In the data processing in this article, the quantitative data is expressed by M (P25, P75) on the psychological state score scale, fear source identification score scale and the behavior change score scale; qualitative data is expressed by the case (%); comparison between groups is expressed by Kruskal–Wallis H test; the influencing factors of students’ psychological changes adopt hierarchical multiple regression analysis. All statistical analyses were performed using SPSS 23.0 for Windows (IBM, Somers, NY, United States). The structural equation model was constructed using AMOS 23.0 software (IBM, Somers, NY, United States) to verify the relationship between variables and the mediation effect, and the Bootstrap method was used to test the mediating effect. The difference is statistically significant when p < 0.05.
Amos 23
Manufactured by IBM
Sourced in United States
AMOS 23.0 is a comprehensive software package for advanced statistical modeling and analysis. It provides a wide range of tools for structural equation modeling, path analysis, and confirmatory factor analysis. The software is designed to handle complex multivariate data and offers advanced features for model specification, estimation, and evaluation.
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Lab products found in correlation
Spss 23, by IBM (10 mentions)
Spss 24, by IBM (5 mentions)
Spss 22, by IBM (5 mentions)
Spss 25, by IBM (4 mentions)
Spss 26, by IBM (3 mentions)
Epidata 3, by IBM (2 mentions)
Spss version 22, by IBM (2 mentions)
Spss 23.0 for window, by IBM (1 mentions)
Spss statistical software version 22, by IBM (1 mentions)
Statistical package for social sciences spss version 23, by IBM (1 mentions)
Spss win 21, by IBM (1 mentions)
Spss software 23, by IBM (1 mentions)
Canoco 5, by Microcomputer Power (1 mentions)
Spss statistics 17, by IBM (1 mentions)
Spss statistics 27, by IBM (1 mentions)
Spss statistics 23, by IBM (1 mentions)
Spss 27, by IBM (1 mentions)
Mplus 7, by IBM (1 mentions)
Spss statistics 25, by IBM (1 mentions)
Spss v23, by IBM (1 mentions)
62 protocols using amos 23
1
Analyzing Students' Psychological Changes
2
Validating Positive Psychological Capital
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Data were analyzed using SPSS 23.0 and AMOS 23.0 software (IBM Corp., Armonk, NY, USA). First, a descriptive statistical analysis was conducted to determine study participants’ general characteristics. Second, the positive psychological capital test was psychometrically validated by performing confirmatory factor analysis and reliability analysis. Third, a two-way (3 × 2) analysis of variance and Pearson’s correlation analysis were performed to examine the difference between the concentration levels and positive psychological capital before and after the exercise routine between the three participant groups. Furthermore, a Bonferroni post hoc test was performed to determine if the interaction effect was statistically significant. Statistical significance was set at p < 0.05.
3
Mediation Analysis of Latent Variables
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All data analyses were performed using SPSS 26.0 and Amos 23 (IBM Inc., Armonk, NY, United States). First, descriptive data were received using SPSS 26.0, and correlations variables were calculated using Pearson’s correlations. Second, according to Baron and Kenny (1986) (link), we analyzed the mediation effects using two measurement models to examine how well the indicators represented each latent variable. Second, we tested the hypothesized relationships among latent variables. Maximum likelihood (ML) estimation was used to test the two structural models in the Amos 23.0 program. When Tucker-Lewis index (TLI) >0.90, comparative fit index (CFI) >0.90, and Root Mean Square Error of Approximation (RMSEA) <0.06, the model fits well, according to Hu and Bentler (1999) (link). We followed the stepwise method to structure the best-fitting model for the mediated effects and bootstrapping with 5,000 replications to measure the chain mediation model. All data analyses were two-tailed, with significance levels of P < 0.01 and P < 0.05.
4
Assessing Factors Influencing Student Well-Being
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An assessment battery was formed by gathering the five scales to measure the research variables, self-concept, stress (factors, symptoms), coping, and attachment. In order to reduce the effects of possible factors out of the scope of the current study, the university students were considered as a convenient sample, since they are in a similar environment, and relatively more homogeneous community, compared to the society in general. Participants were a convenience sample from university students. The survey was administered to 10-30-person groups in classroom conditions and lasted 30 minutes on average. Statistical analyses of the means, standard deviations, correlations among the research variables and their mediating roles, were conducted by IBM SPSS 23, and the path analysis was conducted by using IBM AMOS 23.
5
Evaluating Theoretical Model of Online Health Behaviors
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To test the first hypothesized relationships, we applied path analysis with Amos 23 (IBM Corporation). To obtain a comprehensive model fit, we included the suggested indices by Hair et al [19 ]: the χ2 statistic, the ratio of χ2 to its df, the standardized root mean residual (SRMR<0.08), the Tucker-Lewis index (TLI>0.90), the comparative fit index (CFI>0.95), and the root mean square error of approximation (RMSEA<0.06). These fit indices are typically used to represent the 3 categories of model fit: absolute, parsimonious, and incremental. We added covariates between the health status variables. The correlations between internet attitude, material access, internet skills, internet health use, and health outcomes were not high enough to cause multicollinearity concerns. To test for moderator effects of age and education, we applied multigroup analyses. First, the model was estimated for each of the subgroups separately to confirm its acceptable fit for each group. Then, multigroup analysis was used to test the significance of the χ2 difference.
6
Confirmatory Factor Analysis Procedure
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Confirmatory factor analysisIn the present study, the confirmatory factor analysis was applied. The AMOS 23 (IBM Corp., Armonk, NY, USA) program was used for confirmatory factor analysis.
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Confirmatory factor analysisIn the present study, the confirmatory factor analysis was applied. The AMOS 23 (IBM Corp., Armonk, NY, USA) program was used for confirmatory factor analysis.
7
Statistical Analysis with IBM Tools
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Data were analyzed with IBM Amos 23 software and the IBM SPPS 24 software.
8
Mediation Analysis of Psychological Factors
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All data analyses were performed using SPSS 26.0 and Amos 23 (IBM Inc., Armonk, NY, USA). First, descriptive data were received using SPSS 26.0, and correlations variables were calculated using Pearson’s correlations. Second, according to Baron and Kenny [40 (link)], we analyzed the mediation effects using two measurement models to examine how well the indicators represented each latent variable. Second, we tested the hypothesized relationships among latent variables. Maximum likelihood (ML) estimation was used to test the two structural models in the Amos 23.0 program. When TLI > 0.90, CFI > 0.90, and RMSEA < 0.06, the model fits well, according to Hu and Bentler [41 ]. We followed the stepwise method to structure the best-fitting model for the mediated effects and bootstrapping with 5,000 replications to measure the chain mediation model. All data analyses were two-tailed, with significance levels of P < 0.01 and P < 0.05.
9
Data Entry and Statistical Analysis Protocol
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EpiData 3.0 (Odense. Denmark) was used for data entry, and two researchers completed data entry separately to ensure data accuracy. The IBM SPSS statistical software version 22.0 (Armonk, NY, USA) for Windows was used to perform basic descriptive analyses. Descriptive statistics were reported as mean ± standard deviation (SD) for variables with normal distributions and as median (interquartile range, IQR) for variables with skewed distributions. The reliability (internal consistency) was tested using the Cronbach’s alpha coefficient, which indicates the connectedness of items within a scale. Analysis of variance, the chi-squared test, and the rank sum test were used to analyze differences between the GSQ group and the PSQ group for each factor. Stratified linear regression analysis was used to create the regression equations. A p value of <0.05 was accepted as statistically significant. The IBM AMOS 23 (Armonk, NY, USA) program was used to analyze the relationships between the constructs involved in the structural model. The bootstrap self-sampling count was set to 5000 for validation. Once the theoretical model was developed, path analysis was performed based on the relationships of the matrix identified via the structural equation analysis.
10
Validation of the Problematic YouTube Use Scale
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All statistical analyses were carried out using SPSS 23 (IBM Corp., Armonk, NY, USA) and AMOS 23 (IBM Corp.). First, CFA was used to evaluate the factor structure of the PYUS. In order to determine goodness of fit of the CFA, root mean square residuals (RMSEA), standardized root mean square residuals (SRMR), comparative fit index (CFI), and goodness of fit index (GFI) were utilized. According to Hu and Bentler [41 ], RMSEA and SRMR lower than 0.05 is good and RMSEA and SRMR lower than 0.08 is adequate; CFI and GFI higher than 0.95 is good and CFI and GFI higher than 0.90 is acceptable. Frequency and descriptive statistics were computed with regard to gender, age, YouTube use, and mukbang watching. Following this, Pearson’s correlation and t-tests were applied to examine the correlation coefficients between PYU, PMW, depression, and loneliness, and significance of the score differences between genders. Finally, path analysis was conducted in order to examine possible mediating role of study variables using a saturated model [37 ]. More specifically, depression and loneliness were included as independent variables, PMW as the mediator, and PYU as the outcome variable into the model. Path analysis was carried out using maximum likelihood discrepancy with 5,000 bootstrapped samples and 95% biascorrected confidence intervals.
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