The collected data were analyzed by using the SPSS/WIN 21.0 program (International Business Machines Corp., Armonk, NY, USA) and the AMOS 21.0 program. Structural equation modeling is a multivariate statistical framework that incorporates regression and path analysis, which is used to model multiple relationships between directly and indirectly latent variables. First, descriptive statistics were used to analysis participants’ characteristics. Kurtosis and skewness were obtained to confirm the normality of the sample. Correlations between latent variables were analyzed using Pearson’s correlation coefficients. Second, parameter estimation of the hypothesized model was analyzed using the maximum likelihood estimation. Fit indices including χ2, χ2/df, Root Mean -square Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Normed Fit Index (NFI), Comparative Fit Index (CFI), and Tuker-Lewis Index (TLI) were calculated to evaluate the model’s fitness level. Third, our hypotheses were verified by using structural analyses. Statistical significance of the direct effects, indirect effects, and total effects were verified using a bootstrapping method. The number of bootstrapping was set at 500 times.
Spss win 21
Manufactured by IBM
Sourced in United States
SPSS/WIN 21.0 is a statistical software package developed by IBM. It provides data management, analysis, and reporting capabilities for researchers and analysts. The software offers a wide range of statistical procedures, including regression, analysis of variance, and multivariate techniques. SPSS/WIN 21.0 is designed to help users interpret data and make informed decisions based on statistical evidence.
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Lab products found in correlation
18 protocols using spss win 21
1
Structural Equation Modeling Analysis of Research Data
2
Health Promotion Behavior Analysis
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The general and lifestyle characteristics of the participants were analyzed using descriptive statistics with SPSS/WIN 21.0 (IBM Corp, Armonk, NY, USA). The differences in health promotion behavior according to general and lifestyle characteristics were confirmed by an independent t-test and one-way analysis of variance. Post hoc testing was performed using Scheffé’s test. Variables affecting health promotion behavior were examined using Pearson’s correlation coefficient and multiple regression analysis.
3
Statistical Analysis of Experimental Data
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Data analysis performed using SPSS Win 21.0 (IBM Inc., New York, NY, USA) to describe descriptive data, including means and standard deviations.
4
Validating the Cardiovascular Risk Scale
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Data were analyzed to determine the reliability and validity of the CVRS using SPSS WIN 21.0 (IBM, Armonk, NY, USA) and LISREL 8.80 (Scientific Software International, Lincolnwood, IL, USA, 2006). The general characteristics of the participants are presented as descriptive statistics. The validity of the scale was evaluated by testing for construct validity. An exploratory factor analysis using a polychoric correlation matrix was performed to analyze the construct validity of the CVRS. The Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were used to evaluate whether the sample was adequate to perform a satisfactory factor analysis. A known-groups comparison was performed to assess how CVRS scores differed among participants’ demographic subgroups, and an independent t-test, ANOVA, Mann–Whitney test, and Kruskal–Wallis test were performed. Pearson’s correlation was used to assess the external construct validity. The reliability of the scale was calculated in terms of internal consistency by computing Cronbach’s alpha coefficient. In addition, Cronbach’s alpha coefficients were computed to determine the reliability of each factor.
5
Psychosocial Adaptation and Self-Efficacy
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The collected data were analyzed using SPSS/WIN 21.0 (IBM Corp., Armonk, NY, USA). First, the x2 test and t-test were performed to assess intergroup homogeneity and, given the sample size (n=28), the Kolmogorov-Smirnov test was performed to verify the normality of the data on the dependent variables. The results showed that all of the quantitative variables had a normal distribution.
Second, the paired t-test was conducted to analyze the statistical differences in psychosocial adaptation, PTSS, cultural adaptation stress, and self-efficacy between the experimental group and the control group. Third, analysis of covariance was performed to confirm the differences (posttest scores) between the groups, with the pretest scores as a covariate.
Second, the paired t-test was conducted to analyze the statistical differences in psychosocial adaptation, PTSS, cultural adaptation stress, and self-efficacy between the experimental group and the control group. Third, analysis of covariance was performed to confirm the differences (posttest scores) between the groups, with the pretest scores as a covariate.
6
Psychometric Evaluation of Research Tool
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The collected data were analyzed utilizing SPSS/WIN 21.0 and AMOS 21.0 (IBM Corp., Armonk, NY, USA). We analyzed the following: (a) participants’ characteristics, including demographic and disease-related characteristics, using frequency, percentage, mean, and standard deviation; (b) reliability to determine the internal consistency of the tool using Cronbach’s α coefficient; and (c) validity using exploratory factor analysis. Before conducting the exploratory factor analysis, we determined the correlation coefficients of the correlation matrix and Bartlett’s unit matrix; confirmatory factor analysis for convergent and discriminant validity, with the significance level set at P<0.05.
7
Modeling Smartphone Dependency Factors
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Data were analyzed using SPSS/WIN 21.0(IBM, Armonk, NY, USA) and AMOS 23.0(SPSS, Chicago, IL USA). Skewness and kurtosis were examined to confirm that study variables were normally distributed, and significance was set at .05. Descriptive statistics included frequency, mean, standard deviation, and percentage, which were used to describe participants’ sociodemographic characteristics and study variables. Multicollinearity was evaluated using Pearson’s correlation coefficients among variables.
For the goodness-of-fit of the model, we used the χ2 statistic, Root Mean Square Error of Approximation, Comparative Fit Index, and Normed Fit Index complementarily to evaluate model fitness. To identify the direct and indirect effects of variables related to smartphone dependency, the measurement model and the structural model were analyzed. To estimate the fitness of the model and path coefficient and analyze the effects, we used the maximum-likelihood estimation method and a bootstrapping procedure.
For the goodness-of-fit of the model, we used the χ2 statistic, Root Mean Square Error of Approximation, Comparative Fit Index, and Normed Fit Index complementarily to evaluate model fitness. To identify the direct and indirect effects of variables related to smartphone dependency, the measurement model and the structural model were analyzed. To estimate the fitness of the model and path coefficient and analyze the effects, we used the maximum-likelihood estimation method and a bootstrapping procedure.
8
Menstrual Problems, Psychological Distress
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The data were analyzed using the SPSS/WIN 21.0 (IBM Corp., Armonk, NY, USA). Means, standard deviations, and ranges were used to analyze general and menstrual characteristics, anxiety, depression, and somatization level. Pearson correlation coefficients were used to analyze correlations between anxiety, depression, somatization, and total number of menstrual problems.
9
Quality of Life, Self-Care, and Mental Health
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Data analysis was performed using SPSS/WIN 21.0 program (IBM Corp., Armonk, NY, USA). An independent t-test and one-way analysis of variance analysis were conducted to verify the difference in quality of life according to participants’ general characteristics, implementation of self-care, and stress and depression scores. Moreover, the relationships between the quality of life, implementing self-care, stress, depression were analyzed using the Pearson correlation coefficient, and a multiple regression analysis was conducted to analyze the factors affecting the quality of life. Cronbach’s α coefficient was also used to evaluate the reliability of the tools. Statistical significance was set at P<0.05.
10
Cognitive and Exercise Dual-Task Program
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The data were analyzed as follows using the SPSS/WIN 21.0 program (IBM, Armonk, NY, USA). First, the general characteristics of the study participants were analyzed using descriptive statistics. Second, the homogeneity test for, and difference in, the general characteristics of the study participants, and study variables between the intervention group and the control group, were analyzed using the chi-square test, Fisher’s exact test, and independent t-test. Third, the effects of the cognitive/exercise dual-task program were verified by using the independent t-test.
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