Statistical analysis
Following data collection, the survey responses were coded and entered
into a customized database using the Statistical Package for the Social
Sciences (SPSS), Version 24.0 (IBM Corp., Armonk, New York, USA).
Descriptive results were presented as means and standard deviations for
continuous variables and percentages for qualitative variables. A one
way ANOVA test was performed to analyse regional differences in
perception scores. All tests were two-tailed. A P-value of
<0.05 was considered statistically significant.
Correlation between awareness score (out of 20) and the COVID-19
statistics of cases and deaths announced at the beginning and end of the
study period (12th to 22th of April
2020) for the countries which had at least one case at the beginning of
the study was also conducted.
Linear regression was used to screen for the factors affecting
participants’ awareness score about coronavirus pandemic versus chosen
independent variables in the study , i.e. age, area of residency (city
and urban areas or rural areas), country, region, having children,
educational level, university type (the university where participants
had studied and/or are studying at; public versus private), years of
experience, number of professional education workshops attended during
the last year, work setting, source of previous knowledge about
epidemics and pandemics, source of updates about COVID-19 management,
and current satisfaction with knowledge about COVID-19. These predictor
measures (independent variables) were considered as candidates for
linear regression modelling if they had a significance value p ≤0.25 in
univariate analyses. The candidate variables were subjected to backward
linear regression, where finally only the significant variables (i.e. p
≤0.05) were retained with the model equation constant. Variables were
selected after checking their independence, where tolerance values
> 0.1 and Variance Inflation Factor (VIF) values were
< 10 were selected to indicate the absence of
multicollinearity between the independent variables in regression
analysis. The homoscedasticity assumption for multiple linear regression
was checked using Breusch-Pagan test, with a p≥0.05 indicating the
absence of heteroscedasticity.