Spss process

In this study, double-entry verification through Epidata 3.1 was used on the questionnaires, and SPSS V25.0 was used for data analysis. Descriptive statistical analysis was used to describe sample information. The Kolmogorov-Smirnov test was used to evaluate the normality of related variables. Control variables were obtained using an independent sample t-test and one-way ANOVA to compare the differences between job stress, job satisfaction, psychological capital, and sleep quality scores among participants with different demographic characteristics. Pearson correlation analysis was used to analyze the correlation between work stress, job satisfaction, sleep quality, and psychological capital. The regression and mediating factors were developed using SPSS PROCESS and the macro calculation by Preacher and Hayes (2013) (link). Work stress was the independent variable (X), and psychological capital (M), job satisfaction, and sleep quality were dependent variables (Y). The possibility of multiple collinearities among variables was also considered in this study. P < 0.05 was considered statistically significant (two-tailed).

Statistical analysis was performed using SPSS version 24. Categorical variables were presented as frequencies and percentages. Continuous variables were expressed as mean ± standard deviation (SD). Associations between variables were assessed using Pearson correlation analysis. The SPSS Process was used to test the effect of social support on coping style through psychological resilience (Model 4). The significance of the mediation effect was tested by Bootstrap and a 95% confidence interval (CI) for the mediation effect was calculated by sampling 5,000 times in the original data using repeated random sampling. If the 95%CI does not include 0, the path is significant. The conceptualized model was shown in Figure 1.

This study uses SPSS 21.0 to analyze the mediating role of emotional contagion between a leader's work passion and an employee's work passion.
Model 1 in Table 3 indicates that a leader's work passion had a significantly positive effect on an employee's work passion (β = 0.102^*, p < 0.05), which thus supports H1. As shown in Model 2 and Model 3, the positive effect of a leader's work passion on an employee's work passion was not significant (β = 0.054, p > 0.05) after adding emotional contagion into the regression equation, and emotional contagion had a positive effect on employee's work passion (β = 0.442^***, p < 0.001), which indicates that emotional contagion fully mediated the relationship between a leader's work passion and employee's work passion. Further analysis was conducted using SPSS-PROCESS to confirm the indirect effect of a leader's work passion on an employee's work passion via emotional contagion. The results show that with a formal two-tailed significance test, the indirect effect was significant (Sobel z = 2.472, p < 0.05). Bootstrap estimation confirmed the Sobel test, with 5,000 bootstrap samples and a 95% confidence interval of 0.016–0.092 around the indirect effect not containing 0. Therefore, the indirect effect of a leader's work passion on an employee's work passion via emotional contagion was significant, and H1-2 was fully supported.

We used SPSS 22.0 to calculate the correlations, descriptive statistics, and Cronbach’α coefficients for each variable in this study. Mediation and moderated mediation analysis were conducted using SPSS PROCESS (84 ). We used a bootstrapping procedure with 5,000 resamples to assess the unconditional indirect effects, which were considered significant when the 95% bias-corrected and accelerated confidence intervals (95% CI) did not contain zero.

Based on a questionnaire survey and SPSS 26.0, this study used to perform reliability and discriminant validity tests, descriptive statistics, correlation analysis, and regression analysis. This study used hierarchical regression and the Bootstrapping method in the SPSS-PROCESS program model 7 to test for mediating effects and mediating effects with moderation. To further clearly demonstrate the mediating effect with moderation, previous studies usually made a plot of the moderating effect with a simple slope of one standard deviation of the moderating variable. However, the method of grouping moderating variables can only test the difference of indirect effects under two values of leader emotional trust. Thus, we cannot show the full process of the effect of the continuous variable of moderating variables on indirect effects.

The data were manually encoded, and strict measures were taken to ensure the integrity of the data by verifying if the respondents met the inclusion criteria. Additionally, demographic information of participants who refused to join was documented to analyze potential selection biases in the dropout rate. Data cleaning was given utmost importance and was carried out meticulously, including the handling of outliers, problematic cases, missing values, and normality.
For the actual analysis, the data were examined in accordance with the purpose of the study. Multiple linear regression analysis was used to draw inferences related to hypotheses one and two. To investigate the extent to which identity commitment can moderate the relationship between emotional autonomy and wellbeing, moderation analysis using Model 1 was performed. The first two objectives were analyzed using the software SPSS v. 28, while SPSS—PROCESS was used for the third objective.

In this study, SPSS 22.0 were used to manage and analyze the data. Among them, descriptive statistics and correlation analysis were completed. Among them, the SPSS process component compiled by Hayes was used to test the mediation model (35 ). Besides, AMOS23.0 was used to construct the structural equation modeling in this study.