Measurement Invariance of the Drivers of COVID-19 Vaccination Acceptance Scale: Comparison between Taiwanese and Mainland Chinese-Speaking Populations.

Abstract

The impacts of novel coronavirus disease-2019 (COVID-19) on human life continue to be serious. To control the spread of COVID-19, the production of effective vaccines is likely to be one of the best solutions. However, vaccination hesitancy may decrease individuals' willingness to get vaccinated. The Drivers of COVID-19 Vaccination Acceptance Scale (DrVac-COVID19S) was recently developed to help healthcare professionals and researchers better understand vaccination acceptance. The present study examined whether DrVac-COVID19S is measurement invariant across different subgroups (Taiwanese vs. mainland Chinese university students; males vs. females; and health-related program majors vs. non-health-related program majors). Taiwanese (n = 761; mean age = 25.51 years; standard deviation (SD) = 6.42; 63.5% females) and mainland Chinese university students (n = 3145; mean age = 20.72 years; SD = 2.06; 50.2% females) were recruited using an online survey between 5 January and 21 February 2021. Factor structure and measurement invariance of the two DrVac-COVID19S scales (nine-item and 12-item) were tested using confirmatory factor analysis (CFA). The findings indicated that the DrVac-COVID19S had a four-factor structure and was measurement invariant across the subgroups. The DrVac-COVID19S's four-factor structure was supported by the CFA results is a practical and valid instrument to quickly capture university students' willingness to get COVID-19 vaccination. Moreover, the DrVac-COVID19S can be used to compare university students' underlying reasons to get COVID-19 vaccination among different subgroups.

• Authors: Ya-Chin Yeh, I-Hua Chen, Daniel K Ahorsu, Nai-Ying Ko, Kuan-Lin Chen, Ping-Chia Li, Cheng-Fang Yen, Chung-Ying Lin, Mark D Griffiths, Amir H Pakpour
• Journal: Vaccines (Vaccines (Basel)) Vol. 9 Issue 3 (Mar 22 2021) ISSN: 2076-393X [Print] Switzerland
• PMID: 33810036 (Publication Type: Journal Article)