Measurement invariance of the drivers of covid-19 vaccination acceptance scale: Comparison between taiwanese and mainland chinese-speaking populations

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

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號297
期刊Vaccines
9
發行號3
DOIs
出版狀態Published - 2021 三月

All Science Journal Classification (ASJC) codes

  • 免疫學
  • 藥理
  • 藥物發現
  • 傳染性疾病
  • 藥學(醫學)

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