## 6 Analysis of the Data  
For statistical data analysis, the study employed SPSS statistical package version 23 and AMOS version 22. The distribution of respondents' demographic information was determined through descriptive analysis, i.e., frequency, percentage, and mean values. Then, Cronbach's alpha was used to determine the total and subscale reliabilities, while the contribution of each item to the internal consistency was assessed through item-total and item-subscale correlations [19; 20]. Item discrimination was tested through a critical ratio to ascertain if the items could be used to effectively measure the differences among participants. The critical ratio is the \(t\)-value estimated when comparing the means between two groups, i.e., the upper 27% and the lower 27% of the sample [19; 21; 22]. In addition, a \(t\) value greater than 1.96 (a significance level of 0.05) indicates that the item is able to measure differences among participants. Next, exploratory factor analysis (EFA) was used to restructure the MBI-HSS based on data collected for healthcare professionals in Saudi Arabia. Furthermore, this study employed confirmatory factor analysis (CFA) to evaluate the model fit to the data. In terms of sample size, a total of 888 respondents is sufficient for both EFA and CFA. For EFA, a study by [23] advised that sample sizes of 50, 100, 200, 300, 500, and 1000 or more are very poor, poor, fair, good, very good, and excellent, respectively. Authors [24; 25] suggested sampling at least 100 subjects. Moreover, [26] suggested sampling three to six subjects per variable, [24] suggested at least five subjects per variable, and [27; 28] suggested sampling at least 10 subjects per variable. For CFA using AMOS, [29] suggested a minimum of 10 respondents per estimated parameter as a sufficient sample size; [30] indicated 200 to be a sufficient sample size. Based on the recommendations above, the data were then randomly allocated to two subsamples using the function to select a random sample of cases in SPSS, resulting in 300 randomly selected samples for EFA and 588 randomly selected samples for CFA. The strategy was to use EFA (\(n\) = 300) to identify the underlying factor in the MBI-HSS, and CFA (\(n\) = 588) was then used to confirm the factor structure. In the EFA analysis, the Kaiser-Meyer-Olkin test was used to assess the sampling adequacy, while the Bartlett test of sphericity was used to determine the adequacy of the sampling prior to analysis of the factors [31]. Exploratory factor analysis was employed to identify a new structure of the MDI-HSS from the gathered data. In addition, the study retained the factor number as well as items using the following criteria: (1) eigenvalues \(>\)1, (2) factor structure of at least four items, (3) factor loadings above 0.4, and (4) items that did not load to more than one factor [15; 22; 31]. Furthermore, principal component analysis (PCA) with direct oblimin rotation (oblique rotations) was used to determine the factor structure of the MBI-HSS, with the assumption that factors were correlated in the factor model. With the new factorial structure of the MBI-HSS, CFA was performed with the second subsample to confirm the revised structure among healthcare professionals in the Gulf Region. The maximum likelihood estimation was used to estimate the factor loading of the items and model. Then, the model fit to the data was assessed using goodness-of-fit indices as follows: (1) a non-significant chi square, (2) a goodness-of-fit index (GFI) \(>\)0.90, (3) an adjusted goodness-of fit index (AGFI) \(>\)0.80 and (4) a root mean square error of approximation (RMSEA) \(<\)0.08 [15].