marriage_as_iv

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))))))))

Functions

graphs_continuous <- function(dv, df, dvname){
  
  dist <- ggplot(data = df, aes(x = dv, fill = married)) +
    geom_density(alpha = 0.5) + labs(title = paste("Distribution Of ", dvname), x = dvname, y = "Density") +  scale_fill_discrete(labels = c("Married", "Unmarried"))  +
    jrothsch_theme
  
  gender <- ggplot(data = df,  aes(y = dv, x = married_gender, color = MALE)) +
    geom_boxplot() +
    labs(title = paste(dvname, "By Marriage And Gender"), x = "Marriage/Gender", y = dvname, color = "") +
     coord_flip() + 
    jrothsch_theme

  #Age
  age <- ggplot(data = df, aes(x = age, y = dv, color = married)) +
      geom_point(alpha = 0.1) +
      geom_smooth() +
      labs(title = paste("Effect Of Marriage On", dvname, " -- Age"), x = "Age", y = dvname, color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
    scale_color_discrete(labels = c("Married", "Unmarried")) +
      jrothsch_theme
  
  #Duration
    dur <- ggplot(data = df, aes(x = Years_PrimaryPartner, y = dv, color = married)) +
      geom_point(alpha = 0.1) +
      geom_smooth() +
     labs(title = paste("Effect Of Marriage On", dvname, " -- Duration"), x = "Duration", y = dvname, color = "") + 
      scale_x_continuous(limits = c(0, 50)) + scale_color_discrete(labels = c("Married", "Unmarried")) +
      jrothsch_theme
    
    grid.arrange(dist, gender, age, dur, ncol = 2)
}

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

graphs_discrete <- function(dv, df, dvname){
  df2 = df[!is.na(dv),]
  dv2 = dv[!is.na(dv)]

  
  bar <- ggplot(data = df2, aes(x = dv2)) +
    geom_bar(position = 'dodge', aes(fill = married)) +
   labs(title = paste("Distribution Of ", dvname), x = dvname, y = "Number Of Observations")  +
    jrothsch_theme +
    coord_flip() +
    facet_wrap(~married)

  dv2 <- as.numeric(dv2)
  
  gender <- ggplot(data = df2,  aes(y = dv2, x = married_gender, color = MALE)) +
    geom_boxplot() +
    labs(title = paste(dvname, "By Marriage And Gender"), x = "Marriage/Gender", y = dvname, color = "") +
     coord_flip() +  
    scale_fill_discrete(labels = c("Unmarried", "Married")) +
    jrothsch_theme

  #Age
  age <- ggplot(data = df2, aes(x = age, y = dv2, color = married))  +
      geom_smooth() +
      labs(title = paste("Effect Of Marriage On", dvname, " -- Age"), x = "Age", y = dvname, color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
    scale_color_discrete(labels = c("Unmarried", "Married")) +
      jrothsch_theme
  
  #Duration
    dur <- ggplot(data = df2, aes(x = Years_PrimaryPartner, y = dv2, color = married)) +
      geom_smooth() +
     labs(title = paste("Effect Of Marriage On", dvname, " -- Duration"), x = "Duration", y = dvname, color = "") + 
      scale_x_continuous(limits = c(0, 50)) + scale_color_discrete(labels = c("Unmarried", "Married")) +
      jrothsch_theme
    
    grid.arrange(bar, gender, age, dur)
}


#############################################################################################################################################################################################################################################################
reg_simple <- 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, "light green", "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_more<- 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, 5)) %>%
     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, "limegreen", "white"))))
     
  rownames(tx1) <- c("Intercept", "Married: True", "Age (years)", "Duration", "Male")
    
kable(tx1, row.names = T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8)  


}

reg_interaction<- function(dv, df){
  dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + age + Years_PrimaryPartner + MALE + 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, 5)) %>%
     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, "limegreen", "white"))))
     
  rownames(tx1) <- c("Intercept", "Married: True", "Age (years)", "Duration", "Male", "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)  


}

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Like

Graphs

graphs_continuous(sex_data$totlike, sex_data, "Like")

Regression - Like ~ Marriage

reg_simple(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

Regression - Like ~ Marriage, Age, Gender, Duration

reg_more(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 (years)	-0.0852356	0.0095901	-8.888	0
Duration	-0.0098875	0.0058960	-1.677	0.09361
Male	1.9027594	0.2944406	6.462	0

Regression - Like – Interaction

reg_interaction(sex_data$totlike, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	39.0758142	0.6638263	58.865	0
Married: True	0.2502633	0.8721652	0.287	0.77417
Age (years)	-0.0380305	0.0160538	-2.369	0.01788
Duration	-0.0064684	0.0059616	-1.085	0.27798
Male	1.9417501	0.2942417	6.599	0
Married x Age	-0.0706070	0.0192714	-3.664	0.00025

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Want

Graphs

graphs_continuous(sex_data$want, sex_data, "Want")

Regression - Want ~ Marriage

reg_simple(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

Regression - Want ~ Marriage, Age, Gender, Duration

reg_more(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 (years)	-0.1788330	0.0079330	-22.543	0
Duration	-0.0164498	0.0049803	-3.303	0.00096
Male	4.5428634	0.2430139	18.694	0

Regression - Want – Interaction

reg_interaction(sex_data$want, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	31.1512237	0.5506366	56.573	0
Married: True	1.5349626	0.7211617	2.128	0.03335
Age (years)	-0.1144583	0.0133182	-8.594	0
Duration	-0.0115843	0.0050284	-2.304	0.02128
Male	4.5912970	0.2422920	18.949	0
Married x Age	-0.0956272	0.0159220	-6.006	0

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General Desire

Graphs

graphs_discrete(sex_data$gendesir, sex_data, "Desire")

Regression - Desire ~ Marriage

reg_simple(sex_data$gendesir, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.0234310	0.0568414	105.969	0
Married: True	-0.6143401	0.0685983	-8.956	0

Regression - Desire ~ Marriage, Age, Gender, Duration

reg_more(sex_data$gendesir, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.1873397	0.0924286	66.942	0
Married: True	-0.4328352	0.0727681	-5.948	0
Age (years)	-0.0155807	0.0020443	-7.621	0
Duration	-0.0012408	0.0011670	-1.063	0.28773
Male	0.9636750	0.0639932	15.059	0

Regression - Desire – Interaction

reg_interaction(sex_data$gendesir, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.6517235	0.1367860	41.318	0
Married: True	0.4706434	0.1853605	2.539	0.01115
Age (years)	-0.0019689	0.0032795	-0.6	0.54829
Duration	-0.0003695	0.0011744	-0.315	0.75309
Male	0.9850041	0.0638942	15.416	0
Married x Age	-0.0212580	0.0040138	-5.296	0

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Frequency

Graphs

graphs_discrete(sex_data$freq_num, sex_data, "Frequency")

Regression - Frequency ~ Marriage

reg_simple(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

Regression - Frequency ~ Marriage, Age, Gender, Duration

reg_more(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	0.00238
Age (years)	-0.0334322	0.0014244	-23.471	0
Duration	-0.0032137	0.0010801	-2.975	0.00294
Male	0.2339988	0.0411009	5.693	0

kable(table1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8)  

	country	year	cases	population
1	Afghanistan	1999	745	19987071
2	Afghanistan	2000	2666	20595360
3	Brazil	1999	37737	172006362
4	Brazil	2000	80488	174504898
5	China	1999	212258	1272915272
6	China	2000	213766	1280428583

Regression - Frequency – Interaction

reg_interaction(sex_data$freq_num, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	4.2367447	0.0959036	44.177	0
Married: True	0.8666272	0.1249834	6.934	0
Age (years)	-0.0175985	0.0022859	-7.699	0
Duration	-0.0010892	0.0010983	-0.992	0.32138
Male	0.2448805	0.0407930	6.003	0
Married x Age	-0.0241874	0.0027448	-8.812	0

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Enjoyment

Graphs

graphs_discrete(sex_data$EnjoySex, sex_data, "Enjoyment")

Regression - Enjoyment ~ Marriage

reg_simple(sex_data$EnjoySex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.5439815	0.0325894	78.062	0
Married: True	0.3113073	0.0407129	7.646	0

Regression - Enjoyment ~ Marriage, Age, Gender, Duration

reg_more(sex_data$EnjoySex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.3745682	0.0547840	43.344	0
Married: True	0.2147854	0.0446972	4.805	0
Age (years)	0.0089273	0.0012293	7.262	0
Duration	-0.0002870	0.0005917	-0.485	0.62761
Male	-0.4154260	0.0396967	-10.465	0

Regression - Enjoyment – Interaction

reg_interaction(sex_data$EnjoySex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.6268289	0.0757455	34.68	0
Married: True	-0.2869863	0.1136357	-2.525	0.01162
Age (years)	0.0024859	0.0018164	1.369	0.17125
Duration	-0.0006072	0.0005927	-1.024	0.30578
Male	-0.4294962	0.0396244	-10.839	0
Married x Age	0.0113299	0.0023610	4.799	0

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Masturbation Frequency

Graphs

graphs_discrete(sex_data$MasturbationFreq, sex_data, "Masturbation Frequency")

Regression - Masturbation Frequency ~ Marriage

reg_simple(sex_data$MasturbationFreq, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	3.0515625	0.0467702	65.246	0
Married: True	0.4885266	0.0612082	7.981	0

Regression - Masturbation Frequency ~ Marriage, Age, Gender, Duration

reg_more(sex_data$MasturbationFreq, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.5914198	0.0781435	33.162	0
Married: True	0.2181090	0.0654661	3.332	0.00088
Age (years)	0.0199617	0.0018361	10.872	0
Duration	0.0021769	0.0009869	2.206	0.02755
Male	-0.6384301	0.0609351	-10.477	0

Regression - Masturbation Frequency – Interaction

reg_interaction(sex_data$MasturbationFreq, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.8267475	0.1045275	27.043	0
Married: True	-0.2949152	0.1654023	-1.783	0.07478
Age (years)	0.0139103	0.0025618	5.43	0
Duration	0.0018506	0.0009883	1.872	0.06133
Male	-0.6564739	0.0609644	-10.768	0
Married x Age	0.0116304	0.0034457	3.375	0.00076

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Masturbation Enjoyment

Graphs

graphs_discrete(sex_data$MasturbationOrgasm, sex_data, "Masturbation Enjoyment")

Regression - Masturbation Enjoyment ~ Marriage

reg_simple(sex_data$MasturbationOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.621835	0.0703292	94.155	0
Married: True	-0.442290	0.0921871	-4.798	0

Regression - Masturbation Enjoyment ~ Marriage, Age, Gender, Duration

reg_more(sex_data$MasturbationOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	7.2708803	0.1223579	59.423	0
Married: True	-0.1861058	0.1021175	-1.822	0.06858
Age (years)	-0.0105014	0.0028709	-3.658	0.00026
Duration	-0.0006169	0.0015316	-0.403	0.68718
Male	-0.5468093	0.0952324	-5.742	0

Regression - Masturbation Enjoyment – Interaction

reg_interaction(sex_data$MasturbationOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.8713903	0.1629557	42.167	0
Married: True	0.6931999	0.2589616	2.677	0.00751
Age (years)	-0.0002835	0.0039790	-0.071	0.9432
Duration	-0.0000694	0.0015324	-0.045	0.96389
Male	-0.5146718	0.0952344	-5.404	0
Married x Age	-0.0198692	0.0053816	-3.692	0.00023

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Importance of Sex

Graphs

graphs_discrete(sex_data$seximpor, sex_data, "Importance Of Sex")

Regression - Importance ~ Marriage

reg_simple(sex_data$seximpor, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.4155731	0.0670462	95.689	0
Married: True	-0.6053714	0.0807888	-7.493	0

Regression - Importance ~ Marriage, Age, Gender, Duration

reg_more(sex_data$seximpor, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	7.5084237	0.1083403	69.304	0
Married: True	-0.1241771	0.0854983	-1.452	0.14648
Age (years)	-0.0355943	0.0023789	-14.963	0
Duration	-0.0006182	0.0013465	-0.459	0.64619
Male	0.5898628	0.0747640	7.89	0

Regression - Importance – Interaction

reg_interaction(sex_data$seximpor, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.6452367	0.1594625	41.673	0
Married: True	1.3333085	0.2161325	6.169	0
Age (years)	-0.0136149	0.0038162	-3.568	0.00036
Duration	0.0007914	0.0013506	0.586	0.55793
Male	0.6203199	0.0743479	8.343	0
Married x Age	-0.0342664	0.0046731	-7.333	0

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Unrecriprocated desire for sex - self

Graphs

graphs_discrete(sex_data$i_do, sex_data, "Unrecriprocated desire for sex")

Regression - Unreciprocated Desire ~ Marriage

reg_simple(sex_data$i_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.0662069	0.0309435	66.774	0
Married: True	0.0875064	0.0368394	2.375	0.0176

Regression - Unrepricoted Desire ~ Marriage, Age, Gender, Duration

reg_more(sex_data$i_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.0416868	0.0495172	41.232	0
Married: True	0.1333318	0.0388053	3.436	6e-04
Age (years)	-0.0077222	0.0010862	-7.109	0
Duration	0.0017354	0.0006811	2.548	0.01087
Male	0.6432502	0.0333158	19.308	0

Regression - Unreciprocated Desire – Interaction

reg_interaction(sex_data$i_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.0267944	0.0755943	26.811	0
Married: True	0.1570900	0.0990368	1.586	0.11276
Age (years)	-0.0073391	0.0018274	-4.016	6e-05
Duration	0.0017644	0.0006902	2.556	0.01061
Male	0.6435452	0.0333381	19.304	0
Married x Age	-0.0005700	0.0021862	-0.261	0.7943

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Unrecriprocated desire for sex - Partner

Graphs

graphs_discrete(sex_data$they_do, sex_data, "Unrecriprocated desire for sex - Partner")

Regression - Unreciprocated Desire ~ Marriage

reg_simple(sex_data$they_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.0668966	0.0302177	68.4	0
Married: True	-0.1141044	0.0359754	-3.172	0.0015

Regression - Unrepricotated Desire ~ Marriage, Age, Gender, Duration

reg_more(sex_data$they_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.7029611	0.0476259	56.754	0
Married: True	0.0384113	0.0373232	1.029	0.30346
Age (years)	-0.0092245	0.0010447	-8.83	0
Duration	0.0002504	0.0006551	0.382	0.70234
Male	-0.5976379	0.0320433	-18.651	0

Regression - Unreciprocated Desire – Interaction

reg_interaction(sex_data$they_do, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	2.5117432	0.0726179	34.588	0
Married: True	0.3434655	0.0951374	3.61	0.00031
Age (years)	-0.0043048	0.0017554	-2.452	0.01423
Duration	0.0006233	0.0006630	0.94	0.34725
Male	-0.5938506	0.0320255	-18.543	0
Married x Age	-0.0073194	0.0021001	-3.485	5e-04

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Orgasm Satisfaction

Graphs

graphs_discrete(sex_data$o_satis, sex_data, "Orgasm Satisfaction")

Regression - Orgasm Satisfaction ~ Marriage

reg_simple(sex_data$o_satis, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	8.0124567	0.0506999	158.037	0
Married: True	-0.3879279	0.0603938	-6.423	0

Regression - Orgasm Satisfaction ~ Marriage, Age, Gender, Duration

reg_more(sex_data$o_satis, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	8.3617676	0.0835554	100.075	0
Married: True	-0.1916989	0.0654108	-2.931	0.0034
Age (years)	-0.0134706	0.0018345	-7.343	0
Duration	-0.0013043	0.0011471	-1.137	0.25555
Male	0.3558681	0.0562196	6.33	0

Regression - Orgasm Satisfaction – Interaction

reg_interaction(sex_data$o_satis, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	7.9212414	0.1272410	62.254	0
Married: True	0.5118999	0.1668085	3.069	0.00216
Age (years)	-0.0021385	0.0030764	-0.695	0.487
Duration	-0.0004548	0.0011596	-0.392	0.6949
Male	0.3648875	0.0561394	6.5	0
Married x Age	-0.0168791	0.0036825	-4.584	0

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Fake Orgasm

Graphs

graphs_discrete(sex_data$FakeOrgasm, sex_data, "Fake Orgasm")

Regression - Fake Orgasm ~ Marriage

reg_simple(sex_data$FakeOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	1.3936545	0.0270229	51.573	0
Married: True	-0.1049173	0.0339768	-3.088	0.002

Regression - Fake Orgasm ~ Marriage, Age, Gender, Duration

reg_more(sex_data$FakeOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	1.6369896	0.0460866	35.52	0
Married: True	-0.0437367	0.0375927	-1.163	0.24477
Age (years)	-0.0042270	0.0010352	-4.083	5e-05
Duration	0.0013319	0.0004924	2.705	0.00689
Male	-0.2238837	0.0335479	-6.674	0

Regression - Fake Orgasm – Interaction

reg_interaction(sex_data$FakeOrgasm, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	1.5546947	0.0636259	24.435	0
Married: True	0.1221120	0.0961059	1.271	0.204
Age (years)	-0.0021195	0.0015278	-1.387	0.16548
Duration	0.0014319	0.0004950	2.893	0.00386
Male	-0.2194912	0.0336115	-6.53	0
Married x Age	-0.0037436	0.0019967	-1.875	0.06093

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Thinking About Sex

Graphs

graphs_discrete(sex_data$thought, sex_data, "Thinking About Sex")

Regression - Sex Thoughts ~ Marriage

reg_simple(sex_data$thought, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	4.9725840	0.0557839	89.14	0
Married: True	-0.8959437	0.0663011	-13.513	0

Regression - Sex Thoughts ~ Marriage, Age, Gender, Duration

reg_more(sex_data$thought, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.8493204	0.0863407	67.747	0
Married: True	-0.3721875	0.0677267	-5.495	0
Age (years)	-0.0371692	0.0018926	-19.639	0
Duration	-0.0028712	0.0011919	-2.409	0.01604
Male	1.1562792	0.0579697	19.946	0

Regression - Sex thought – Interaction

reg_interaction(sex_data$thought, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.4014015	0.1317941	40.984	0
Married: True	0.3402770	0.1724148	1.974	0.04848
Age (years)	-0.0256396	0.0031869	-8.045	0
Duration	-0.0019961	0.0012055	-1.656	0.09782
Male	1.1650735	0.0578917	20.125	0
Married x Age	-0.0171027	0.0038075	-4.492	1e-05

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Comfort Discussing Sex

Graphs

graphs_discrete(sex_data$comfort, sex_data, "Comfort Discussing Sex")

Regression - Comfort Discussing ~ Marriage

reg_simple(sex_data$comfort, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.5650865	0.0476178	116.87	0
Married: True	-0.4565023	0.0568567	-8.029	0

Regression - Comfort Discusing ~ Marriage, Age, Gender, Duration

reg_more(sex_data$comfort, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.6686792	0.0787410	71.991	0
Married: True	-0.3353835	0.0616217	-5.443	0
Age (years)	-0.0066364	0.0017307	-3.835	0.00013
Duration	-0.0024910	0.0010453	-2.383	0.01721
Male	0.3399399	0.0532421	6.385	0

Regression - Comfort Discussing – Interaction

reg_interaction(sex_data$comfort, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.4387274	0.1194017	45.55	0
Married: True	0.0353290	0.1573261	0.225	0.82233
Age (years)	-0.0006952	0.0028939	-0.24	0.81017
Duration	-0.0020791	0.0010570	-1.967	0.04924
Male	0.3446437	0.0532405	6.473	0
Married x Age	-0.0089034	0.0034770	-2.561	0.01048

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Have More Sex When Happy

Graphs

graphs_discrete(sex_data$happy_moresex, sex_data, "More Sex When Happy")

Regression - More Sex Hhen Happy ~ Marriage

reg_simple(sex_data$happy_moresex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.4318690	0.0429588	126.444	0
Married: True	-0.3629252	0.0512766	-7.078	0

Regression - More Sex When Happy ~ Marriage, Age, Gender, Duration

reg_more(sex_data$happy_moresex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.2776496	0.0693580	90.511	0
Married: True	-0.0433788	0.0542584	-0.799	0.42405
Age (years)	-0.0241874	0.0015223	-15.889	0
Duration	0.0003706	0.0009246	0.401	0.68859
Male	0.1463213	0.0468547	3.123	0.0018

Regression - More Sex When Happy – Interaction

reg_interaction(sex_data$happy_moresex, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.8619075	0.1050098	55.822	0
Married: True	0.6255343	0.1381779	4.527	1e-05
Age (years)	-0.0134635	0.0025414	-5.298	0
Duration	0.0011242	0.0009329	1.205	0.22826
Male	0.1554121	0.0467483	3.324	0.00089
Married x Age	-0.0160622	0.0030531	-5.261	0

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Feel Happier With More Sex

Graphs

graphs_discrete(sex_data$regsex_morehappy, sex_data, "Happier With More Sex")

Regression - Feel Happier With More Sex ~ Marriage

reg_simple(sex_data$regsex_morehappy, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.5465376	0.0416587	133.142	0
Married: True	-0.3311676	0.0497247	-6.66	0

Regression -Feel Happier With More Sex ~ Marriage, Age, Gender, Duration

reg_more(sex_data$regsex_morehappy, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.8829738	0.0678255	86.737	0
Married: True	-0.1488451	0.0530596	-2.805	0.00505
Age (years)	-0.0154938	0.0014887	-10.408	0
Duration	0.0003163	0.0009042	0.35	0.72649
Male	0.5210109	0.0458194	11.371	0

Regression - Feel Happier With More Sex – Interaction

reg_interaction(sex_data$regsex_morehappy, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.5614478	0.1028079	54.096	0
Married: True	0.3684779	0.1352804	2.724	0.00648
Age (years)	-0.0072001	0.0024881	-2.894	0.00382
Duration	0.0008991	0.0009134	0.984	0.32497
Male	0.5280415	0.0457681	11.537	0
Married x Age	-0.0124222	0.0029891	-4.156	3e-05

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Feel Physically Attractive

Graphs

graphs_discrete(sex_data$PhysicallyAttractive, sex_data, "Feel Physically Attractive")

Regression - Feel Physically Attractive~ Marriage

reg_simple(sex_data$PhysicallyAttractive, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	5.9405941	0.0609770	97.423	0
Married: True	-0.4999782	0.0769465	-6.498	0

Regression - Feel Physically Attractive ~ Marriage, Age, Gender, Duration

reg_more(sex_data$PhysicallyAttractive, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.4060252	0.1045882	61.25	0
Married: True	-0.3171995	0.0857432	-3.699	0.00022
Age (years)	-0.0139702	0.0023516	-5.941	0
Duration	0.0012972	0.0010973	1.182	0.23727
Male	0.1329357	0.0767150	1.733	0.08326

Regression - Feel Physically Attractive – Interaction

reg_interaction(sex_data$PhysicallyAttractive, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	6.2193710	0.1438303	43.241	0
Married: True	0.0610548	0.2177849	0.28	0.77924
Age (years)	-0.0091526	0.0034679	-2.639	0.00837
Duration	0.0015142	0.0011027	1.373	0.16981
Male	0.1431872	0.0768613	1.863	0.06261
Married x Age	-0.0085938	0.0045489	-1.889	0.059