*2.11. Preprocessing and Factor Analyses*

For active engagement, attributes, and functions, mean scores of the original factors were computed twice per person: once for the questionnaires describing the situation before and once during the lockdown. All analyses were performed using R Statistics 3.5.1.

The factor structure of the other questionnaires was determined with a factor analysis using oblimin rotation. Parallel analysis was used to determine the number of factors to keep in the factor analysis. Items with loadings < |0.3| were excluded (three items) as well as one factor that did not explain much of the variance and consisted of the two items on social contacts which only applied to a small percentage of the participants. After repeating the factor analysis without these items, factor scores were extracted, and latent variables were created to be used in the statistical models. Descriptive statistics, the details of the factor analysis, and the correlations of all latent variables can be found in the Supplementary Materials. The multicollinearity of each model was checked with variance inflation factors (VIF), the vif() function, accepting VIFs < 3.
