Multivariate Normality Test

To test for the multivariate normality assumption with Mardia’s multivariate kurtosis and skewness, Henze–Zirkler’s consistent test, Doornik–Hansen omnibus test, and Energy test.


Detecting outliers

To detect outliers with Mahalanobis distance.


CFA on each study measures

To confirm the factor structure of MBI-HSS and CD-RISC 10 in this special Greek population (OTs), ensuring no misspecifications in the SEM model.


Cronbach’s alpha & McDonald’s omega (1999)

To calculate the internal consistency reliability and the model-based reliability of MBI-HSS and CD-RISC 10, before the SEM measurement model with ωt coefficient (McDonald, 1999) .


Spearman rho Correlations, and means

Descriptive statistics and burnout scores.


Test the SEM measurement model and model-based reliability

To evaluate the measurement model fit with a CFA and to evaluate reliability of the measurement variables and the reliability latent variables with ωt coefficient (McDonald, 1999) .


Test the full SEM model fit

To evaluate if the structural model fit is adequate.


A priori & post hoc power analysis of the full SEM model

To calculate the required sample for achieving a power of 80% to reject a wrong model. An alpha level of 0.05 was assumed with an RMSEA misspecification of 0.05 (MacCallum, Browne, & Sugawara, 1996) .


Primary Hypotheses testing (Primary hypotheses H1 - H5)

To test the hypothesized relationships between 4 latent variables with 4 direct associations, and 1 mediation. No covariates were used.


Latent Profile Analysis (LPA) (Secondary Hypotheses H1 - H2)

Use the scores of the latent variables of the SEM model to profile the sample and check if the profiles that emerged confirm the hypotheses tested with the SEM model.