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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorStok, Marijn
dc.contributor.authorSilva de Sousa, Mariana da
dc.date.accessioned2023-07-27T00:00:56Z
dc.date.available2023-07-27T00:00:56Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/44323
dc.description.abstractThis study investigates to what extent risk perception and governmental trust correlate with students’ compliance with COVID-19 masking and testing behavior over time. A multi-regression data analysis was conducted using an existing longitudinal quantitative set that researched 100 freshman psychology students at Konstanz University in Germany. Findings indicated that high-risk perception was prevalent when the reported death cases were high and there was high trust in the government present among the students but no correlation with the stringency of measures. Furthermore, the results also note that adherence to masking and testing decreased as the reported death cases decreased and the stringency of measures decreased. This can indicate that students adhere more to preventative measures when the situation is serious (leading to death) and the government puts more stringency of measures in place. However, this study found no significant relationship between risk perception, governmental trust, and student adherence to COVID-19 preventative measures.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectA multi-regression analysis using an existing longitudinal quantitative set was conducted to investigate to what extent risk perception and governmental trust correlate with students’ compliance with COVID-19 masking and testing behavior over time.
dc.titleStudent POV: The Relationship Dynamic of Factors and Adherence to COVID-19 Preventative Measures
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsCOVID-19, preventative measures, risk-perception, governmental trust, student adherence, masking behavior, testing behavior
dc.subject.courseuuSocial Policy and Public Health
dc.thesis.id20240


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