Utilizing Multiple Linear Regression Models to Predict Intentions to Vaccinate for COVID-19

Abstract

The purpose of this research is to develop a multiple linear regression model to predict the intention to vaccinate for COVID-19. An anonymous cross-sectional online survey utilizing Qualtrics software was conducted. Descriptive statistics and inter-correlations between predictors and criterion variables were presented. Multiple linear regression was used to analyze associations among predictors and criterion variables. Overall, results indicated that while 79% of participants reported their intentions to get vaccinated against COVID-19 when a vaccine becomes available, 21% reported not being likely to. Further, results showed that six socio-demographic, health-related, and belief factors had positive effects on intentions to vaccinate for COVID-19. Specifically, the predictive variables of the belief in the effectiveness of the COVID-19 vaccine, political party affiliation, previous influenza vaccinations, employment status, perceived knowledge of COVID-19 and the COVID-19 vaccine, as well as education level have crucial roles in predicting the dependent variable of the intention to vaccinate for COVID-19. This research contributes to our understanding of the various factors that influence the decision to vaccinate for COVID-19. Overall, the proposed regression model with the variables present in this study represents a strong effect and explains the proportion of the variability in the intention to vaccinate with over 70% accuracy. These results have important practical as well as theoretical implications for public health policymakers. With significant percentages of the population that are still hesitant to vaccinate, future studies should focus on finding this missing link and implementing any social/public health policies to level up individual intentions.