Virtual Reality Training for Police Officers: A Comparison of Training Responses in VR and Real-life Training 3 59 In addition to assessing sense of presence factors, the ITC-SOPI contains a section in which the participant’s background information is obtained. Specifically, participant characteristics such as age, level of computer experience, gaming frequency, prior experience with VR, and knowledge about VR. Statistical analysis To investigate whether VR and RL SBT elicit differences in training responses in police officers, we conducted five paired-samples t-tests using each of the training response variables (average HR, maximum HR, average activity, invested mental effort, perceived stress). To further examine whether the psychological training responses in VR were influenced by factors that are hypothesized to impact the VR experience (e.g., age, technology experience, cybersickness in VR as obtained with the ITC-SOPI), we performed two separate hierarchical multiple linear regression analyses for invested mental effort in VR and perceived stress in VR. For each of the models, the predictor variables were entered in two pre-determined steps. First, we entered participants characteristics consisting of age, VR knowledge, gaming frequency, and prior VR experience. Second, we entered the VR sense of presence factors from the ITCSOPI consisting of spatial presence, engagement, ecological validity, and negative effects. Entering the predictors in two separate steps allowed us to investigate the change in explained variance for each block of predictors and evaluate the relative importance of each predictor with each step. For each model, we conducted residual analyses to ensure no violations of assumptions. To check for multicollinearity, we used correlation coefficients above 0.7 among independent variables, Variance Inflation Factor (VIF) above 10, and Tolerance values of less than .10 as indicators for multicollinearity (Miles, 2005). To detect multivariate outliers, we used visual inspection of the scatterplot of the standardized residuals (cut-off for outliers > 3.3 or < −3.3), Mahalanobis Distance (critical value of Chi-Square for eight predictor variables = 26.13 at p = .001) and Cook’s Distance (critical value > 1; Tabachnick & Fidell, 2018) (critical value > 1; Tabachnick & Fidell, 2018). P-values of < 0.05 were considered statistically significant. Cohen’s d was calculated as an estimate for effect size. A value of d = 0.2 indicated a small effect size, a value of d = 0.5 indicated a medium effect size and a value of d = 0.8 indicated a large effect size (Cohen, 1969). All statistical analyses were performed using IBM SPSS, version 27.
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