Analysis

Description

Rationale

1

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.

2

Detecting outliers

To detect outliers with Mahalanobis distance.

3

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.

4

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) .

5

Spearman rho Correlations, and means

Descriptive statistics and burnout scores.

6

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) .

7

Test the full SEM model fit

To evaluate if the structural model fit is adequate.

8

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) .

9

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.

10

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.