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The psychometric properties of motors of COVID-19 vaccination acceptance scale (MoVac-COVID19S): A dataset across five regions
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Chinese Academy of Education Big Data, Qufu Normal University, Qufu City, Shandong, China.
Kabir Medical College, Gandhara University, Peshawar, Pakistan.
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2022 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 42, article id 108103Article in journal (Refereed) Published
Abstract [en]

The novel coronavirus disease 2019 (COVID-19) continues to plague the world. Hence, there is been an effort to mitigate this virus and its effects with several means including vaccination which is one of the most effective ways of controlling the virus. However, efforts at getting people to vaccinate have met several challenges. To help with understanding the reasons underlying an individual's willingness to take COVID-19 vaccine or not, a scale called Motors of COVID-19 Vaccination Acceptance Scale (MoVac-COVID19S) was developed. To expand its usability worldwide (as it has currently been limited to only China and Taiwan), data were collected in other countries (regions) too. Therefore, this MoVac-COVID19S data is from five countries (that is, India, Ghana, Afghanistan, Taiwan, and mainland China) which cut across five regions. A total of 6053 participants across the stated countries completed the survey between January and March 2021 using a cross-sectional survey design. The different sections of the survey solicited sociodemographic information (e.g., country, age, gender, educational level, and profession) and the MoVac-COVID19S data from the participants. The data collected from this survey were analyzed using descriptive statistics, which were carried out using the IBM SPSS version 22.0.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 42, article id 108103
Keywords [en]
Afghanistan, China, COVID-19, Ghana, India, Measurement Invariance, Taiwan
National Category
Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:hj:diva-56342DOI: 10.1016/j.dib.2022.108103ISI: 000793580800015PubMedID: 35372646Scopus ID: 2-s2.0-85127815830Local ID: GOA;intsam;809054OAI: oai:DiVA.org:hj-56342DiVA, id: diva2:1655485
Note

Data article.

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2025-02-20Bibliographically approved

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Pakpour, Amir H.

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