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mating psychology.Rmd
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mating psychology.Rmd
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---
title: "mating psychology"
author:
- William Costello, Swansea University
- Vania Rolón, Brunel University London
- Andrew Thomas, Swansea University
- David Schmitt, Brunel University London
output:
html_document:
toc: yes
---
```{r message=FALSE, warning=FALSE}
library(readr)
library(tidyverse)
library(psych)
library(mosaic)
library(car)
library(Hmisc)
library(rstatix)
library(lm.beta)
library(ppcor)
options(scipen = 999)
data <- read_csv("C:/Users/Vania/Desktop/psychology of incels/incel cleaned data.csv")
```
The hypotheses regarding mating-related variables were:
* Incel men will report more external (and internal, exploratory) reasons for being single
* Incel men will report lower minimum mate preferences than non-incel men (15 traits plus overall, use Bonferroni-Holm correction)
* Incel men, more than non-incel men, will overestimate women’s minimum mate preferences and overemphasise the importance of physical-attractiveness
Because in the mental health analyses incels were younger than non-incels were, brief linear regressions controlling for age are conducted throughout to see if results vary drastically. If this is not the case, t-tests will be reported throughout the manuscripts for greater parsimony.
Additionally, to evaluate if effects might be severely affected by the non-single non-incels, we take a look at differences between single and non single non-incels, where, for *relation, 1 = single, 2 = dating around or seeing someone, 3 = in a committed relationship, 4 = married, 5 = divorced, 6 = widowed, 7 = in a consensually non-monogamous relationship.
```{r}
xtabs(~ incel + relation, data = data)
data <- data %>%
mutate(relationship = ifelse(relation == 1, 1,
ifelse(relation == 3 | relation == 4, 0, "")))
data$relationship <- factor(data$relationship, levels = c("0", "1"),
labels = c("not single", "single"))
xtabs(~ incel + relationship, data = data)
```
# Reasons for being single
The hypothesis on external RBS was justified as incels' TIV making it more likely that they would blame external forces. There was no hypothesis for internal reasons, but as many of these relate to internal qualities (some fixable like being shy or lacking confidence and others non-fixable such as being too short or having disability, health, or fertility issues) that seem related to MVS, incels should be expected to show more internal RBS than non-incels as well. Because the mechanisms driving external and internal reasons differ (external are related to an external locus and thus TIV while internal reasons relate to MVS), controlling for these variables shouls also show TIV to have a greater effect on external reasons, and MVS on internal.
```{r}
data_RBS <- data %>%
filter(sex == 0 & incel == 0 & relation == 1 | sex == 0 & incel == 1 & relation == 1) %>%
dplyr::select(., (c(incel, age, RBS_int, RBS_ext, TIV, MVS, relation))) %>%
group_by(incel) %>%
mutate(RBS_intZ = scale(RBS_int), RBS_extZ = scale(RBS_ext),
ageZ = scale(age), TIVZ = scale(TIV), MVSZ = scale(MVS)) %>%
ungroup()
sum(data_RBS$incel == 1 & is.na(data_RBS$TIV) & is.na(data_RBS$MVS))
sum(data_RBS$incel == 0 & is.na(data_RBS$TIV) & is.na(data_RBS$MVS))
```
## External reasons
```{r}
range(data_RBS$RBS_extZ) #outliers on the positive end
sum(data_RBS$RBS_extZ >= 3.30) #2 outliers
data_RBSext <- data_RBS %>%
filter(RBS_extZ >= -3.30 & RBS_extZ <= 3.30)
favstats(~ RBS_ext, incel, data = data_RBSext)
(leveneTest(RBS_ext ~ as.factor(incel), data = data_RBSext))$`Pr(>F)` #homogeneity not met
t.test(RBS_ext ~ incel, data = data_RBSext, var.equal = FALSE)
cohens_d(RBS_ext ~ incel, data = data_RBSext)
set.seed(1993)
RBSext.boot <- Boot(lm(RBS_ext ~ incel, data = data_RBSext), f = coef, R = 5000)
confint(RBSext.boot, level = .95, type = "norm")
hist(lm(RBS_ext ~ as.factor(incel), data = data_RBS)$residuals)
qqPlot(lm(RBS_ext ~ as.factor(incel), data = data_RBS)$residuals)
### controlling for variables ###
favstats(~ RBS_ext, incel, data = na.omit(data_RBSext))
rcorr(as.matrix(na.omit(data_RBSext[, c("RBS_ext", "incel", "age", "TIV", "MVS")]), type = "pearson"))
ext.model <- lm(RBS_ext ~ incel + age + TIV + MVS, data = na.omit(data_RBSext))
summary(ext.model)
lm.beta(ext.model)
set.seed(1993)
ext.mod.boot <- Boot(lm(RBS_ext ~ incel + age + TIV + MVS, data = na.omit(data_RBSext)), f = coef, R = 5000)
confint(ext.mod.boot, level = .95, type = "norm")
sr2_ext <- spcor(na.omit(data_RBSext)[ , c("RBS_ext", "incel", "TIV", "MVS", "age")]) #first row are semi-partial r's, need to square them
sr2_ext$estimate[1, c(2:5)]^2
ext1 <- lm(RBS_ext ~ age + TIV + MVS, data = na.omit(data_RBSext))
summary(ext1) #Radj square is .25 vs .35 in ext.model, thus incel self-identification adds about 10% of variance (which is what sr2 for incel is if rounded up)
anova(ext1, ext.model)
```
## Internal reasons
```{r}
range(data_RBS$RBS_intZ) #outliers on the positive end
sum(data_RBS$RBS_intZ >= 3.30) #1 outliers
data_RBSint <- data_RBS %>%
filter(RBS_intZ >= -3.30 & RBS_intZ <= 3.30,)
favstats(~ RBS_int, incel, data = data_RBSint)
(leveneTest(RBS_int ~ as.factor(incel), data = data_RBSint))$`Pr(>F)` #homogeneity met
t.test(RBS_int ~ incel, data = data_RBSint, var.equal = TRUE)
cohens_d(RBS_int ~ incel, data = data_RBSint)
set.seed(1993)
RBSint.boot <- Boot(lm(RBS_int ~ incel, data = data_RBSint), f = coef, R = 5000)
confint(RBSint.boot, level = .95, type = "norm")
### controlling for variables ###
favstats(~ RBS_int, incel, data = na.omit(data_RBSint))
rcorr(as.matrix(na.omit(data_RBSint[, c("RBS_int", "incel", "age", "TIV", "MVS")]), type = "pearson")) #to compute power on G*Power
int.model <- lm(RBS_int ~ incel + age + TIV + MVS, data = na.omit(data_RBSint))
summary(int.model)
lm.beta(int.model)
set.seed(1993)
int.mod.boot <- Boot(lm(RBS_int ~ incel + age + TIV + MVS, data = na.omit(data_RBSint)), f = coef, R = 5000)
confint(int.mod.boot, level = .95, type = "norm")
sr2_int <- spcor(na.omit(data_RBSint)[ , c("RBS_int", "incel", "TIV", "MVS", "age")]) #first row are semi-partial r's, need to square them
sr2_int$estimate[1, c(2:5)]^2
```
Incels reported more internal reasons for being single (*M* = 7.83, *SD* = 4.79, *n* = 151) than did non-incels (*M* = 3.10, *SD* = 4.22, *n* = 375) , *t*(248.66) = 10.60, *p* < .001, *d* = 1.05, *95%BootCI*[3.85, 5.61], 1 - $\beta$ = .99.
Incels reported more external reasons for being single (*M* = 2.12, *SD* = 1.84, *n* = 151) than did non-incels (*M* = .59, *SD* = 1.03, *n* = 372) , *t*(189.40) = 9.66, *p* < .001, *d* = 1.03, *95%BootCI*[1.23, 1.85], 1 - $\beta$ = .99.
# Minimum mate preferences
```{r}
data_mmp <- data %>%
filter(sex == 0 & incel == 0 | sex == 0 & incel == 1) %>%
dplyr::select(., (c(incel, age, ownminprefer_1:ownminprefer_15, TIV, MVS))) %>%
group_by(incel) %>%
mutate( Z1 = scale(ownminprefer_1), Z2 = scale(ownminprefer_2), Z3 = scale(ownminprefer_3), Z4 = scale(ownminprefer_4),
Z5 = scale(ownminprefer_5), Z6 = scale(ownminprefer_6), Z7 = scale(ownminprefer_7), Z8 = scale(ownminprefer_8),
Z9 = scale(ownminprefer_9), Z10 = scale(ownminprefer_10), Z11 = scale(ownminprefer_11), Z12 = scale(ownminprefer_12),
Z13 = scale(ownminprefer_13), Z14 = scale(ownminprefer_14), Z15 = scale(ownminprefer_15),
TIVZ = scale(TIV), MVSZ = scale(MVS)
) %>%
ungroup() #n = 529
########## Trait 1; facial attractiveness ##########
data_mmp1 <- data_mmp %>%
filter(Z1 >= -3.30 & Z1 <= 3.30) #409
favstats(~ ownminprefer_1, incel, data = data_mmp1)
(leveneTest(ownminprefer_1 ~ as.factor(incel), data = data_mmp1))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_1 ~ incel, data = data_mmp1, var.equal = FALSE)
p_mmp1 <- t.test(ownminprefer_1 ~ incel, data = data_mmp1, var.equal = FALSE)$p.value
summary(lm(ownminprefer_1 ~ incel + age, data = data_mmp1))
cohens_d(ownminprefer_1 ~ incel, data = data_mmp1)
set.seed(1993)
mmp1.boot <- Boot(lm(ownminprefer_1 ~ incel, data = data_mmp1), f = coef, R = 5000)
confint(mmp1.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp1_control <- data_mmp %>%
filter(Z1 >= -3.30 & Z1 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp1 <- lm(ownminprefer_1 ~ incel + MVS + TIV , data = data_mmp1_control)
summary(lm_mmp1)
lm.beta(lm_mmp1)
set.seed(1993)
lm_mmp1.boot <- Boot(lm_mmp1, f = coef, R = 5000)
confint(lm_mmp1.boot, level = .95, type = "norm")
```
```{r}
########## Trait 2; body attractiveness ##########
data_mmp2 <- data_mmp %>%
filter(Z2 >= -3.30 & Z2 <= 3.30) #n = 408
favstats(~ ownminprefer_2, incel, data = data_mmp2)
(leveneTest(ownminprefer_2 ~ as.factor(incel), data = data_mmp2))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_2 ~ incel, data = data_mmp2, var.equal = FALSE)
p_mmp2 <- t.test(ownminprefer_2 ~ incel, data = data_mmp2, var.equal = FALSE)$p.value
summary(lm(ownminprefer_2 ~ incel + age, data = data_mmp2))
cohens_d(ownminprefer_2 ~ incel, data = data_mmp2)
set.seed(1993)
mmp2.boot <- Boot(lm(ownminprefer_2 ~ incel, data = data_mmp2), f = coef, R = 5000)
confint(mmp2.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp2_control <- data_mmp %>%
filter(Z2 >= -3.30 & Z2 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp2 <- lm(ownminprefer_2 ~ incel + MVS + TIV, data = data_mmp2_control)
summary(lm_mmp2)
lm.beta(lm_mmp2)
set.seed(1993)
lm_mmp2.boot <- Boot(lm_mmp2, f = coef, R = 5000)
confint(lm_mmp2.boot, level = .95, type = "norm")
```
```{r}
########## Trait 3; fashion sense ##########
data_mmp3 <- data_mmp %>%
filter(Z3 >= -3.30 & Z3 <= 3.30) #n = 413
favstats(~ ownminprefer_3, incel, data = data_mmp3)
(leveneTest(ownminprefer_3 ~ as.factor(incel), data = data_mmp3))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_3 ~ incel, data = data_mmp3, var.equal = TRUE)
p_mmp3 <- t.test(ownminprefer_3 ~ incel, data = data_mmp3, var.equal = TRUE)$p.value
summary(lm(ownminprefer_3 ~ incel + age, data = data_mmp3))
cohens_d(ownminprefer_3 ~ incel, data = data_mmp3)
set.seed(1993)
mmp3.boot <- Boot(lm(ownminprefer_3 ~ incel, data = data_mmp3), f = coef, R = 5000)
confint(mmp3.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp3_control <- data_mmp %>%
filter(Z3 >= -3.30 & Z3 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp3 <- lm(ownminprefer_3 ~ incel + MVS + TIV , data = data_mmp3_control)
summary(lm_mmp3)
lm.beta(lm_mmp3)
set.seed(1993)
lm_mmp3.boot <- Boot(lm_mmp3, f = coef, R = 5000)
confint(lm_mmp3.boot, level = .95, type = "norm")
```
```{r}
########## Trait 4; good job/financial prospects ##########
data_mmp4 <- data_mmp %>%
filter(Z4 >= -3.30 & Z4 <= 3.30) #n = 413
favstats(~ ownminprefer_4, incel, data = data_mmp4)
(leveneTest(ownminprefer_4 ~ as.factor(incel), data = data_mmp4))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_4 ~ incel, data = data_mmp4, var.equal = TRUE)
p_mmp4 <- t.test(ownminprefer_4 ~ incel, data = data_mmp4, var.equal = TRUE)$p.value
summary(lm(ownminprefer_4 ~ incel + age, data = data_mmp4))
cohens_d(ownminprefer_4 ~ incel, data = data_mmp4)
set.seed(1993)
mmp4.boot <- Boot(lm(ownminprefer_4 ~ incel, data = data_mmp4), f = coef, R = 5000)
confint(mmp4.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp4_control <- data_mmp %>%
filter(Z4 >= -3.30 & Z4 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp4 <- lm(ownminprefer_4 ~ incel + MVS + TIV , data = data_mmp4_control)
summary(lm_mmp4)
lm.beta(lm_mmp4)
set.seed(1993)
lm_mmp4.boot <- Boot(lm_mmp4, f = coef, R = 5000)
confint(lm_mmp4.boot, level = .95, type = "norm")
```
```{r}
########## Trait 5; kind and understanding ##########
data_mmp5 <- data_mmp %>%
filter(Z5 >= -3.30 & Z5 <= 3.30) #n = 408
favstats(~ ownminprefer_5, incel, data = data_mmp5)
(leveneTest(ownminprefer_5 ~ as.factor(incel), data = data_mmp5))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_5 ~ incel, data = data_mmp5, var.equal = FALSE)
p_mmp5 <- t.test(ownminprefer_5 ~ incel, data = data_mmp5, var.equal = FALSE)$p.value
summary(lm(ownminprefer_5 ~ incel + age, data = data_mmp5))
cohens_d(ownminprefer_5 ~ incel, data = data_mmp5)
set.seed(1993)
mmp5.boot <- Boot(lm(ownminprefer_5 ~ incel, data = data_mmp5), f = coef, R = 5000)
confint(mmp5.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp5_control <- data_mmp %>%
filter(Z5 >= -3.30 & Z5 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp5 <- lm(ownminprefer_5 ~ incel + MVS + TIV , data = data_mmp5_control)
summary(lm_mmp5)
lm.beta(lm_mmp5)
set.seed(1993)
lm_mmp5.boot <- Boot(lm_mmp5, f = coef, R = 5000)
confint(lm_mmp5.boot, level = .95, type = "norm")
```
```{r}
########## Trait 6; loyal and dependable ##########
data_mmp6 <- data_mmp %>%
filter(Z6 >= -3.30 & Z6 <= 3.30) #n = 406
favstats(~ ownminprefer_6, incel, data = data_mmp6)
(leveneTest(ownminprefer_6 ~ as.factor(incel), data = data_mmp6))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_6 ~ incel, data = data_mmp6, var.equal = FALSE)
p_mmp6 <- t.test(ownminprefer_6 ~ incel, data = data_mmp6, var.equal = FALSE)$p.value
summary(lm(ownminprefer_6 ~ incel + age, data = data_mmp6))
cohens_d(ownminprefer_6 ~ incel, data = data_mmp6)
set.seed(1993)
mmp6.boot <- Boot(lm(ownminprefer_6 ~ incel, data = data_mmp6), f = coef, R = 5000)
confint(mmp6.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp6_control <- data_mmp %>%
filter(Z6 >= -3.30 & Z6 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp6 <- lm(ownminprefer_6 ~ incel + MVS + TIV , data = data_mmp6_control)
summary(lm_mmp6)
lm.beta(lm_mmp6)
set.seed(1993)
lm_mmp6.boot <- Boot(lm_mmp6, f = coef, R = 5000)
confint(lm_mmp6.boot, level = .95, type = "norm")
```
```{r}
########## Trait 7; emotional stability and maturity ##########
data_mmp7 <- data_mmp %>%
filter(Z7 >= -3.30 & Z7 <= 3.30) #n = 407
favstats(~ ownminprefer_7, incel, data = data_mmp7)
(leveneTest(ownminprefer_7 ~ as.factor(incel), data = data_mmp7))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_7 ~ incel, data = data_mmp7, var.equal = FALSE)
p_mmp7 <- t.test(ownminprefer_7 ~ incel, data = data_mmp7, var.equal = FALSE)$p.value
summary(lm(ownminprefer_7 ~ incel + age, data = data_mmp7))
cohens_d(ownminprefer_7 ~ incel, data = data_mmp7)
set.seed(1993)
mmp7.boot <- Boot(lm(ownminprefer_7 ~ incel, data = data_mmp7), f = coef, R = 5000)
confint(mmp7.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp7_control <- data_mmp %>%
filter(Z7 >= -3.30 & Z7 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp7 <- lm(ownminprefer_7 ~ incel + MVS + TIV , data = data_mmp7_control)
summary(lm_mmp7)
lm.beta(lm_mmp7)
set.seed(1993)
lm_mmp7.boot <- Boot(lm_mmp7, f = coef, R = 5000)
confint(lm_mmp7.boot, level = .95, type = "norm")
```
```{r}
########## Trait 8; sociability ##########
data_mmp8 <- data_mmp %>%
filter(Z8 >= -3.30 & Z8 <= 3.30) #n = 413
favstats(~ ownminprefer_8, incel, data = data_mmp8)
(leveneTest(ownminprefer_8 ~ as.factor(incel), data = data_mmp8))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_8 ~ incel, data = data_mmp8, var.equal = FALSE)
p_mmp8 <- t.test(ownminprefer_8 ~ incel, data = data_mmp8, var.equal = FALSE)$p.value
summary(lm(ownminprefer_8 ~ incel + age, data = data_mmp8))
cohens_d(ownminprefer_8 ~ incel, data = data_mmp8)
set.seed(1993)
mmp8.boot <- Boot(lm(ownminprefer_8 ~ incel, data = data_mmp8), f = coef, R = 5000)
confint(mmp8.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp8_control <- data_mmp %>%
filter(Z8 >= -3.30 & Z8 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp8 <- lm(ownminprefer_8 ~ incel + MVS + TIV , data = data_mmp8_control)
summary(lm_mmp8)
lm.beta(lm_mmp8)
set.seed(1993)
lm_mmp8.boot <- Boot(lm_mmp8, f = coef, R = 5000)
confint(lm_mmp8.boot, level = .95, type = "norm")
```
```{r}
########## Trait 9; exciting personality ##########
data_mmp9 <- data_mmp %>%
filter(Z9 >= -3.30 & Z9 <= 3.30) #n = 413
favstats(~ ownminprefer_9, incel, data = data_mmp9)
(leveneTest(ownminprefer_9 ~ as.factor(incel), data = data_mmp9))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_9 ~ incel, data = data_mmp9, var.equal = FALSE)
p_mmp9 <- t.test(ownminprefer_9 ~ incel, data = data_mmp9, var.equal = FALSE)$p.value
summary(lm(ownminprefer_9 ~ incel + age, data = data_mmp9))
cohens_d(ownminprefer_9 ~ incel, data = data_mmp9)
set.seed(1993)
mmp9.boot <- Boot(lm(ownminprefer_9 ~ incel, data = data_mmp9), f = coef, R = 5000)
confint(mmp9.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp9_control <- data_mmp %>%
filter(Z9 >= -3.30 & Z9 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp9 <- lm(ownminprefer_9 ~ incel + MVS + TIV , data = data_mmp9_control)
summary(lm_mmp9)
lm.beta(lm_mmp9)
set.seed(1993)
lm_mmp9.boot <- Boot(lm_mmp9, f = coef, R = 5000)
confint(lm_mmp9.boot, level = .95, type = "norm")
```
```{r}
########## Trait 10; sense of humour ##########
data_mmp10 <- data_mmp %>%
filter(Z10 >= -3.30 & Z10 <= 3.30) #n = 413
favstats(~ ownminprefer_10, incel, data = data_mmp10)
(leveneTest(ownminprefer_10 ~ as.factor(incel), data = data_mmp10))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_10 ~ incel, data = data_mmp10, var.equal = FALSE)
p_mmp10 <- t.test(ownminprefer_10 ~ incel, data = data_mmp10, var.equal = FALSE)$p.value
summary(lm(ownminprefer_10 ~ incel + age, data = data_mmp10))
cohens_d(ownminprefer_10 ~ incel, data = data_mmp10)
set.seed(1993)
mmp10.boot <- Boot(lm(ownminprefer_10 ~ incel, data = data_mmp10), f = coef, R = 5000)
confint(mmp10.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp10_control <- data_mmp %>%
filter(Z10 >= -3.30 & Z10 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp10 <- lm(ownminprefer_10 ~ incel + MVS + TIV , data = data_mmp10_control)
summary(lm_mmp10)
lm.beta(lm_mmp10)
set.seed(1993)
lm_mmp10.boot <- Boot(lm_mmp10, f = coef, R = 5000)
confint(lm_mmp10.boot, level = .95, type = "norm")
```
```{r}
########## Trait 11; intelligence ##########
data_mmp11 <- data_mmp %>%
filter(Z11 >= -3.30 & Z11 <= 3.30) #n = 406
favstats(~ ownminprefer_11, incel, data = data_mmp11)
(leveneTest(ownminprefer_11 ~ as.factor(incel), data = data_mmp11))$`Pr(>F)` #homogeneity not met
t.test(ownminprefer_11 ~ incel, data = data_mmp11, var.equal = FALSE)
p_mmp11 <- t.test(ownminprefer_11 ~ incel, data = data_mmp11, var.equal = FALSE)$p.value
summary(lm(ownminprefer_11 ~ incel + age, data = data_mmp11))
cohens_d(ownminprefer_11 ~ incel, data = data_mmp11)
set.seed(1993)
mmp11.boot <- Boot(lm(ownminprefer_11 ~ incel, data = data_mmp11), f = coef, R = 5000)
confint(mmp11.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp11_control <- data_mmp %>%
filter(Z11 >= -3.30 & Z11 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp11 <- lm(ownminprefer_11 ~ incel + MVS + TIV , data = data_mmp11_control)
summary(lm_mmp11)
lm.beta(lm_mmp11)
set.seed(1993)
lm_mmp11.boot <- Boot(lm_mmp11, f = coef, R = 5000)
confint(lm_mmp11.boot, level = .95, type = "norm")
```
```{r}
########## Trait 12; attractive political beliefs ##########
data_mmp12 <- data_mmp %>%
filter(Z12 >= -3.30 & Z12 <= 3.30) #n = 413
favstats(~ ownminprefer_12, incel, data = data_mmp12)
(leveneTest(ownminprefer_12 ~ as.factor(incel), data = data_mmp12))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_12 ~ incel, data = data_mmp12, var.equal = TRUE)
p_mmp12 <- t.test(ownminprefer_12 ~ incel, data = data_mmp12, var.equal = TRUE)$p.value
summary(lm(ownminprefer_12 ~ incel + age, data = data_mmp12))
cohens_d(ownminprefer_12 ~ incel, data = data_mmp12)
set.seed(1993)
mmp12.boot <- Boot(lm(ownminprefer_12 ~ incel, data = data_mmp12), f = coef, R = 5000)
confint(mmp12.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp12_control <- data_mmp %>%
filter(Z12 >= -3.30 & Z12 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp12 <- lm(ownminprefer_12 ~ incel + MVS + TIV , data = data_mmp12_control)
summary(lm_mmp12)
lm.beta(lm_mmp12)
set.seed(1993)
lm_mmp12.boot <- Boot(lm_mmp12, f = coef, R = 5000)
confint(lm_mmp12.boot, level = .95, type = "norm")
```
```{r}
########## Trait 13; similar interests ##########
data_mmp13 <- data_mmp %>%
filter(Z13 >= -3.30 & Z13 <= 3.30) #n = 413
favstats(~ ownminprefer_13, incel, data = data_mmp13)
(leveneTest(ownminprefer_13 ~ as.factor(incel), data = data_mmp13))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_13 ~ incel, data = data_mmp13, var.equal = TRUE)
p_mmp13 <- t.test(ownminprefer_13 ~ incel, data = data_mmp13, var.equal = TRUE)$p.value
summary(lm(ownminprefer_13 ~ incel + age, data = data_mmp13))
cohens_d(ownminprefer_13 ~ incel, data = data_mmp13)
set.seed(1993)
mmp13.boot <- Boot(lm(ownminprefer_13 ~ incel, data = data_mmp13), f = coef, R = 5000)
confint(mmp13.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp13_control <- data_mmp %>%
filter(Z13 >= -3.30 & Z13 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp13 <- lm(ownminprefer_13 ~ incel + MVS + TIV , data = data_mmp13_control)
summary(lm_mmp13)
lm.beta(lm_mmp13)
set.seed(1993)
lm_mmp13.boot <- Boot(lm_mmp13, f = coef, R = 5000)
confint(lm_mmp13.boot, level = .95, type = "norm")
```
```{r}
########## Trait 14; sexually skilful ##########
data_mmp14 <- data_mmp %>%
filter(Z14 >= -3.30 & Z14 <= 3.30) #n = 413
favstats(~ ownminprefer_14, incel, data = data_mmp14)
(leveneTest(ownminprefer_14 ~ as.factor(incel), data = data_mmp14))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_14 ~ incel, data = data_mmp14, var.equal = TRUE)
p_mmp14 <- t.test(ownminprefer_14 ~ incel, data = data_mmp14, var.equal = TRUE)$p.value
summary(lm(ownminprefer_14 ~ incel + age, data = data_mmp14))
lm.beta(lm(ownminprefer_14 ~ incel + age, data = data_mmp14))
cohens_d(ownminprefer_14 ~ incel, data = data_mmp14)
set.seed(1993)
mmp14.boot <- Boot(lm(ownminprefer_14 ~ incel, data = data_mmp14), f = coef, R = 5000)
confint(mmp14.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp14_control <- data_mmp %>%
filter(Z14 >= -3.30 & Z14 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp14 <- lm(ownminprefer_14 ~ incel + MVS + TIV , data = data_mmp14_control)
summary(lm_mmp14)
lm.beta(lm_mmp14)
set.seed(1993)
lm_mmp14.boot <- Boot(lm_mmp14, f = coef, R = 5000)
confint(lm_mmp14.boot, level = .95, type = "norm")
```
```{r}
########## Trait 15; good cook/homemaker ##########
data_mmp15 <- data_mmp %>%
filter(Z15 >= -3.30 & Z15 <= 3.30) #n = 413
favstats(~ ownminprefer_15, incel, data = data_mmp15)
(leveneTest(ownminprefer_15 ~ as.factor(incel), data = data_mmp15))$`Pr(>F)` #homogeneity met
t.test(ownminprefer_15 ~ incel, data = data_mmp15, var.equal = TRUE)
p_mmp15 <- t.test(ownminprefer_15 ~ incel, data = data_mmp15, var.equal = TRUE)$p.value
summary(lm(ownminprefer_15 ~ incel + age, data = data_mmp15))
cohens_d(ownminprefer_15 ~ incel, data = data_mmp15)
set.seed(1993)
mmp15.boot <- Boot(lm(ownminprefer_15 ~ incel, data = data_mmp15), f = coef, R = 5000)
confint(mmp15.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp15_control <- data_mmp %>%
filter(Z15 >= -3.30 & Z15 <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp15 <- lm(ownminprefer_15 ~ incel + MVS + TIV , data = data_mmp15_control)
summary(lm_mmp15)
lm.beta(lm_mmp15)
set.seed(1993)
lm_mmp15.boot <- Boot(lm_mmp15, f = coef, R = 5000)
confint(lm_mmp15.boot, level = .95, type = "norm")
```
```{r}
########## TOTAL ##########
data_tot <- data_mmp15 %>%
mutate(mmp_tot = dplyr::select(., c(ownminprefer_1:ownminprefer_15)) %>%
rowSums()) %>%
group_by(incel) %>%
mutate(Zmmp = scale(mmp_tot)) %>%
ungroup() %>%
filter(Zmmp >= -3.30 & Zmmp <= 3.30) #n = 409
favstats(~ mmp_tot, incel, data = data_tot)
(leveneTest(mmp_tot ~ as.factor(incel), data = data_tot))$`Pr(>F)` #homogeneity not met
t.test(mmp_tot ~ incel, data = data_tot, var.equal = FALSE)
p_mmp <- t.test(mmp_tot ~ incel, data = data_tot, var.equal = FALSE)$p.value
summary(lm(mmp_tot ~ incel + age, data = data_tot))
lm.beta(lm(mmp_tot ~ incel + age, data = data_tot))
cohens_d(mmp_tot ~ incel, data = data_tot)
set.seed(1993)
tot.boot <- Boot(lm(mmp_tot ~ incel, data = data_tot), f = coef, R = 5000)
confint(tot.boot, level = .95, type = "norm")
###controlling for TIV and perceived mate value###
data_mmp_control <- data_mmp %>%
mutate(mmp_tot = dplyr::select(., c(ownminprefer_1:ownminprefer_15)) %>%
rowSums()) %>%
group_by(incel) %>%
mutate(Zmmp = scale(mmp_tot)) %>%
ungroup() %>%
filter(Zmmp >= -3.30 & Zmmp <= 3.30,
TIVZ >= -3.30 & TIVZ <= 3.30,
MVSZ >= -3.30 & MVSZ <= 3.30)
lm_mmp <- lm(mmp_tot ~ incel + MVS + TIV , data = data_mmp_control)
summary(lm_mmp)
lm.beta(lm_mmp)
set.seed(1993)
lm_mmp.boot <- Boot(lm_mmp, f = coef, R = 5000)
confint(lm_mmp.boot, level = .95, type = "norm")
```
```{r}
########## Adjusting p-values w/Holm-Bonferroni ##########
p.values <- c(p_mmp1, p_mmp2, p_mmp3, p_mmp4, p_mmp5, p_mmp6, p_mmp7, p_mmp8, p_mmp9, p_mmp10, p_mmp11, p_mmp12, p_mmp13, p_mmp14, p_mmp15, p_mmp)
p.adjust(p.values, method = "holm")
########## Formal age comparison to confirm that the groups do in fact differ ##########
(leveneTest(age ~ as.factor(incel), data = data_mmp))$`Pr(>F)`
t.test(age ~ incel, data = data_mmp, var.equal = FALSE)
```
# Overestimation of women's minimum mate preferences
A new factor variable *participant* was created with 3 levels: 0 = *woman*, 1 = *male non-incel*, and 2 = *male incel*. Using this variable, 15 new variables were created for the 15 traits on the minimum mate preferences questionnaire. If the participant was a heterosexual female, the values for these new variables were her reported minimum mate preferences on the 15 traits (i.e., ownminprefer_1:ownminprefer_15). If the participant was a male, regardless of whether he was an incel or not, the values for these traits were what he believed or estimated to be the average woman's minimum mate preferences (i.e., otherminpref_1:otherminpref_15).
```{r}
data_prefs <- data %>%
filter(sex == 0 & incel == 0 | sex == 0 & incel == 1 | sex == 1 & incel == 0 & sexorien == 0) %>%
dplyr::select(., (c(sex, incel, ownminprefer_1:otherminpref_15))) %>%
mutate(participant = ifelse(sex == 1, 0,
ifelse(incel == 1, 2, 1)))
data_prefs$participant <- factor(data_prefs$participant, levels = c("0", "1", "2"),
labels = c("Female", "Male non-incel", "Male incel"))
data_prefs <- data_prefs %>%
mutate(face = ifelse(participant == "Female", ownminprefer_1, otherminpref_1),
body = ifelse(participant == "Female", ownminprefer_2, otherminpref_2),
fashion = ifelse(participant == "Female", ownminprefer_3, otherminpref_3),
finances = ifelse(participant == "Female", ownminprefer_4, otherminpref_4),
kindness = ifelse(participant == "Female", ownminprefer_5, otherminpref_5),
loyalty = ifelse(participant == "Female", ownminprefer_6, otherminpref_6),
maturity = ifelse(participant == "Female", ownminprefer_7, otherminpref_7),
sociability = ifelse(participant == "Female", ownminprefer_8, otherminpref_8),
personality = ifelse(participant == "Female", ownminprefer_9, otherminpref_9),
humor = ifelse(participant == "Female", ownminprefer_10, otherminpref_10),
intelligence = ifelse(participant == "Female", ownminprefer_11, otherminpref_11),
politics = ifelse(participant == "Female", ownminprefer_12, otherminpref_12),
interests = ifelse(participant == "Female", ownminprefer_13, otherminpref_13),
sex_skill = ifelse(participant == "Female", ownminprefer_14, otherminpref_14),
homemaker = ifelse(participant == "Female", ownminprefer_15, otherminpref_15),
)
```
Once these new variables were created, data were screened for univariate outliers and then 15 one-way ANOVAs were ran with the *participant* variable as the IV and the 15 traits as outcomes. Were homogeneity of variance was not met, a Welch's ANOVA was used, as well as a Games-Howell correction for multiple comparisons instead of Tukey's HSD
```{r}
data_prefs <- data_prefs %>%
group_by(participant) %>%
mutate(Z1 = scale(face), Z2 = scale(body), Z3 = scale(fashion), Z4 = scale(finances),
Z5 = scale(kindness), Z6 = scale(loyalty), Z7 = scale(maturity), Z8 = scale(sociability),
Z9 = scale(personality), Z10 = scale(humor), Z11 = scale(intelligence), Z12 = scale(politics),
Z13 = scale(interests), Z14 = scale(sex_skill), Z15 = scale(homemaker)) %>%
ungroup()
options("contrasts") #to see current contrasts and set ANOVA to type III SS for rstatix functions
options(contrasts = c("contr.helmert", "contr.poly"))
options("contrasts")
##### Facial attractiveness #####
data_face <- data_prefs %>%
filter(Z1 >= -3.30 & Z1 <= 3.30)
stats_face <- favstats(~ face, participant, data = data_face)
stats_face
leveneTest(face ~ participant, data = data_face) #homogeneity met
anova_test(face ~ participant, data = data_face, type = 3)
p_face <- anova_test(face ~ participant, data = data_face, type = 3)$p
data_face %>% tukey_hsd(face ~ participant)
##### Body attractiveness #####
data_body <- data_prefs %>%
filter(Z2 >= -3.30 & Z2 <= 3.30)
stats_body <- favstats(~ body, participant, data = data_body)
stats_body
leveneTest(body ~ participant, data = data_body) #homogeneity met
anova_test(body ~ participant, data = data_body, type = 3)
p_body <- anova_test(body ~ participant, data = data_body, type = 3)$p
data_body %>% tukey_hsd(body ~ participant)
##### Fashion sense #####
data_fashion <- data_prefs %>%
filter(Z3 >= -3.30 & Z3 <= 3.30)
stats_fashion <- favstats(~ fashion, participant, data = data_fashion)
stats_fashion
leveneTest(fashion ~ participant, data = data_fashion) #homogeneity met
anova_test(fashion ~ participant, data = data_fashion, type = 3)
p_fashion <- anova_test(fashion ~ participant, data = data_fashion, type = 3)$p
data_fashion %>% tukey_hsd(fashion ~ participant)
##### Good job/financial prospects #####
data_finances <- data_prefs %>%
filter(Z4 >= -3.30 & Z4 <= 3.30)
stats_finances <- favstats(~ finances, participant, data = data_finances)
stats_finances
leveneTest(finances ~ participant, data = data_finances) #homogeneity NOT met
welch_anova_test(finances ~ participant, data = data_finances)
p_finances <- welch_anova_test(finances ~ participant, data = data_finances)$p
data_finances %>% games_howell_test(finances ~ participant)
##### Kind and understanding #####
data_kindness <- data_prefs %>%
filter(Z5 >= -3.30 & Z5 <= 3.30)
stats_kindness <- favstats(~ kindness, participant, data = data_kindness)
stats_kindness
leveneTest(kindness ~ participant, data = data_kindness) #homogeneity NOT met
welch_anova_test(kindness ~ participant, data = data_kindness)
p_kindness <- welch_anova_test(kindness ~ participant, data = data_kindness)$p
data_kindness %>% games_howell_test(kindness ~ participant)
##### Loyal and dependable #####
data_loyalty <- data_prefs %>%
filter(Z6 >= -3.30 & Z6 <= 3.30)
stats_loyalty <- favstats(~ loyalty, participant, data = data_loyalty)
stats_loyalty
leveneTest(loyalty ~ participant, data = data_loyalty) #homogeneity NOT met
welch_anova_test(loyalty ~ participant, data = data_loyalty)
p_loyalty <- welch_anova_test(loyalty ~ participant, data = data_loyalty)$p
data_loyalty %>% games_howell_test(loyalty ~ participant)
##### Emotional stability and maturity #####
data_maturity <- data_prefs %>%
filter(Z7 >= -3.30 & Z7 <= 3.30)
stats_maturity <- favstats(~ maturity, participant, data = data_maturity)
stats_maturity
leveneTest(maturity ~ participant, data = data_maturity) #homogeneity NOT met
welch_anova_test(maturity ~ participant, data = data_maturity)
p_maturity <- welch_anova_test(maturity ~ participant, data = data_maturity)$p
data_maturity %>% games_howell_test(maturity ~ participant)
##### Sociability #####
data_sociability <- data_prefs %>%
filter(Z8 >= -3.30 & Z8 <= 3.30)
stats_sociability <- favstats(~ sociability, participant, data = data_sociability)
stats_sociability
leveneTest(sociability ~ participant, data = data_sociability) #homogeneity NOT met
welch_anova_test(sociability ~ participant, data = data_sociability)
p_sociability <- welch_anova_test(sociability ~ participant, data = data_sociability)$p
data_sociability %>% games_howell_test(sociability ~ participant)
##### Exciting personality #####
data_personality <- data_prefs %>%
filter(Z9 >= -3.30 & Z9 <= 3.30)
stats_personality <- favstats(~ personality, participant, data = data_personality)
stats_personality
leveneTest(personality ~ participant, data = data_personality) #homogeneity met (barely at .05)
anova_test(personality ~ participant, data = data_personality, type = 3)
p_personality <- anova_test(personality ~ participant, data = data_personality, type = 3)$p
data_personality %>% tukey_hsd(personality ~ participant)
##### Sense of humour #####
data_humor <- data_prefs %>%
filter(Z10 >= -3.30 & Z10 <= 3.30)
stats_humor <- favstats(~ humor, participant, data = data_humor)
stats_humor
leveneTest(humor ~ participant, data = data_humor) #homogeneity NOT met
welch_anova_test(humor ~ participant, data = data_humor)
p_humor <- welch_anova_test(humor ~ participant, data = data_humor)$p
data_humor %>% games_howell_test(humor ~ participant)
##### Intelligence #####
data_intelligence <- data_prefs %>%
filter(Z11 >= -3.30 & Z11 <= 3.30)
stats_intelligence <- favstats(~ intelligence, participant, data = data_intelligence)
stats_intelligence
leveneTest(intelligence ~ participant, data = data_intelligence) #homogeneity NOT met
welch_anova_test(intelligence ~ participant, data = data_intelligence)
p_intelligence <- welch_anova_test(intelligence ~ participant, data = data_intelligence)$p
data_intelligence %>% games_howell_test(intelligence ~ participant)
##### Atractive political beliefs (to the average woman) #####
data_politics <- data_prefs %>%
filter(Z12 >= -3.30 & Z12 <= 3.30)
stats_politics <- favstats(~ politics, participant, data = data_politics)
stats_politics
leveneTest(politics ~ participant, data = data_politics) #homogeneity NOT met
welch_anova_test(politics ~ participant, data = data_politics)
p_politics <- welch_anova_test(politics ~ participant, data = data_politics)$p
data_politics %>% games_howell_test(politics ~ participant)
##### Similar interests #####
data_interests <- data_prefs %>%
filter(Z13 >= -3.30 & Z13 <= 3.30)
stats_interests <- favstats(~ interests, participant, data = data_interests)
stats_interests
leveneTest(interests ~ participant, data = data_interests) #homogeneity NOT met
welch_anova_test(interests ~ participant, data = data_interests)
p_interests <- welch_anova_test(interests ~ participant, data = data_interests)$p
data_interests %>% games_howell_test(interests ~ participant)
##### Sexually skillful #####
data_sex_skill <- data_prefs %>%
filter(Z14 >= -3.30 & Z14 <= 3.30)
stats_sex_skill <- favstats(~ sex_skill, participant, data = data_sex_skill)
stats_sex_skill
leveneTest(sex_skill ~ participant, data = data_sex_skill) #homogeneity met
anova_test(sex_skill ~ participant, data = data_sex_skill, type = 3)
p_sex_skill <- anova_test(sex_skill ~ participant, data = data_sex_skill, type = 3)$p
data_sex_skill %>% tukey_hsd(sex_skill ~ participant)
##### Good cook/homemaker #####
data_homemaker <- data_prefs %>%
filter(Z15 >= -3.30 & Z15 <= 3.30)
stats_homemaker <- favstats(~ homemaker, participant, data = data_homemaker)
stats_homemaker
leveneTest(homemaker ~ participant, data = data_homemaker) #homogeneity met
anova_test(homemaker ~ participant, data = data_homemaker, type = 3)
p_homemaker <- anova_test(homemaker ~ participant, data = data_homemaker, type = 3)$p
data_homemaker %>% tukey_hsd(homemaker ~ participant)
##### Total #####
data_totprefs <- data_prefs %>%
mutate(totprefs = dplyr::select(., c(face:homemaker)) %>%
rowSums()) %>%
group_by(participant) %>%
mutate(Ztotprefs = scale(totprefs)) %>%
ungroup() %>%
filter(Ztotprefs >= -3.30 & Ztotprefs <= 3.30)
stats_totprefs <- favstats(~ totprefs, participant, data = data_totprefs)
stats_totprefs
leveneTest(totprefs ~ participant, data = data_totprefs) #homogeneity NOT met
welch_anova_test(totprefs ~ participant, data = data_totprefs)
p_totprefs <- welch_anova_test(totprefs ~ participant, data = data_totprefs)$p
data_totprefs %>% games_howell_test(totprefs ~ participant)
########## Adjusting p-values w/Holm-Bonferroni ##########
p.values <- c(p_face, p_body, p_fashion, p_finances, p_kindness, p_loyalty, p_maturity, p_sociability, p_personality, p_humor, p_intelligence, p_politics, p_interests, p_sex_skill, p_homemaker, p_totprefs)
p.adjust(p.values, method = "holm")
```
Regarding power, given unbalanced samples, methods like G*Power would not accurately calculate power. A few options found on stats forums included simulating power (assuming the standard deviations are the same, though it is unclear if this simply means assuming homogeneity is met and SDs are not significantly different). Separately, a package was found that is supposed to be able to compute power for unbalanced samples. Both methods are used and applied to the 15 minimum mate preferences traits.
Homogeneity of variances was NOT met for financial prospects, kind and understanding, loyal and dependable, emotional stability and maturity, sociability, humour, intelligence, political beliefs, similar interests,
Might be worth discussing effect sizes, as eta squared and partial eta squared have been shown to be biased, especially when samples are small or homogeneity is not met (both often cases with our data). Omega squared and partial omega seem to hold better should effect sizes for the ANOVAs be of interest, though there are no packages or functions that can compute these. Alternatively, it may be of interest to compute Cohen's d comparing incels and non-incels, and, more importantly, incels and hetero females.
```{r}
########## Facial attractiveness ##########
#Simulating power
n_sim <- 10000
sampsi <- c(stats_face$n[1], stats_face$n[2], stats_face$n[3])
mus <- c(stats_face$mean[1], stats_face$mean[2], stats_face$mean[3])
sds <- c(c(stats_face$sd[1], stats_face$sd[2], stats_face$sd[3]))
p_vals <- NULL
set.seed(1993)
for(i in 1:n_sim) {
dat_tmp <- data.frame(
y = rnorm(sum(sampsi), mean = rep(mus, times = sampsi), sd = rep(sds, times = sampsi))
, group = factor(rep(seq_along(mus), times = sampsi))
)
mod <- anova(lm(y~group, data = dat_tmp))
p_vals[i] <- mod$`Pr(>F)`[1]
rm(dat_tmp)
}
cat("Simulated power is:", mean(p_vals <= 0.05)*100, "%")
#Using the power package
install.packages("remotes")
remotes::install_github("gilberto-sassi/power")
library(power)
pwr_anova_unbalanced(c(stats_face$mean[1], stats_face$mean[2], stats_face$mean[3]),
c(stats_face$n[1], stats_face$n[2], stats_face$n[3]),
mean(data_face$face, na.rm = TRUE), sd(data_face$face, na.rm = TRUE), sig_level = 0.05)
```
Compare women's reported preferences to their estimated preferences of other women?
```{r}
data_women <- data %>%
filter(sex == 1 & incel == 0 & sexorien == 0) %>%
dplyr::select(., (c(age, ownminprefer_1:otherminpref_15)))
table_women <- matrix(ncol = 15, nrow = 4)
table_women[1,] <- as.numeric(data_women %>% dplyr::select(., (c(ownminprefer_1:ownminprefer_15))) %>% summarise_if(is.numeric, mean, na.rm = TRUE))
table_women[2,] <- as.numeric(data_women %>% dplyr::select(., (c(otherminpref_1:otherminpref_15))) %>% summarise_if(is.numeric, mean, na.rm = TRUE))
table_women[3,] <- c(t.test(data_women$ownminprefer_1, data_women$otherminpref_1, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_2, data_women$otherminpref_2, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_3, data_women$otherminpref_3, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_4, data_women$otherminpref_4, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_5, data_women$otherminpref_5, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_6, data_women$otherminpref_6, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_7, data_women$otherminpref_7, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_8, data_women$otherminpref_8, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_9, data_women$otherminpref_9, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_10, data_women$otherminpref_10, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_11, data_women$otherminpref_11, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_12, data_women$otherminpref_12, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_13, data_women$otherminpref_13, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_14, data_women$otherminpref_14, paired = TRUE)$p.value,
t.test(data_women$ownminprefer_15, data_women$otherminpref_15, paired = TRUE)$p.value)
table_women[4,] <- p.adjust(as.numeric(table_women[3,]), method = "holm")
table_women <- as.table(t(table_women))
rownames(table_women) <- c("Facial attractiveness", "Body attractiveness", "Fashion sense", "Good job", "Kind and understanding", "Loyal and dependable", "Emotional stability", "Sociability", "Exciting personality", "Sense of humour", "Intelligence", "Attractive political beliefs", "Similar interests","Sexually skilful", "Good cook/homemaker")
colnames(table_women) <- c("Own preferences", "Estimated preferences", "p-value", "adjusted p-value")
round(table_women, digits = 4)
```
# Exploratory questions
```{r}
data_exp <- data %>%
filter(sex == 0 & incel == 0 | sex == 0 & incel == 1) %>%
dplyr::select(., (c(incel, soiatt, forums, feminist, sexpartners_5_TEXT, sexpartners_6_TEXT, poly, polypartner,
forcesex, femopinion))) %>%