Spss process macro

The full data set can be found in the Supplementary Materials (as Supplementary Table 1). To test H1 and H2, an ANOVA and a pair of between-subjects t-tests (separated based on condition) were performed, with the dependent variable of fSPP. An ANOVA was performed to test H3 (testing Condition A compared to Conditions B and C) on the dependent variable of the DIS. A power analysis using G^∗Power (Faul et al., 2007 (link)) indicated a total sample of 159 people would be needed to detect a medium effect size (f = 0.25) with 80% power for an ANOVA with three groups (α = 0.05). A mediational analysis was also performed to test for H4, with the SPSS PROCESS macro using 5000 bootstrap samples (Preacher and Hayes, 2008 (link)).

Using SPSS 24, we computed descriptive statistics, an analysis of variance (ANOVA), and a two-way ANOVA to explore the relationship of the variables of interest under high vs. low RC and TC (hypotheses 1–4). To test the proposed hypotheses, the influence of RC (and TC) levels (low vs. high) and conflict management styles (cooperative vs. competitive) on work engagement and emotional exhaustion were assessed using multivariate analysis of covariance (MANCOVA) and analysis of covariance (ANCOVA), whereas emotion regulation was retained as a covariate (hypotheses 5–8). As an additional analysis, we tested the moderating role of emotion regulation in the link between RC (and TC) on emotional exhaustion and work engagement, and we conducted moderation analysis using the SPSS PROCESS macro; Model 1, developed by Hayes (2012 ).

We used SPSS Statistics 22.0 and the SPSS PROCESS macro (Hayes, 2012 ) to analyze the data. After performing descriptive statistics and correlational analysis, we verified the mediating effect of the main variable. In addition, we conducted bootstrapping to verify the significance of the mediating effect. To verify the moderated mediation effect, we examined the presence of the moderating effect of the main variable (1) in the relationship between the independent variable and the dependent variable, as well as (2) in the relationship between the independent variable and the mediating variable. Additionally, we confirmed the simple slope of the moderating effect.

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 25. Descriptive statistics from SPSS were used to characterize the participants’ demographics. Pearson’s correlation analysis was used to examine the relationships between borderline pathological celebrity worship, impulsive buying intent, and empathy and the related gender differences. Additionally, we used the SPSS PROCESS macro (Hayes, 2013 ) to test our hypothesized moderated mediation model. First, the raw scores were transformed to z-scores before testing the moderated mediation effect to obtain the standardized regression coefficients; gender was coded (male = 1; female = 2). Second, PROCESS Model 4 was used to test the mediation effect of empathy. Third, PROCESS Model 59 was used to test the full moderated mediation model. Specifically, the bootstrapping method was applied to test the effects’ significance to obtain robust standard errors for parameter estimation (Chi et al., 2019 (link)). This method produced 95% bias-corrected confidence intervals (CIs) for these effects from 1,000 resamples of the data. CIs that do not contain zero indicate significant effects.