I need an expert in R CODING
Budget: ₹1,500 – ₹12,500 INR
Overview
Two years since it first emerged, the Covid-19 pandemic continues to disrupt lives and livelihoods across the planet. Almost a full year ago, vaccines started in a few select countries and, despite a very uneven distribution across countries, over 4 billion people have received at least one dose (according to the New York Times). However, several of the countries that have an abundance of available vaccines are plateauing in their vaccine rollout. The United States is one of those countries: despite being the first country to start vaccinating, only around 60 percent of the total population (70 percent of the vaccine-eligible population) is fully vaccinated.
There are several potential reasons why people might hesitate to get vaccinated, or let their children be vaccinated, against Covid-19. Some people may be worried about the fast rollout of the vaccine, others are worried about side effects or efficacy, and yet others are affected by vaccine-critical news coverage or partisan politics. Regardless of the cause of concern, however, researchers have noted that various social-economic or demographic factors may influence individual decision-making. Poverty, employment, education, and income can all affect an individual’s decision to get vaccinated or not.
Your task in this analysis will be to investigate, using OLS regression, which demographic factors best explain an individuals’s vaccination decisions for themselves and their children. To that end, you have been provided with a dataset, hps.csv, which is extracted from one of the waves of the Household Pulse Survey, conducted by the US Census Bureau during between September 29 and October 11, 2021. It includes survey responses from several thousand individuals. None of them have been vaccinated yet, nor have any children in the household received the vaccine yet.
Dependent variable: You may choose one of two potential dependent variables for your analysis (see important note below on their construction). Either you can study an individual’s decision to get themselves vaccinated (GETVAC) or get their kids vaccinated (GETKIDVAC) Be sure to adjust your theory about potential explanatory variables accordingly. Both variables are structured in the same way, they are interval level variables, that measure an individual’s willingness to get themselves or their children vaccinated. The scale ranges from 0-100, where 0 indicates an individual that will defi- nitely get (themselves or their kids) vaccinated and 100 indicates an individual who will definitely not get themselves or their kids vaccinated.
Independent variables: You will have a variety of potential explanatory variables to choose from. A codebook has been provided as a separate .pdf document, which lists the variable names in the dataset, the wording of the question, and the way the reponses were coded. This codebook has been slightly modified from the original version to fit the variables included. **NOTE: Be careful to check the codebook when interpreting your results. Sometimes, variables are coded such that 1=No and 0=Yes and sometimes it is the opposite (i.e., 1=Yes and 0=No).**
1
Task
Your goal is to estimate the effect of a number of individual characteristics (of your own choosing) on willingness to be vaccinated or have your children vaccinated, as measured by the two potential dependent variables and to persuade your readers of the accuracy and robustness of your estimate. After an introduction and overview, your paper should roughly follow the following trajectory:
• Formulate null and alternative hypotheses for testing.
• Be sure to include descriptive statistics of your chosen variables. • Design a statistical model to test your hypothesis.
• Present your models and interpret your findings.
Be sure to discuss the substantive effect in a meaningful way. Could income or education influence your result? What about other race, employment, or other socio-economic variables? Are there inter- action effects
Two years since it first emerged, the Covid-19 pandemic continues to disrupt lives and livelihoods across the planet. Almost a full year ago, vaccines started in a few select countries and, despite a very uneven distribution across countries, over 4 billion people have received at least one dose (according to the New York Times). However, several of the countries that have an abundance of available vaccines are plateauing in their vaccine rollout. The United States is one of those countries: despite being the first country to start vaccinating, only around 60 percent of the total population (70 percent of the vaccine-eligible population) is fully vaccinated.
There are several potential reasons why people might hesitate to get vaccinated, or let their children be vaccinated, against Covid-19. Some people may be worried about the fast rollout of the vaccine, others are worried about side effects or efficacy, and yet others are affected by vaccine-critical news coverage or partisan politics. Regardless of the cause of concern, however, researchers have noted that various social-economic or demographic factors may influence individual decision-making. Poverty, employment, education, and income can all affect an individual’s decision to get vaccinated or not.
Your task in this analysis will be to investigate, using OLS regression, which demographic factors best explain an individuals’s vaccination decisions for themselves and their children. To that end, you have been provided with a dataset, hps.csv, which is extracted from one of the waves of the Household Pulse Survey, conducted by the US Census Bureau during between September 29 and October 11, 2021. It includes survey responses from several thousand individuals. None of them have been vaccinated yet, nor have any children in the household received the vaccine yet.
Dependent variable: You may choose one of two potential dependent variables for your analysis (see important note below on their construction). Either you can study an individual’s decision to get themselves vaccinated (GETVAC) or get their kids vaccinated (GETKIDVAC) Be sure to adjust your theory about potential explanatory variables accordingly. Both variables are structured in the same way, they are interval level variables, that measure an individual’s willingness to get themselves or their children vaccinated. The scale ranges from 0-100, where 0 indicates an individual that will defi- nitely get (themselves or their kids) vaccinated and 100 indicates an individual who will definitely not get themselves or their kids vaccinated.
Independent variables: You will have a variety of potential explanatory variables to choose from. A codebook has been provided as a separate .pdf document, which lists the variable names in the dataset, the wording of the question, and the way the reponses were coded. This codebook has been slightly modified from the original version to fit the variables included. **NOTE: Be careful to check the codebook when interpreting your results. Sometimes, variables are coded such that 1=No and 0=Yes and sometimes it is the opposite (i.e., 1=Yes and 0=No).**
1
Task
Your goal is to estimate the effect of a number of individual characteristics (of your own choosing) on willingness to be vaccinated or have your children vaccinated, as measured by the two potential dependent variables and to persuade your readers of the accuracy and robustness of your estimate. After an introduction and overview, your paper should roughly follow the following trajectory:
• Formulate null and alternative hypotheses for testing.
• Be sure to include descriptive statistics of your chosen variables. • Design a statistical model to test your hypothesis.
• Present your models and interpret your findings.
Be sure to discuss the substantive effect in a meaningful way. Could income or education influence your result? What about other race, employment, or other socio-economic variables? Are there inter- action effects