Statistical methods
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REGRESSION
Statistical analyses were conducted in R (Version 4.1, Due to the large number of potential covariates and the explorative nature of the research questions two through four, it was not feasible to set up hypothesis driven models as these do not converge with the given sample size. We therefore opted to use a data driven approach and identify the best fitting model based on Akaike’s Information Criterion which penalises additional covariates Akaike’s Information Criterion (AIC; To ensure that presented effects are not carried by outliers, for each resulting model, we trimmed observations based on residuals beyond ±2 standard deviations of the model prediction and refitted the model. Each model then underwent model criticism to ensure that reported models fulfil regression assumptions of independence of observations as well as a normal and homoscedastic distribution of residuals. Usually, model fit is evaluated by the squared correlation between the observed and the fitted values (As several of the fitted models did not show intervention effects, the Results section will focus on those models that show effects related to the gaming conditions and our research aims (see 