Gender Research Proposal
Arianna Zavala
2023-01-09
Proposal Script
STEP1: Bringing data
library(rio)
lkData = "https://github.com/AriannaNKZC/UniMaGenderStudies/blob/main/data_gender_studies_fin.xlsx?raw=true"
data=import(lkData)
data$`Same Sex Marriage`## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
## [38] 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0
## [75] 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0STEP2: Exploring how is data distribution by making a graphic
library(ggplot2)
ggplot(data, aes(x = Year, fill = factor(`Same Sex Marriage`))) +
ylab("Number of Latin-American Countries") +
geom_bar(stat = "count") +
scale_fill_discrete(name = "Acceptance of Same Sex Marriage",
labels = c("no", "yes"))
This indicates that there has been an increase in Latin American countries recognizing same-sex marriage in the last 12 years.
Lets change one name variable to make it easier to work witk
names(data)[3] = "sexmarriage"
names(data)[6] = "USA"STEP 2: Logistic regresion
Binominal variable
Hypothesis one
H1. The odds of a Latin-America country legalizing same-sex marriage increase when critical countries have a high index of LGBT legislation.
Consideration of critical countries
set.seed(2019)
#first model - considering just Spain
rlog1 <- glm(sexmarriage ~ Spain, data = data, family = 'binomial')
#second model - considering just Portugal
rlog2 <- glm(sexmarriage ~ Portugal, data = data, family = 'binomial')
#third model - considering just France
rlog3 <- glm(sexmarriage ~ France, data = data, family = 'binomial')
#fourth model - considering just Germany
rlog4 <- glm(sexmarriage ~ Germany, data = data, family = 'binomial')
#fifht model = considering just USA
rlog5 <- glm(sexmarriage ~ USA, data = data, family = 'binomial')
#sixth model = considering just China
rlog6 <- glm(sexmarriage ~ China, data = data, family = 'binomial')
#seventh model = considering just Brazil
rlog7 <- glm(sexmarriage ~ Brazil, data = data, family = 'binomial')
#eight model = considering just Argentina
rlog8 <- glm(sexmarriage ~ Argentina, data = data, family = 'binomial')
#nine model - the aggrupation one
rlog9 <- glm(sexmarriage ~ Spain + Portugal + France + Germany + USA + China + Brazil + Argentina, data = data, family = 'binomial')
rlog91 <- glm(sexmarriage ~ Spain + Portugal + France + Germany + USA + Brazil + Argentina, data = data, family = 'binomial')#a nicer way to see the models
library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazerto see as group
stargazer(rlog9, rlog91, type="text",report=("vc*p"))##
## ==============================================
## Dependent variable:
## ----------------------------
## sexmarriage
## (1) (2)
## ----------------------------------------------
## Spain 0.040 0.034
## p = 0.498 p = 0.530
##
## Portugal -0.126 -0.102
## p = 0.500 p = 0.525
##
## France 0.015 0.018
## p = 0.773 p = 0.727
##
## Germany -0.106 -0.077
## p = 0.567 p = 0.598
##
## USA 0.052 0.051
## p = 0.451 p = 0.462
##
## China 0.018
## p = 0.801
##
## Brazil 0.063 0.053
## p = 0.490 p = 0.521
##
## Argentina 0.003 0.002
## p = 0.977 p = 0.979
##
## Constant 3.515 1.129
## p = 0.852 p = 0.945
##
## ----------------------------------------------
## Observations 260 260
## Log Likelihood -117.612 -117.644
## Akaike Inf. Crit. 253.223 251.287
## ==============================================
## Note: *p<0.1; **p<0.05; ***p<0.01to see individually
stargazer(rlog1,rlog2, rlog3, rlog4, rlog5, rlog6, rlog7, rlog8, type="text", report=("vc*p"))##
## ==============================================================================================================
## Dependent variable:
## --------------------------------------------------------------------------------------------
## sexmarriage
## (1) (2) (3) (4) (5) (6) (7) (8)
## --------------------------------------------------------------------------------------------------------------
## Spain 0.084***
## p = 0.005
##
## Portugal 0.150***
## p = 0.0004
##
## France 0.071***
## p = 0.0001
##
## Germany 0.093***
## p = 0.00004
##
## USA 0.040***
## p = 0.0001
##
## China 0.060***
## p = 0.0002
##
## Brazil 0.062***
## p = 0.00004
##
## Argentina 0.144***
## p = 0.0002
##
## Constant -8.047*** -14.198*** -7.048*** -9.417*** -4.625*** -4.318*** -6.539*** -12.905***
## p = 0.001 p = 0.0001 p = 0.00001 p = 0.00001 p = 0.00000 p = 0.00000 p = 0.00000 p = 0.00003
##
## --------------------------------------------------------------------------------------------------------------
## Observations 260 260 260 260 260 260 260 260
## Log Likelihood -125.057 -120.711 -118.555 -119.601 -119.474 -120.836 -119.144 -119.737
## Akaike Inf. Crit. 254.114 245.422 241.109 243.201 242.948 245.672 242.289 243.474
## ==============================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01hypothesis 2
H2. The odds of a Latin-America country legalizing same-sex marriage increased when Spain and Portugal had a high index of LGBT legislation.
Consideration of Spain and Portugal
log1 <- glm(sexmarriage ~ Spain + Portugal, data = data, family = 'binomial')
stargazer(rlog1,rlog2, log1, type="text", report=("vc*p"))##
## ==================================================
## Dependent variable:
## --------------------------------
## sexmarriage
## (1) (2) (3)
## --------------------------------------------------
## Spain 0.084*** 0.051
## p = 0.005 p = 0.107
##
## Portugal 0.150*** 0.132***
## p = 0.0004 p = 0.003
##
## Constant -8.047*** -14.198*** -16.637***
## p = 0.001 p = 0.0001 p = 0.00002
##
## --------------------------------------------------
## Observations 260 260 260
## Log Likelihood -125.057 -120.711 -119.440
## Akaike Inf. Crit. 254.114 245.422 244.881
## ==================================================
## Note: *p<0.1; **p<0.05; ***p<0.01hypothesis 3
H3. The odds of a Latin-America country legalizing same-sex marriage increased when the United States and China had a high index of LGBT legislation since they are superpowers.
hypothesis 4
H4. The odds of a Latin-America country legalizing same-sex marriage increased when the United States had a high index of LGBT legislation since they are superpowers.
Consideration of superpowers (China & US) and US
log2 <- glm(sexmarriage ~ USA + China, data = data, family = 'binomial')
stargazer(rlog5,log2, type="text",report=("vc*p"))##
## ==============================================
## Dependent variable:
## ----------------------------
## sexmarriage
## (1) (2)
## ----------------------------------------------
## USA 0.040*** 0.049*
## p = 0.0001 p = 0.089
##
## China -0.015
## p = 0.735
##
## Constant -4.625*** -4.609***
## p = 0.00000 p = 0.00000
##
## ----------------------------------------------
## Observations 260 260
## Log Likelihood -119.474 -119.417
## Akaike Inf. Crit. 242.948 244.835
## ==============================================
## Note: *p<0.1; **p<0.05; ***p<0.01hypothesis 5
H5. The odds of a Latin-America country legalizing same-sex marriage increased when Argentina and Brazil had a high index of LGBT legislation since they are superpowers.
Consideration of Latinoamerican countries (Argentina and Brazil)
log3 <- glm(sexmarriage ~ Brazil + Argentina, data = data, family = 'binomial')
stargazer(rlog7, rlog8, log3, type="text",report=("vc*p"))##
## ===================================================
## Dependent variable:
## ---------------------------------
## sexmarriage
## (1) (2) (3)
## ---------------------------------------------------
## Brazil 0.062*** 0.040
## p = 0.00004 p = 0.138
##
## Argentina 0.144*** 0.062
## p = 0.0002 p = 0.338
##
## Constant -6.539*** -12.905*** -9.648***
## p = 0.00000 p = 0.00003 p = 0.007
##
## ---------------------------------------------------
## Observations 260 260 260
## Log Likelihood -119.144 -119.737 -118.658
## Akaike Inf. Crit. 242.289 243.474 243.317
## ===================================================
## Note: *p<0.1; **p<0.05; ***p<0.01stargazer(rlog9, log1, rlog5,log2, log3, type="text",report=("vc*p"))##
## =========================================================================
## Dependent variable:
## -------------------------------------------------------
## sexmarriage
## (1) (2) (3) (4) (5)
## -------------------------------------------------------------------------
## Spain 0.040 0.051
## p = 0.498 p = 0.107
##
## Portugal -0.126 0.132***
## p = 0.500 p = 0.003
##
## France 0.015
## p = 0.773
##
## Germany -0.106
## p = 0.567
##
## USA 0.052 0.040*** 0.049*
## p = 0.451 p = 0.0001 p = 0.089
##
## China 0.018 -0.015
## p = 0.801 p = 0.735
##
## Brazil 0.063 0.040
## p = 0.490 p = 0.138
##
## Argentina 0.003 0.062
## p = 0.977 p = 0.338
##
## Constant 3.515 -16.637*** -4.625*** -4.609*** -9.648***
## p = 0.852 p = 0.00002 p = 0.00000 p = 0.00000 p = 0.007
##
## -------------------------------------------------------------------------
## Observations 260 260 260 260 260
## Log Likelihood -117.612 -119.440 -119.474 -119.417 -118.658
## Akaike Inf. Crit. 253.223 244.881 242.948 244.835 243.317
## =========================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01