Spss amos 26

Calculations of descriptive statistics and Spearman’s correlations were performed using IBM SPSS 28.0. To test H2, mediation effects were verified by indirect effects (Williams and MacKinnon 2008 (link)) using IBM SPSS Amos 26. Bias-corrected bootstrapped point estimates for the indirect effects of FMS on mathematics achievement were estimated, considering 95% confidence intervals. Significant indirect effects were considered if 95% confidence intervals did not include zero. Bias corrected intervals supported by 5000 bootstrapping samples were used to make inferences. Bootstrapping procedures have been recommended as more efficient and powerful in detecting indirect effects than alternative approaches (Williams and MacKinnon 2008 (link)).

Statistical analyses were conducted using SPSS version 25 for Windows (RRID: SCR 016479) to determine the influence of various variables on the studied parameters. The Shapiro-Wilk test was selected as the normality test. The Kruskal–Wallis test was used to statistically compare means when data were presented as mean ± standard deviation (SD), with a significance level of p<0.05. Differences between pre-and post-treatment outcomes were evaluated using the paired t-test, while unpaired t-tests were utilized to compare changes in patient pre- and post-treatment results between groups 1 and 2. One-way ANOVA was conducted to compare the measured parameters, followed by post-hoc Tukey's test to determine significant differences among the groups. Additionally, the chi-square test was employed to identify significant correlations among demographic variables. To test the hypotheses and confirm relationships, relationships between observed and latent (unobserved) variables were conducted using IBM SPSS Amos 26. For regression (impact) testing, the study depended on the structural model using the structural modeling equation (SEM) approach.

Data were analyzed via SPSS 16.0 and IBM SPSS AMOS 26. First, correlations among the study variables were determined, and the reliability of constructs was conducted in SPSS. To analyze the conceptual model, SEM was performed using AMOS 26; the perceived epidemic risk of COVID-19 was identified as a predictor, fear and powerlessness as mediators, and information-avoidance behavior as the outcome. The fitness of the model was good [χ² (84) = 338.20; p < 0.001, RMSEA = 0.07 90% CI = (0.065, 0.082), SRMR = 0.05, NFI = 0.93, and CFI = 0.94]. A non-parametric bootstrap method (5,000 samples) was used to test the significance of the mediating effects, with a 95% CI failing to contain zero, indicating a significant mediation effect (Hu and Bentler, 1999 (link)).

To evaluate factor loading of the subscales of the COHIP-SF 19 JP, confirmatory factor analysis (CFA) was conducted utilizing SPSS AMOS 26 (IBM Corp., Armonk, NY, USA).
The goodness of fit of the explored models was evaluated using several different model indices, including χ²/DF = chi-squared/degree of freedom, RMSEA = Root Mean Square Error of Approximation, GFI = Goodness of Fit Index, AGFI = Adjusted Goodness of Fit Index, CFI = Comparative Fit Index, AIC = Akaike Information Criterion. Values for acceptable fit were determined with reference to the literature [34 ], as follows: χ²/DF ≤ 3, RMSEA ≤0.08, 0.90 ≤ GFI, 0.85 ≤ AGFI, 0.95 ≤ CFI, AIC < AIC for comparison model.

Statistical analyses were performed using IBM SPSS 27 for Mac (IBM, Corp, Armonk, NY) while confirmatory facto analysis was performed using an IBM SPSS Amos 26 (IBM, Corp, Armonk, NY). To calculate generalizability coefficients, we utilized G-String IV software (available at https://healthsci.mcmaster.ca/merit/research/g_string_v). We used descriptive statistics of mean, standard deviation (SD), frequencies, and percentages to summarize the data.

A confirmatory factor analysis was performed to evaluate the 3-factor structure of the OES in this new data set. The three factors (latent traits/unobserved factors) and their respective observed indicators (items) are as follows: Function—items 1,2,3,4; Pain—items 7,8,11,12; Social psychological—items 5,6,9,10. First, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) test and Bartlett’s Test of Sphericity were performed to assess the adequacy of the sample size for factor analysis calculation. Goodness of fit was then analyzed based on the factor loading, chi-square significance levels, relative χ² (ratio of chi-square to degrees of freedom (χ²/df), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), non-normed fit index (NNFI), root mean square error of approximation (RMSEA), and standard root mean square residual (SRMR) [19 ]. Calculation estimates were carried out using IBM SPSS AMOS 26, and values were compared to their thresholds.

This quantitative study uses the structural equation model to verify the relationship between EI as the independent variable, WE as the intermediary variable and TfC as the dependent variable, and to verify whether the intermediary effect is significant. The data were analyzed with the IBM Statistics SPSS 24 program and IBM SPSS Amos 26 software. First, we used the IBM Statistics SPSS 24 program to describe the data and the correlation between the variables. Descriptive statistics analyzes also determined the means and standard deviations of these variables. Then, structural equation modeling (SEM) Amos 26 was conducted to examine between the components and examine multiple metrics of model fit. For the structural equation model, we used several statistics to determine the fit of each model, including the chi-square, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). Finally, we used bootstrapping tests as estimators for testing mediation effects; zero was not straddled in the 95% confidence interval generated by the bias-corrected bootstrap method set to 5,000 reiterations for unstandardized and standardized estimates.