Files in this item

FilesDescriptionFormat

application/pdf

application/pdfPredictors of C ... and Non-Student Adults.pdf (1MB)
Undergraduate ThesisPDF

application/pdf

application/pdfThesis Certification Form.pdf (171kB)
Thesis Certification FormPDF

Description

Title:Predictors of COVID-19 Vaccine Intention among U.S. Undergraduates and Non-Student Adults
Author(s):Gupta, Suryaa
Contributor(s):Laurent, Sean (Undergraduate Faculty Advisor)
Department / Program:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Genre:Thesis
Subject(s):COVID-19
Physical Health
Applied Social Science
Abstract:As COVID-19 has disrupted societies across the world, scientists have been rapidly working to develop a COVID-19 vaccine in order to achieve herd immunity and save lives. Understanding the factors impacting whether people will receive this vaccine is therefore of utmost importance. Past work on vaccine hesitancy—declining of vaccinations despite availability—has focused on hypothetical, lower-threat or eradicated (e.g., influenza, polio) diseases in the US. The current research takes a timelier approach, extending past work by examining predictors of COVID-19 vaccine intention during the ongoing pandemic. Study 1 (N=346 undergraduate college students) was administered online as the pandemic escalated in the US (February-April 2020). Study 2 (preregistered; N=676) was administered to an age-diverse panel of US adults in July 2020. As hypothesized, receiving flu shots and vaccine confidence were significant unique predictors of COVID-19 vaccine intentions in both studies. Collective responsibility uniquely predicted COVID-19 vaccine intention in Study 1 but not in Study 2, and constraint, self-community overlap, perceived vaccine danger, and disease vulnerability uniquely predicted vaccine intention in Study 2 but not Study 1. Vaccine knowledge, complacency, calculation, analytical thinking, conspiracy belief, mistrust in science, and political ideology were not significant predictors in regression models containing all predictors.
Issue Date:2021
Genre:Dissertation / Thesis
Type:Text
Language:English
URI:http://hdl.handle.net/2142/110002
Sponsor:The Office of Undergraduate Research
Rights Information:Copyright 2021 Suryaa Gupta
Date Available in IDEALS:2021-05-28


This item appears in the following Collection(s)

Item Statistics