*2.4. Statistical Analysis*

Descriptive analyses were conducted to describe sociodemographic characteristics, COVID-19-related aspects, and occupational balance. The categorical variables were expressed as absolute frequencies and percentages, while the continuous variables were expressed in terms of mean and standard deviation (SD). The compliance of the normality criteria of the quantitative variables was assessed by the Kolmogorov–Smirnov test. In cases where the normal distribution could not be assumed, the contributions made by Blanca et al. [33] were considered. To evaluate the association between the variation in the occupational balance and the categorical variables, the analysis of variance (ANOVA) and the post-hoc less significant differences (LSD) test were used. The effect size differences were calculated using partial eta squared (η<sup>2</sup> *p*) or Hedge's g and interpreted according to the following criteria: If 0 <sup>≤</sup> <sup>η</sup><sup>2</sup> *<sup>p</sup>* or *<sup>g</sup>* <sup>&</sup>lt; 0.01, there is no effect; if 0.01 <sup>≤</sup> <sup>η</sup><sup>2</sup> *<sup>p</sup>* or g <sup>&</sup>lt; 0.06, the effect is minimal; if 0.06 <sup>≤</sup> <sup>η</sup><sup>2</sup> *<sup>p</sup>* or g <sup>&</sup>lt; 0.14, the effect is moderate; and if <sup>η</sup><sup>2</sup> *<sup>p</sup>* or g <sup>≥</sup> 0.14, the effect is strong. The relationship between the differential score obtained in the OBQ and the quantitative variables was analyzed using the Pearson correlation. A forward stepwise multiple lineal regression analysis, adjusted by sex and age, was performed to identify possible independent predictive factors, related to COVID-19 infection, for a higher occupational balance. In this model, variables with a *p*-value < 0.05 in the univariate analysis were included. Statistical analysis was performed with SPSS version 25 software (IBM-Inc, Chicago, IL, USA). For the analysis of statistical significance, a value of *p* < 0.05 was established.
