2.4. Data Analysis

A non-experimental, cross-sectional design was adopted in this study. An a priori power analysis was carried out using G*Power version 3.1.9.7 [37] to identify the minimum sample size necessary to test the study hypotheses. The results indicated that the required sample size to achieve over 90% power for detecting significant differences in a model of linear regression with 15 independent variables at a significance level of 5% was n = 200. The obtained sample size of n = 205 is adequate to test the study hypotheses.

Preliminary analyses (means, standard deviations, partial correlations, Cronbach’s alphas) for all variables were performed first. Demographic differences in coronavirus impacts, mental health, and resilience were examined using independent samples t-tests and one-way ANOVAs. The potential role of GHQ as moderator in the relationship between COVID-19 impacts and resilience was tested using PROCESS v3.3 (model 1) [38]. With this method, significance is inferred if there is a significant interaction between the moderator and the independent variable, while conditional effects of the predictor variable at low, medium, and high levels of the moderator further elucidate the relationship between the various variables. All data analyses were performed using SPSS v22.0 (IBM, Armonk, NY, USA).

In the next section, the results of the study are described in detail. Findings from the descriptive analyses are presented first to set the scene for results from further inferential analyses to be demonstrated subsequently.