library(haven)
library(foreign) 
library(readr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(broom)
library(car)
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
library(readr) 
library(MASS) 
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
library(car) 
library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(alr3)
## 
## Attaching package: 'alr3'
## The following object is masked from 'package:MASS':
## 
##     forbes
library(zoo)
library(nortest)
library(plotrix)
library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:plotrix':
## 
##     rescale
## The following object is masked from 'package:readr':
## 
##     col_factor
library(tableone)
library(Weighted.Desc.Stat)
library(mitools)
library(survey)
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
## 
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
## 
##     dotchart
library(questionr)
library(grid)
library(Matrix)
library(survival)
anes2016<-read_dta("C:\\Users\\Jaire\\OneDrive\\Desktop\\Stats for Dem Data 2\\Homework 2\\ANES2016.dta")

Research Question:

How do demographic characteristics influence support and opposition toward the Patient Protection and Affordable Care Act of 2010?

Coding

Outcome

anes2016$favACA<-as.factor(anes2016$V161113)
anes2016$favACA<- recode(anes2016$favACA, recodes = "1=1; 2:3=0; -8=NA")
table(anes2016$favACA)
## 
##    0    1 
## 2689 1578

Predictors

# Sex
anes2016$males<-as.factor(anes2016$V161342)
anes2016$males<- recode(anes2016$males, recodes = "1=1; 2:3=0; -9=NA")
table(anes2016$males)
## 
##    0    1 
## 2243 1987
# Age
anes2016$Agec<-recode(anes2016$V161267, recodes = "-9:-8=NA")
table(anes2016$Agec)
## 
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 
## 28 39 54 53 44 56 55 63 70 70 60 60 69 78 86 73 81 79 69 85 66 75 51 66 67 51 
## 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 
## 46 70 83 66 53 67 59 72 59 81 78 78 88 76 94 96 89 80 69 72 75 69 88 78 94 55 
## 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 
## 54 49 53 38 45 34 41 26 29 21 24 24 24 12 13 12 14  9 12  6 27
# non-Hispanic white race
anes2016$nhwhite<-as.factor(anes2016$V161310X)
anes2016$nhwhite<-recode(anes2016$nhwhite, recodes = "1=1; 2:6=0;-9=NA")
table(anes2016$nhwhite)
## 
##    0    1 
## 1200 3038
# non-Hispanic black race
anes2016$nhblack<-as.factor(anes2016$V161310X)
anes2016$nhblack<-recode(anes2016$nhblack, recodes = "1=0; 2=1; 3:6=0;-9=NA")
table(anes2016$nhblack)
## 
##    0    1 
## 3840  398
# Hispanic race

anes2016$Hispanic<-as.factor(anes2016$V161310X)
anes2016$Hispanic<-recode(anes2016$Hispanic, recodes = "5=1; 1:4=0; 6=0;-9=NA")
table(anes2016$Hispanic)
## 
##    0    1 
## 3788  450
# other race
anes2016$otherrace<-as.factor(anes2016$V161310X)
anes2016$otherrace<-recode(anes2016$otherrace, recodes = "3=1; 4=1; 6=1; 1:2=0; 5=0;-9=NA")
table(anes2016$otherrace)
## 
##    0    1 
## 3886  352
# famliy income
anes2016$familyincome<-recode(anes2016$V168023, recodes = "-9:-1=NA")
table(anes2016$familyincome)
## 
##   1   2   3   4   5   6   7 
##  87 183 247 246 152 153  23

Survey Design and Analytical Dataset

sub<-dplyr::select(anes2016, favACA,nhwhite,nhblack,Hispanic,otherrace,familyincome,males) %>%
  filter(complete.cases(.))


options(survey.lonely.psu = "adjust")
des<-svydesign(nest = TRUE, ids=~anes2016$V160202, strata =~anes2016$V160201, weights =~anes2016$V160101, data=anes2016)

Analysis

Logit Regression

fit.logit<-svyglm(favACA~nhwhite+nhblack+Hispanic+otherrace+familyincome+males, 
                  design= des,
                  family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
summary(fit.logit)
## 
## Call:
## svyglm(formula = favACA ~ nhwhite + nhblack + Hispanic + otherrace + 
##     familyincome + males, design = des, family = binomial)
## 
## Survey design:
## svydesign(nest = TRUE, ids = ~anes2016$V160202, strata = ~anes2016$V160201, 
##     weights = ~anes2016$V160101, data = anes2016)
## 
## Coefficients: (1 not defined because of singularities)
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.37289    0.35982  -1.036  0.30893   
## nhwhite1     -0.72861    0.25426  -2.866  0.00781 **
## nhblack1      1.20917    0.36501   3.313  0.00256 **
## Hispanic1    -0.08143    0.33244  -0.245  0.80829   
## familyincome  0.17127    0.06418   2.668  0.01253 * 
## males1       -0.21918    0.16977  -1.291  0.20724   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 0.9377897)
## 
## Number of Fisher Scoring iterations: 4

Odds Ratios and Confidence Intervals

knitr::kable(data.frame(OR = exp(coef(fit.logit)), ci=exp(confint(fit.logit))))
OR ci.2.5.. ci.97.5..
(Intercept) 0.6887433 0.3402316 1.3942484
nhwhite1 0.4825803 0.2931833 0.7943282
nhblack1 3.3507180 1.6384659 6.8523312
Hispanic1 0.9217997 0.4804622 1.7685363
familyincome 1.1868127 1.0465207 1.3459116
males1 0.8031767 0.5758431 1.1202578

Results

Research Question:

How do demographic characteristics influence support and opposition toward the Patient Protection and Affordable Care Act of 2010?

Based on the logit model, non-Hispanic whites are significantly less likely to favor the Patient Protection and Affordable Care Act of 2010 than non-Hispanic other race. Non-Hispanic blacks are significantly more likely to favor the Patient Protection and Affordable Care Act of 2010 than non-Hispanic other race. Additionally, as family income increases the likelihood of favoring the Patient Protection and Affordable Care Act of 2010 significantly increases.