Descriptives

Library

library(apaTables)
library(formr)
# library(effects)
# library(effectsize)
# library(lme4)
# library(sjstats)
# library(lmerTest)
library(ggplot2)
library(psych)

## 
## Attache Paket: 'psych'

## Die folgenden Objekte sind maskiert von 'package:ggplot2':
## 
##     %+%, alpha

library(knitr)
library(dplyr)

## 
## Attache Paket: 'dplyr'

## Die folgenden Objekte sind maskiert von 'package:formr':
## 
##     first, last

## Die folgenden Objekte sind maskiert von 'package:stats':
## 
##     filter, lag

## Die folgenden Objekte sind maskiert von 'package:base':
## 
##     intersect, setdiff, setequal, union

library(jtools)
library(raincloudplots)
library(countrycode)

apatheme = theme_bw() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        axis.line = element_line(),
        legend.title = element_blank(),
        plot.title = element_text(hjust = 0.5))

Data

Load selected data based on 03_codebook

data_included_documented = read.csv(file = "data_included_documented.csv")[,-1]

data_included_documented = data_included_documented %>%
  filter(!is.na(political_orientation))

data_included_documented = data_included_documented %>%
  select(sex, age, language, country,
         political_orientation,
         pref_politicalsim, pref_ethnicalsim, pref_religioussim,
         pref_level_financially_secure_successful_ambitious,
         pref_level_financially_secure, pref_level_successful_ambitious,
         pref_level_confident_assertive, 
         pref_level_confident, pref_level_assertive,
         pref_level_intelligence_educated,
         pref_level_intelligence, pref_level_educated,
         pref_level_kind_supportive,
         pref_level_kind, pref_level_supportive,
         pref_level_attractiveness,
         pref_level_attractive_body, pref_level_attractive_face,
         imp_age, ideal_age,
         imp_height, ideal_height,
         interest_single, interest_sexrel, interest_nonmonrel, interest_monrel
         )

Missingness Pattern

# crosstabs(~ is.na(data_included_documented$pref_level_kind) +
#             is.na(data_included_documented$pref_level_supportive))

Summary

describe(data_included_documented %>%
           select_if(is.numeric)) %>%
  kable()

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
age	1	13253	23.7202897	6.8965345	21.0	22.419975	4.4478	18.0	67	49.0	1.5479844	2.0877431	0.0599065
political_orientation	2	13257	2.5280229	1.3660201	3.0	2.531347	1.4826	0.0	6	6.0	0.0656681	0.0450769	0.0118641
pref_politicalsim	3	12949	3.2265040	1.9164802	3.0	3.283081	1.4826	0.0	6	6.0	-0.3496801	-0.9267668	0.0168417
pref_ethnicalsim	4	8643	3.2731690	1.4399195	3.0	3.305134	1.4826	0.0	6	6.0	-0.1432807	0.0624201	0.0154884
pref_religioussim	5	12937	2.9507614	2.1996731	3.0	2.938460	2.9652	0.0	6	6.0	-0.0393900	-1.3764222	0.0193393
pref_level_financially_secure_successful_ambitious	6	12551	4.3512469	0.8958860	4.5	4.373519	0.7413	0.0	6	6.0	-0.2566121	-0.0312702	0.0079968
pref_level_financially_secure	7	12700	4.2515748	1.1123663	4.0	4.267618	1.4826	0.0	6	6.0	-0.3160348	0.0383197	0.0098707
pref_level_successful_ambitious	8	12779	4.4361844	1.0750048	4.0	4.471589	1.4826	0.0	6	6.0	-0.3398636	-0.1077220	0.0095096
pref_level_confident_assertive	9	12697	4.3850516	0.8409224	4.5	4.398169	0.7413	1.0	6	5.0	-0.1581146	-0.1908187	0.0074629
pref_level_confident	10	12890	4.6868115	1.0241349	5.0	4.752521	1.4826	0.0	6	6.0	-0.3084903	-0.5199185	0.0090205
pref_level_assertive	11	12761	4.0814983	1.0425599	4.0	4.064355	1.4826	0.0	6	6.0	-0.1613944	0.1139068	0.0092291
pref_level_intelligence_educated	12	12723	4.7293091	0.8202860	5.0	4.757687	0.7413	0.5	6	5.5	-0.3358695	-0.1434036	0.0072723
pref_level_intelligence	13	12887	4.7858307	0.8929550	5.0	4.827078	1.4826	0.0	6	6.0	-0.3397771	-0.2641457	0.0078660
pref_level_educated	14	12784	4.6665363	1.0306818	5.0	4.736899	1.4826	0.0	6	6.0	-0.4708239	0.0313713	0.0091157
pref_level_kind_supportive	15	12729	5.1395632	0.6994721	5.0	5.201866	0.7413	2.0	6	4.0	-0.6345107	0.1082785	0.0061997
pref_level_kind	16	12919	5.2586888	0.8538259	5.0	5.370901	1.4826	0.0	6	6.0	-0.9896444	0.5289154	0.0075120
pref_level_supportive	17	12767	5.0206783	0.8933643	5.0	5.090945	1.4826	0.0	6	6.0	-0.6191681	0.0430046	0.0079065
pref_level_attractiveness	18	12526	4.0443078	0.9095593	4.0	4.049192	0.7413	0.0	6	6.0	-0.2176994	0.3431934	0.0081269
pref_level_attractive_body	19	12603	3.8400381	1.0168988	4.0	3.838937	1.4826	0.0	6	6.0	-0.0761180	0.3254029	0.0090582
pref_level_attractive_face	20	12728	4.2283941	1.0459340	4.0	4.227023	1.4826	0.0	6	6.0	-0.2899305	0.0912565	0.0092709
imp_age	21	13113	3.6313582	1.4196495	4.0	3.713945	1.4826	0.0	6	6.0	-0.5754470	0.2444746	0.0123974
ideal_age	22	12061	26.2319874	7.2850679	25.0	25.254431	7.4130	10.0	100	90.0	1.5176713	4.9783139	0.0663349
imp_height	23	13026	3.9486412	1.4911290	4.0	4.080695	1.4826	0.0	6	6.0	-0.7604326	0.3638011	0.0130650
ideal_height	24	12524	1.0282657	0.4357638	1.0	1.008283	0.0000	-2.0	2	4.0	-0.0964272	3.7226173	0.0038939
interest_single	25	13208	2.2229709	1.7639180	2.0	2.096139	1.4826	0.0	6	6.0	0.3091694	-0.8417319	0.0153483
interest_sexrel	26	13193	2.4040779	1.9823903	2.0	2.263288	2.9652	0.0	6	6.0	0.3040934	-1.0910713	0.0172591
interest_nonmonrel	27	13145	0.8615443	1.4981224	0.0	0.520871	0.0000	0.0	6	6.0	1.8220336	2.5365563	0.0130667
interest_monrel	28	13165	4.8196734	1.7517171	6.0	5.216273	0.0000	0.0	6	6.0	-1.6203776	1.6662237	0.0152670

Political Orientation

mean(data_included_documented$political_orientation) %>% round(., 2)

## [1] 2.53

sd(data_included_documented$political_orientation) %>% round(., 2)

## [1] 1.37

range(data_included_documented$political_orientation)

## [1] 0 6

hist <- ggplot(data_included_documented, aes(x = political_orientation)) + 
  geom_histogram(col = "grey", binwidth = 0.5, center = 0) +
  labs(x = "Political Orientation", y = "Number of Participants")+ 
  theme(text = element_text(size=15), axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 10))+
 scale_x_continuous(breaks=c(0, 1, 2, 3, 4, 5, 6))+
  apatheme

hist

Save Image

jpeg("PO_Histogram.jpeg", width = 1580, height = 836, res = 300)
hist
dev.off()

## png 
##   2

Correlation between political orientation and age

cor.test(data_included_documented$political_orientation,
         data_included_documented$age,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$political_orientation and data_included_documented$age
## t = -6.0286, df = 13251, p-value = 1.698e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06926286 -0.03530510
## sample estimates:
##        cor 
## -0.0522991

Correlations for Ideal Partner Preferences

cor.test(data_included_documented$pref_level_financially_secure,
         data_included_documented$pref_level_successful_ambitious,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$pref_level_financially_secure and data_included_documented$pref_level_successful_ambitious
## t = 42.601, df = 12549, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3400739 0.3706444
## sample estimates:
##       cor 
## 0.3554542

cor.test(data_included_documented$pref_level_confident,
         data_included_documented$pref_level_assertive,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$pref_level_confident and data_included_documented$pref_level_assertive
## t = 39.3, df = 12695, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3137402 0.3447564
## sample estimates:
##       cor 
## 0.3293371

cor.test(data_included_documented$pref_level_intelligence,
         data_included_documented$pref_level_educated,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$pref_level_intelligence and data_included_documented$pref_level_educated
## t = 58.552, df = 12721, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.446951 0.474328
## sample estimates:
##       cor 
## 0.4607491

cor.test(data_included_documented$pref_level_kind,
         data_included_documented$pref_level_supportive,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$pref_level_kind and data_included_documented$pref_level_supportive
## t = 33.231, df = 12727, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2664929 0.2984644
## sample estimates:
##       cor 
## 0.2825571

cor.test(data_included_documented$pref_level_attractive_body,
         data_included_documented$pref_level_attractive_face,
         conf.level = 0.95)

## 
##  Pearson's product-moment correlation
## 
## data:  data_included_documented$pref_level_attractive_body and data_included_documented$pref_level_attractive_face
## t = 79.881, df = 12524, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5692519 0.5924574
## sample estimates:
##       cor 
## 0.5809727

Language

n_part = nrow(data_included_documented)

table(data_included_documented$language)

## 
##    chinese     danish    english     french     german    italian   japanese 
##         96        385       2849       2385       2187        999        287 
## portuguese    russian    spanish 
##        825        207       3037

round((table(data_included_documented$language)/n_part)*100,2)

## 
##    chinese     danish    english     french     german    italian   japanese 
##       0.72       2.90      21.49      17.99      16.50       7.54       2.16 
## portuguese    russian    spanish 
##       6.22       1.56      22.91

Country

country_absolute = as.data.frame(table(data_included_documented$country))

country_freq = as.data.frame(round((table(data_included_documented$country)/n_part)*100,2))

country = left_join(country_absolute, country_freq, by = "Var1")
country = country %>% rename(Country = Var1,
                   n = Freq.x,
                   percentage = Freq.y)
kable(country)

Country	n	percentage
Afghanistan	1	0.01
Albania	2	0.02
Algeria	7	0.05
Andorra	6	0.05
Antigua and Barbuda	2	0.02
Argentina	217	1.64
Armenia	2	0.02
Aruba	1	0.01
Australia	133	1.00
Austria	197	1.49
Bahamas, The	1	0.01
Bahrain	3	0.02
Barbados	1	0.01
Belarus	13	0.10
Belgium	102	0.77
Belize	1	0.01
Benin	2	0.02
Bolivia	23	0.17
Bosnia and Herzegovina	6	0.05
Botswana	1	0.01
Brazil	806	6.08
Bulgaria	9	0.07
Burma	1	0.01
Cameroon	3	0.02
Canada	338	2.55
Central African Republic	2	0.02
Chile	154	1.16
China	90	0.68
Colombia	387	2.92
Costa Rica	47	0.35
Cote d’Ivoire	1	0.01
Croatia	6	0.05
Cuba	1	0.01
Czechia	11	0.08
Denmark	395	2.98
Dominica	2	0.02
Dominican Republic	41	0.31
East Timor (see Timor-Leste)	1	0.01
Ecuador	102	0.77
Egypt	3	0.02
El Salvador	26	0.20
Estonia	12	0.09
Ethiopia	1	0.01
Fiji	1	0.01
Finland	31	0.23
France	2013	15.18
Georgia	3	0.02
Germany	1846	13.92
Ghana	3	0.02
Greece	4	0.03
Grenada	1	0.01
Guatemala	61	0.46
Guinea-Bissau	1	0.01
Guyana	3	0.02
Haiti	4	0.03
Honduras	18	0.14
Hong Kong	8	0.06
Hungary	9	0.07
Iceland	5	0.04
India	45	0.34
Indonesia	18	0.14
Iran	7	0.05
Iraq	1	0.01
Ireland	59	0.45
Israel	13	0.10
Italy	968	7.30
Jamaica	6	0.05
Japan	290	2.19
Jordan	2	0.02
Kazakhstan	9	0.07
Kenya	6	0.05
Kuwait	1	0.01
Kyrgyzstan	2	0.02
Latvia	8	0.06
Lebanon	3	0.02
Liechtenstein	1	0.01
Lithuania	3	0.02
Luxembourg	13	0.10
Macedonia	1	0.01
Madagascar	1	0.01
Malaysia	17	0.13
Maldives	1	0.01
Mali	2	0.02
Malta	2	0.02
Marshall Islands	1	0.01
Mauritania	1	0.01
Mauritius	2	0.02
Mexico	1157	8.73
Micronesia	1	0.01
Monaco	1	0.01
Montenegro	1	0.01
Morocco	16	0.12
Namibia	4	0.03
Nepal	1	0.01
Netherlands	44	0.33
New Zealand	34	0.26
Nicaragua	15	0.11
Nigeria	6	0.05
Norway	12	0.09
Pakistan	8	0.06
Palestinian Territories	2	0.02
Panama	15	0.11
Paraguay	11	0.08
Peru	119	0.90
Philippines	36	0.27
Poland	7	0.05
Portugal	51	0.38
Qatar	2	0.02
Romania	24	0.18
Russia	155	1.17
Saint Lucia	2	0.02
Saint Vincent and the Grenadines	1	0.01
Saudi Arabia	6	0.05
Senegal	5	0.04
Serbia	5	0.04
Singapore	26	0.20
Sint Maarten	1	0.01
Slovakia	3	0.02
Slovenia	3	0.02
South Africa	29	0.22
South Korea	5	0.04
South Sudan	1	0.01
Spain	562	4.24
Sri Lanka	3	0.02
Swaziland	1	0.01
Sweden	27	0.20
Switzerland	280	2.11
Syria	1	0.01
Taiwan	7	0.05
Tanzania	1	0.01
Thailand	4	0.03
Trinidad and Tobago	8	0.06
Tunisia	7	0.05
Turkey	8	0.06
Turkmenistan	2	0.02
Uganda	1	0.01
Ukraine	21	0.16
United Arab Emirates	14	0.11
United Kingdom	499	3.76
United States of America	1254	9.46
Uruguay	24	0.18
Venezuela	67	0.51
Vietnam	2	0.02
Zimbabwe	1	0.01

write.table(country, file = "country.txt", sep = ",")

kable(country %>% arrange(-n))

Country	n	percentage
France	2013	15.18
Germany	1846	13.92
United States of America	1254	9.46
Mexico	1157	8.73
Italy	968	7.30
Brazil	806	6.08
Spain	562	4.24
United Kingdom	499	3.76
Denmark	395	2.98
Colombia	387	2.92
Canada	338	2.55
Japan	290	2.19
Switzerland	280	2.11
Argentina	217	1.64
Austria	197	1.49
Russia	155	1.17
Chile	154	1.16
Australia	133	1.00
Peru	119	0.90
Belgium	102	0.77
Ecuador	102	0.77
China	90	0.68
Venezuela	67	0.51
Guatemala	61	0.46
Ireland	59	0.45
Portugal	51	0.38
Costa Rica	47	0.35
India	45	0.34
Netherlands	44	0.33
Dominican Republic	41	0.31
Philippines	36	0.27
New Zealand	34	0.26
Finland	31	0.23
South Africa	29	0.22
Sweden	27	0.20
El Salvador	26	0.20
Singapore	26	0.20
Romania	24	0.18
Uruguay	24	0.18
Bolivia	23	0.17
Ukraine	21	0.16
Honduras	18	0.14
Indonesia	18	0.14
Malaysia	17	0.13
Morocco	16	0.12
Nicaragua	15	0.11
Panama	15	0.11
United Arab Emirates	14	0.11
Belarus	13	0.10
Israel	13	0.10
Luxembourg	13	0.10
Estonia	12	0.09
Norway	12	0.09
Czechia	11	0.08
Paraguay	11	0.08
Bulgaria	9	0.07
Hungary	9	0.07
Kazakhstan	9	0.07
Hong Kong	8	0.06
Latvia	8	0.06
Pakistan	8	0.06
Trinidad and Tobago	8	0.06
Turkey	8	0.06
Algeria	7	0.05
Iran	7	0.05
Poland	7	0.05
Taiwan	7	0.05
Tunisia	7	0.05
Andorra	6	0.05
Bosnia and Herzegovina	6	0.05
Croatia	6	0.05
Jamaica	6	0.05
Kenya	6	0.05
Nigeria	6	0.05
Saudi Arabia	6	0.05
Iceland	5	0.04
Senegal	5	0.04
Serbia	5	0.04
South Korea	5	0.04
Greece	4	0.03
Haiti	4	0.03
Namibia	4	0.03
Thailand	4	0.03
Bahrain	3	0.02
Cameroon	3	0.02
Egypt	3	0.02
Georgia	3	0.02
Ghana	3	0.02
Guyana	3	0.02
Lebanon	3	0.02
Lithuania	3	0.02
Slovakia	3	0.02
Slovenia	3	0.02
Sri Lanka	3	0.02
Albania	2	0.02
Antigua and Barbuda	2	0.02
Armenia	2	0.02
Benin	2	0.02
Central African Republic	2	0.02
Dominica	2	0.02
Jordan	2	0.02
Kyrgyzstan	2	0.02
Mali	2	0.02
Malta	2	0.02
Mauritius	2	0.02
Palestinian Territories	2	0.02
Qatar	2	0.02
Saint Lucia	2	0.02
Turkmenistan	2	0.02
Vietnam	2	0.02
Afghanistan	1	0.01
Aruba	1	0.01
Bahamas, The	1	0.01
Barbados	1	0.01
Belize	1	0.01
Botswana	1	0.01
Burma	1	0.01
Cote d’Ivoire	1	0.01
Cuba	1	0.01
East Timor (see Timor-Leste)	1	0.01
Ethiopia	1	0.01
Fiji	1	0.01
Grenada	1	0.01
Guinea-Bissau	1	0.01
Iraq	1	0.01
Kuwait	1	0.01
Liechtenstein	1	0.01
Macedonia	1	0.01
Madagascar	1	0.01
Maldives	1	0.01
Marshall Islands	1	0.01
Mauritania	1	0.01
Micronesia	1	0.01
Monaco	1	0.01
Montenegro	1	0.01
Nepal	1	0.01
Saint Vincent and the Grenadines	1	0.01
Sint Maarten	1	0.01
South Sudan	1	0.01
Swaziland	1	0.01
Syria	1	0.01
Tanzania	1	0.01
Uganda	1	0.01
Zimbabwe	1	0.01

country = country %>% 
  mutate(continent = countrycode(Country,
                                 origin = "country.name",
                                 destination = "continent"),
         continent = ifelse(Country == "Micronesia",
                            "Oceania",
                            continent))

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `continent = countrycode(Country, origin = "country.name",
##   destination = "continent")`.
## Caused by warning:
## ! Some values were not matched unambiguously: Micronesia

continents <- country %>%
  group_by(continent) %>%
  summarize(n = sum(n)) %>%
  mutate(freq = round((n/n_part)*100, 2)) %>%
  arrange(-n)

Descriptives and Correlation Table

cor = data_included_documented %>%
  select(political_orientation,
         age,
         pref_politicalsim, 
         pref_ethnicalsim, 
         pref_religioussim,
         pref_level_financially_secure_successful_ambitious,
         pref_level_confident_assertive,
         pref_level_intelligence_educated, 
         pref_level_kind_supportive, 
         pref_level_attractiveness, 
         imp_age, 
         ideal_age, 
         imp_height, 
         ideal_height,
         interest_single,
         interest_sexrel, 
         interest_nonmonrel, 
         interest_monrel, 
         ) 
kable(round(cor(cor, use="pairwise.complete.obs"),2))

	political_orientation	age	pref_politicalsim	pref_ethnicalsim	pref_religioussim	pref_level_financially_secure_successful_ambitious	pref_level_confident_assertive	pref_level_intelligence_educated	pref_level_kind_supportive	pref_level_attractiveness	imp_age	ideal_age	imp_height	ideal_height	interest_single	interest_sexrel	interest_nonmonrel	interest_monrel
political_orientation	1.00	-0.05	-0.12	0.15	0.12	0.16	0.12	0.06	0.03	0.05	0.10	-0.05	0.13	0.01	-0.04	0.06	-0.05	-0.02
age	-0.05	1.00	0.11	0.03	0.11	0.07	0.10	0.07	0.04	-0.02	-0.05	0.90	-0.04	-0.05	-0.03	-0.11	0.01	0.07
pref_politicalsim	-0.12	0.11	1.00	0.14	0.43	0.06	-0.02	0.13	0.06	0.12	0.11	0.11	0.05	-0.01	0.01	-0.03	-0.02	0.11
pref_ethnicalsim	0.15	0.03	0.14	1.00	0.24	0.15	0.03	0.06	0.06	0.16	0.15	0.03	0.11	0.00	-0.06	0.03	-0.04	-0.01
pref_religioussim	0.12	0.11	0.43	0.24	1.00	0.16	0.09	0.11	0.10	0.08	0.19	0.11	0.13	0.01	-0.03	0.10	-0.08	0.07
pref_level_financially_secure_successful_ambitious	0.16	0.07	0.06	0.15	0.16	1.00	0.36	0.43	0.24	0.27	0.18	0.10	0.25	0.10	0.00	0.04	-0.02	0.03
pref_level_confident_assertive	0.12	0.10	-0.02	0.03	0.09	0.36	1.00	0.32	0.24	0.13	0.12	0.12	0.20	0.05	0.04	0.06	0.03	-0.04
pref_level_intelligence_educated	0.06	0.07	0.13	0.06	0.11	0.43	0.32	1.00	0.20	0.22	0.16	0.09	0.22	0.07	0.04	0.03	0.00	0.01
pref_level_kind_supportive	0.03	0.04	0.06	0.06	0.10	0.24	0.24	0.20	1.00	0.13	0.10	0.04	0.07	0.01	-0.06	0.05	-0.05	0.01
pref_level_attractiveness	0.05	-0.02	0.12	0.16	0.08	0.27	0.13	0.22	0.13	1.00	0.16	-0.03	0.27	0.09	-0.04	-0.04	0.02	0.01
imp_age	0.10	-0.05	0.11	0.15	0.19	0.18	0.12	0.16	0.10	0.16	1.00	-0.04	0.35	0.03	0.01	0.04	-0.05	0.03
ideal_age	-0.05	0.90	0.11	0.03	0.11	0.10	0.12	0.09	0.04	-0.03	-0.04	1.00	-0.01	-0.03	-0.02	-0.10	0.02	0.06
imp_height	0.13	-0.04	0.05	0.11	0.13	0.25	0.20	0.22	0.07	0.27	0.35	-0.01	1.00	0.29	0.02	0.02	-0.01	0.02
ideal_height	0.01	-0.05	-0.01	0.00	0.01	0.10	0.05	0.07	0.01	0.09	0.03	-0.03	0.29	1.00	0.00	-0.02	-0.01	0.03
interest_single	-0.04	-0.03	0.01	-0.06	-0.03	0.00	0.04	0.04	-0.06	-0.04	0.01	-0.02	0.02	0.00	1.00	0.18	0.18	-0.08
interest_sexrel	0.06	-0.11	-0.03	0.03	0.10	0.04	0.06	0.03	0.05	-0.04	0.04	-0.10	0.02	-0.02	0.18	1.00	0.12	0.01
interest_nonmonrel	-0.05	0.01	-0.02	-0.04	-0.08	-0.02	0.03	0.00	-0.05	0.02	-0.05	0.02	-0.01	-0.01	0.18	0.12	1.00	-0.23
interest_monrel	-0.02	0.07	0.11	-0.01	0.07	0.03	-0.04	0.01	0.01	0.01	0.03	0.06	0.02	0.03	-0.08	0.01	-0.23	1.00

apa_table_cor = apa.cor.table(cor, filename = "descriptives.doc")