -1.119 0.26320
d_jobConstruction 0.0110444440 0.0471180253 0.234 0.81468
d_jobEducation -0.0741440454 0.0433126239 -1.712 0.08697 .
d_jobEntertainment / Media 0.0318127577 0.0469953255 0.677 0.49847
d_jobHospitality 0.0132730106 0.0466476999 0.285 0.77601
d_jobLaw -0.0123986104 0.0580948332 -0.213 0.83101
d_jobManagement -0.0471599811 0.0437218981 -1.079 0.28079
d_jobMedicine -0.0324692081 0.0445924229 -0.728 0.46656
d_jobMilitary 0.0842953455 0.0624553812 1.350 0.17716
d_jobOther 0.0059724518 0.0386197781 0.155 0.87710
d_jobPolitics / Government -0.0796933768 0.0488412291 -1.632 0.10279
d_jobRather not say 0.0799904664 0.0662619116 1.207 0.22740
d_jobRetired -0.0227340350 0.0685754848 -0.332 0.74026
d_jobSales / Marketing 0.0121280329 0.0421580853 0.288 0.77360
d_jobScience / Engineering -0.0061959865 0.0431248072 -0.144 0.88576
d_jobStudent -0.0115598122 0.0424359140 -0.272 0.78532
d_jobTechnology 0.0480117491 0.0397852648 1.207 0.22756
d_jobTransportation -0.0241852832 0.0478803142 -0.505 0.61349
d_jobUnemployed 0.0777749841 0.0604340038 1.287 0.19816
sexMale 0.1833308436 0.0134120495 13.669 < 0.0000000000000002 ***
raceAsian 0.2517193709 0.0406684507 6.190 0.00000000063881840 ***
raceBlack 0.1706439489 0.0292609650 5.832 0.00000000573749773 ***
raceHispanic / Latin 0.1312387506 0.0283104534 4.636 0.00000362439209106 ***
raceIndian -0.1127114767 0.0764837739 -1.474 0.14062
raceMiddle Eastern 0.0193798517 0.1153212768 0.168 0.86655
raceOther 0.0759863135 0.0367770064 2.066 0.03885 *
racePacific Islander 0.4556078405 0.1299900822 3.505 0.00046 ***
raceSelected over 1 race 0.1286448282 0.0176056372 7.307 0.00000000000030464 ***
d_age -0.0107252098 0.0007601062 -14.110 < 0.0000000000000002 ***
numincome -0.0000008618 0.0000001109 -7.774 0.00000000000000871 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4302 on 6733 degrees of freedom
(61605 observations deleted due to missingness)
Multiple R-squared: 0.09429, Adjusted R-squared: 0.08999
F-statistic: 21.91 on 32 and 6733 DF, p-value: < 0.00000000000000022
> lr <- lm(data=okcdataset, numincome ~ anime + race + sexuality + sex + d_age)
> summary(lr)
Call:
lm(formula = numincome ~ anime + race + sexuality + sex + d_age,
data = okcdataset)
Residuals:
Min 1Q Median 3Q Max
-91419 -22750 -8675 9797 726537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6753.81 3037.28 -2.224 0.02621 *
anime -11014.84 1385.17 -7.952 0.00000000000000214 ***
raceAsian 13310.53 4645.28 2.865 0.00418 **
raceBlack 3678.85 3346.16 1.099 0.27162
raceHispanic / Latin -80.18 3229.89 -0.025 0.98020
raceIndian 8402.99 8716.94 0.964 0.33509
raceMiddle Eastern 21950.91 13152.32 1.669 0.09517 .
raceOther 5712.45 4195.61 1.362 0.17339
racePacific Islander 14836.07 14844.60 0.999 0.31762
raceSelected over 1 race 1058.70 2015.67 0.525 0.59944
sexualityBisexual -6514.68 2472.17 -2.635 0.00843 **
sexualityGay -6205.32 2680.03 -2.315 0.02062 *
sexualityOther -3728.32 8861.97 -0.421 0.67398
sexMale 12684.20 1531.48 8.282 < 0.0000000000000002 ***
d_age 1446.80 83.96 17.231 < 0.0000000000000002 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 49120 on 6751 degrees of freedom
(61605 observations deleted due to missingness)
Multiple R-squared: 0.08747, Adjusted R-squared: 0.08558
F-statistic: 46.22 on 14 and 6751 DF, p-value: < 0.00000000000000022
q455 Would the world be a better place if people with low IQs were not allowed to reproduce?
> okcdataset %>% group_by(q455) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q455 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2434 14135
2 Yes 0.2812 6046
3 NA 0.2520 3611
q1100 Are you better looking than most people?
> okcdataset %>% group_by(q1100) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q1100 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.2795 4784
2 Yes 0.2379 3233
3 NA 0.2500 15775
q4018 Are you happy with your life?
> okcdataset %>% group_by(q4018) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 3 × 3
q4018 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 No 0.3552 2379
2 Yes 0.2412 19659
3 NA 0.2645 1754
q24013 Consciousness is...
> okcdataset %>% group_by(q24013) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q24013 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 A big, scary word. 0.2857 98
2 A good topic for debate, my answer is too long. 0.2804 4615
3 Biological - Brain, senses. 0.2460 2577
4 Spiritual - Soul, heart, spirit. 0.2297 1267
5 NA 0.2496 15235
q37545 Is 'just be yourself' good advice?
> okcdataset %>% group_by(q37545) %>% summarise(into_anime = mean(anime, na.rm=T), sum(!is.na(anime)))
# A tibble: 5 × 3
q37545 into_anime `sum(!is.na(anime))`
<chr> <dbl> <int>
1 Absolutely. 0.2403 4307
2 It's vacuous. Who else can I be? 0.2951 2162
3 No. I misrepresent myself as needed. 0.3697 165
4 Not quite. I mind how I come off. 0.2690 803
5 NA 0.2507 16355
"TL;DR: people who claim to be really into anime are more likely to be men, childless, poor, bicurious, nonwhite, racist, unathletic, bullied in high school, into Asians, kinky, unconscientious, and eccentric."
Thanks for putting this first, but I could have told you this without any data.
Highly autogynephilic