In this study, we applied SPSS 25.0 (IBM, Armonk, NY, USA) and the Hayes SPSS macro program PROCESS 3.3 (http://www.afhayes.com , accessed on 24 October 2021) to analyze the data. This PROCESS macro for SPSS is specifically designed to examine sophisticated models involving both mediating and moderating variables [46 ]. The analyses were conducted in three steps. Firstly, we calculated descriptive statistics and correlation analysis for each variable. Secondly, we used the PROCESS macro (Model 4) to test the mediating role played by envy in the relationship between weight-related/competency-related teasing and internet harassment. Finally, we used Model 8 to test the moderated mediation models, investigating the moderating role acted by the Zhong-Yong thinking style. The mediation and moderating effects were examined by using the bias-corrected percentile bootstrap method. A bootstrap sample of 5000 was drawn, obtaining 95% confidence intervals (CI). If the 95% confidence interval excludes zero, it means that the effects are statistically significant. All variables were standardized prior to the formal data processing.
Process 3
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Process 3.5 is a high-performance laboratory equipment designed for precise sample processing. It features advanced technology for efficient and reliable data collection. The core function of Process 3.5 is to provide accurate and consistent results for a variety of laboratory applications.
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17 protocols using process 3
1
Examining Mediating and Moderating Factors in Internet Harassment
2
Moderation Analysis of Gray Matter Volume
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Using the regions-of-interest tool within CAT12.5 (r1363), we extracted estimated mean GMV for each participant based on the neuromorphometrics atlas. These volumes of interest (VOI) were dependent variables in moderation analyses conducted in PROCESS 3.3 [57 ] for SPSS (Version 25.0, IBM Corp., Armonk, NY). Interactions of Total PLE × distress for Perceptual and Delusional distress severity were examined as estimators of VOI. Due to the low item and score range, we refrained from including Negative symptoms in moderation analyses. We corrected coefficient p values for multiple comparisons for the number of dependent variables (VOI) for each PLE subscale using FDR adjusted p values. FDR corrections for multiple comparisons [58 ] were carried out in R [59 ].
3
Delay Discounting and Relapse Risk in Recovery
4
Bedtime Procrastination, Sleep Hygiene, and Quality
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Data analyses were processed using SPSS 21.0 (IBM Corp., Armonk, NY, USA) and PROCESS 3.4. First, the comparisons between bedtime procrastination, sleep hygiene awareness and sleep quality according to the MEQ scores were tested using analysis of covariance (ANCOVA) with gender and age as covariates. Next, Pearson’s correlations were performed to determine the links between variables of interest. The third step was to test the mediation model and moderated mediation model using the SPSS macro PROCESS version 3.4 (model 4 and model 59) suggested by Hayes with the bootstrapping method [5000 samples]. A significant indirect effect was considered to be established when the 95% bias-corrected CIs did not contain 0 [53 ]. Finally, to further understand the nature of the moderation effect, conditional direct and indirect effects (“simple slopes”) were estimated by using the “pick-a-point” approach [54 (link)], with the sample plus and minus 1 SD from the mean representing “high” and “low” sleep hygiene awareness.
5
Mediating Role of Insomnia and Compassion Fatigue in PTSD
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Descriptive analysis was used for the basic sociodemographic characteristics of the participants, whereas correlation analysis was used to examine the associations among research variables. SPSS 24.0 (IBM Crop) and a PROCESS 3.2 macro were used to analyze the research variables. Statistical significance was set to P < 0.05. Moreover, the study employed one-way ANOVA to analyze differences between sociodemographic variables in PTSD. Multiple linear regression and the PROCESS macro were used to verify the mediating effect of insomnia severity and the moderating effect of compassion fatigue on all paths of the model. Finally, the study used 95% bootstrap confidence intervals (95% CI) based on 5,000 bootstrapped samples.
6
Reliability and Validity of Paper Folding
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First, the reliability and construct validity of the paper folding task were examined using the factor analysis. Second, the age and gender effects in the map-use and paper folding tasks were explored by MANOVA analysis with age (3) and gender (2) as independent variables and SA and folding skills as dependent variables. Third, the relationships between the study variables were explored using the correlation analysis. Fourth, the possible contributors to young children’s folding performance and its predictive power of map-use performance were investigated by two sets of hierarchical regression analyses. Last, based on the above analyses, a bootstrapping analysis using IBM SPSS Statistics version 23.0 and macro-program PROCESS 3.2 was conducted to test the mediation effect of the paper-folding performance. The bias-corrected bootstrap method with 5,000 resamples was employed to calculate the 95% confidence intervals (CI).
7
Mediation Analysis of Pain and Mental Health
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All analyses were performed using the Statistical Package for the Social Sciences (SPSS; version 25). Descriptive statistics were computed to provide information about the demographic characteristics and for the independent variable, mediators, and dependent variable. Correlation analysis was conducted to examine relationships among variables. Finally, a serial mediation analysis was conducted to examine physical health QOL and ability to participate as mediators between pain intensity and mental health QOL. The SPSS PROCESS 3.4.1 [34 ] was used to conduct mediation analysis. A bootstrapping approach was also used to test the significance of indirect effects of pain intensity on mental health QOL with 5000 bootstrap samples through (a) physical health QOL, (b) ability to participate in social roles and activities, and (c) both physical health QOL and ability to participate. Although there is not a consensus how many bootstrap samples should be generated, researchers recommended at least 5000 resamples for final reporting [35 (link)].
8
Serial Mediation Analysis with SPSS
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We conducted descriptive analyses and Pearson correlations with SPSS 26.0. The statistical significance of the mediation effects of the serial multiple mediation model tested in the study was investigated by using the ordinary least squares regression method. Analyses were conducted through SPSS macro PROCESS 3.4.1 (model 6) [67 ], which allowed us to estimate simultaneously the indirect effects of successive mediators in a single model. We generated 5000 bootstrapped samples to estimate the confidence interval of the model effect. A 95% confidence interval without zero indicates statistical significance.
9
Exploring Mediators of Burnout in Teachers
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First, the Harman single-factor test and variance inflation factor (VIF) values were examined to ensure that there was no serious common method deviation or multicollinearity concern. Then, the chain mediation effects were tested using bias-corrected bootstrapping with 5000 resamples to compute 95% confidence intervals (CIs). If the 95% CI for the indirect effect estimate excluded zero, the mediation effect was considered statistically significant. Multiple regression analyses were also conducted to identify the coefficients between major variables in the chain mediation model incorporating (1) control variables (gender, school phase, school quality, highest education, years of teaching, homeroom teacher status); (2) an independent variable (external accountability); (3) mediators (internal accountability, surface acting/deep acting/expression of naturally felt emotions); and (4) the outcome variable burnout. Hypotheses were tested along the line, and SPSS 26 and Process 3.4 were applied for data analysis.
10
Well-being Correlates and Predictors
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In this study, a cross-sectional design was used with convenience samples mated by professional characteristics. Correlations, regressions, and mediational analyses were conducted amongst explanatory and explained variables of well-being, using SPSS 24 and process 3.4, Model 4. Scores were standardized and the correlations weighted by the inverse of the variance using the CMA program (Borenstein et al., 2014 ) for estimate a global effect size in this study.
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