Novelty and boredom

rm(list = ls())
setwd("~/Downloads")
library(foreign)
sex_data <- read.spss("sex_data.sav", use.value.label=TRUE, to.data.frame=TRUE)

Libraries/themes

library(ggplot2)
library(tidyverse)
library(gridExtra)
library(finalfit)
library(kableExtra)
library(xtable)
jrothsch_theme <-  theme_bw() + 
  theme(text = element_text(size = 10, face = "bold", color = "deepskyblue4"),panel.grid = element_blank(),axis.text = element_text(size = 10, color = "gray13"), axis.title = element_text(size = 10, color = "red"), legend.text = element_text(colour="Black", size=10), legend.title = element_text(colour="Black", size=7), plot.subtitle = element_text(size=14, face="italic", color="black"))

---

Removing obserations without our key variables

sex_data <- sex_data[!is.na(sex_data$status),]
sex_data <- sex_data[!is.na(sex_data$age),]
sex_data <- sex_data[!is.na(sex_data$Years_PrimaryPartner),]
sex_data <- sex_data[!is.na(sex_data$MALE),]

---

Making variables

sex_data <- sex_data %>%
  mutate(married = status == "Married")

sex_data <- sex_data %>%
  mutate(married_gender = ifelse(married, 
                                 ifelse(MALE == "Male", "Married Male", "Married Female"),
                                 ifelse(MALE == "Male", "Unmarried Male", "Unmarried Female ")))

  married_num =   length(sex_data$married[sex_data$married == T])
  unmarried_num =   length(sex_data$married[sex_data$married == T])
  
  sex_data <- sex_data %>% mutate(freq_num =  as.numeric(ifelse(sexfreq == "At least once per day", 6, 
                                ifelse(sexfreq == "3-4 times per week", 5,
                                  ifelse(sexfreq == 'At least once a week', 4,
                                       ifelse(sexfreq == 'At least once per month', 3, 
                                              ifelse(sexfreq =='At least once per year', 2,
                                                     ifelse( sexfreq =="Less than once a year", 1, 0))))))))
  
  
  female <- sex_data %>%
  filter(sex_data$MALE == "Female")

---

Binary graphs

bin_graphs_want <- function(df, mod, modname, modlabs,  interaction_labs){
  
  
  df2 = df[!is.na(df$want) & !is.na(mod),]
  mod = mod[!is.na(df$want) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = want, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Want", " -- Age"), x = "Age", y = "Want", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by( mod_married) %>%
      summarize(DV = mean(want))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "Black") + 
      scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Want", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}

bin_graphs_like <- function(df, mod, modname, modlabs, interaction_labs){
  

  
  df2 = df[!is.na(df$totlike) & !is.na(mod),]
  mod = mod[!is.na(df$totlike) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = totlike, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Like", " -- Age"), x = "Age", y = "Like", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by( mod_married) %>%
      summarize(DV = mean(totlike))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "black") +
         scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Like", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}
bin_graphs_freq <- function(df, mod, modname, modlabs, interaction_labs){
  

  
  df2 = df[!is.na(df$freq_num) & !is.na(mod),]
  mod = mod[!is.na(df$freq_num) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = freq_num, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Frequency", " -- Age"), x = "Age", y = "Frequency", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by(mod_married) %>%
      summarize(DV = mean(freq_num))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "black") +    
      scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Frequency", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}

---

Small continuous graphs

contsmall <- function(df, mod, modname, modlabs){

  df2 = df[!is.na(df$want) & !is.na(df$totlike) & !is.na(df$freq_num ) & !is.na(mod) ,]
  mod = mod[!is.na(df$want) & !is.na(df$totlike) & !is.na(df$freq_num ) & !is.na(mod)]
  
want <- ggplot(df2, aes(x=mod, y=  want)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
  labs(title = paste0("Interaction between marriage and ", modname ), x = modname, y = "Want")
  
like <- ggplot(df2, aes(x=mod, y = totlike)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
    labs( x = modname, y = "Like")


freq <- ggplot(df2, aes(x=mod, y=  freq_num)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
      labs( x = modname, y = "Frequency")

  

grid.arrange(want, like, freq)

}

---

Regressions – Standard

###################################################################################################################################
#REgressions
#########################################################################################################################
  reg_no_mod <- function(dv, df){
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }


###########################################################################################################################
  reg_no_mod_i <- function(dv, df){

  dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + age + married*age, data = .)

  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: TRUE", "Age",  "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }

##################################################################################################################

  reg_no_mod_control <- function(dv, df){

  dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + age +  Years_PrimaryPartner + MALE, data = .)

  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: TRUE", "Age",  "Duration", "MALE")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }

##################################################################################################################

  reg_just_mod_i <- function(dv, df, mod, modname){
    
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname)
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }
  
#############################################################################################################################################################################################################################################################
  reg_full_control <- function(dv, df, mod, modname){
     
     dv <- as.numeric(dv)
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
  }


 


#############################################################################################################################################################################################################################################################
  reg_full_control_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + married*age, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male", "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

---

Subsets where controls can’t be used – e.g. one gender subsets

     reg_full_control_gend <- function(dv, df, mod, modname){

    
      dv <- as.numeric(dv)
      
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration" )
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
     }

 reg_full_control_gend_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + age*married, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

---

Looking at modertators interaction with marriage

  ireg_just_mod_i <- function(dv, df, mod, modname){
    
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }
  
#############################################################################################################################################################################################################################################################
  ireg_full_control <- function(dv, df, mod, modname){
     
     dv <- as.numeric(dv)
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + married*mod, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
  }


 


#############################################################################################################################################################################################################################################################
  ireg_full_control_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + age*married + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male", "Married X Age",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }
  
  
  
   
    
    #############################################################################################################################################################################################################################################################
   #For subsets wheere we can't use all controls
    
    #############################################################################################################################################################################################################################################################
    
     ireg_full_control_gend <- function(dv, df, mod, modname){

    
      dv <- as.numeric(dv)
      
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + married*mod, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r",  "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
     }

 ireg_full_control_gend_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + age*married + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Married X Age",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

Marriage on Want/Age/Like without mediators, with or without controls, for comparison

Want - Marriage

reg_no_mod(sex_data$want, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	28.927462	0.2358428	122.656	0
Married: True	-5.062436	0.2804827	-18.049	0

Like - Marriage

reg_no_mod(sex_data$totlike, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	38.524460	0.2654955	145.104	0
Married: True	-3.954296	0.3168241	-12.481	0

Frequency - Marriage

reg_no_mod(sex_data$freq_num, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	3.6641623	0.0408283	89.746	0
Married: True	-0.6595098	0.0480844	-13.716	0

Want - Marriage, controls

reg_no_mod_control(sex_data$want, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	33.6515234	0.3616805	93.042	0
Married: TRUE	-2.4498192	0.2836310	-8.637	0
Age	-0.1788330	0.0079330	-22.543	0
Duration	-0.0164498	0.0049803	-3.303	0.001
MALE	4.5428634	0.2430139	18.694	0

Like - Marriage, controls

reg_no_mod_control(sex_data$totlike, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	40.9088281	0.4368889	93.637	0
Married: TRUE	-2.6911441	0.3412552	-7.886	0
Age	-0.0852356	0.0095901	-8.888	0
Duration	-0.0098875	0.0058960	-1.677	0.0936
MALE	1.9027594	0.2944406	6.462	0

Frequency - Marriage, controls

reg_no_mod_control(sex_data$freq_num, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	4.8698702	0.0640338	76.052	0
Married: TRUE	-0.1485242	0.0488622	-3.04	0.0024
Age	-0.0334322	0.0014244	-23.471	0
Duration	-0.0032137	0.0010801	-2.975	0.0029
MALE	0.2339988	0.0411009	5.693	0

---

Monotony – Negative Effect, No Clear Interaction With Marriege

Graphs

sex_data$MonoNum = as.numeric(sex_data$satis5)

contsmall(sex_data, sex_data$MonoNum, "Monotony", "")

Want - Only Monotony

reg_just_mod_i(sex_data$want, sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	29.5259136	0.3511769	84.077	0
Married: True	-4.9183274	0.2844883	-17.288	0
Monotony	-0.2051366	0.0927987	-2.211	0.0271

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	35.5128795	0.4728986	75.096	0
Married: True	-2.3731787	0.2860717	-8.296	0
Monotony	-0.5559344	0.0873297	-6.366	0
Age (years)	-0.1857905	0.0081737	-22.73	0
Duration	-0.0148084	0.0049804	-2.973	0.003
Male	4.4861607	0.2468294	18.175	0

Like - Only Monotony

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	42.784049	0.3820126	111.996	0
Married: True	-4.227152	0.3098335	-13.643	0
Monotony	-1.535484	0.1011579	-15.179	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	46.9634370	0.5466499	85.911	0
Married: True	-2.6328889	0.3306209	-7.963	0
Monotony	-1.7679711	0.1010195	-17.501	0
Age (years)	-0.1154494	0.0094498	-12.217	0
Duration	-0.0074340	0.0057137	-1.301	0.1933
Male	1.8702236	0.2852568	6.556	0

Frequency - Only Monotony

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7307217	0.0597737	62.414	0
Married: True	-0.6360546	0.0485138	-13.111	0
Monotony	-0.0197453	0.0157821	-1.251	0.211

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$MonoNum, "Monotony")

	Estimate	Std. Error	t_value	p_value
Intercept	5.1952864	0.0820679	63.305	0
Married: True	-0.1445071	0.0489517	-2.952	0.0032
Monotony	-0.0971658	0.0148454	-6.545	0
Age (years)	-0.0345372	0.0014561	-23.719	0
Duration	-0.0027521	0.0010713	-2.569	0.0102
Male	0.2256946	0.0415652	5.43	0

---

Fantasy Of Other Partner – Strong Negative Effect, No Clear Interaction With Marriege

Graphs

sex_data$OtherNum = as.numeric(sex_data$satis14)

contsmall(sex_data, sex_data$OtherNum, "Fantasize About Other Sex Partner", "")

Want - Only Monotony

reg_just_mod_i(sex_data$want, sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	30.2063690	0.3359159	89.922	0
Married: True	-4.9715910	0.2852515	-17.429	0
Fantasy Other Partner	-0.7041937	0.1449588	-4.858	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	35.7117038	0.4340663	82.272	0
Married: True	-2.5159268	0.2866776	-8.776	0
Fantasy Other Partner	-1.2303163	0.1353922	-9.087	0
Age (years)	-0.1810275	0.0080917	-22.372	0
Duration	-0.0136193	0.0049551	-2.749	0.006
Male	4.8201704	0.2499502	19.285	0

Like - Only Monotony

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	43.273313	0.3602976	120.104	0
Married: True	-4.242085	0.3059558	-13.865	0
Fantasy Other Partner	-2.913021	0.1554803	-18.736	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	46.4198309	0.4944105	93.889	0
Married: True	-2.8816458	0.3265317	-8.825	0
Fantasy Other Partner	-3.2163329	0.1542145	-20.856	0
Age (years)	-0.1039780	0.0092167	-11.282	0
Duration	-0.0051358	0.0056440	-0.91	0.3629
Male	2.7726414	0.2846984	9.739	0

Frequency - Only Monotony

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7295288	0.0573182	65.067	0
Married: True	-0.6361004	0.0485324	-13.107	0
Fantasy Other Partner	-0.0219594	0.0248557	-0.883	0.377

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$OtherNum, "Fantasy Other Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	4.9729976	0.0756913	65.701	0
Married: True	-0.1628016	0.0493060	-3.302	0.001
Fantasy Other Partner	-0.0748040	0.0233300	-3.206	0.0014
Age (years)	-0.0324980	0.0014478	-22.447	0
Duration	-0.0028291	0.0010725	-2.638	0.0084
Male	0.2498086	0.0422698	5.91	0

---

Hurried Sex - Strongly Harms Like, Doesn’t Change Marriage Much

Graphs

sex_data$HurryNum = as.numeric(sex_data$satis16)

contsmall(sex_data, sex_data$HurryNum, "Sex Completed Too Hurriedly", "")

Want - Only Hurry

reg_just_mod_i(sex_data$want, sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	30.275498	0.3397738	89.105	0
Married: True	-4.821224	0.2852910	-16.899	0
Too Quick	-0.570011	0.1132467	-5.033	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	35.9840531	0.4513280	79.729	0
Married: True	-2.1978202	0.2878586	-7.635	0
Too Quick	-0.9559470	0.1055536	-9.057	0
Age (years)	-0.1844556	0.0081382	-22.665	0
Duration	-0.0137789	0.0049548	-2.781	0.0054
Male	4.6001122	0.2475807	18.58	0

Like - Only Hurry

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	44.248025	0.3591588	123.199	0
Married: True	-3.574343	0.3015677	-11.853	0
Too Quick	-2.680510	0.1197077	-22.392	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	48.1806020	0.5054212	95.328	0
Married: True	-1.9419931	0.3223594	-6.024	0
Too Quick	-2.9202466	0.1182045	-24.705	0
Age (years)	-0.1176091	0.0091136	-12.905	0
Duration	-0.0048224	0.0055486	-0.869	0.3848
Male	2.2470082	0.2772541	8.105	0

Frequency - Only Hurry

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7815070	0.0576402	65.605	0
Married: True	-0.6274247	0.0485768	-12.916	0
Too Quick	-0.0413275	0.0191751	-2.155	0.0312

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$HurryNum, "Too Quick")

	Estimate	Std. Error	t_value	p_value
Intercept	5.1187966	0.0784816	65.223	0
Married: True	-0.1291410	0.0494262	-2.613	0.009
Too Quick	-0.1092619	0.0179364	-6.092	0
Age (years)	-0.0333392	0.0014525	-22.953	0
Duration	-0.0026991	0.0010694	-2.524	0.0116
Male	0.2424752	0.0417810	5.803	0

---

Distracted during - Strong Negative Effect, Doesn’t Change Marriage Much

Graphs

sex_data$DistractNum = as.numeric(sex_data$satis18)

contsmall(sex_data, sex_data$DistractNum, "Gets Distracted", "")

Want - Only Distracted

reg_just_mod_i(sex_data$want, sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	32.172881	0.3311453	97.156	0
Married: True	-5.055033	0.2805466	-18.019	0
Gets Distracted	-1.545575	0.1158295	-13.344	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	38.0551853	0.4521564	84.164	0
Married: True	-2.3906136	0.2812337	-8.5	0
Gets Distracted	-1.7687953	0.1086924	-16.273	0
Age (years)	-0.1922949	0.0079836	-24.086	0
Duration	-0.0128899	0.0048612	-2.652	0.008
Male	4.0684648	0.2439992	16.674	0

Like - Only Distracted

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	44.716559	0.3515265	127.207	0
Married: True	-4.258508	0.2978136	-14.299	0
Gets Distracted	-3.072837	0.1229585	-24.991	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	49.2125749	0.5106058	96.381	0
Married: True	-2.5933206	0.3175883	-8.166	0
Gets Distracted	-3.2994353	0.1227429	-26.881	0
Age (years)	-0.1196268	0.0090157	-13.269	0
Duration	-0.0051364	0.0054896	-0.936	0.3495
Male	1.1212549	0.2755405	4.069	0

Frequency - Only Distracted

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7240482	0.0572071	65.098	0
Married: True	-0.6355050	0.0485176	-13.098	0
Gets Distracted	-0.0149976	0.0200057	-0.75	0.4535

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$DistractNum, "Gets Distracted")

	Estimate	Std. Error	t_value	p_value
Intercept	5.0677481	0.0803318	63.085	0
Married: True	-0.1550681	0.0492552	-3.148	0.0017
Gets Distracted	-0.0877909	0.0189700	-4.628	0
Age (years)	-0.0330110	0.0014538	-22.707	0
Duration	-0.0027907	0.0010711	-2.605	0.0092
Male	0.2069520	0.0421376	4.911	0

---

Tries New Things – Massive Effect, Decimates Marriage Effect

Graphs

sex_data$NewNum = as.numeric(sex_data$satis24)

contsmall(sex_data, sex_data$NewNum, "Tries New Things", "")

Want - Only New

reg_just_mod_i(sex_data$want, sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	17.277493	0.3635358	47.526	0
Married: True	-2.457230	0.2549766	-9.637	0
New	3.485618	0.0883668	39.445	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	20.9415194	0.4754371	44.047	0
Married: True	-1.0224475	0.2577227	-3.967	1e-04
New	3.1395434	0.0857641	36.607	0
Age (years)	-0.1153445	0.0073363	-15.722	0
Duration	-0.0158141	0.0044020	-3.592	3e-04
Male	4.1302188	0.2200485	18.77	0

Like - Only New

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	21.4035724	0.3518714	60.828	0
Married: True	-0.4013391	0.2467954	-1.626	0.104
New	5.0655671	0.0855314	59.225	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	20.4167057	0.4852004	42.079	0
Married: True	-0.3920576	0.2630151	-1.491	0.1361
New	5.0809793	0.0875253	58.052	0
Age (years)	0.0094618	0.0074870	1.264	0.2064
Duration	-0.0104979	0.0044924	-2.337	0.0195
Male	1.3246685	0.2245672	5.899	0

Frequency - Only New

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	2.2343526	0.0652348	34.251	0
Married: True	-0.3344510	0.0461251	-7.251	0
New	0.4410455	0.0160447	27.489	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$NewNum, "New")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4179222	0.0860175	39.735	0
Married: True	0.0062303	0.0470354	0.132	0.8946
New	0.3676306	0.0155445	23.65	0
Age (years)	-0.0261641	0.0013851	-18.889	0
Duration	-0.0025752	0.0010116	-2.546	0.0109
Male	0.1880966	0.0395304	4.758	0

---

Interesting/Mysterious Partner – Accounts For Part Of Marriage Effect

Graphs

sex_data$MystNum = as.numeric(sex_data$satis25)

contsmall(sex_data, sex_data$MystNum, "Partner Mysterious and Interesting", "")

Want - Only Partner Interesting

reg_just_mod_i(sex_data$want, sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	21.435459	0.3580187	59.872	0
Married: True	-3.327966	0.2716355	-12.252	0
Interesting	2.647642	0.0974496	27.169	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	26.2125777	0.4601101	56.97	0
Married: True	-1.4323576	0.2750976	-5.207	0
Interesting	2.2809680	0.0928068	24.578	0
Age (years)	-0.1456037	0.0077300	-18.836	0
Duration	-0.0164465	0.0046997	-3.499	5e-04
Male	3.9779383	0.2356056	16.884	0

Like - Only Partner Interesting

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	29.254788	0.3908048	74.858	0
Married: True	-2.040204	0.2965110	-6.881	0
Interesting	3.219601	0.1063737	30.267	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	30.8720969	0.5316977	58.063	0
Married: True	-1.3186588	0.3178995	-4.148	0
Interesting	3.0974577	0.1072464	28.882	0
Age (years)	-0.0468673	0.0089327	-5.247	0
Duration	-0.0112585	0.0054310	-2.073	0.0382
Male	1.2109024	0.2722630	4.448	0

Frequency - Only Partner Interesting

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	2.9456605	0.0633786	46.477	0
Married: True	-0.4839334	0.0482961	-10.02	0
Interesting	0.2655542	0.0174524	15.216	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$MystNum, "Interesting")

	Estimate	Std. Error	t_value	p_value
Intercept	4.1953493	0.0820903	51.106	0
Married: True	-0.0717980	0.0489997	-1.465	0.1429
Interesting	0.2088167	0.0165309	12.632	0
Age (years)	-0.0299742	0.0014304	-20.954	0
Duration	-0.0026900	0.0010549	-2.55	0.0108
Male	0.1867203	0.0413268	4.518	0

---

Interesting/Mysterious Partner – Accounts For Part Of Marriage Effect

Graphs

sex_data$DiscNewNum = as.numeric(sex_data$satis26)

contsmall(sex_data, sex_data$DiscNewNum, "Discovers New Things About Partner", "")

Want - Only Discovering

reg_just_mod_i(sex_data$want, sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	19.015735	0.3619703	52.534	0
Married: True	-2.747458	0.2626967	-10.459	0
Discovering	3.137622	0.0912593	34.381	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	23.3240442	0.4646886	50.193	0
Married: True	-1.0989919	0.2652105	-4.144	0
Discovering	2.7911754	0.0873766	31.944	0
Age (years)	-0.1337139	0.0074652	-17.912	0
Duration	-0.0133074	0.0045246	-2.941	0.0033
Male	4.0860144	0.2262791	18.057	0

Like - Only Discovering

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	25.108310	0.3779806	66.428	0
Married: True	-1.075977	0.2743160	-3.922	1e-04
Discovering	4.191605	0.0952958	43.985	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	25.7549054	0.5139211	50.115	0
Married: True	-0.7097562	0.2933089	-2.42	0.0156
Discovering	4.1146860	0.0966340	42.58	0
Age (years)	-0.0260560	0.0082561	-3.156	0.0016
Duration	-0.0067482	0.0050040	-1.349	0.1775
Male	1.3110139	0.2502528	5.239	0

Frequency - Only Discovering

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	2.7055783	0.0657384	41.157	0
Married: True	-0.4229947	0.0480013	-8.812	0
Discovering	0.3144845	0.0167285	18.799	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$DiscNewNum, "Discovering")

	Estimate	Std. Error	t_value	p_value
Intercept	3.9637027	0.0849400	46.665	0
Married: True	-0.0420060	0.0486551	-0.863	0.388
Discovering	0.2488899	0.0159668	15.588	0
Age (years)	-0.0291566	0.0014203	-20.528	0
Duration	-0.0022667	0.0010459	-2.167	0.0303
Male	0.1941551	0.0408757	4.75	0

---

Intellectually Stimulating – Strongly Reduces Marriage Effect

Graphs

sex_data$IntNum = as.numeric(sex_data$intellectualstim)

contsmall(sex_data, sex_data$IntNum, "Intellectually Stimulating Partner", "")

Want - Only Intellectually stimulating

reg_just_mod_i(sex_data$want, sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	16.194569	0.4892062	33.104	0
Married: True	-4.686088	0.2627498	-17.835	0
Intellectually Stimulating	2.294247	0.0778895	29.455	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	20.9372811	0.5386318	38.871	0
Married: True	-2.4122527	0.2648919	-9.107	0
Intellectually Stimulating	2.1794216	0.0721333	30.214	0
Age (years)	-0.1600354	0.0074552	-21.466	0
Duration	-0.0158778	0.0045697	-3.475	5e-04
Male	4.3161638	0.2285628	18.884	0

Like - Only Intellectually Stimuating

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	17.513912	0.4800842	36.481	0
Married: True	-3.605761	0.2578504	-13.984	0
Intellectually Stimulating	3.745942	0.0764371	49.007	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	19.4060631	0.5650010	34.347	0
Married: True	-2.6414464	0.2778599	-9.506	0
Intellectually Stimulating	3.6999956	0.0756647	48.9	0
Age (years)	-0.0622205	0.0078201	-7.956	0
Duration	-0.0104218	0.0047934	-2.174	0.0297
Male	1.6438567	0.2397523	6.856	0

Frequency - Only Intellectually Stimulating

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	2.4224503	0.0863825	28.043	0
Married: True	-0.6075684	0.0470778	-12.906	0
Intellectually Stimulating	0.2277971	0.0137416	16.577	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$IntNum, "Intellectually Stimulating")

	Estimate	Std. Error	t_value	p_value
Intercept	3.6439135	0.0968524	37.623	0
Married: True	-0.1499368	0.0479399	-3.128	0.0018
Intellectually Stimulating	0.2089352	0.0128012	16.322	0
Age (years)	-0.0308807	0.0014053	-21.975	0
Duration	-0.0028770	0.0010416	-2.762	0.0058
Male	0.2048077	0.0407177	5.03	0