1— title: “SMI205 Replication Project (2023)” author: “220175229” date: “22/05/2023” output: html_document: code_download: true toc: true toc_depth: 2 toc_float: collapsed: false smooth_scroll: true —
Does the factor of Age have an impact on Vaccine Hesitancy?
Rpubs link: https://rpubs.com/mic21rc
GitHub Repository: https://github.com/220175229/SMI205-Assessment-2/tree/main
Study Preregistration form: https://rpubs.com/mic21rc/1186388
Information about this replication project
- Replication project based on paper
Bullock, J., Lane, J. E., & Shults, F. L. (2022). What causes COVID-19 vaccine hesitancy? Ignorance and the lack of bliss in the United Kingdom. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01092-w
- Replication method:
- Own replication following methods section of the paper
Workspace setup
YAML settings
output: html_document: code_download: true toc: true toc_depth: 2 toc_float: collapsed: false smooth_scroll: true
Global settings of R chunks
# Global options
opts_chunk$set(echo=TRUE,
cache=TRUE,
comment=NA,
message=FALSE,
warning=FALSE)
knitr::opts_knit$set(root.dir = "/Users/rosiecave/Desktop/SMI205_Assessment_2/Replication_project")Libraries
# All used libraries
library(rmarkdown)
library(knitr)
library(tidyverse)
library(psych)
library(foreign)
library(haven)
library(janitor)
library(glmmTMB)
library(psych)
library(Matrix)
library(sjPlot)
library(ggplot2)
library(stats)Versions of used packages
$rmarkdown
[1] '2.25'
$knitr
[1] '1.45'My enviroment
[1] "R version 4.3.2 (2023-10-31)"1. Introduction
This report will aim to further explore factors that affect the likelihood to take made up or unavailable vaccination, which was first studied in Vaccine Hesitancy (2022). Within the original report there is a focus on if where people receive their information from has an impact, especially regarding social media. One of the main aims of this study was to investigate social media’s effect on people’s choices and fears, seeing whether their view on vaccinations can be warped. They believed that different social media such as Twitter, Facebook and Instagram would have effects on people’s knowledge about and willingness to take the vaccines.
For my replication project I will be extending the investigation on fake/ unavailable vaccinations by adding in the factor of age. I believe this follows on nicely from the current study as age will have an impact on where people receive their information from, especially in regards to social media. Age has been featured in many different studies on vaccine hesitancy and it has been found that vaccines are understood and approached differently depending on age group (Hudson and Montelpare, 2021). My predictions are no different, I think age will have impacts on the likelihood to agree to taking the vaccinations.
For example, a study conducted by Troiano and Nardi (2021) found that 86.1% of students or 77.6% of the general population had decided to take a covid vaccination. Although both are fairly high, this presents an almost 10% difference in the likelihood of taking. Students in the UK are on a ratio of 4:1 for being under 21 compared to a “mature” student (above 21) (Bolton, 2024). This therefore suggests the likelihood of choosing to vaccinate is higher among younger age groups, leading me to believe this will be the same during my investigation.
I believe that this will create an interesting perspective in relation to social media usage, how much it’s believed and how much it is followed. It must be considered that different age groups will use different media more and are therefore more influenced by it (Bontcheva, Gorrell and Wessels, 2013). For example, older age groups are much less likely to use social media than younger generations, especially apps like Instagram.
According to research carried out by Freese and Peterson (2017), there are 4 different forms or types of replication. They are verifiability, robustness, repeatability and generalizability. The interpretation stems from how and why the replication has taken place. Verifiability occurs when the author’s intentions are questioned and robustness tests whether certain decisions were appropriate by replicating, both more critiquing the old project. Whereas repeatability investigates how similar the report is when repeated and generalizability checks whether it can be used in other environments (Freese and Peterson, 2017). I believe my replication tests the robustness of the original research, as I am questioning whether age should have been an added variable and if it makes a difference to the final model and result.
2. Data and methods
2.1. Data
The data used in both the original report and my replication was collected solely for the investigation. It is primary data taken from an online survey created by Bullock, Lane and Shults (2022) and is not officially, or directly referenced at any point, a link is just provided. The survey was composed of various questions on age, ethnicity, gender, political and economic beliefs, various social media usage then finally eagerness, willingness, and hesitance to take various covid vaccinations (Bullock, Lane and Shults, 2022). The survey gained 537 responses in total from UK adults.
The only direct changes I made to the original data set was the exclusion of any participants with NA answers to one or more of my independent or dependent variables and removing the one observation that was from the US. The sample size I was left with is 476. To my vaccine dependent variables, however, I did carry out transformations as I felt the order in which they were numerically sorted did not make sense for my analysis. I therefore made their numerical ordering 1 = strongly agreed, 2 = agreed, 3 = slightly agreed, 4 = slightly disagreed, 5 = disagreed and 6 = strongly disagreed. I also chose to make transformations to the age factor, changing it into a categorical variable instead of numerical. I believe this just made it easier to see trends across the groups. I decided to sort into 3 categories under 35s, 36-50 and then 50+.
Below are summary statistics for the 4 vaccine variables and a boxplot for my age variable
2.2. Methods
The method I used, and the method used in the original paper is OLS regression. A simple form of linear regression frequently used during research. Regression techniques are used by researchers to examine the specific effects variables have on one another and they are the most widely used statistical tools for social research (Fox, 2015). OLS regression was chosen due to easily presenting a comparison between demographic factors and their effect on knowledge and willingness to take vaccinations without overcomplicating.
In order to conduct my analysis and compare answers between the 4 different vaccinations, I ran 4 separate regression tables. Each vaccination Medicare, Thernaos, Sputnik, Sinovac therefore each have their own regression tables, but these will not be presented in the final project. I found in order to keep the 4 vaccine results separate this was the simplest method to take. They were then compiled at the end to create one regression model containing values for all 4 vaccinations. I will be using P-values to determine statistical significance and estimates to see the effect each independent variable had on likelihood to take the 4 vaccinations.
3. Results
|
Would Take Vaccine Medicare USA |
Would Take Vaccine Theranos USA |
Would Take Vaccine Sputnik VRUSSIA |
Would Take Vaccine Sinovac CHINA |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.03 | 2.33 – 3.73 | <0.001 | 3.07 | 2.37 – 3.77 | <0.001 | 3.63 | 2.90 – 4.36 | <0.001 | 3.52 | 2.79 – 4.26 | <0.001 |
| FreqTwitter | 0.06 | -0.00 – 0.12 | 0.060 | 0.06 | -0.00 – 0.12 | 0.064 | 0.03 | -0.03 – 0.09 | 0.345 | 0.03 | -0.03 – 0.09 | 0.371 |
| FreqFacebook | 0.04 | -0.02 – 0.10 | 0.195 | 0.04 | -0.03 – 0.10 | 0.256 | 0.00 | -0.06 – 0.07 | 0.909 | 0.01 | -0.05 – 0.08 | 0.721 |
| FreqReddit | 0.03 | -0.05 – 0.12 | 0.473 | 0.04 | -0.05 – 0.12 | 0.392 | -0.01 | -0.09 – 0.08 | 0.896 | 0.00 | -0.09 – 0.09 | 0.955 |
| FreqInsta | 0.00 | -0.06 – 0.06 | 0.970 | -0.00 | -0.07 – 0.06 | 0.925 | 0.04 | -0.02 – 0.11 | 0.220 | 0.02 | -0.05 – 0.09 | 0.541 |
| politics social | 0.00 | -0.01 – 0.01 | 0.878 | 0.00 | -0.01 – 0.01 | 0.611 | 0.00 | -0.01 – 0.02 | 0.428 | 0.00 | -0.01 – 0.02 | 0.435 |
| politics economics | 0.01 | -0.00 – 0.02 | 0.149 | 0.01 | -0.00 – 0.02 | 0.158 | 0.01 | -0.00 – 0.02 | 0.212 | 0.01 | -0.00 – 0.02 | 0.074 |
| age group | -0.29 | -0.48 – -0.10 | 0.003 | -0.31 | -0.49 – -0.12 | 0.001 | -0.17 | -0.37 – 0.02 | 0.085 | -0.18 | -0.38 – 0.02 | 0.077 |
| Observations | 476 | 476 | 476 | 476 | ||||||||
| R2 / R2 adjusted | 0.043 / 0.029 | 0.052 / 0.038 | 0.035 / 0.020 | 0.049 / 0.034 | ||||||||
Age had statistically significant impacts on both Medicare and Theranos, and notably low estimates for Sputnik and Sinovac (although not statistically significant). Age had the biggest influence on whether or not someone would take Theranos, with a P-value of 0.001 and an estimate of -0.31. This implies that a younger age group is more likely to have agreed to take the made up vaccination. This is similar for the Medicare vaccination which also has a statistically significant P-value of 0.003 and a negative estimate, of -0.29, implying a correlation between younger age groups and willingness to take the vaccination.
For Sputnik and Sinovac age is not presented as statistically significant, with P-values of 0.085 and 0.077 respectively. But these are still low, which does suggest a possible correlation but cannot be claimed with total confidence. The estimates are again negative, just like for Medicare and Theranos, suggesting lower age groups are more likely to agree to taking the vaccination.
One possible reason Sputnik and Sinovac cannot be directly compared to Medicare and Theranos, is the fact they have much less willingness to be taken overall. This can be seen in the descriptive statistics in the data section, presented as bar graphs. It can also be seen in the estimates of the intercepts in the final regression table and in the original report. The lower the intercept estimate the more likely it was to be taken overall and Sputnik and Sinovac were significantly less than Medicare and Theranos. This difference can also be spotted across other variables, with Medicare and Theranos almost always presenting more significant P-values and estimates. In the original report this is put down to people being more skeptical or fearing foreign vaccinations (Bullock, Lane and Shults, 2022. pg.5). Although this may have had no effect and is a theory that cannot be proven by the current model, it could offer a possible explanation for their continuous difference in all results.
Overall, the rest of my final model presents similar results to the original report. In terms of social media, Twitter is viewed as having the largest effect on all the vaccinations, showing as statistically significant for Medicare and Thernanos. An increase in the use of twitter is presented as causing people to be more likely to take the made up vaccinations due to positive estimates across all vaccinations. Another similarity to the original report is that social political view has the smallest effect on all 4 of the vaccinations with estimate changes of 0 across all 4. My final model, however, does not and could not present the same results as the original report. This is due to a difference in observations that were included, 324 observations were used in the original report compared to my 476. But again, there is no explanation as to why this was within the methodology.
4. Conclusions
My findings overall aligned with my hypothesis, that age would have an effect on the likelihood of taking the 4 made up or unavailable vaccinations. In my results two statistically significant P-values, for Medicare and Thernaos, and notable estimate values for all 4 vaccinations give a large indication of willingness changing with age. I found and have concluded that the younger age group of my study were more willing to agree to taking the vaccinations, shown by the negative estimates.
As previously mentioned, age has been featured in many different studies on vaccine hesitancy and it came as a shock that it wasn’t considered in the original report. I received different results, due to adding in a new variable, but due to nothing else being changed there was not a large different across the other variables included.
I was expecting to find a difference in younger generations compared to older ones, due to Troiano and Nardi (2021) along with Hudson and Montelpare (2021) both studies I earlier examined. These state that vaccines are understood and approached differently depending on age group, and that age is a vital factor when investigating vaccine hesitancy. Especially for vaccines during the Covid 19 pandemic due to social media being such a huge factor in people’s lives during that time.
References
Bolton, P. (2024). Higher education student numbers, House of Commons Library, [online] (7857). Available at: https://commonslibrary.parliament.uk/research-briefings/cbp-7857/.
Bontcheva, K., Gorrell, G. and Wessels, B. (2013). Social Media and Information Overload: Survey Results, [online] Available at: https://arxiv.org/pdf/1306.0813 [Accessed 17 May 2024].
Bullock, J., Lane, J.E. and Shults, F.L. (2022). What causes COVID-19 vaccine hesitancy? Ignorance and the lack of bliss in the United Kingdom, Humanities and Social Sciences Communications, 9(1). doi:https://doi.org/10.1057/s41599-022-01092-w.
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models, [online] Google Books. SAGE Publications. Available at: https://books.google.co.uk/books?hl=en&lr=&id=3wrwCQAAQBAJ&oi=fnd&pg=PT15&ots=3B6sFdxj9k&sig=buBHtHoJJm5C6qfO_LBsd9ywr7A&redir_esc=y#v=onepage&q&f=false [Accessed 27 May 2024].
Freese, J., & Peterson, D. (2017). Replication in social science. Annual Review of Sociology, 43, 147-165, doi: 10.1146.
Hudson, A. and Montelpare, W.J. (2021). Predictors of Vaccine Hesitancy: Implications for COVID-19 Public Health Messaging, International Journal of Environmental Research and Public Health, 18(15), p.8054. doi:https://doi.org/10.3390/ijerph18158054
Troiano, G. and Nardi, A. (2021). Vaccine hesitancy in the era of COVID-19, Public Health, 194(1), pp.245–251. doi:https://doi.org/10.1016/j.puhe.2021.02.025.
Appendix
Appendix 1. My enviroment (full information)
# Detailed information about my environment
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/London
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.45 rmarkdown_2.25
loaded via a namespace (and not attached):
[1] digest_0.6.34 R6_2.5.1 codetools_0.2-19 fastmap_1.1.1
[5] xfun_0.42 cachem_1.0.8 htmltools_0.5.7 lifecycle_1.0.4
[9] cli_3.6.2 sass_0.4.8 jquerylib_0.1.4 compiler_4.3.2
[13] rstudioapi_0.15.0 tools_4.3.2 evaluate_0.23 bslib_0.6.1
[17] rlang_1.1.3 jsonlite_1.8.8 Appendix 2. Entire R code used in the project
# Opening key libraries first
library(rmarkdown)
library(knitr)
# Global options
opts_chunk$set(echo=TRUE,
cache=TRUE,
comment=NA,
message=FALSE,
warning=FALSE)
knitr::opts_knit$set(root.dir = "/Users/rosiecave/Desktop/SMI205_Assessment_2/Replication_project")
# All used libraries
library(rmarkdown)
library(knitr)
library(tidyverse)
library(psych)
library(foreign)
library(haven)
library(janitor)
library(glmmTMB)
library(psych)
library(Matrix)
library(sjPlot)
library(ggplot2)
library(stats)
# Versions of used packages
packages <- c("rmarkdown", "knitr")
names(packages) <- packages
lapply(packages, packageVersion)
# What is my R version?
version[['version.string']]
library(readr)
vaccinedata1 <- read_csv("DataExport_CLEANED.csv")
vaccinedata <- subset(vaccinedata1,(Country != "United States"))
na.omit(vaccinedata)
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_MedicareUSA) +
geom_bar(fill="lightblue") +
labs(x = "Responses to whether they would take Medicare", y = "value",
title = "Agreement with the statement 'I would take the vaccination Medicare if offered'")
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_TheranosUSA) +
geom_bar(fill="lightblue") +
labs(x = "Responses to whether they would take Theranos", y = "value",
title = "Agreement with the statement 'I would take the vaccination Theranos if offered'")
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_SputnikVRUSSIA) +
geom_bar(fill="lightblue") +
labs(x = "Responses to whether they would take Sputnik", y = "value",
title = "Agreement with the statement 'I would take the vaccination Sputnik if offered'")
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_SinovacCHINA) +
geom_bar(fill="lightblue") +
labs(x = "Responses to whether they would take Sinovac", y = "value",
title = "Agreement with the statement 'I would take the vaccination Sinovac if offered'")
ggplot(vaccinedata, aes(x=DOB, y=Country)) +
geom_boxplot(fill="lightblue") +
labs(title = "A boxplot presenting the data of birth varibable", x = "Year of birth", y = " ")
vaccinedata[vaccinedata$DOB > 1989, "age_group"] <- ">35"
vaccinedata[vaccinedata$DOB < 1988 & vaccinedata$DOB >1974, "age_group"] <- "36-50"
vaccinedata[vaccinedata$DOB < 1973, "age_group"] <- "50+"
vaccinedata$age_group <- factor(vaccinedata$age_group, levels=c('>35', '36-50', '50+'))
vaccinedata$age_group <- as.numeric(vaccinedata$age_group)
vaccinedata$FreqTwitter <- as.numeric(as.factor(vaccinedata$FreqTwitter))
vaccinedata$FreqFacebook <- as.numeric(as.factor(vaccinedata$FreqFacebook))
vaccinedata$FreqReddit <- as.numeric(as.factor(vaccinedata$FreqReddit))
vaccinedata$FreqInsta <- as.numeric(as.factor(vaccinedata$FreqInsta))
vaccinedata$politics_social <- as.numeric(vaccinedata$politics_social)
vaccinedata$politics_economics <- as.numeric(vaccinedata$politics_economics)
vaccinedata$WouldTakeVaccine_MedicareUSA <- factor(vaccinedata$WouldTakeVaccine_MedicareUSA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_MedicareUSA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_MedicareUSA))
modmedicare <- lm(data = vaccinedata,
WouldTakeVaccine_MedicareUSA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics +
age_group)
vaccinedata$WouldTakeVaccine_TheranosUSA <- factor(vaccinedata$WouldTakeVaccine_TheranosUSA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_TheranosUSA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_TheranosUSA))
modtheranos <- lm(data = vaccinedata,
WouldTakeVaccine_TheranosUSA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics+
age_group)
vaccinedata$WouldTakeVaccine_SputnikVRUSSIA <- factor(vaccinedata$WouldTakeVaccine_SputnikVRUSSIA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_SputnikVRUSSIA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_SputnikVRUSSIA))
modspuntnik <- lm(data = vaccinedata,
WouldTakeVaccine_SputnikVRUSSIA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics +
age_group)
vaccinedata$WouldTakeVaccine_SinovacCHINA <- factor(vaccinedata$WouldTakeVaccine_SinovacCHINA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_SinovacCHINA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_SinovacCHINA))
modsinovac <- lm(data = vaccinedata,
WouldTakeVaccine_SinovacCHINA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics+
age_group)
tab_model(modmedicare, modtheranos, modspuntnik, modsinovac)
# Detailed information about my environment
sessionInfo()
#calling required packages
library(tidyverse)
library(psych)
library(foreign)
library(haven)
library(janitor)
library(glmmTMB)
library(psych)
library(Matrix)
library(sjPlot)
library(ggplot2)
library(stats)
#accessing the dataset
library(readr)
vaccinedata <- read_csv("Replication_project/DataExport_CLEANED.csv")
View(vaccinedata)
#finding class of all required variables
class(vaccinedata$WouldTakeVaccine_MedicareUSA)
class(vaccinedata$FreqTwitter)
class(vaccinedata$FreqFacebook)
class(vaccinedata$FreqReddit)
class(vaccinedata$FreqInsta)
class(vaccinedata$politics_social)
class(vaccinedata$politics_economics)
class(vaccinedata$age_group)
#changing class to numeric
vaccinedata$FreqTwitter <- as.numeric(as.factor(vaccinedata$FreqTwitter))
vaccinedata$FreqFacebook <- as.numeric(as.factor(vaccinedata$FreqFacebook))
vaccinedata$FreqReddit <- as.numeric(as.factor(vaccinedata$FreqReddit))
vaccinedata$FreqInsta <- as.numeric(as.factor(vaccinedata$FreqInsta))
vaccinedata$politics_social <- as.numeric(vaccinedata$politics_social)
vaccinedata$politics_economics <- as.numeric(vaccinedata$politics_economics)
#removing any NAs
na.omit(vaccinedata)
#removing the one US observation
vaccinedata <- subset(vaccinedata,(Country != "United States"))
#boxplot for the date of birth variable
ggplot(vaccinedata, aes(x=DOB, y=Country)) +
geom_boxplot(fill="lightblue") +
labs(title = "A boxplot presenting the data of birth varibable", x = "Year of birth", y = " ")
#changing my age variable into categories
library(lubridate)
vaccinedata[vaccinedata$DOB > 1989, "age_group"] <- ">35"
vaccinedata[vaccinedata$DOB < 1988 & vaccinedata$DOB >1974, "age_group"] <- "36-50"
vaccinedata[vaccinedata$DOB < 1973, "age_group"] <- "50+"
#changing the age groups into a numerical order
vaccinedata$age_group <- factor(vaccinedata$age_group, levels=c('>35', '36-50', '50+'))
vaccinedata$age_group <- as.numeric(vaccinedata$age_group)
#MEDICARE
#Frequency graph on responses
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_MedicareUSA) +
geom_bar() +
labs(x = "Responses to whether they would take Medicare", y = "value",
title = "Agreement with the statement 'I would take the vaccination Medicare if offered'")
#Reordering the factors before changing to numerical
vaccinedata$WouldTakeVaccine_MedicareUSA <- factor(vaccinedata$WouldTakeVaccine_MedicareUSA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
#Changing responses to numerical
vaccinedata$WouldTakeVaccine_MedicareUSA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_MedicareUSA))
#Running OLS regression
modmedicare <- lm(data = vaccinedata,
WouldTakeVaccine_MedicareUSA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics +
age_group)
#viewing the model (tab_model creates a more aesthetic version)
summary(modmedicare)
tab_model(modmedicare)
#THERANOS
#for code comments please view MEDICARE section as code is the same
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_TheranosUSA) +
geom_bar() +
labs(x = "Responses to whether they would take Theranos", y = "value",
title = "Agreement with the statement 'I would take the vaccination Theranos if offered'")
vaccinedata$WouldTakeVaccine_TheranosUSA <- factor(vaccinedata$WouldTakeVaccine_TheranosUSA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_TheranosUSA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_TheranosUSA))
modtheranos <- lm(data = vaccinedata,
WouldTakeVaccine_TheranosUSA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics+
age_group)
summary(modtheranos)
tab_model(modtheranos)
#SPUTNIK
#for code comments please view MEDICARE section as code is the same
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_SputnikVRUSSIA) +
geom_bar() +
labs(x = "Responses to whether they would take Sputnik", y = "value",
title = "Agreement with the statement 'I would take the vaccination Sputnik if offered'")
vaccinedata$WouldTakeVaccine_SputnikVRUSSIA <- factor(vaccinedata$WouldTakeVaccine_SputnikVRUSSIA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_SputnikVRUSSIA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_SputnikVRUSSIA))
modspuntnik <- lm(data = vaccinedata,
WouldTakeVaccine_SputnikVRUSSIA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics +
age_group)
summary(modspuntnik)
tab_model(modspuntnik)
#SINOVAC
#for code comments please view MEDICARE section as code is the same
ggplot(data = vaccinedata) + aes(x = WouldTakeVaccine_SinovacCHINA) +
geom_bar() +
labs(x = "Responses to whether they would take Sinovac", y = "value",
title = "Agreement with the statement 'I would take the vaccination Sinovac if offered'")
vaccinedata$WouldTakeVaccine_SinovacCHINA <- factor(vaccinedata$WouldTakeVaccine_SinovacCHINA, levels=c('Strongly agree', 'Agree', 'Slightly agree', 'Slightly disagree', 'Disagree', 'Strongly disagree'))
vaccinedata$WouldTakeVaccine_SinovacCHINA <- as.numeric(as.factor(vaccinedata$WouldTakeVaccine_SinovacCHINA))
modsinovac <- lm(data = vaccinedata,
WouldTakeVaccine_SinovacCHINA ~ FreqTwitter +
FreqFacebook +
FreqReddit +
FreqInsta +
politics_social +
politics_economics+
age_group)
summary(modsinovac)
tab_model(modsinovac)
#combining models to create final one
tab_model(modmedicare, modtheranos, modspuntnik, modsinovac)