### INTRODUCTION

### METHODS

### Subjects and Design

### Instruments

### Statistical Analysis

^{2}/df), root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker–Lewis index (TLI), standardized root mean square residual (SRMR), coefficient of determination (CD), Akaike information criterion (AIC), and Bayesian information criterion (BIC) [18]. Modification indices, which show additional relationships by pairing error terms, were used to improve the goodness-of-fit [25]. Once the model was estimated, the Stata software recommended the parameters that covaried, and data fit was improved by pairing error terms. However, only those with a theoretical basis should be accepted. Then, a bifactor model was tested to determine whether the measure was sufficiently unidimensional to support using a total score, while still accounting for the multidimensionality that was found [26].

*r*>0.80) [27]. Construct validity was defined as the presence of substantial and significant correlations between different instruments designed to assess a common (convergent) and different (divergent) construct [28]. For this purpose, 2-tailed Pearson correlation coefficients were calculated for the PSS versions and the ECQ to ascertain the convergent validity, whereas the UWES was used to study their divergent validity. Positive correlations were considered to furnish evidence of convergent validity, while negative correlations and the absence of correlation were considered evidence of an absence of validity [29]. In addition, convergent and discriminant internal validity was also studied. The average variance extracted (AVE) and composite reliability (CR) were used to inform convergent validity, according to Hair et al. [23], Values of AVE >0.5 and CR≥0.7 indicate adequate convergent validity. To determine discriminant validity (whether each component measures a different dimension from the rest), phi-square coefficients (φ

^{2}) were calculated to measure the degree of association between components. If φ

^{2}is close to 0 and less than the AVE, discriminant validity has been achieved [30].

### RESULTS

### Demographic Characteristics

### Exploratory Factor Analysis

^{2}(45)=968.852,

*p*<0.001). The EFA resulted in a 2-component model according to eigenvalues, a scree plot, and information criteria. In the bidimensional solution, all items had loadings >0.60. The first component, named “stress,” showed high loadings for items 1, 2, 3, 6, 9, and 10, which explained 34% of the variance. Component 2, named “coping,” had high loadings for items 4, 5, 7, and 8, explaining 26% of the variance. The U and SMC showed the absence of multicollinearity and singularity; thus, no item had to be removed.

^{2}(6)=179.000,

*p*<0.001). However, this version did not achieve a good KMO value (0.572).

### Confirmatory Factor Analysis

^{2}/df=2.111, RMSEA= 0.064, CFI=0.958, TLI=0.942, SRMR=0.050, CD=0.964, and low AIC and BIC. Figure 1 demonstrates the measurement model. Moreover, CFA supported a bifactor structure for PSS-10, which demonstrated a superior fit. Since the items assessed conceptually linked processes, the suggestions made by the Stata software were accepted: item 4 (“Confident about your ability to handle your personal problems?”) was grouped with item 7 (“Dealt successfully with irritating life hassles?”). The standardized regressions were significant (T>1.96), ranging from 0.61 to 0.75 for component 1 and from 0.57 to 0.81 for component 2. The inter-component covariation was 0.29.

### Internal Consistency

### Convergent and Divergent Validity

*r*=-0.35,

*p*<0.001), vigor (

*r*=-0.37,

*p*<0.001), dedication (

*r*=-0.37,

*p*<0.001), and absorption (

*r*=-0.27,

*p*<0.001). Lower correlation coefficients were found between the total PSS-4 and vigor (

*r*=-0.33,

*p*< 0.001), dedication (

*r*=-0.30,

*p*<0.001), absorption (

*r*=-0.20,

*p*<0.001), and total UWES (

*r*=-0.30,

*p*<0.001).

*r*=0.61,

*p*<0.001), executive attention (

*r*=0.54,

*p*<0.001), behavioral flexibility (

*r*= 0.53,

*p*<0.001), and inhibitory control (

*r*=0.43,

*p*<0.001). Similarly, the PSS-4 score was also positively correlated with the ECQ (

*r*=0.49,

*p*<0.001), executive attention (

*r*=0.42,

*p*<0.001), behavioral flexibility (

*r*=0.44,

*p*<0.001), and inhibitory control (

*r*=0.32,

*p*<0.001).

^{2}value of 0.085 indicated that there was no problem with internal discriminant validity. It was not possible to estimate these statistics for the PSS-4 because the 2-component model did not converge.