*2.3. Data Analysis*

For the basic descriptive analysis and cross-tabs, the statistical software package IBM SPSS® was used in version 25.0 for Windows. The normality and homogeneity of the sample was established through the Kolmogorov–Smirnov test.

Following this, binary logistic regression (odds ratio and 95% confidence intervals) was performed. High resilience provided the exposure variable as it was one of the specific objectives being considered. The model examined its association with socio-demographic, work, and academic variables. Likewise, Cox and Snell's R2 was employed to examine the model fit, whilst the Hosmer–Lemeshow test was used to determine the goodness of fit. Variables were introduced into the model manually if they met the criteria of having shown significant associations in prior bivariate analysis carried out through crosstabs. Variables that did not show significance at this prior stage were excluded from the model (*p* ≥ 0.05).

Given that the proposed analysis was binary, participants with low and moderate resilience were categorised into a single "not high resilience" group. Likewise, given the dichotomous nature of analysis, the remaining variables were categorised as follows: Sex (0 = female and 1 = male), professional occupation (0 = employed and 1 = not employed), level of study (0 = without higher education and 1 = higher education), professional occupation related with the emergency services (0 = not emergency services and 1 = emergency services), responsible for dependents (0 = does not have dependents and 1 = has dependents), associated with individuals with COVID-19 (0 = not associated with anybody with COVID-19 and 1 = associated with somebody with COVID-19), and time-period (0 = period from the 15–22 March and 1 = period from the 23–31 March).
